Note: Descriptions are shown in the official language in which they were submitted.
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METHODS FOR USING CO-REGULATED GENESETS TO ENHANCE
DETECTION AND CLASSIFICATION OF GENE EXPRESSION PATTERNS
This is a continuation-in-part of application serial no. 09/220,275, filed on
December 23, 1998, which is a continuation-in-part of application serial no.
09/179,569
filed October 27, 1998, each of which is incorporated herein, by reference, in
its entirety.
1. FIELD OF THE INVENTION
The; field of this invention relates to methods for enhanced detection of
biological
responses to perturbations. In particular, it relates to methods for analyzing
structure in
biological expression patterns for the purposes of improving the ability to
detect certain
specific gene regulations and to classify more accurately the actions of
compounds that
produce complex patterns of gene regulation in the cell.
2. BACKGROUND OF THE INVENTION
Within the past decade, several technologies have made it possible to monitor
the
expression level of a large number oftranscripts at any one time (see, e.g.,
Schena et al.,
1995, Quantitative monitoring of gene expression patterns with a complementary
DNA
micro-array, Science 270:467-470; Lockhart et al., 1996, Expression monitoring
by
hybridization to high-density oligonucleotide arrays, Nature Biotechnolo~ev
14:1675-1680;
Blanchard et al., 1996, Sequence to array: Probing the genome's secrets,
Nature
BiotechnoloQV 14, 1649; U.S. Patent 5,569,588, issued October 29, 1996 to
Ashby et al.
entitled "Methods for Drug Screening"). In organisms for which the complete
genome is
known, it is. possible to analyze the transcripts of all genes within the
cell. With other
organisms, such as human, for which there is an increasing knowledge of the
genome, it is
possible to simultaneously monitor large numbers of the genes within the cell.
Such monitoring technologies have been applied to the identification of genes
which
are up regulated or down regulated in various diseased or physiological
states, the analyses
of members of signaling cellular states, and the identification of targets for
various drugs.
See, e.g., Friend and Hartwell, U.S. Provisional Patent Application Serial No.
60/039,134,
filed on February 28, 1997; Stoughton, U.S. Patent Application Serial No.
09/099,722, filed
on June 19, 1998; Stoughton and Friend, U.S. Patent Application Serial No.
09/074,983,
filed on bled on May 8, 1998; Friend and Hartwell, U.S. Provisional
Application Serial No.
60/056,109., filed on August 20, 1997; Friend and Hartwell, U.S. Application
Serial No.
09/031,216.. filed on February 26, 1998; Friend and Stoughton, U.S.
Provisional
Application Serial Nos. 60/084,742 (filed on May 8, 1998), 60/090,004 (filed
on June 19,
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1998) and 60,090,046 (filed on June 19, 1998), all incorporated herein by
reference for all
purposes.
Levels of various constituents of a cell are known to change in response to
drug
treatments and other perturbations of the cell's biological state.
Measurements of a plurality
of such "cellular constituents"' therefore contain a wealth of information
about the effect of
perturbations and their effect on the cell's biological state. Such
measurements typically
comprise measurements of gene expression levels of the type discussed above,
but may also
include levels of other cellular components such as, but by no means limited
to, levels of
protein abundances, or protein activity levels. The collection of such
measurements is
generally referred to as the "profile" of the cell's biological state.
The number of cellular constituents is typically on the order of a hundred
thousand
for mammalian cells. The profile of a particular cell is therefore typically
of high
complexity. .Any one perturbing agent may cause a small or a large number of
cellular
constituents to change their abundances or activity levels. Not knowing what
to expect in
response to my given perturbation will therefore require measuring
independently the
responses of these about 105 constituents if the action of the perturbation is
to be completely
or at least mostly characterized. The complexity of the biological response
data coupled
with measurement errors makes such an analysis of biological response data a
challenging
task.
Current techniques for quantifying profile changes suffer from high rates of
measurement error such as false detection, failures to detect, or inaccurate
quantitative
determinations. Therefore, there is a great demand in the art for methods to
enhance the
detection of structure in biological expression patterns. In particular, there
is a need to find
groups and structure in sets of measurements of cellular constituents, e.g.,
in the profile of a
cell's biological state. Examples of such structure include associations
between the
regulation of the expression levels of different genes, associations between
different drug or
drug candidates, and association between the drugs and the regulation of sets
of genes.
Discussion or citation of a reference herein shall not be construed as an
admission
that such reference is prior arl: to the present invention.
3. SUMMARY OF THE INVENTION
This invention provides methods for enhancing detection of structures in the
response of biological systems to various perturbations, such as the response
to a drug, a
drug candidate or an experimental condition designed to probe biological
pathways as well
as changes in biological systems that correspond to a particular disease or
disease state, or
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to a treatment of a particular disease or disease state. The methods of this
invention have
extensive applications in the areas of drug discovery, drug therapy
monitoring, genetic
analysis, and clinical diagnosis. This invention also provides apparatus and
computer
instructions for performing the enhanced detection of biological response
patterns, drug
discovery, monitoring of drug therapies, genetic analysis, and clinical
diagnosis.
One aspect of the invention provides methods for classifying cellular
constituents
(measurable biological variables, such as gene transcripts and protein
activities) into groups
based upon the co-variation among those cellular constituents. Each of the
groups
has cellular constituents that co-vary in response to perturbations. Those
groups are termed
cellular constituent sets.
In some specific embodiments, genes are grouped according to the degree of co-
variation of their transcription, presumably co-regulation. Groups of genes
that have co-
varying transcripts are termed genesets. Cluster analysis or other statistical
classification
methods are used to analyze the co-variation of transcription of genes in
response to a
variety of perturbations. In preferred embodiments, the cluster analysis or
other statistical
classification methods use a novel "distance" or "similarity" metric to
evaluate the
similarity (i. e., the co-variance) of two or more genes (or other cellular
constituents) in
response to tree variety of perl:urbations. In one specific embodiment,
clustering algorithms
are applied to expression pros files (e.g., a collection of transcription
rates of a number of
genes) obtained under a variety of cellular perturbations to construct a
"similarity tree" or
"clustering tree" which relates cellular constituents by the amount of co-
regulation
exhibited. Gc,nesets are defined on the branches of a clustering tree by
cutting across the
clustering tree at different levels in the branching hierarchy. In some
embodiments, the
cutting level i s chosen based upon the number of distinct response pathways
expected for
the genes measured. In some ather embodiments, the tree is divided into as
many branches
as they are tmly distinct in teems of minimal distance value between the
individual
branches.
In some preferred embodiments, objective statistical tests are employed to
define
truly distinct branches. One exemplary embodiment of such a statistical test
employs
Monte Carlo randomization of the perturbation index for each gene's responses
across all
perturbations tested. In some; preferred embodiments, the cut off level is set
so that
branching is significant at the 95% confidence level. In preferred
embodiments, clusters
with one or t'vo genes are discarded. .In some other embodiments, hoawever,
small clusters
with one or two genes are included in genesets. In more detail, the preferred
statistical tests
of the invention comprise (a) obtaining a measure of the "compactness" of
clusters (i.e.,
cellular constituent sets such .as gene sets) determined by the above
mentioned cluster
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analysis or other statistical to;chniques, and (b) comparing the thus obtained
measure of
compactness to a hypothetical measure of compactness of cellular constituents
regrouped in
an increased number of clusters. Such a comparison typically comprises
determining the
difference in the compactness of the two sets of clusters. Further, by
employing Monte
Carlo randomization of the perturbation index for each gene's responses across
all
perturbations tested, a statistical distribution of the difference in the
compactness is thus
generated. T'he statistical significance of the actual difference in
compactness can then be
determined by comparing this actual difference in compactness to the
statistical distribution
of the differences in compactness from the Monte Carlo randomizations.
As the diversity of perturbations in the clustering set becomes very large,
the
genesets which are clearly distinguishable get smaller and more numerous.
However, it is a
discovery of the inventors that even over very large experiment sets, there is
a number of
genesets that retain their coherence. These genesets are termed irreducible
genesets. In
some embodiments of the invention, a large number of diverse perturbations are
applied to
obtain these iirreducible genesets.
Statistically derived genesets may be refined using regulatory sequence
information
to confirm members that are co-regulated, or to identify more tightly co-
regulated
subgroups. In such embodirr~ents, genesets may be defined by their response
pattern to
individual biological experimental perturbations such as specific mutations,
or specific
growth conditions, or specific compounds. The statistically derived genesets
may be fiuther
refined based, upon biological understanding of gene regulation. In another
preferred
embodiment, classification o:f genes into genesets is based first upon the
known regulatory
mechanisms of genes. Sequence homology of regulatory regions is used to define
the
genesets. In some embodiments, genes with common promoter sequences are
grouped into
one geneset.
In preferred embodiments, the cluster analysis and statistical classification
methods
of this invention analyze co-variation, e.g., of transcription levels of
individual genes, by
means of an objective, quantitative "similarity" or "distance" function which
provides a
useful measurement of the similarity of expression levels for two or more
cellular
constituents (e.g., for two or more genes). Accordingly, the present invention
provides
novel similarity or distance function which are particularly useful for
analyzing the co-
variation of cellular constituents, including the co-variation of gene
transcript levels. The
invention-also provides objective statistical tests, in particular Monte.Carlo
procedures, for
assessing the significance of the cellular constituent sets or genesets
obtained by the
methods of this invention. Finally, the clustering methods of this invention
are equally
applicable to the clustering o:f both cellular constituents and biological
profiles according to
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their similarities. Thus, in another aspect, the present invention provides
methods for
simultaneous clustering in both dimension of a tabular data set. In preferred
embodiments,
the data set is a table of numbers representing the levels or changes in
level, of a plurality
of cellular constituents in response to different conditions, perturbations,
or conditions
$ pairs.
Another aspect of the invention provides methods for expressing the state (or
biological responses) of a biological sample on the basis of co-varying
cellular constituent
sets. In some; embodiments, a profile containing a plurality of measurements
of cellular
constituents in a biological sample is converted into a projected profile
containing a
plurality of cellular constituent set values according to a definition of co-
varying basis
cellular constituent sets. In some preferred embodiments, the cellular
constituent set values
are the average of the cellular constituent values within a cellular
constituent set. In some
other embodiments, the cellular constituent set values are derived from a
linear projection
process. The projection operation expresses the profile on a smaller and
biologically more
meaningful set of coordinates, reducing the effects of measurement errors by
averaging
them over each cellular constituent sets, and aiding biological interpretation
of the profile.
The method of the invention is particularly useful for the analysis of gene
expression
profiles. In some embodiments, a gene expression profile, such as a collection
of
transcription rates of a number of genes, is converted to a projected gene
expression profile.
The projected gene expression profile is a collection of geneset expression
values. The
conversion is achieved, in some embodiments, by averaging the transcription
rate of the
genes within f;ach geneset. In some other embodiments, other linear projection
processes
may be used.
In yet another aspect of the invention, methods for comparing cellular
constituent set
2$ values, particularly, geneset expression values are provided. In some
embodiments, the
expression of at least 10, preferably more than 100, more preferably more than
1,000 genes
of a biological system is monitored. A known drug is applied to the system to
generate a
known drug response profile in terms of genesets. A drug candidate is also
applied to the
biological system to obtain a drug candidate response profile in terms of
genesets. The drug
candidate's response profile is then compared with the known drug response
profile to
detenmine whether the drug cau~didate induces a response similar to the
response to a known
drug.
In some other embodiments, the comparison of projected profiles is_achieved by
using an objective measure of'similarity. In some preferred embodiments, the
objective
3$ measure is the; generalized angle between the vectors representing the
projections of the two
profiles being compared (the ''normalized dot product'). In some other
embodiments, the
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projected profiles are analyzed by applying threshold to the amplitude
associated with each
geneset for th.e projected profile. If the change of a geneset is above a
threshold, it is
declared that a change is present in the geneset.
The rr~ethods of the present invention may also be used to group biological
response
profiles according to the similarity of the responses of measured cellular
constituents.
Accordingly, in alternative embodiments, the present invention provides
methods for
grouping biological responses (i.e., response profiles) according to the
degree of similarity
of the cellulw constituents' responses by means of the cluster analysis or
other statistical
classification methods described supra for classification of cellular
constituents {e.g., genes)
into co-varying sets (e.g., genesets). Such methods may also be used, e.g.,
for enhancing
detection of structures in the responses of biological systems to various
perturbations. Still
further, the present invention also provides "two-dimensional" methods of
analyzing
biological response profile data. Such methods simply comprise (1) grouping
cellular
constituents (e.g., genes) according to their degree ofco-variation in the
response profile
data, and (2) grouping response profiles according to the similarity of their
cellular
constituents' responses.
The clustering methods ofthe invention are particularly useful, e.g., for
identifying
and/or characterizing perturbations (for example, drugs, drug candidates or
genetic
mutations) affecting particular cellular constituents or particular groups of
cellular
constituents. For example, the clustering methods can be used to identify
cellular
constituents (e.g., genes and proteins) and/or sets of co-varying cellular
constituents such as
genesets whose changes in expression or abundance are associated with a
particular
biological effect such as a particular disease state or the effect of one or
more drugs.
Further, the c'.lustering methods of the invention are also useful, e.g., for
identifying cellular
constituents, :>uch as genes or gene transcripts, involved in a particular
biological response
or pathway. Thus, the invention further provides methods for identifying
cellular
constituents, such as genes or gene transcripts, associated with a particular
biological
response or pathway by means of the cluster analysis methods described supra.
The
invention still further provides methods for identifying biological
"perturbations", for
example drugs, drug candidates, or genetic mutations which "perturb" a
biological system,
effecting particular cellular constituents or particular groups of cellular
constituents by
means of the cluster analysis methods described supra. The cellular
constituents and
perturbations identified by thc; methods of the invention may be known or
previously _,
unknown. Thus, the invention provides methods for identifying, e.g., novel
genes and
drugs or drug candidates as well previously known genes and drugs/drug
candidates which
were not previously known to~ be associated with a particular biological
effect of interest.
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The .methods of the present invention may also be used to remove one or more
artifacts from a measured biological profile (i.e., from a measure profile
comprising a
plurality of measurements of cellular constituents). Thus, the invention
provides methods
for removing; such artifacts from a measured biological profile by subtracting
one or more
S artifact patterns from the measured biological profile, wherein each
artifact pattern
corresponds to a particular artifact.
The methods of the invention are preferably implemented with a computer system
capable of executing cluster analysis and projection operations. In some
embodiments, a
computer system contains a computer-usable medium having computer readable
program
code embodied. The computer code is used to effect retrieving a definition of
basis
genesets from a database and converting a gene expression profile into a
projected
expression profile according to the retrieved definition.
4. BRII?F DESCRIPTION OF THE DRAWINGS
Fig. 1 illustrates an embodiment of the cluster analysis.
Fig. 2; illustrates the projection process.
Fig. 3 illustrates an exemplary geneset database management system.
Fig. 4A illustrates two different possible responses to receptor activation.
Fig. 4B illustrates three main clusters of yeast genes with distinct temporal
behavior.
Fig. 5~ illustrates a computer system useful for embodiments of the invention.
Fig. 6 shows a clustering tree derived from 'hclust' algorithm operating on a
table of
18 experiments by 48 mRNA, levels.
Fig. i' shows a clustering tree derived from 34 experiments.
Fig. 8A-E shows amplitudes of the individual elements of the projected
profile.
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Fig. S~ shows results of correlating the profile of FK506 (16 ltg/ml)
treatment with
the profiles of each of the 34 experiments used to generate the basis
genesets.
Fig. 10 illustrates an exemplary signaling cascade which includes a group of
up-
regulated genes (Gl, G2, and. G3) and a group of down regulated genes (G4, GS,
and G~.
Fig. 11 is the clustering tree obtained by the hclust algorithm to identify
clusters
(i.e., genesets) among 185 genes whose expression levels were measured in 34
perturbation
response profiles.
Fig. 12 illustrates an exemplary, two-dimensional embodiment of the Monte
Carlo
method for assigning significance to cluster subdivisions.
Fig. 13 shows the transcriptional response of the largest responding genes of
S.
cerevisiae to different concentrations of the drug FK506.
Fig. 14 shows projected titration curves obtained by projecting the titration
curves of
Fig. 13.
Fig. 1 S is chi-squared plotted around the values of the two Hill coefficients
n and uo
derived for each geneset in Fig. 14.
Fig. 16A-D illustrates an exemplary application of the methods of the
invention;
Fig. 16A is a grey scale display of 185 genetic transcripts of S. Cerevisiae
(horizontal axis)
measured in 34 different perturbation experiments (vertical axis); Fig. 16B
shows the co-
regulation tree obtained by c:~lustering the genetic transcripts of Fig. 16A
using the 'hclust'
algorithm; F'ig. 16C is an illustration of the same experimental data in which
the transcripts
(horizontal axis) have been re-ordered according to the genesets defined from
Fig. 16B;
Fig. 16D is another illustration of the experimental data in which the
experimental index
(vertical axis) has also been reordered according to similarity of the
response profiles.
Fig. 17 is another illustration of the data in Fig. 16 in which the genetic
transcripts
(horizontal axis) and experiments (vertical axis) are ordered according to
similarity;
individual genesets are identified above the false color image, while the
biological pathways
arld~or responses with which each geneset is associated are indicated below
the image; the
label on the vertical axis surrmnarizes each experiment.
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Fig. l8 shows the correlation of the expression profiles of a (believed to be)
uncontaminated experiment measuring the effect of deletion of the gene YJL107c
in S.
cerevisiae and an identical experiment unintentionally contaminated with an
artifact (poor
control of R1VA concentration during reverse transcription.
S
Fig. K 9 shows a profile, plotted as gene expression ratio vs. mean expression
level,
corresponding to poor control of RNA concentration in a reverse transcription
procedure
during hybridization sample preparation.
Fig. 20 shows the correlation of the expression profile of a (believed to be)
uncontaminated experiment measuring the effect of deletion of the gene YJL107c
in S
cerevisiae and an identical experiment unintentionally contaminated with an
artifact (poor
control of Rl'JA concentration during reverse transcription) wherein the data
from the
contaminated has been "cleaned" using the response profile in Fig. 19 as a
"template" of the
artifact.
S. DETAILED DESCRIPTION
This section presents a detailed description of the invention and its
applications.
This description is by way of'several exemplary illustrations, in increasing
detail and
specificity, o:f the general methods of this invention. These examples are non-
limiting, and
related variants will be apparent to one of skill in the art.
Although, for simplicity, this disclosure often makes references to gene
expression
profiles, transcriptional rate, transcript levels, etc., it will be understood
by those skilled in
the art that the methods of the inventions are useful for the analysis of any
biological
response profile. In particular, one skilled in the art will recognize that
the methods of the
present invention are equally applicable to biological profiles which comprise
measurements of other cellul;~r constituents such as, but not limited to,
measurements of
protein abundance or protein activity levels.
5.1. INTRODUCTION
The state of a cell or other biological sample is represented by cellular
constituents
(any measurable biological v~u-iables) as defined in Section 5.1.1, infra.
Those cellular
constituents vary in response to perturbations. A group of cellular
constituents may co-vary
in response to particular perturbations. Accordingly, one aspect of the
present invention
provides methods for grouping co-varying cellular constituents. Each group of
co-varying
cellular constituents is termed a cellular constituent set. This invention is
partially premised
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upon a discovery of the inventors that the state of a biological sample can be
more
advantageously represented using cellular constituent sets rather than
individual cellular
constituents. It is also a discovery of the inventors that the response of a
biological sample
can be better analyzed in terns of responses of co-varying cellular
constituent sets rather
$ than cellular constituents.
In some preferred specific embodiments of this invention, genes are grouped
into
basis genesets according to the regulation of their expression.
Transcriptional rates of
individual genes within a geneset are combined to obtain a single gene
expression value for
the geneset by a proj ection process. The expression values of genesets,
rather than the
~'anscriptional rate of individual genes, are then used as the basis for the
comparison and
detection of biological responses with greatly enhanced sensitivity.
This section first presents a background about representations of biological
state and
biological responses in terms of cellular constituents. Next, a schematic and
non-limiting
overview of the invention is presented, and the representation of biological
states and
1$ biological responses according to the method of this invention is
introduced. The following
sections present specific non-limiting embodiments of this invention in
greater detail.
$.1.1. DEFINTTION OF BIOLOGICAL STATE
As used in herein, the term "biological sample" is broadly defined to include
any
cell, tissue, organ or multiceillular organism. A biological sample can be
derived, for
example, from cell or tissue cultures in vitro. Alternatively, a biological
sample can be
derived from a living organism or from a population of single cell organisms.
The state of a biological sample can be measured by the content, activities or
structures of its cellular constituents. The state of a biological sample, as
used herein, is
2$ taken from tlae state of a collection of cellular constituents, which are
sufficient to
characterize the cell or organism for an intended purpose including, but not
limited to
characterizing the effects of :a drug or other perturbation. The team
"cellular constituent" is
also broadly defined in this disclosure to encompass any kind of measurable
biological
variable. The measurements and/or observations made on the state of these
constituents can
be of their abundances (i.e., :nnounts or concentrations in a biological
sample), or their
activities, or their states of rn, odification (e.g., phosphorylation), or
other measurements
relevant to the biology of a biological sample. In various embodiments, this
invention
includes making such measurements. and/or observations on
different_collections of cellular
constituents. These different collections of cellular constituents are also
called herein
3$ aspects of the biological state of a biological sample.
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One aspect of the biological state of a biological sample (e.g., a cell or
cell culture)
usefully measured in the present invention is its transcriptional state. In
fact, the
transcription,al state is the currently preferred aspect of the biological
state measured in this
invention. The transcriptional state of a biological sample includes the
identities and
abundances of the constituent RNA species, especially mRNAs, in the cell under
a given set
of conditions. Preferably, a substantial fraction of all constituent RNA
species in the
biological sample are measured, but at least a sufficient fraction is measured
to characterize
the action of a drug or other perturbation of interest. The transcriptional
state of a
biological sample can be conveniently determined by, e.g., measuring cDNA
abundances by
any of several existing gene c;xpression technologies. One particularly
preferred
embodiment of the invention employs DNA arrays for measuring mRNA or
transcript level
of a large nwnber of genes.
Another aspect of the biological state of a biological sample usefully
measured in
the present invention is its translational state. The translational state of a
biological sample
includes the identities and abundances of the constituent protein species in
the biological
sample under a given set of conditions. Preferably, a substantial fraction of
all constituent
protein species in the biological sample is measured, but at least a
sufficient fraction is
measured to characterize the .action of a drug of interest. As is known to
those of skill in the
art, the transcriptional state is. often representative of the translational
state.
Other aspects of the biological state of a biological sample are also of use
in this
invention. For example, the activity state of a biological sample, as that
term is used herein,
includes the activities of the constituent protein species (and also
optionally catalytically
active nucleic; acid species) in the biological sample under a given set of
conditions. As is
known to those of skill in the art, the translational state is often
representative of the activity
state.
This invention is also adaptable, where relevant, to "mixed" aspects of the
biological
state of a biological sample in which measurements of different aspects of the
biological
state of a biological sample are combined. For example, in one mixed aspect,
the
abundances of certain RNA species and of certain protein species, are combined
with
measurements of the activities of certain other protein species. Further, it
will be
appreciated from the following that this invention is also adaptable to other
aspects of the
biological state of the biological sample that are measurable.
The biological state of a biological sample (e.g., a cell or cell.culture) is
represented
by a profile of some number of cellulax constituents. Such a profile of
cellular constituents
c~ be represented by the vector S.
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S = ~S, , . " S; , . . Sk ~ (1)
Where S; is the level of the i'th cellular constituent, for example, the
transcript level of
gene i, or alternatively, the abundance or activity level of protein i.
In some embodiments, cellular constituents are measured as continuous
variables.
For example" transcriptional rates are typically measured as number of
molecules
synthesized per unit of time. 'rranscriptional rate may also be measured as
percentage of a
control rate. However, in some other embodiments, cellular constituents may be
measured
as categorical variables. For example, transcriptional rates may be measured
as either "on"
or "off', where the value "on" indicates a transcriptional rate above a
predetermined
threshold and value "off' indicates a transcriptional rate below that
threshold.
5.1.2. REPRESENTATION OF BIOLOGICAL RESPONSES
The responses of a biological sample to a perturbation, such as the
application of a
drug, can be measured by observing the changes in the biological state of the
biological
sample. A response profile is a collection of changes of cellular
constituents. In the present
invention, the response profile of a biological sample (e.g., a cell or cell
culture) to the
perturbation .m is defined as the vector v~m~:
V~m~ _ ~V~m~ . . V~m~ . . V(m)1 (2)
1 ~ r ~ ,k
Where v,'." is the amplitude of response of cellular constituent i under the
pe~bation ,m. In some particularly preferred embodiments of this invention,
the
biological response to the application of a drug, a drug candidate or any
other perturbation,
is measured by the induced change in the transcript level of at least 2 genes,
preferably more
than 10 genes, more preferably more than 100 genes and most preferably more
than 1,000
genes.
In some embodiments of the invention, the response is simply the difference
between biological variables before and after perturbation. In some preferred
embodiments,
the response is defined as the; ratio of cellular constituents before and
after a perturbation is
applied. In other embodiments, the response may be a function of time after
the
perturbation, i.e., v~'"~ = v~"''(t). For example v~'"~(t) may be the
difference or ratio of cellular
c°nstituents before the pertwbation and at time t after the
perturbation.
In some preferred embodiments, v,"' is set to zero if the response of gene i
is below
some threshold amplitude or confidence level determined from knowledge of the
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measurement error behavior. In such embodiments, those cellular constituents
whose
measured responses are lower than the threshold are given the response value
of zero,
whereas those cellular constituents whose measured responses are greater than
the threshold
retain their measured response values. This truncation of the response vector
is a good
S strategy when most of the smaller responses are expected to be greatly
dominated by
measurement error. After the truncation, the response vector v~"'~ also
approximates a
'matched detector' (see, e.g., Van Trees, 1968, Detection. Estimation and
Modulation
Theory Vol. I, Wiley & Sons) for the existence of similar perturbations. It is
apparent to
those skilled in the art that the truncation levels can be set based upon the
purpose of
detection and the measurement errors. For example, in some embodiments, genes
whose
transcript level changes are lower than two fold or more preferably four fold
are given the
value of zero.
In some preferred embodiments, perturbations are applied at several levels of
strength. For example, different amounts of a drug may be applied to a
biological sample to
1 S observe its response. In such embodiments, the perturbation responses may
be interpolated
by approximating each by a single parameterized "model" function of the
perturbation
strength u. An exemplary model function appropriate for approximating
transcriptional
state data is the Hill function" which has adjustable parameters a, uo, and n.
a(u l uo)"
H(u) 1 + (u l uo)" (3)
The adjustable parameters are selected independently for each cellular
constituent of the
perturbation response. Preferably, the adjustable parameters are selected for
each cellular
constituent so that the sum of"the squares of the differences between the
model function
2S (e.g.~ ~e Hill function, Equation 3) and the corresponding experimental
data at each
perturbation strength is minimized. This preferable parameter adjustment
method is well
known in the art as a least squares fit. Other possible model functions are
based on
polynomial fitting, for example by various known classes of polynomials. More
detailed
description of model fitting and biological response has been disclosed in
Friend and
Stoughton, Methods of Determining Protein Activity Levels Using Gene
Expression
Profiles, U.S. Provisional Application Serial No. 60/084,742, filed on May 8,
1998, which
is incorporated herein by reference for all purposes.
S.1.3. OVERVIEW OF THE INVENTION
3S ~s invention provides a method for enhanced detection, classification, and
pattern
recognition oPbiological states and biological responses. It is a discovery of
the inventors
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that biological state and response measurements, i.e., cellular constituents
and changes of
cellular constituents can be classified into co-varying sets. Expressing
biological states and
responses in terms of those c:o-varying sets offers many advantages over
representation of
profiles of biological states and responses.
One aspect of the invention provides methods for defining co-varying cellular
constituent sets. Fig. 1 is a schematic view of an exemplary embodiment of
this aspect of
invention. First, a biological sample (or a population of biological samples)
is subject to a
wide variety of perturbations ( 101 ). The biological sample may be repeatedly
tested under
different perl:urbations sequentially or many biological samples may be used
and each of the
biological samples can be te.cted for one perturbation. For a particular type
of perturbation,
such as a drug, different doses of the perturbation may be applied.
In some particularly preferred embodiments, different chemical compounds,
mutations, temperature changes, etc., are used as perturbations to generate a
large data set.
In most embodiments, at least S, preferably more than 10, more preferably more
than 50,
most preferably more than 100 different perturbations are employed.
In the. preferred embodiment of the invention, the biological samples used
here for
cluster analysis are of the same type and from the same species as the species
of interest.
For example, human kidney cells are tested to define cellular constituent sets
that are useful
for the analysis of human kidney cells. In some other preferred embodiments,
the biological
samples used here for cluster analysis are not of the same type or not from
the same species.
For example, yeast cells may be used to define certain yeast cellular
constituent sets that are
useful for hwnan tissue analysis.
The biological samples subjected to perturbation are monitored for their
cellular
constituents (level, activity, o~r structure change, etc.) (102). Those
biological samples are
occasionally :referred to herein as training samples and the data obtained are
referred to as
training data. The term "monitoring" as used herein is intended to include
continuous
measuring as well as end point measurement. In some embodiments, the cellular
constituents of the biological samples are measured continuously. In other
embodiments,
the cellular constituents before and after perturbation are measured and
compared. In still
other embodiments, the cellular constituents are measured in a control group
of biological
samples under no perturbation, and the cellular constituents of several
experimental groups
are measured and compared with those of the control group. It is apparent to
those skilled
in the art that other experimental designs are also suitable for the method of
this invention
to detect the change in cellular constituents in response to perturbations.
The responses of cellular constituents to various perturbations are analyzed
to
generate co-varying sets (103). 'The data are first grouped by cluster
analysis according to
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the method described in Section 5.2., infra, to generate a cluster tree which
depicts the
similarity ofthe responses of cellular constituents to perturbation (104). A
cut off value is
set so that the number of sets (branches) is preferably matched with the
number of known
pathways involving the cellular constituents studied (105). In some
embodiments where the
number of pathways is unknown, cellular constituents are clustered into the
maximal
number of truly distinct branches (or sets).
The cellular constituent sets may be refined by utilizing the ever increasing
knowledge about biological pathways and regulatory pathways obtained from the
art (106).
Conversely, the cluster analysis method of the invention is useful for
deciphering complex
biological pathways.
In another aspect of the invention, biological state and biological responses
of a
biological sample are represented by combined values for cellular constituent
sets. In one
exemplary embodiment as depicted in Fig. 2, the cellular constituents (202) of
a biological
sample (201;1 are grouped into three predefined cellular constituent sets
(203), (204) and
(205). The measurements of the cellular constituents (202) within a cellular
constituent set
are combined to generate set values {206), (207) and {208). This step of
converting from
cellular constituent values to set values is termed 'projection.' This
projection operation
expresses the prof le on a smaller and biologically more meaningful set of
coordinates,
reducing the effects of measurement errors by averaging them over each set,
and aiding
biological interpretation of the profile.
Using set values does not necessarily cause loss of information by combining
individual cellular constituent values. Because the cellular constituents
within a set co-
vary, individual cellular constituents provides little more information than
the combined set
value. In most embodiments" in this step, the quantitative description of a
profile changes
from a list of,, for example, 100 numbers to a substantially shorter list, for
example 10,
representing the amplitude of each individual response pattern {coordinated
change in any
one geneset) needed to closely represent, in a sum, the entire profile.
The conversion of cellular constituent values into set values, however, offers
many
benefits by greatly reducing the measurement errors and random variations and
thus
e~ancing pattern detection.
Another aspect of the invention provides methods for using the simplified
description, or 'projection' of the profile onto cellular constituent sets in
drug discovery,
diagnosis,.genetic analysis and other applications. Profiles of responses
expressed in terms
of cellular constituent sets, particularly genesets in some preferred
embodiments, can be l
compared with enhanced accuracy. In some embodiments of the invention, a
geneset
response profile of a biological sample to an unknown perturbation, such as a
drug
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candidate, is compared with the geneset profiles generated with a number of
known
perturbations. The biological nature, such as its pharmacological activities,
of an unknown
perturbation can he determined by examining the similarity of its response
profile with
known profiles. In some embodiments, an objective measure of similarity is
used. In one
particularly preferred embodiment, the generalized angle between the vectors
representing
the projections of the two profiles being compared (the 'normalized dot
product') is the
objective measure. In some other embodiments, the amplitude associated with
each geneset
for the projected profile can be masked with threshold values to declare the
presence or
absence of a change in that geneset. This will be a more sensitive detector of
changes in
that geneset than one based on individual cellular constituents from that
geneset detected
separately. It is also a more accurate quantitative monitor of the amplitude
of change in that
geneset. Thus, the presence of specific biological perturbations can be
detected more
sensitively, and similarities between the mechanisms of action of different
compounds or
perturbations discovered more efficiently.
5.2. PE IFIC EMBODIMENT: DEFINING BASIS GENESETS
In this section, a preferred embodiment of the invention is described in
detail.
While the b~csis genesets are used as an illustrative embodiment of the
invention, it is
apparent to those skilled in the art that this invention is not limited to
genesets and gene
expression, but is useful for analyzing many types of cellular constituents.
One :particular aspect of the invention provides methods for clustering co-
regulated
genes into genesets. This section provides a more detailed discussion of
methods for
clustering co-regulated gene's.
5.2.1. CO-REGULATED GENES AND GENESETS
Certain genes tend to increase or decrease their expression in groups. Genes
tend to
increase or decrease their rates of transcription together when they possess
similar
regulatory sequence patterns, z.e., transcription factor binding sites. This
is the mechanism
for coordinated response to particular signaling inputs (see, e.g., Madhani
and Fink, 1998,
The riddle of MAP kinase si"naling specificity, Transactions in Genetics
14:151-155;
Arnone and lDavidson, 1997, The hardwiring of development: organization and
function of
genomic regulatory systems, Development 124:1851-1864). Separate genes which
make
different components of a necessary protein or cellular structure will.tend to
co-vary. .
Duplicated genes (see, e.g., \Nagner, 1996, Genetic redundancy caused by gene
duplications
and its evolution in networks. of transcriptional regulators, Biol. Cybern.
74:557-567) will
also tend to c;o-vary to the extent mutations have not led to functional
divergence in the
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regulatory regions. Further, because regulatory sequences are modular (see,
e.g., Yuh et
a1.,1998, Ge:nomic cis-regul;~tory logic: experimental and computational
analysis of a sea
urchin gene, Science 279:1896-1902), the more modules two genes have in
common, the
greater the variety of conditions under which they are expected to co-vary
their
transcriptional rates. Separation between modules also is an important
determinant since
co-activators also are involved. In summary therefore, for any finite set of
conditions, it is
expected that genes will not all vary independently, and that there are
simplifying subsets of
genes and proteins that will co-Vary. These co-varying sets of genes form a
complete basis
in the mathematical sense with which to describe all the profile changes
within that finite
set of conditions. One aspect of the invention classifies genes into groups of
co-varying
genes. The analysis of the responses of these groups, or genesets, allows the
increases in
detection sensitivity and classification accuracy.
5.2.2. GENESE'T CLASSIFICATION BY LUSTER ANALYSIS
For many applications of the present invention, it is desirable to find basis
genesets
that are co-regulated over a wide variety of conditions. This allows the
method of invention
to work well for a large class of profiles whose expected properties are not
well
circumscribed. A preferred embodiment for identifying such basis genesets
involves
clustering algorithms (for reviews of clustering algorithms, see, e.g.,
Fukunaga, 1990,
statistical Pattern Reco ition, 2nd Ed., Academic Press, San Diego; Everitt,
1974, Cluster
Analysis, London: Heinemann Educ. Books; Hartigan, 1975, Clustering
Algorithms, New
York: Wiley; Sneath and Sokal, 1973, Numerical Taxonomy, Freeman; Anderberg,
1973,
Cluster Anal3rsis for Applications, Academic Press: New York).
In some embodiments employing cluster analysis, the expression of a large
number
of genes is monitored as biological samples are subjected to a wide variety of
perturbations
see, section 5.8, infra, for detailed discussion of perturbations useful for
this invention). A
table of data containing the gene expression measurements is used for cluster
analysis. In
order to obtain basis genesets that contain genes which co-vary over a wide
variety of
conditions, at least 10, preferably more than S0, most preferably more than
100
perturbations or conditions are employed. Cluster analysis operates on a table
of data which
has the dimension m x k wherein m is the total number of conditions or
perturbations and k
is the number of genes measured.
A number of clustering algorithms are useful for clustering analysis.
Clustering
algorithms use dissimilarities or distances between objects when forming
clusters. In some
embodiments., the distance used is Euclidean distance in multidimensional
space:
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1/2
I (x~Y) _ ~ (~'; ' ~; (4)
J
where I(xy) is the distance between gene X and gene Y (or between any other
cellular
constituents X and Y); X,. and .Y,. are gene expression response under
perturbation i. The
Euclidean distance may be squared to place progressively greater weight on
objects that are
further apart. Alternatively, the distance measure may be the Manhattan
distance e.g.,
between gene X and Y, which is provided by:
I (x~Y) _ ~ I X; ' ~; I (5)
r
Again, X,. and Y; are gene expression responses under perturbation i. Some
other definitions
of distances are Chebychev distance, power distance, and percent disagreement.
Percent
disagreement, defined as 1(x,.y) _ (number of X, ~ Y;)/i, is particularly
useful for the method
of this invention, if the data i:or the dimensions are categorical in nature.
Another useful
distance definition, which is particularly useful in the context of cellular
response, is
I =1- r, where r is the correlation coefficient between the response vectors
X, Y, also called
the normalized dot product h ~YI~X~ ~Y~. Specifically, the dot product X~Y is
defined by the
equation:
X~Y=~X;xY,.
1
and ~X~ _ (X~.Xyn~ I ~ _ (Y~I~~~~.
Most preferably, the distance measure is appropriate to the biological
questions
being asked, e.g., for identifying co-varying and/or co-regulated cellular
constituents
including co-varying or co-regulated genes. For example, in a particularly
preferred
embodiment, the distance measure I = 1 - r with the correlation coefficient
which
comprises a weighted dot product of the genes X and Y. Specifically, in this
preferred
embodiment, r;~ is preferably defined by the equation
X,Y,
~ ~X~~y~
' 1/2
( ~X) )2( ~ ,2
where ~'~ and o~ are the standard errors associated with the measurement of
genes Xand
Y, respectively, in experiment ;i.
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The c;orrelation coefficients of the normal and weighted dot products above
are
bounded betwveen values of ~-1, which indicates that the two response vectors
are perfectly
correlated and essentially identical, and -1, which indicates that the two
response vectors are
"anti-correlated" or "anti-sense" (i.e., are opposites). These correlation
coefficients are
particularly preferable in embodiments of the invention where cellular
constituent sets or
clusters are sought of constituents which have responses of the same sign.
In other embodiments, it is preferable to identify cellular constituent sets
or clusters
which are co-regulated or involved in the same biological responses or
pathways, but which
comprise sirrular and anti-correlated responses. For example, Fig. 10
illustrates a cascade in
w~ch a signal activates a transcription factor which up-regulated several
genes, identified
as Gl , G2, and G3. In the example presented in Fig. 10, the product of G3 is
a repressor
element for several different genes, e.g., G4, GS, and G6. Thus, it is
preferable to be able
to identify al:l six genes GI - G6 as part of the same cellular constituent
set or cluster. In
such embodiments, it is preferable to use the absolute value of either the
normalized or
weighted dot products described above, i.e., J r~, as the correlation
coefficient.
In still other embodiments, the relationships between co-regulated and/or co-
varying
cellular constituents (such as genes) may be even more complex, such as in
instances
wherein multiple biological pathways (e.g., signaling pathways) converge on
the same
cellular constituent to produce different outcomes. In such embodiments, it is
preferable to
use a correlation coefficient r = r~'"°"gee which is capable of
identifying co-varying and/or co-
regulated cellular constituents irrespective of the sign. The correlation
coefficient specified
by Equation 33 below is particularly useful in such embodiments.
n
n2
2 2
~(~Y'~ ~ Y'I
Various cluster linkage rules are useful for the methods of the invention.
Single
linkage, a nearest neighbor method, determines the distance between the two
closest
objects. By contrast, complei:e linkage methods determine distance by the
greatest distance
between any two objects in flee different clusters. This method is
particularly useful in cases
when genes or other cellular constituents form naturally distinct "clumps."..
Alternatively, .
the unweighted pair-group average defines distance as the average distance
between all
pairs of objects in two different clusters. This method is also very useful
for clustering
genes or other cellular constituents to form naturally distinct "clumps."
Finally, the
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weighted pair-group average method may also be used. This method is the same
as the
unweighted 'pair-group average method except that the size of the respective
clusters is used
as a weight. This method is particularly useful for embodiments where the
cluster size is
suspected to be greatly varied (Sneath and Soka1,1973, Numerical taxonomv, San
Francisco: V~J. H. Freeman & Co.). Other cluster linkage rules, such as the
unweighted and
weighted pair-group centroid and Ward's method are also useful for some
embodiments of
the invention. See., e.g., Ward, 1963, J. Am. Stat Assn. 58:236; Hartigan,
1975, lusterin
al one 'thins, New York: Wiley.
In one particularly preferred embodiment, the cluster analysis is performed
using the
hclust routine (see, e.g., 'hclust' routine from the software package S-Plus,
MathSoft, Inc.,
Cambridge, )VIA). An example of a clustering 'tree' output by the hclust
algorithm of S-
Plus is shown in Fig. 6 (see, also, Example l, section 6.1, infra). The data
set in this case
involved 18 experiments including different drug treatments and genetic
mutations related
to the yeast ~~. cerevisiae biochemical pathway homologous to
immunosuppression in
humans. The set of more than 6000 measured mRNA levels was first reduced to 48
by
selecting only those genes which had a response amplitude of at least a factor
of 4 in at least
one of the experiments. This initial downselection greatly reduces the
confusing effects of
measurement errors, which dominate the small responses of most genes in most
experiments. Clustering using 'hclust' was then performed on the resulting 18
x 48 table of
data, yielding; the clustering tree shown in Fig. 6. When the number and
diversity of
experiments 'in the clustering set is larger, then the fraction of measured
cellular constituents
with significant responses (well above the measurement error level) is also
larger, and
eventually most or all of the set of cellular constituents are retained in the
first down
selection and become represented in the clustering tree. The genesets derived
from the tree
then more completely cover the set of cellular constituents.
As the diversity of perturbations in the clustering set becomes very large,
the
genesets which are clearly distinguishable get smaller and more numerous.
However, it is a
discovery of the inventors that even over very large experiment sets, there
are small
genesets that retain their cohf;rence. These genesets are termed irreducible
genesets. In
some embodiments of the invention, a large number of diverse perturbations are
applied to
obtain such i~Teducible genesets. For example, Geneset No.l at the left in
Figure 6 is found
also when clustering is performed on a much larger set of perturbation
conditions. A data
set of 365-yeast conditions including.the 18 previously mentioned was used for
cluster
analysis. Perturbation conditians include drug treatments at different
concentrations and
measured after different times of treatment, responses to genetic mutations in
various genes,
combinations, of drug treatment and mutations, and changes in growth
conditions such as
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temperature" density, and calcium concentration. Most of these conditions had
nothing to
do with the immunosuppressant drugs used in the 18-experiment set; however,
the geneset
retains its coherence. Genesets No. 2 and No. 3 also retain partial coherence.
Genesets may be defined based on the many smaller branches in the tree, or a
small
number of larger branches by cutting across the tree at different levels - see
the example
dashed line in Fig. 6. The choice of cut level may be made to match the number
of distinct
response pathways expected. If little or no prior information is available
about the number
of pathways, then the tree should be divided into as many branches as are
truly distinct.
'Truly distinct' may be defined by a minimum distance value between the
individual
branches. In Fig. 6, this distance is the vertical coordinate of the
horizontal connector
joining two branches. Typical values are in the range 0.2 to 0.4 where 0 is
perfect
correlation and 1 is zero correlation, but may be larger for poorer quality
data or fewer
experiments in the training sc;t, or smaller in the case of better data and
more experiments
in the training set.
Preferably, 'truly distinct' may be defined with an objective test of
statistical
significance for each bifurcation in the tree. In one aspect of the invention,
the Monte
Carlo randomization of the experiment index for each cellular constituent's
responses
across the set of experiments is used to define an objective test.
In some embodiments, the objective test is defined in the following manner:
Let pk; be the response of constituent k in experiment i. Let II(i) be a
random
permutation of the experiment index. Then for each of a large (about 100 to
1000) number
of different random permutations, construct p,~,~. For each branching in the
original tree,
for each permutation:
(1) perform hierarchical clustering with the same algorithm ('hclust' in this
case)
used on the original unpermuted data;
(2) compute fractional improvement f in the total scatter with respect to
cluster
centers in going from one c luster to two clusters
f=1- ~'Dkn~ / ~Dk~z~
where Dk is the square of the distance measure for constituent k with respect
to the center
(mean) of its ~~ssigned cluster. Superscript 1 or 2 indicates whether it .is
with respect to the.
center of the entire branch or with respect to the center of the appropriate
cluster out of the
~o subcluste:rs. There is considerable freedom in the definition of the
distance function D
used in the clustering procedure. In these examples, D =1- r , where r is the
correlation
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coefficient between the responses of one constituent across the experiment set
vs. the
responses of~the other (or vs. the mean cluster response).
The distribution of fractional improvements obtained from the Monte Carlo
procedure is an estimate of the distribution under the null hypothesis that
particular
branching was not significant, The actual fractional improvement for that
branching with
the unpermu.ted data is then compared to the cumulative probability
distribution from the
null hypothesis to assign significance. Standard deviations are derived by
fitting a log
normal model for the null hypothesis distribution.
The numbers displayed at the bifiu-cations in Fig. 6 are the significance, in
standard
deviations, o~f each bifurcation. Numbers greater than about 2, for example,
indicate that the
branching is significant at the 95% confidence level.
If, for example, the horizontal cut shown in Fig. 6 is used, and only those
branches
with more than two members below the cut are accepted as genesets, three
genesets are
obtained in Fig. 6. These three genesets reflect the pathways involving the
calcineurin
protein, the I'DR gene, and the Gcn4 transcription factor. Therefore, genesets
defined by
cluster analysis have underlying biological significance.
In more detail, an objective statistical test is preferably employed to
determine the
statistical reliability of the grouping decisions of any clustering method or
algorithm.
Preferably, a similar test is used for both hierarchical and non-hierarchical
clustering
methods. More preferably, the statistical test employed comprises (a)
obtaining a measure
of the compactness of the clusters determined by one of the clustering methods
of this
invention, and (b) comparing the obtained measure of compactness to a
hypothetical
measure of compactness of cellular constituents regrouped in an increased
number of
clusters. For example, in embodiments wherein hierarchical clustering
algorithms, such as
hclust, are employed, such a hypothetical measure of compactness preferably
comprises the
measure of compactness for clusters selected at the next lowest branch in a
clustering tree
(e.g., at LEVEL 1 rather than. at LEVEL 2 in Fig. 11). Alternatively, in
embodiments
wherein non-hierarchical clustering methods or algorithms are employed, e.g.,
to generate N
clusters, the hypothetical measure of compactness is preferably the
compactness obtained
for N+1 clusters by the same methods.
Cluster compactness may be quantitatively defined, e.g., as the mean squared
distance of elements of the cluster from the "cluster mean," or, more
preferably, as the ,
inverse of the; mean squared distance of elements from the cluster mean. The
cluster mean
of a particular cluster is generally defined as the mean of the response
vectors of all
elements in the cluster. However, in certain embodiments, e.g., wherein the
absolute value
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of the normalized or weighted dot product is used to evaluate the distance
metric (i.e., l = 1
- ~ r) ) of the clustering algorithm, such a definition of cluster mean is
problematic. More
generally, the above definiticm of mean is problematic in embodiments wherein
response
vectors may be in opposite directions such that the above defined cluster mean
could be
zero. Accordingly, in such embodiments, it is preferable to chose a different
definition of
cluster compactness, such as, but not limited to, the mean squared distance
between all pairs
of elements iin the cluster. Alternatively, the cluster compactness may be
defined to
comprise the; average distance (or more preferably the inverse of the average
distance) from
each element (e.g., cellular constituent) of the cluster to all other elements
in that cluster.
Preferably, Step (b) above of comparing cluster compactness to a hypothetical
compactness comprises generating a non-parametric statistical distribution for
the changed
compactness in an increased number of clusters. More preferably, such a
distribution is
generated using a model which mimics the actual data but has no intrinsic
clustered
structures (i.~s., a "null hypothesis" model). For example, such distributions
may be
generated by (a) randomizing the perturbation experiment index i for each
cellular
constituent ~:, and (b) calculating the change in compactness which occurs for
each
distribution, e.g., by increasing the number of clusters from N to N+1 (non-
hierarchical
clustering mE;thods), or by increasing the branching level at which clusters
are defined
(hierarchical methods).
Such a process is illustrated in Fig. 12 for an exemplary, non-hierarchical
embodiment of the clustering methods wherein the perturbation vectors are two-
dimensional (i.e., there are two perturbation experiment, i =1, 2) and have
lengths ~X~ =2.
Their response vectors are therefore displayed in Fig. 12 as points in two-
dimensional
space. In the: present example, two apparent clusters can be distinguished.
These two
cluster are shown in Fig. 12A, and comprise a circular cluster and a dumbbell-
shaped
cluster. The cluster centers are indicated by the triangle symbol (1). As is
apparent to one
skilled in the art, the distribution of perturbation vectors in Fig. 12 could
also be divided
into three clusters, illustrated in Fig. 12B along with their corresponding
centers. As will
also be apparent to one skilled in the art, the two new clusters in Fig. 12B
are each more
compact than the one dumbbell shaped cluster in Fig. 12A. However, such an
increase in
compactness may not be statistically significant, and so may not be indicative
of the actual
or unique cellular constituent sets. In particular, the compactness of a set
of N clusters may
be defined -in this example as the inverse of the mean squared distance of
each element from
its cluster center, i.e., as 1/Dw~,"eQ". In general, D~'+'~,"~,n < D~N~m~n~
Regardless of whether
there are additional "real" cellular constituent sets. Accordingly, the
statistical methods of
this invention may be used to evaluate the statistical significance of the
increased
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compactness which occurs, e.g., in the present example, when the number of
clusters is
increased fiom N= 2 to N+1 = 3.
In an exemplary embodiment, the increased compactness is given by the
parameter
E, which is defined by the fbrmula
ItN> _ IAN+n
E = mean mean
mean
However, other definitions are apparent to those skilled in the art which may
also be used in
the statistical methods of this invention. In general, the exact definition of
E is not crucial
provided it is monotonically related to increase in cluster compactness.
The statistical methods of this invention provide methods to analyze the
significance
of E. Specifically, these methods provide an empirical distribution approach
for the
analysis of.E by comparing the actual increase in compactness, Eo for actual
experimental
data, to an empirical distribution of E values determined from randomly
permuted data
(e,g.~ by Equation 10 above). In the two-dimensional example illustrated in
Fig 12, such a
translation comprises, first, randomly swapping the perturbation indices i =
1,2 in each
response vector with equal probability. More specifically, the coordinates
(i.e., the indices)
of the vectors in each cluster being subdivided are "reflected" about the
cluster center, e.g.,
by first translating the coordinate axes to the cluster center as shown in
Fig. 12C. The
results of such an operation are shown, for the two-dimensional example, in
Fig. 12D.
Second, the randomly permuted data are re-evaluated by the cluster algorithms
of the
invention, most preferably by the same cluster algorithm used to determine the
original
cluster(s), so that new clusters are determined for the penmutated data, and a
value of E is
evaluated for these new clusters (i. e., for splitting one or more of the new
clusters). Steps
one and two above are repeated for some number of Monte Carlo trials to
generate a
distribution of E values. Preferably, the number of Monte Carlo trials is from
about 50 to
about 1000, and more preferably from about 50 to about 100. Finally, the
actual increase in
compactness, i.e., Eo, is corr~pared to this empirical distribution of E
values. For example, if
M Monte Cs~rlo simulation are performed, of which x have E values greater than
Eo, then the
confidence level in the nurnber of clusters may be evaluated from 1 xlM. In
particular, if
M= 100 and x = 4, then the confidence level that there is no real significance
in increasing
the number of clusters is 1 - 4/100 = 96%.
The above methods are equally applicable to embodiments comprising
hierarchical
clusters and.~or a plurality of elements (e.g., rnore than two cellular
constituents). For
example, the cluster tree illustrated in Fig. 11. This clustering tree was
obtained using the
hclust algorithm for 34 perturbation response profiles comprising 185 cellular
constituents
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which had significant responses. Using the clusters defined by the branches at
LEVEL 2 of
this tree, 100 Monte Carlo simulations were performed randomizing the 34
experimental
indices and f;mpirical distributions for the improvements in compactness E
were generated
for each branching in the tree. The actual improvement in compactness Eo at
each branch
$ was comparc;d with its corresponding distribution. These comparisons are
shown by the
numbers at each branch in Fig. 11. Specifically, these numbers indicate the
number of
standard deviations in the distribution by which Eo exceed the average value
of E. The
indicated significance correspond well with the independently determined
biological
significance of the branches. For example, the main branch indicated in Fig. ?
by the
n~ber five (bottom label) comprises genes regulated via the calcineurin
protein, whereas
the branch labeled number 7 primarily comprises genes regulated by the Gcn4
transcription
factor.
Further, although the Monte Carol methods of the present invention are
described
above, for exemplary purposes, in terms of the permutation of a perturbation
index i, it is
readily appreciated by those skilled in the art that such methods may also be
used by
permuting ar~y index of biological response data which is independent of the
cellular
constituent index. For example, in some embodiments the response profile data
for cellular
constituent h.'may be a function of time, e.g., X(t), with a time index t in
addition to or in
place of a perturbation index. In such embodiments, the Monte Carlo methods of
this
invention many also be used by permuting the time index t.
Another aspect of the cluster analysis method of this invention provides the
definition of basis vectors for use in profile projection described in the
following sections.
A set of basis vectors V has k x n dimensions, where k is the number of genes
and n
is the number of genesets.
V~"~
V = . . . (li)
I~~"~
k k
"~k 1S the amplitude contribution of gene index k in basis vector n. In some
embodiments,
V~"~k = l, if gene k is a member of geneset n, and Y~"~k = 0 if gene k is not
a member of
geneset n. Iri some embodiments, I~"~~ is proportional to the response' of
gene k in geneset n
over the training data set used to define the genesets .
In some preferred embodiments, the elements I~"~k are normalized so that each
basis
vector T~"~ has unit length by dividing by the square root of the number of
genes in geneset
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n. This produces basis vectors which are not only orthogonal (the genesets
derived from
cutting the c'.lustering tree are disjoint), but also orthonormal (unit
length). With this choice
of normalization, random measurement errors in profiles project onto the
T~"~,~ in such a way
that the ampl'.itudes tend to be comparable for each n. Normalization prevents
large
genesets fror~n dominating the results of similarity calculations.
5.2.3. GENESET CLASSIFICATION BASED UPON
1VIECHANISMS OF REGULATION
Genesets can also be defined based upon the mechanism of the regulation of
genes.
Genes whose regulatory regions have the same transcription factor binding
sites are more
likely to be co-regulated. In same preferred embodiments, the regulatory
regions of the
genes of interest are compared using multiple alignment analysis to decipher
possible shared
transcription :factor binding sites (Stormo and Hartzell,1989, Identifying
protein binding
sites from unaligned DNA fragments, Proc Natl Acad Sci 86:1183-1187; Hertz and
Stormo,
1995, Identification of consensus patterns in unaligned DNA and protein
sequences: a large
deviation statistical basis for penalizing gaps, Proc of 3rd Intl Conf on
Bioinformatics and
Genome Research, Lim and Cantor, eds., World Scientific Publishing Co., Ltd.
Singapore,
pp. 201-216). For example, as Example 3, infra, shows, common promoter
sequence
responsive to ~Gcn4 in 20 genes may be responsible for those 20 genes being co-
regulated
over a wide variety of perturbations.
The co-regulation of genes is not limited to those with binding sites for the
same
transcriptional factor. Co-regulated (co-varying) genes may be in the up-
stream/down-
stream relationship where the products of up-stream genes regulate the
activity of down-
stream genes. :ft is well known to those of skill in the art that there are
numerous varieties of
gene regulation networks. One of skill in the art also understands that the
methods of this
invention are riot limited to any particular kind of gene regulation
mechanism. If it can be
derived from the mechanism of regulation that two genes are co-regulated in
terms of their
activity change; in response to perturbation, the two genes may be clustered
into a geneset.
Because of lack of complete understanding of the regulation of genes of
interest, it is
often preferred to combine cluster analysis with regulatory mechanism
knowledge to derive
better defined genesets. For example, in some embodiments statistically
significant
genesets identified in cluster analysis are compared to biologically
significant genesets, e.g.,
that are identified in regulatory mechanism studies. In some
preferred.embodiments, K- .
means clustering may be used to cluster genesets when the regulation of genes
of interest is
partially known. K-means clustering is particularly useful in cases where the
number of
genesets is predetermined by the understanding of the regulatory mechanism. In
general, K-
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mean clustering is constrained to produce exactly the number of clusters
desired. Therefore,
if promoter sequence comparison indicates the measured genes should fall into
three
genesets, K-means clustering may be used to generate exactly three genesets
with greatest
possible distinction between clusters.
5.2.4. REFINEMENT OF GENESETS AND GENESET DEFINITION DATABASE
Gene;sets found as above may be refined with any of several sources of
corroborating
information including searches for common regulatory sequence patterns,
literature
evidence for co-regulation, sequence homology, known shared function, etc.
Databases are particularly useful for the refinement of genesets. In some
embodiments, a database containing raw data for cluster analysis of genesets
is used for
continuously updating geneset definitions. FIG. 3 shows one embodiment of a
dynamic
geneset database. Data from perturbation experiments (301) are input into data
tables {302)
in the perturbation database management system (308). Geneset definitions, in
the form of
basis vectors are continuously generated based upon the updated data in
perturbation
database using cluster analysis (303) and biological pathway definitions (305,
306). The
resulting gen.eset definition datatable (304) contains updated geneset
definitions.
The g;eneset definitions are used for refining (307) the biological pathway
datatables.
The geneset definition tables are accessible by user-submitted projection
requests. A user
(313) can access the database; management system by submitting expression
profiles (31I).
The database management system projects (310) the expression profile into a
projected
expression profile (see, section 5.3, ,infra, for a discussion of the
projection process). The
user-submitted expression profile is optionally added to the perturbation data
tables {302).
This dynamic database is constantly productive in the sense that it provides
useful
geneset definitions with the first, and limited, set of perturbation data. The
dynamically
updated database continuously refines its geneset definitions to provide more
useful geneset
definitions as more perturbation data become available.
In Borne embodiments of the dynamic geneset definition database, the
perturbation
data and geneset definition data are stored in a series of relational tables
in digital computer
storage media. Preferably, the database is implemented in distributed system
environments
with client/serve~r implementation, allowing multiuser and remote access.
Access control
and usage accounting are implemented in some embodiments of the database
system.
Relational-.database management systems and client/server environments are
well
documented nn the art (Math, 1995, The Guide to S(ZL Server, 2°d ed.,
Addison-Wesley
Publishing C~o.).
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5.3. REPf.ESENTATION OF GENE EXPRESSION PROFILES
BASED UPON BASIS GENESETS
One aspect of the invention provides methods for converting the expression
value of
genes into the expression value for genesets. This process is referred to as
projection. In
S some embodiments, the projection is as follows:
P = (P~,.. Pr... P"~ = p. y (12)
wherein,p is the expression profile, P is the projected profile, P; is
expression value for
geneset i and V is a predefined set of basis vectors. The basis vectors have
been previously
defined in Equation 7 (Section 5.2.2, supra) as:
~~1) . Vin)
Y = . . . (13)
1 S T~~') . V ~")
k k
wherein i~"~k is the amplitude of cellular constituent index k of basis vector
n.
In ont; preferred embodiment, the value of geneset expression is simply the
average
of the expression value of the: genes within the geneset. In some other
embodiments, the
average is weighted so that highly expressed genes do not dominate the geneset
value. The
collection of the expression values of the genesets is the projected profile.
5.4. APPLICATION OF PR(ZJECTED PROFILES
The projected profiles, i.e., biological state or biological responses
expressed in
terms of genesets, offer many advantages. This section discusses another
aspect of this
invention which provides methods of analysis utilizing projected profiles.
5.4.1. ADVANTAGE OF THE PROJECTED PROFILE
One advantage of using projected profiles is that projected profiles are less
vulnerable to measurement e~TOrs. Assuming independent measurement errors in
the data
for each cellular constituent, the fractional standard error in the projected
profile element is
approximately M"''~2 times the average fractional standard error for the
individual cellular
constituents, where M" is the number of cellular constituents in the n'th
geneset. Thus if the
average up or down-regulation of the cellular constituents is significant at x
standard
deviations, then the projected profile element will be significant at M"'~ x
standard
deviations. This is a standard result for signal-to-noise ratios of mean
values; averaging
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makes a tremendous difference in the probabilities of detection vs. false
alarm (see, e.g., Van
Trees, 1968, Detection, Estimation, and Modulation Theory Vol I, Wiley &
Sons).
Another advantage of the projected profiles is the reduced dimension of the
data set.
For example, a 48 gene data set is represented by three genesets (example 2)
and 194 gene
data set is represented by 9 genesets (example 3). This reduction of data
dimension greatly
facilitates the: analysis of profiles.
Yet another advantage of the projected profiles is that projected profiles
tend to
capture the underlying biology. For example, FIG. 6 shows a clustering tree of
48 genes.
Three genesets which correspond to three pathways involving the calcineurin
protein, the
PDR gene, and the Gcn4 transcription factor, respectively, are identified
(Example 1, infra).
5.4.2. PROFILE COMPARISON AND CLASSIFICATION
Once the basis genesets are chosen, projected profiles P, may be obtained for
any set
of profiles indexed by i. Similarities between the P; may be more clearly seen
than between
~e original profiles p; for two reasons. First, measurement errors in
extraneous genes have
been excluded or averaged out., Second, the basis genesets tend to capture the
biology of the
profiles p; and so are matched detectors for their individual response
components.
Classification. and clustering of the profiles both are based on an objective
similarity metric,
call it S, where one useful definition is
Sri = S~Pa ~ P.~ - Pt ' Pi ~ ~P; I I P;V {14)
This definition is the generalized angle cosine between the vectors P; and P~.
It is the
projected version of the conventional correlation coefficient between p; and
p~. Profile p; is
deemed most similar to that other profile p~ for which S;~ is maximum. New
profiles may be
classified according to their similarity to profiles of known biological
significance, such as
the response patterns for known drugs or perturbations in specific biological
pathways. Sets
of new profile, may be clustered using the distance metric
D;; =1- S;~ {15)
where this clustering is analogous to clustering in the original larger space
of the entire set of
response measurements, but has the advantages just mentioned of reduced
measurement
error effects and enhanced capture of the relevant biology.
The statistical significance of any observed similarity S;~ may be assessed
using an
empirical probability distribution generated under the null hypothesis of no
correlation. This
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distribution is generated by performing the projection, Equations (9) and (10)
above, for
many different random permutations of the constituent index in the original
profile p.
That is, the ordered setpk are replaced byp~k~ where II'(k) is a permutation,
for 100
to 1000 different random permutations. The probability of the similarity S,~
arising by
chance is then the fraction of these permutations for which the similarity S~
(permuted)
exceeds the similarity observed using the original unpermuted data.
5.4.3. ILLUS'CRATIVE DRUG DISCOVERY APPLICATIONS
One aspect of the invention provides methods for drug discovery. In one
embodiment:, genesets are defined using cluster analysis. The genes within a
geneset are
indicated as potentially co-regulated under the conditions of interest. Co-
regulated genes are
further explored as potentially being involved in a regulatory pathway.
Identification of
genes involved in a regulatory pathway provides useful information for
designing and
screening new drugs.
Some embodiments of the invention employ geneset definition and projection to
identify drug action pathways. In one embodiment, the expression changes of a
large
number of genes in response to the application of a drug are measured. The
expression
change profile is projected into a geneset expression change profile. In some
cases, each of
the genesets represents one particular pathway with a defined biological
purpose. By
examining the change of genesets, the action pathway can be deciphered. In
some other
cases, the expression change profile is compared with a database of projected
profiles
obtained by perturbing many different pathways. If the projected profile is
similar to a
projected profile derived from a known perturbation, the action pathway of the
drug is
indicated as similar to the known perturbation. Identification of drug action
pathways is
useful for dn~g discovery. Sc~e, Stoughton and Friend, Methods for Identifying
pathways of
Drug Action., U.S. Patent Application No. 09/074,983, previously incorporated
by reference.
In some embodiments of the invention, drug candidates are screened for their
therapeutic activity (See, Friend and Hartwell, Drug Screening Method, U.S.
Provisional
Application lVo. 60/056,109, filed on August 20, 1998, previously incorporated
by reference
for all purposes, for a discussion of drug screening methods). In one
embodiment, desired
drug activity is to affect one particular genetic regulatory pathway. In this
embodiment,
drug candidates are screened for their ability to affect the geneset
corresponding to the
regulatory.pathway. In another embodiment, a new drug is desired to_replace an
existing _
drug. In this embodiment, the projected profiles of drug candidates are
compared with that
of the existing drug to deternrine which drug candidate has activities similar
to the existing
drug.
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In some embodiments, the method of the invention is used to decipher pathway
arborization and kinetics. When a receptor is triggered (or blocked) by a
ligand, the
excitation of the downstream pathways can be different depending on the exact
temporal
profile and molecular domains of the ligand interaction with the receptor.
Simple examples
of the differing effects of different ligands are the phenotypical differences
that arise
between responses to agonists, partial agonists, negative antagonists, and
antagonists, and
that are expected to occur ire response to covalent vs. noncovalent binding
and activation of
different molecular domains on the receptor. See, Ross, Pharmacodynamics:
Mechanisms
of Drug Action and the Relationship between Drug Concentration and Effect, in
The
Pharmacological Basis of Therapeutics (Gilman et al. ed.), McGraw Hill, New
York, 1996.
FIG. 4A illustrates two different possible responses of a pathway cascade.
In same embodiments of the invention, ligands for G protein-coupled receptors
(GPCRs) or other receptors may be investigated using the projection method of
the invention
to simplify the observed temporal responses to receptor interactions over the
responding
1 S genes. In some particularly preferred embodiments, the genesets and
temporal profiles
involved are; discovered. The profile of temporal responses of a large number
of genes are
projected onto the predefined genesets to obtain a projected profile of
temporal responses.
The projection process simplifies the observed responses so that different
temporal
responses may be detected and discriminated more accurately.
Figwre 4B gives an example of clustering of genes by their temporal response
profiles across several time points. The experiment here was activation of the
yeast mating
pathway (saJne strains, methods, etc. as described earlier) with the yeast a
mating
pheromone. Expression respanses for all yeast genes ratioed to control (mock
treatment)
baseline were measured immediately after treatment, and at 15 minutes after
treatment, 30,
45, 60, 90, and 120 minutes after treatment. This time series of experiments
provided the
experiment set for clustering analysis. Each line represents one experiment. A
line with an
asterisk represents an experiment that was given low weight in clustering
operation. Three
of the main cluster groups are illustrated in FIG. 4B, showing systematically
distinct
temporal behavior. The first group (early) is responding to the STE12
transcription factor,
the second group (adaptive) .contains members of the main signaling pathway
such as STE2
and STE12 itself that fatigue (show decreasing response) with continued
treatment, and the
third group (cell cycle) is associated with the cell cycle perturbations
inflicted by the mating
response. -
It is possible to define augmented basis vectors whose indices cover
constituents and
time points. Projection onto these basis vectors picks out the amplitudes of
response in
specific gene groups and of ;specific temporal profiles. Thus, for example, we
could
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efficiently detect responses such as those shown in the third group in FIG. 4B
by projecting
a time series of expression profiles onto an augmented basis vector whose
elements were
nonzero only for the genes included in the third group, and whose nonzero
amplitudes varied
over the time index according to the average of the temporal response in the
third group.
5.4.4. ILLUSTRATIVE DIAGNOSTIC APPLICATIONS
One aspect of the invention provides methods for diagnosing diseases of
humans,
animals and plants. Those methods are also useful for monitoring the
progression of
diseases and the effectiveness of treatments.
In one embodiment of the invention, a patient cell sample such as a biopsy
from a
patient's diseased tissue, is assayed for the expression of a large number of
genes. The gene
expression profile is projected into a profile of geneset expression values
according to a
definition oiFgenesets. The projected profile is then compared with a
reference database
containing reference projected profiles. If the projected profile of the
patient matches best
with a cancer profile in the database, the patient's diseased tissue is
diagnosed as being
cancerous. Similarly, when the best match is to a profile of another disease
or disorder, a
diagnosis of'such other disease or disorder is made.
In another embodiment, a tissue sample is obtained from a patient's tumor. The
tissue sample is assayed for the expression of a large number of genes of
interest. The gene
expression profile is projected into a profile of geneset expression values
according to a
definition of genesets. The projected profile is compared with projected
profiles previously
obtained from the same tumor to identify the change of expression in genesets.
A reference
library is usc;d to determine whether the geneset changes indicate tumor
progression. A
similar method is used to stage other diseases and disorders. Changes of
geneset expression
values in a profile obtained .from a patient under treatment can be used to
monitor the
effectiveness of the treatment, for example, by comparing the projected
profile prior to
treatment with that after treatment.
5.4.5. RESPONSE PROFILE CLASSIFICATION BY CLUSTER ANALYSIS
The methods of the present invention are not simply limited to grouping
cellular
constituents, such as genes, according to their degrees of co-variation (e.g.,
by co-
regulation). In particular, the cluster analysis and other statistical
classification methods
described.above to analyze the co-variation of cellular constituents may also
be used to
analyze biological response profiles and to group or cluster such profiles
according to the
similarity of their biological responses. Thus, for example, whereas Section
5.2.2 above
describes methods for analy:;ing cellular constituent "vectors" X = {X,.}
where i is the
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response profile index, the methods and equations described in Section 5.2.2
may also be
used to analyze response profile vectors v~'"~ _ {v;~"'~} where m is the
response profile index,
and i is the cellular consdtue:nt index.
Such analyses may be performed, e.g., using the exact same clustering
algorithms,
$ including 'hclust,' as described in Section 5.2.2 above, and using the exact
same distance
metrics. For example, Section 5.2.2 above describes using the distance metric
I= 1 - r,
where r is the normalized dot product X Yl ~Y~ ~Y~, for the comparison of
cellular constituent
vectors X and Y. As is readily apparent to those skilled in the art, the same
distance metric
may also be 'used to evaluate response profile vectors v~'"~ and v~"~, by
evaluating
r = v(mJ,v~")/ w~'"~ ~ w~"~ ~. SimilaJ- application of the other aspects of
the clustering methods
described above in Section 5.2.2, including the other distance metrics and the
significance
tests, are also apparent to those skilled in the art and may be used in the
present invention.
The analytical methods of this invention thus include methods of "two-
dimensional"
cluster analysis. Such two-dimensional cluster analysis methods simply
comprise (1)
clustering cellular constituents into sets that are co-varying in biological
profiles, and (2)
clustering biological profiles into sets that effect similar cellular
constituents (preferably in
similar ways;l. The two clustering steps may be performed in any order and
according to the
methods described above.
Such two-dimensional clustering techniques are useful, as noted above, for
identifying sets of genes and perturbations of particular interest. For
example, the two-
dimensional clustering techniques of this invention may be used to identify
sets of cellular
constituents (i.e., changes in levels of expression or abundance) and/or
experiments that are
associated with a particular biological effect of interest, such as a drug
effect or a particular
disease or disease state. The two-dimensional clustering techniques of this
invention may
also be used, e.g., to identify sets of cellular constituents and/or
experiments that are
associated with a particular biological pathway of interest.
Still further, the above described two-dimensional clustering techniques can
be used
to identify perturbations that cause changes in the levels of expression or
abundance of
particular cellular constituents of interest or in particular co-varying sets
of cellular
constituents (e.g., particular genesets) of interest. For example, in one
preferred
embodiment of the invention, such sets of cellular constituents and/or
perturbations are used
to determine consensus profiles for a particular biological response of
interest. In other
embodiments, identification of such sets of cellular constituents and/or.
experiments provide
more precise indications of groupings cellular constituents, such as
identification of genes
involved in a particular biological pathway or response of interest.
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Accordingly, another preferred embodiment of the present invention provides
methods for identifying cellular constituents, particularly genes (e.g., new
genes) or
genesets, whose change (e.g., in levels of expression or abundance) is
associated with and/or
involved in a particular biological effect of interest e.g., a particular
biological pathway, the
effect of one or more drugs, a particular disease or disease state or,
alternatively, a particular
treatment or therapy (e.g., a particular drug treatment or drug therapy). Such
cellular
constituents are identified according to the cluster-analysis methods
described above. Such
cellular constituents (e.g., ge:nes) may be previously unknown cellular
constituents, or
known cellular constituents that were not previously known to be associated
with the
biological effect of interest.
Considering, for example, the particular embodiment of identifying cellular
constituents associated with a disease or disease state, using the two-
dimensional clustering
methods described hereinabove biological profiles that cluster with
perturbations associated
with a particular disease or disease state can be identified and examined to
identify cellular
constituents and/or cellular constituent sets (e.g., genesets) that
consistently change (e.g., in
levels of expression or abundance) within such profiles. Such cellular
constituents are useful
as markers (c~.g., genetic markers in the case of genes and genesets) for the
particular disease
or disease state. In particular, changes in such markers (e.g., in their level
of expression or
abundance) observed in a biological sample obained, e.g., from a patient, can
therefore be
used to diagnose the particular disease or disease state in that patient.
Those cellular
constituents that are particul~~rly useful as markers (e.g., of a disease or
disease state), and
are therefore preferred in the present invention, are those cellular
constituents that change
(e.g., in their level of expression or abundance) in perturbations associated
with a particular
biological effect (e.g., a particular disease or disease state) of interest
but do not change in
over perturbations; i.e., in perturbations that are not associated with the
particular
biological effect of interest.
The present invention further provides methods for the iterative refinement of
cellular constituent sets and/or clusters of response profiles (such as
consensus profiles). In
particular, dominant features in each set of cellular constituents and or
profiles identified by
the cluster analysis methods ~of this invention may be blanked out, e.g., by
setting their
elements to zero or to the mean data value of the set. The blanking out of
dominant features
may done by a user, e.g., by manually selecting features to blank out, or
automatically, e.g.,
by automatically blanking out those elements whose response amplitudes are
above a
selected threshold. The cluster analysis methods of the invention are then
reapplied to the'
cellular constituent and/or profile data. Such iterative refinement methods
may be used, e.g.,
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to identify other potentially interesting but more subtle cellular constituent
andlor
experiment associations that were not identified because of the dominant
features.
More generally, and as is also apparent to those skilled in the art, the
clustering
methods of this invention may be used to cluster each dimension of any N-
dimensional array
of biological (or other) data, wherein N may be any positive integer. For
example, in some
embodiments, the biological data may comprise matrices (i.e., tables) of
values v~'"~, (t) which
describe the change of cellular constituent i in response to perturbation m
after a time t. The
clustering methods of the present invention may be used, in such embodiments,
to cluster (1)
the cellular constituent index i, (2) the perturbation response index m, and
(3) the time index
t. Other embodiments are also apparent to those skilled in the art.
5.4.6. REMOVAL OF PROFILE ARTIFACTS
The projection methods of the present invention, including the methods
described in
Section 5.2 above, may also be used to remove unwanted response components
(i.e.,
"artifacts") from biological profile data. Frequently, when such profile data
are obtained
there are one or more poorly controlled variables which lead to measured
patterns of cellular
constituents (e.g., measured gene expression patterns) which are, in fact,
artifacts of the
measurement process and are not part of the actual biological state or
response (such as a
perturbation response) being measured. Exemplary variables which may produce
artifacts in
biological profile data include, but are by no means limited to, cell culture
density and
temperature; and hybridization temperature, as well as concentrations of total
RNA and/or
hybridization reagents.
For example, Di Risi et al. (1997, Science 278:680-686) describe measurements
using microarrays of S. cerevisiae cDNA levels during the change from
anaerobic to aerobic
~'o~h (i.e., the "diauxic sh.ifl"). However, if one of two nominally identical
cell cultures
has unintentionally progressed further into the diauxic shift than the other,
their expression
ratios will reflect that transc:riptional changes associated with this shift.
Such artifacts
potentially confuse the measurements of the true transcriptional responses
being sought.
These artifacts may be "projected out" by removing or suppressing their
patterns in the data.
In preferred embodiments, the artifact patterns in the data are known. In
general,
artifact patterns may be determined from any source of knowledge of the genes
and relative
amplitudes ~of response associated with such artifacts. For example, the
artifact patterns may
be derived from experiments with intentional perturbations of the suspected
causative .
variables. In another embodiment, the artifact patterns may be determined from
clustering
analysis of control experimc,nts where the artifacts arise spontaneously.
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In such preferred embodiments, the contribution of known artifacts may be
solved
for and subtracted from the measured biological profile p = { p; }, e.g., by
determining the
best scaling; coefficients an for the contribution of artifact n to the
profile. Preferably, the
coefficients a" are found by determining the values of a" which minimize an
objective
function of the difference between the measured profile and the scaled
contribution of the
artifacts. For example, the .coefficients a" may be determined by the least
square
minimization
2
min ~ Pa - ~ an ~,> ''''r
l0 X ' n
wherein A".; is the amplitude; of artifact n on the measurement of cellular
constituent i. w; is
an optional weighting factor selected by a user according to the relative
certainty or
significance of the measured value of cellular constituent i (i. e., of p;).
1 S The "cleaned" protifile p~''e°"~ in which the artifacts are
effectively removed, is then
given by the; equation
pi 'lean) ' Pr _ ~ Q'n An,i (1 ~
n
wherein the coefficients a~ are determined, e.g., from equation 16 above.
20 In other embodiments, the profile p may be compared to a library of
artifact
signatures As = { As,; } of different severity. In such embodiments, the
"cleaned" profile is
determined by pattern matching against this library to determine the
particular template
which has greatest similarity to the profilep. In such embodiments, the
cleaned profile is
25 given by pk~'l'°"~ = pk _ As,,, wherein the signature AS is
determined, e.g., by solving the
equation
2
min ~(p; - fls;) w; (is)
5.4. i'. PROJECTED TITRATION CURVES
In many instances, it may be 'desirable to measure the response of a
biological system
to a plurality of graded levels of exposure to a particular perturbation. For
example, during
the process of drug discovery, it is often necessary or desirable to measure
the response of a
biological system to graded levels of exposure to a particular drug or drug
candidate, e.g., to
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determine the therapeutic and/or toxic effects of the drug or drug candidate.
In other
instances, it may be desirable; to measure the effect on a biological system,
e.g., of graded
expression of a particular gene or gene product, such as by the methods
described in Section
5.8.1 below. For example, Fig. 13 shows the transcriptional responses of the
largest
responding genes of S. cerevrsiae to different concentrations of the drug
FK506, as described
by Marton et al., 1998, Nature Medecine 4:1293-1301).
The methods of the present invention may also be used to project such
"titration
responses" onto co-varying cellular constituent sets, such as onto genesets.
Such "titration
responses" typically comprise a plurality of biological responses at graded
levels of exposure
to a particular perturbation (e.g., graded levels of exposure to the drug
FK506, as illustrated
in Fig. 13). Thus, projected titration responses may be generated by
projecting the
biological response profile obtained at each level of the perturbation (e.g.,
at each
concentration of the drug) according to any of the methods described above in
Sections 5.2
and 5.3. For example, Fig. 1:> shows the projected titration response curves
of Fig. 13. In
this particula~~ example, the projection comprises averaging the response of
each geneset
with normalisation such that the length of each basis geneset is unity, as
described, e.g., in
Section 5.3 above.
In preferred embodiments, the projected titration responses are interpolated,
e.g., by
fitting to some model function of the perturbation. For example, in Fig. 14
the projected
titration response curves have been fit to Hill Functions of the form shown in
Equation 3
above. However, other model function known in the art may be used.
Alternatively, the
projected titration response cwves may be interpolated by means of spline-
fitting, wherein
each projected titration curve is interpolated by summing products of an
appropriate spline
interpolation function S multiplied by the measured data values, as provided
by the equation
P(u) ~ ~,$'(u- u,)P(u,) (19)
r
The variable "u" refers to an arbitrary value of the perturbation (e.g., the
drug exposure level
or concentration) where the projected titration response P is to be evaluated.
The variable
"u," refers to discrete values of the perturbation at which response profiles
were actually
measured. In general, S may be any smooth, or at least piece-wise continuous,
function of
limited support having a width characteristic of the structure expected in the
projected
titration response functions. An exemplary width can be chosen to be the
distance over
which the projected titration response function being interpolated rises from
10% to 90% of
its asymptotic: value. Exemplary S functions include linear and Gaussian
interpolation.
Compared to the confi,~sing tangle of curves in Fig. 13, it is clear from the
projected
geneset titration responses shown in Fig. 14 that certain genesets respond at
different critical
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concentrations of FK506 (given by uo in Equation 3), and with different power
law exponent
(n in Equation 3) than do other genesets. Fig. 15 shows the contours of chi-
squared plotted
around the values of the two Hill coefficients (uo and n in Equation 3)
derived for each
geneset. The plot shows that the apparent visual distinctions in Fig. 14 are
statistically
significant. Specifically, the Hill coefficients are distinguished in both
their sharpness (i.e.,
the power law exponent n, vertical axis) and in their critical concentrations
(i.e., u~,
horizontal axis). Thus, individual genesets may be distinguished, e.g.,
according to the form
of their titration responses.
As expected, the different genesets in a titration response profile are also
biologically
significant. For example, supporting experiments using FK506 in gene deletion
strains of S
cerevisiae and the analysis of gene regulatory sequence regions show that the
geneset
identified in Fig. 14 for the titration response of S. cerevisiae to FK506
have biological
identities (sec; Marton et al., supra). These identities are indicated by the
annotations in Fig.
14. Thus, the; titration behaviors of different genesets are also indicative
of different
biological pathways. For example, the curves labeled "GCN4-dependent" in Fig.
14 are
responses of the sets of genes whose responses are mediated via the
transcription factor
protein Gcn4 (see, Marton et al., supra), while the gentler responses in Fig.
14, labeled
"GCN4-independent" are for the sets of genes which response to FK506 whether
or not the
calcineurin or Gcn4 proteins ;are present.
In other instances, it may be desirable to measure the state of a biological
sample
over a time interval. In particular, it is often desirable to monitor the
changing biological
state of a sample that occurs over time, e.g., in association with a
particular biological
process or effect. Such biological processes rnay include, but are by no means
limited to,
meiosis, mitosis, and cell differentiation. Changes in the biological state of
a sample that
occur over a time interval ma;y also include changes in response to a
particular perturbation
such as exposure to one or more drugs, or a change in the environment.
Monitoring changes
of the biological state of a sample over time may simply comprise a plurality
of
measurements of the time intE;rval during which the biological process or
effect of interest
occurs. The methods of the present invention may be used to project such
"temporal
measurements" of the biological state onto co-varying cellular constituent
sets such as onto
genesets. In particular, as is apparent to those skilled in the art, such
temporal measurements
may be analy:aed according to the methods described above for measuring
titration
responses.
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5.4.8. ~.1SE OF GENESETS IN MICROARRAYS
The genesets of the present invention are also useful in the design and
preparation of
microarrays. In particular, using the methods of the invention a skilled
artisan can readily
select and prepare probes for' a microarray wherein the microarray contains
specific
individual probes for less than all the genes in the genome and less than all
the genes in a
geneset. In such embodiments, the microarray contains one or two or more
individual
probes, each of which hybridises to an expression product (e.g., mRNA, or cDNA
or cRNA
derived therefrom) within a single geneset for a desired number of genesets.
Thus, for
example, changes in the expression of all or most of the genes in the entire
genome of a cell
or organism can thereby be monitored by use of a surrogate and on a single
microarray by
measuring expression of the group of genesets that are representative of all
or most of the
genes of the ;genome. Such rnicroarrays can be prepared, e.g., as described in
Section 5.7,
below, using the selected probes and are therefore part of the present
invention.
For e:Kample, in preferred embodiments, genesets are identified, as described
in the
above sections, for a biological sample (e.g., a cell or organism) of
interest. In general, the
number of genesets identified and for which probes appear in a microarray can
be anywhere
from 50 to 1,000. Preferably, however, the number of genesets for which probes
appear in a
microarray will be fewer than 500, more preferably from 100 to 500, and still
more
preferably from 100 to 200. Representative genes are then selected from each
geneset
identified, and probes are prepared that hybridize to the nucleotide sequence
of each
representative gene. Preferably, no more than ten representative genes are
selected from
each geneset. More preferably, however, the number of representative genes
selected from
each geneset for which probes appear on the rnicroarray is no more than five,
no more than
four, no more; than three or no more than two. In fact, most preferably only a
single
representative gene is selected from each geneset for which one or more probes
appear on
the microarray. For at least one geneset, and preferably for most or all of
the genesets, the
number of representative genes for which probes appear on the microarray is
less than the
total number of genes in the l;eneset. In certain preferred embodiments, at
least one
representative gene for which probes appear on the microarray is selected from
all of the
genesets identified for the cell or organism. In other embodiments, the
representative genes
for which probes appear on the microarray are selected solely from genesets
that are
associated with one or more particular biological states of interest. For
example, in certain
embodiments, the representative genes are selected from genesets associated
with a
particular disease or disease state. In other embodiments, the representative
genes are
selected from. genesets whose; change is expression is associated with a
particular drug or
with a particular therapy including, for example, genesets whose change is
expression is
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associated with drug or therapeutic efficacy or genesets whose change in
expression is
associated with drug resistance or therapeutic failure. Thus, for example, in
certain
embodiments the total number of genesets for which probes are present on a
microarray is
less than 1,(100, less than 500, less than 200, less than 100, less than 50,
less than 30, less
$ than 20, or less than 10.
5.~. COMPUTER IMPLEMENTATION
The analytic methods described in the previous subsections can preferably be
implemented by use of the following computer systems and according to the
following
programs arid methods. FICi. 5 illustrates an exemplary computer system
suitable for
implementation of the analytic methods of this invention. Computer system 501
is
illustrated as comprising internal components and being linked to external
components. The
internal components of this computer system include processor element 502
interconnected
with main rr~emory 503. For example, computer system SO1 can be an Intel
Pentium~-
15 based processor of 200 MHa or greater clock rate and with 32 MB or more of
main memory.
The external components include mass storage 504. This mass storage can be one
or
more hard disks (which are typically packaged together with the processor and
memory).
Such hard disks are typically of 1 GB or greater storage capacity. Other
external
components include user interface device SOS, which can be a monitor, together
with
inputing device 506, which can be a "mouse", or other graphic input devices
(not illustrated),
andlor a keyboard. A printing device 508 can also be attached to the computer
501.
Typically, computer system 501 is also linked to network link 507, which can
be part
of an Ethernet link to other local computer systems, remote computer systems,
or wide area
communication networks, such as the Internet. This network link allows
computer system
501 to share data and processing tasks with other computer systems.
Loaded into memory during operation of this system are several software
components,, which are both standard in the art and special to the instant
invention. These
software components collectively cause the computer system to function
according to the
methods of this invention. These software components are typically stored on
mass storage
504. Software component S 10 represents the operating system, which is
responsible for
managing computer system :501 and its network interconnections. This operating
system can
be, for example, of the Microsoft Windows' family, such as Windows 95, Windows
98, or
Windows NT. Software component.511 represents common languages and functions .
conveniently present on this system to assist programs implementing the
methods specific to
~s invention. Many high or low level computer languages can be used to program
the
analytic methods of this invf;ntion. Instructions can be interpreted during
run-time or
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compiled. Preferred languages include C/ C++, FORTRON and JAVA~. Most
preferably,
the methods of this invention are programmed in mathematical software packages
which
allow symbolic entry of equations and high-level specification of processing,
including
algorithms to be used, thereby freeing a user of the need to procedurally
program individual
equations or algorithms. Such packages include Matlab from Mathworks (Natick,
MA),
Mathematica from Wolfram Research (Champaign, IL), or S-Plus from Math Soft
(Cambridge, MA). Accordingly, software component 512 represents the analytic
methods of
this invention as programmed in a procedural language or symbolic package. In
a preferred
embodiment, the computer system also contains a database 513 of perturbation
response
curves.
In an exemplary implementation, to practice the methods of the present
invention, a
user first loads expression profile data into the computer system 501. These
data can be
directly entered by the user from monitor 505 and keyboard 506, or from other
computer
systems linked by network <;onnection 507, or on removable storage media such
as a CD-
ROM or floppy disk (not illustrated) or through the network (507). Next the
user causes
execution of expression pro:Eile analysis software S 12 which performs the
steps of clustering
co-varying genes into genesets.
In another exemplary implementation, a user first loads expression profile
data into
the computer system. Geneset profile definitions are loaded into the memory
from the
storage media (504) or from a remote computer, preferably from a dynamic
geneset database
system, through the network (507). Next the user causes execution of
projection software
which performs the steps of converting expression profile to projected
expression profiles.
In ye;t another exemplary implementation, a user first loads a projected
profile into
the memory. The user then causes the loading of a reference profile into the
memory. Next,
the user causes the execution of comparison software which performs the steps
of
objectively comparing the profiles.
This invention also provides software for geneset definition, projection, and
analysis
for projected profiles. One embodiment of the software contains a module
capable of
executing the cluster analysis of the invention. The module is capable of
causing a processor
of a computer system to execute steps of (a) receiving a perturbation
experiment data table,
(b) receiving the criteria for geneset selection, (c) cluster the perturbation
data into a
clustering tree, and (d) defining genesets based upon the clustering tree and
the criteria for
geneset selection.
Another embodiment of the software contains a module capable of executing the
projection operation by causing a processor of a computer system to execute
steps of (a)
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receiving a geneset definition, (b) receiving an expression profile, and (c)
calculating a
projected profile based upon the geneset definition and the expression
profile.
Yet another embodiment of the software contains a module capable of executing
the
comparison operation by causing a processor of a computer system to execute
steps of
(a) receiving a projected pro:6le of a biological sample, (b) receiving a
reference profile, and
(c) calculating an objective measurement of the similarity between the two
profiles.
Alternative computer systems and software for implementing the analytic
methods of
this invention will be apparent to one of skill in the art and are intended to
be comprehended
within the accompanying claims. In particular, the accompanying claims are
intended to
include the alternative progr~~rn structures for implementing the methods of
this invention
that will be readily apparent to one of skill in the art.
5.6. ANALYTIC KIT IMPLEMENTATION
In a preferred embodiment, the methods of this invention can be implemented by
use
of kits for determining the responses or state of a biological sample. Such
kits contain
microarrays, such as those described in Subsections below. The microarrays
contained in
such kits comprise a solid phase, e.g., a surface, to which probes are
hybridized or bound at a
known location of the solid phase. Preferably, these probes consist of nucleic
acids of
known, different sequence, with each nucleic acid being capable of hybridizing
to an RNA
species or to a cDNA species derived therefrom. In particular, the probes
contained in the
kits of this invention are nucieic acids capable of hybridizing specifically
to nucleic acid
sequences derived from RNA species which are known to increase or decrease in
response to
perturbations to the particular protein whose activity is determined by the
kit. The probes
contained in the kits of this invention preferably substantially exclude
nucleic acids which
hybridize to RNA species that are not increased in response to perturbations
to the particular
protein whose activity is determined by the kit.
In a preferred embodiment, a kit of the invention also contains a database of
geneset
definitions such as the databases described above or an access authorization
to use the
database described above from a remote networked computer.
In another preferred embodiment, a kit of the invention further contains
expression
profile projection and analysis software capable of being loaded into the
memory of a
computer system such as the one described supra in the subsection, and
illustrated in FIG. 5.
The expression profile analysis software contained in the kit of this
invention, is essentially
identical to the expression profile analysis software 512 described above.
Alternative kits for implementing the analytic methods of this invention will
be
apparent to one of skill in the. art and are intended to be comprehended
within the
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accompanying claims. In particular, the accompanying claims are intended to
include the
alternative program structures for implementing the methods of this invention
that will be
readily apparent to one of skill in the art.
5.7. METHODS FOR DETERMINING BIOLOGICAL RESPQNSE
This invention utilizea the ability to measure the responses of a biological
system to a
large variety of perturbations. This section provides some exemplary methods
for measuring
biological responses. One of. skill in the art would appreciate that this
invention is not
limited to thf; following specific methods for measuring the responses of a
biological system.
5.7.1. TRANSCRIPT ASSAY USING DNA ARRAY
This invention is particularly useful for the analysis of gene expression
profiles. One
aspect of the invention provides methods for defining co-regulated genesets
based upon the
correlation o f gene expression. Some embodiments of this invention are based
on measuring
the transcriptional rate of genes.
The t;ranscriptional rate can be measured by techniques of hybridization to
arrays of
nucleic acid or nucleic acid mimic probes, described in the next subsection,
or by other gene
expression technologies, such as those described in the subsequent subsection.
However
measured, the result is either the absolute, relative amounts of transcripts
or response data
including values representing RNA abundance ratios, which usually reflect DNA
expression
ratios (in the absence of differences in RNA degradation rates).
In va~~ious alternative embodiments of the present invention, aspects of the
biological
state other than the transcriptional state, such as the translational state,
the activity state, or
mixed aspects can be measured.
Preferably, measurement of the transcriptional state is made by hybridization
to
transcript arrays, which are described in this subsection. Certain other
methods of
transcriptional state measurement are described later in this subsection.
In a preferred embodiment the present invention makes use of "transcript
arrays"
(also called herein "microarrays"). Transcript arrays can be employed for
analyzing the
~anscription;~l state in a biological sample and especially for measuring the
transcriptional
states of a biological sample exposed to graded levels of a drug of interest
or to graded
perkurbations to a biological pathway of interest.
In one embodiment, transcript. arrays are produced by hybridizing detectably
labeled
polynucleotides representing the mRNA transcripts present in a cell (e.g.,
fluorescently
labeled cDNA synthesized from total cell mRNA) to a microan:ay. A microarray
is a surface
with an ordered array of binding (e.g., hybridization) sites for products of
many of the genes
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in the genome of a cell or organism, preferably most or almost all of the
genes. Microarrays
can be made in a number of ways, of which several are described hereinbelow.
However
produced, microarrays share certain characteristics: The arrays are
reproducible, allowing
multiple copies of a given array to be produced and easily compared with each
other.
Preferably, the microarrays are made from materials that are stable under
binding (e.g.,
nucleic acid hybridization) conditions. The microarrays are preferably small,
e.g., between
about 5 cmz 2nd 25 cmz, preferably about 12 to 13 cm2. However, both larger
and smaller
arrays are also contemplated and may be preferable, e.g., for simultaneously
evaluating a
very large number of different probes.
Preferably, a given binding site or unique set of binding sites in the
microarray will
specifically bind (e.g., hybrid.ize) to the product of a single gene or gene
transcript from a
cell or organism (e.g., to a specific mRNA or to a specific cDNA derived
therefrom).
However, as .discussed above, in general other, related or similar sequences
will cross
hybridize to a given binding site.
The microarrays used in the methods and compositions of the present invention
include one or more test probes, each of which has a polynucleotide sequence
that is
complementary to a subsequence of RNA or DNA to be detected. Each probe
preferably has
a different nucleic acid sequence, and the position of each probe on the solid
surface of the
array is preferably known. Indeed, the microarrays are preferably addressable
arrays, more
preferably positionally addressable arrays. More specifically, each probe of
the array is
preferably located at a known, predetermined position on the solid support
such that the
identity (i.e., the sequence) of each probe can be determined from its
position on the array
(i.e., on the support or surface;).
Preferably, the density of probes on a microarray is about 100 different
(i.e., non-
identical) probes per 1 cm2 or' higher. More preferably, a microarray used in
the methods of
the invention will have at least 550 probes per 1 cmz, at least 1,000 probes
per 1 cm2, at least
1,500 probes per 1 cmz or at least 2,000 probes per 1 cm2. In a particularly
preferred
embodiment, the microarray is a high density array, preferably having a
density of at least
about 2,500 different probes per 1 cm2. The microarrays used in the invention
therefore
preferably contain at least 2,500, at least 5,000, at least 10,000, at least
15,000, at least
20,000, at least 25,000, at least 50,000 or at least 55,000 different (i.e.,
non-identical)
probes.
In one: embodiment, the microarray is an array (i.e., a matrix) in which each
positions
represents a discrete binding site for a product encoded by a gene (i.e., for
an mRNA or for a
cDNA derived therefrom). For example, in various embodiments, the microarrays
of the
invention can comprise binding sites for products encoded by fewer than 50% of
the genes
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in the genome of an organsim. Alternatively, the microarrays of the invention
can have
binding sites for the products encoded by at least 50%, at least 75%, at least
85%, at least
90%, at least 95%, at least 99% or 100% of the genes in the genome of an
organism or,
alternatively, for representative genes of genesets encompassing the foregoing
percentages
of genes in tree genome. In other embodiments, the microarrays of the
invention can having
binding sites for products encoded by fewer than 50%, by at Ieast 50%, by at
least 75%, by
at least 85%, by at least 90%, by at least 95%, by at least 99% or by 100% of
the genes
expressed by a cell of an organism or, alternatively, for representative genes
of genesets
encompassing the foregoing percentages of genes in the genome. The binding
site can be a
DNA or DNA analog to which a particular RNA can specifically hybridize. The
DNA or
DNA analog can be, e.g., a syntehtic oligomer, a full length cDNA, a less-than
full length
cDNA, or a gene fragment.
Preferably, the micro~urays used in the invention have binding sites (i.e.,
probes) for
one or more genes relevant to the action of a drug of interest or in a
biological pathway of
interest. A "gene" is identified as an open reading frame (ORF) that encodes a
sequence of
preferably at least 50, 75, or ~)9 amino acid residues from which a messenger
RNA is
transcribed in the organism o:r in some cell or cells of a multicellular
organism. The number
of genes in a genome can be estimated from the number of mRNAs expressed by
the cell or
organism, or by extrapolation. of a well characterized portion of the genome.
When the
genome of the organism of interest has been sequenced, the number of ORFs can
be
determined and mRNA coding regions identified by analysis of the DNA sequence.
For
example, the genome of Saccharomyces cerevisiae has been completely sequenced
and is
reported to have approximately 6275 ORFs encoding sequences longer the 99
amino acid
residues in length. Analysis of these ORFs indicates that there are 5,885 ORFs
that are
likely to encode protein products (Goffeau et al., 1996, Science 274:546-567).
In contrast,
the human genome is estimated to contain approximately 105 genes.
It will be appreciated that when cDNA complementary to the RNA of a cell is
made
and hyhridize;d to a microarray under suitable hybridization conditions, the
level of
hybridization to the site in the array corresponding to any particular gene
will reflect the
prevalence in the cell of mRNA transcribed from that gene. For example, when
detectably
labeled (e.g., with a fluorophore) cDNA complementary to the total cellular
mRNA is
hybridized to a microarray, the site on the array corresponding to a gene
(i.e., capable of
specifically binding the product of the gene) that is not transcribed in the
cell will have little
or no signal (~e.g., fluorescent signal), and a gene for which the encoded
mRNA is prevalent
will have a relatively strong signal.
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In preferred embodiments, cDNAs from two different cells are hybridized to the
binding sites of the microarray. In the case of drug responses one biological
sample is
exposed to a virug and another biological sample of the same type is not
exposed to the drug.
In the case of pathway responses one cell is exposed to a pathway perturbation
and another
cell of the same type is not exposed to the pathway perturbation. The cDNA
derived fiom
each of the two cell types are differently labeled so that they can be
distinguished. In one
embodiment, for example, cDNA from a cell treated with a drug (or exposed to a
pathway
perturbation) is synthesized using a fluorescein-labeled dNTP, and cDNA from a
second
cell, not drug-exposed, is synthesized using a rhodamine-labeled dNTP. When
the two
cDNAs are mixed and hybridized to the microarray, the relative intensity of
signal from each
cDNA set is determined for each site on the array, and any relative difference
in abundance
of a particular mRNA detected.
In the example described above, the cDNA from the drug-treated (or pathway
perturbed) cell will fluoresce green when the fluorophore is stimulated and
the cDNA from
the untreated cell will fluoresce red. As a result, when the drug treatment
has no effect,
either directly or indirectly, on the relative abundance of a particular mRNA
in a cell, the
mRNA will be equally prevalent in both cells and, upon reverse transcription,
red-labeled
and green-labeled cDNA will be equally prevalent. When hybridized to the
microarray, the
binding site(s;) for that species of RNA will emit wavelengths characteristic
of both
fluorophores I;and appear brown in combination). In contrast, when the drug-
exposed cell is
treated with a drug that, direci;ly or indirectly, increases the prevalence of
the mRNA in the
cell, the ratio of green to red fluorescence will increase. When the drug
decreases the
mRNA prevalence, the ratio vrill decrease.
The use of a two-color fluorescence labeling and detection scheme to define
alterations in ;gene expression has been described, e.g., in Shena et al.,
1995, Quantitative
monitoring of gene expression patterns with a complementary DNA microarray,
Science
270:467-470, which is incorporated by reference in its entirety for all
purposes. An
advantage of using cDNA labeled with two different fluorophores is that a
direct and
internally controlled comparison of the rnRNA levels corresponding to each
arrayed gene in
two cell state.. can be made, and variations due to minor differences in
experimental
conditions (e.~:, hybridization conditions) will not affect subsequent
analyses. However, it
will be recognized that it is also possible to use cDNA from a single cell,
and compare, for
example, the absolute amount of a particular mRNA in, e.g., a drug-treated or
pathway-
perturbed cell and an untreated cell.
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5.7.1.1. PREPARING NUCLEIC ACIDS FOR MICROARRAYS
As noted above, the "binding site" to which a particular cognate cDNA
specifically
hybridizes is usually a nucleic acid or nucleic acid analogue attached at that
binding site. In
one embodirr~ent, the binding sites of the microarray are DNA polynucleotides
corresponding to at least a portion of each gene in an organism's genome.
These DNAs can
be obtained by, e.g., polymerase chain reaction (PCR) amplification of gene
segments from
genomic DN,A, cDNA (e.g., by RT-PCR), or cloned sequences. PCR primers are
chosen,
based on the known sequence; of the genes or cDNA, that result in
amplification of unique
fragments (i.E~., fragments that do not share more than 10 bases of contiguous
identical
sequence with any other fragment on the microanray). Computer programs are
useful in the
design of prirners with the required specificity and optimal amplification
properties. See,
e.g., Oligo version 5.0 (National Biosciences). In the case of binding sites
corresponding to
very long genes, it will sometimes be desirable to amplify segments near the
3' end of the
gene so that when oligo-dT primed cDNA probes are hybridized to the
microarray, less-
~~-~11 length probes will bind efficiently. Typically each gene fragment on
the
microarray will be between 5~0 by and 50,000 bp, between 50 by and 2000 bp,
more typically
between 100. by and 1000 bp, and usually between 300 by and 800 by in length.
PCR
methods are well known and are described, for example, in Innis et al. eds.,
1990, PCR
Protocols: A Guide to Methods and Applications, Academic Press Inc., San
Diego, CA,
which is incorporated by reference in its entirety for all purposes. It will
be apparent that
computer controlled robotic systems are useful for isolating and amplifying
nucleic acids.
An alternative, preferred means for generating the polynucleotide probes for a
microanray used in the methods and compositions of the invention is by
synthesis of
synthetic polynucleotides or c>ligonucleotides, e.g., using N-phosphonate or
phosphoramidite
chemistries (hroehler et al., 1'986, Nucleic Acid Res. 14:5399-5407; MeBride
et al., 1983,
Tetrahedron ~Gett. 24:246-248). Synthetic sequences are typically between 4
and 500 bases
in length, between 15 and 500 bases in length, more typically between 4 and
200 bases in
length, even more preferably 'between 15 and 150 bases in length and still
more preferably
between 20 and SO bases in length. In embodiments wherein shorter
oligonucleotide probes
are used, synthetic nucleic acid sequences less than 40 bases in length are
preferred, more
preferably between 15 and 30 bases in length. In embodiments wherein longer
oligonucleotide probes are used, synthetic nucleic acid sequences are
preferably between 40
and 80 bases :in length, more preferably between 40 and 70 bases in length and
even more
preferably between 50 and 60 bases in length. In some embodiments, synthetic
nucleic acids
include non-natural bases, such as, but not limited to, insoine. As noted
above, nucleic acid
analogs may be used as binding sites for hybridization. An example of a
suitable nucleic
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acid analog is peptide nucleic acid (see, e.g., Egholm et al., 1993, Nature
363:566-568; U.S.
Patent No. 5,539,083).
In an alternative embodiment, the binding (hybridization) sites are made from
plasmid or phage clones of genes, cDNAs (e.g., expressed sequence tags), or
inserts
therefrom (Nguyen et al., 1995, Differential gene expression in the marine
thymus assayed
by quantitative hybridization of arrayed cDNA clones, Genomics 29:207-209). In
yet
another embodiment, the polynucleotide of the binding sites is RNA.
5.'7.1.2. ATTACHING NUCLEIC ACIDS TO THE SOLID SURFACE
The nucleic acid or analogue are attached to a solid support, which may be
made
from glass, plastic (e.g., polypropylene, nylon), polyacrylamide,
nitrocellulose, or other
materials. A preferred method for attaching the nucleic acids to a surface is
by printing on
glass plates, as is described f;enerally by Schena et al., 1995, Quantitative
monitoring of
gene expression patterns with a complementary DNA microarray, Science 270:467-
470.
This method is especially useful for preparing microarrays of cDNA. See also
DeRisi et al.,
1996, Use of a cDNA microarray to analyze gene expression patterns in human
cancer,
Nature Genetics 14:457-460;, Shalon et al., 1996, A DNA microarray system for
analyzing
complex DNA samples using two-color fluorescent probe hybridization, Genome
Res.
6:639-645; and Schena et al., 1995, Parallel human genome analysis; microarray-
based
expression o:f 1000 genes, Proc. Natl. Acad. Sci. USA 93:10539-11286.
A second preferred method for making microarrays is by making high-density
oligonucleotide arrays. Techniques are known for producing arrays containing
thousands of
oligonucleotides complementary to defined sequences, at defined locations on a
surface
using photolithographic techniques for synthesis in situ (see, Fodor et al.,
1991, Light-
directed spatially addressable parallel chemical synthesis, Science 251:767-
773; Pease et al.,
1994, Light-directed oligonucleotide arrays for rapid DNA sequence analysis,
Proc. Natl.
Acad. Sci. USA 91:5022-50'.6; Lockhart et al., 1996, Expression monitoring by
hybridization to high-density oligonucleotide arrays, Nature Biotech 14:1675;
U.S. Patent
Nos. 5,578,832; 5,556,752; and 5,510,270, each of which is incorporated by
reference in its
entirety for all purposes) or other methods for rapid synthesis and deposition
of defined
oligonucleotides (Blanchard et al., 1996, High-Density Oligonucleotide arrays,
Biosensors
& Bioelectronics 11: 687-90;). When these methods are used, oligonucleotides
(e.g., 20-
mers) of known sequence are; synthesized directly on a surface such as a
derivatized glass
slide. Usually, the array produced contains multiple probes against each
target transcript.
Oligonucleotide probes can be chosen to detect alternatively spliced mRNAs or
to serve as
various type of control.
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Another preferred method of making microarrays is by use of an inkjet printing
process to synthesize oligonucleotides directly on a solid phase, as
described, e.g., in
co-pending tl.S. patent application Serial No. 09/008,120 filed on January 16,
1998, by
Blanchard entitled "Chemical Synthesis Using Solvent Microdroplets", which is
incorporated by reference herein in its entirety.
Other methods for making microarrays, e.g., by masking (Maskos and Southern,
1992, Nuc. Acids Res. 20:16.79-1684), may also be used. 1n principal, any type
of array, for
example, dot: blots on a nylon hybridization membrane (see Sambrook et al.,
Molecular
Cloning - A Laboratory Manual (2nd Ed.), Vol. 1-3, Cold Spring Harbor
Laboratory, Cold
Spring Harbor, New York, 1989), could be used, although, as will be recognized
by those of
skill in the art, very small aways will be preferred because hybridization
volumes will be
smaller.
In a particularly preferred embodiment, micorarrays used in the invention are
manufactured by means of an ink jet printing device for oligonucleotide
synthesis, e.g.,
using the methods and systems described by Blanchard in International Patent
Publication
No. WO 98/41531, published on September 24, 1998; Blanchard et al., 1996,
Biosensors
and Bioeletn~nics 11:687-690; Blanchard, 1998, in Synthetic DNA Arrays in
Genetic
Engineering" Vol. 20, J.K. Setlow, ed., Plenum Press, New York at pages 111-
123.
Specifically, the oligonucleotide probes in such microarrays are preferably
synthesized by
serially depositing individual nucleotides for each probe sequence in an array
of
"microdrople;ts" of a high surface tension solvent such a propylene carbonate.
The
microdroplets have small volumes (e.g., 100 pL or less, more preferably 50 pL
or less) and
are separatedl from each other on the microarray (e.g., by hydrophobic
domains) to form
circular surface tension wells which define the locations of the array
elements (i.e., the
different probes).
5.7.1.3. TARGET POLYNUCLEOTIDE MOLECULES
Methods for preparing total and poly(A)+ RNA are well known and are described
generally in Sambrook et al., supra. In one embodiment, RNA is extracted from
cells of the
v~ous types of interest in this invention using guanidinium thiocyanate lysis
followed by
CsCI centrifiigation (Chirgwin et al., 1979, Biochemistry 18:5294-5299).
Poly(A)+ RNA is
selected by selection with oligo-dT cellulose (see Sambrook et al., supra).
Cells of interest
include wild-type cells, drug-exposed, wild-type cells, modified cells, and
drug-exposed ,
modified cells.
Labeled cDNA is prepared from mRNA by oligo dT-primed or random-primed
reverse transcription, both of which are well known in the art (see, e.g.,
Klug and Berger,
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1987, Methods Enzymol. 1$2:316-32$). Reverse transcription may be earned out
in the
presence of a dNTP conjugated to a detectable label, most preferably a
fluorescently labeled
dNTP. Alternatively, isolated mRNA can be converted to labeled antisense RNA
synthesized by in vitro transcription of double-stranded cDNA in the presence
of labeled
$ dNTPs (Lockhart et al., 199ti, Expression monitoring by hybridization to
high-density
oligonucleotide arrays, Nature Biotech. 14:167$, which is incorporated by
reference in its
entirety for a.ll purposes). In alternative embodiments, the cDNA or RNA probe
can be
synthesized in the absence of detectable label and may be labeled
subsequently, e.g., by
incorporating biotinylated dNTPs or rNTP, or some similar means (e.g., photo-
cross-linking
a psoralen derivative of biotin to RNAs), followed by addition of labeled
streptavidin (e.g.,
phycoerythrin-conjugated streptavidin) or the equivalent.
When fluorescently-labeled probes are used, many suitable fluorophores are
known,
including fluorescein, lissamine, phycoerythrin, rhodamine (Perkin Elrner
Cetus), Cy2, Cy3,
Cy3.5, CyS, Cy5.5, Cy7, FluorX (Amersham) and others (see, e.g., Kricka, 1992,
1$ Nonisotopic DNA Probe Techniques, Academic Press San Diego, CA). It will be
appreciated That pairs of fluoxophores are chosen that have distinct emission
spectra so that
they can be easily distinguished.
In another embodiment, a label other than a fluorescent label is used. For
example, a
radioactive label, or a pair of radioactive labels with distinct emission
spectra, can be used
{see Zhao et al., 199$, High density cDNA filter analysis: a novel approach
for large-scale,
quantitative analysis of gene expression, Gene 1$6:207; Pietu et al., 1996,
Novel gene
transcripts preferentially expressed in human muscles revealed by quantitative
hybridization
of a high density cDNA array, Genome Res. 6:492). However, because of
scattering of
radioactive particles, and the consequent requirement for widely spaced
binding sites, use of
2$ radioisotopes is a less-preferred embodiment.
In one; embodiment, labeled cDNA is synthesized by incubating a mixture
containing
0.$ mM dGT:P, dATP and dC'TP plus 0.1 mM dTTP plus fluorescent
deoxyribonucleotides
(e.g., 0.1 mM: Rhodamine 110 UTP (Perken I:liner Cetus) or 0.1 mM Cy3 dUTP
(Amersham)) with reverse transcriptase (e.g., SuperScriptTM II, LTI Inc.) at
42° C for 60
mm.
$.7.1.4. HYBRIDIZATION TO MICROARRAYS
Nucleic acid hybridization and wash conditions are optimally chosen so that
the
probe "specifically binds" ar '"specifically hybridizes" to a specific array
site, i.e., the probe
hybridizes, duplexes or binds to a sequence array site with a complementary
nucleic acid
3$ sequence but does not hybridize to a site with a non-complementary nucleic
acid sequence.
As used herein, one polynucleotide sequence is considered complementary to
another when,
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if the shorter of the polynucleotides is less than or equal to 25 bases, there
are no
mismatches using standard base-pairing rules or, if the shorter of the
polynucleotides is
longer than 2.5 bases, there is no more than a 5% mismatch. Preferably, the
polynucleotides
are perfectly complementary (no mismatches). It can easily be demonstrated
that specific
hybridization conditions result in specific hybridization by carrying out a
hybridization assay
including negative controls (see, e.g., Shalon et al., supra, and Chee et al.,
supra).
Optimal hybridization conditions will depend on the length (e.g., oligomer
versus
polynucleotide greater than 200 bases) and type (e.g., RNA, DNA, PNA) of
labeled probe
and immobilized polynucleotide or oligonucleotide. General parameters for
specific (i.e.,
stringent) hybridization conditions for nucleic acids are described in
Sambrook et al., supra,
and in Ausubel et al., 1987, (:urrent Protocols in Molecular Biology, Greene
Publishing and
Wiley-Interscience, New York. When the cDNA microarrays of Schena et al. are
used,
typical hybridization conditions are hybridization in 5 X SSC plus 0.2% SDS at
65 ° C for 4
hours followed by washes at :25 ° C in low stringency wash buffer (1 X
SSC plus 0.2% SDS)
followed by 1.0 minutes at 25 ° C in high stringency wash buffer (0.1 X
SSC plus 0.2% SDS)
(Shena et al., 1996, Proc. Natl. Acad. Sci. USA, 93:10614). Useful
hybridization conditions
are also provided in, e.g., Tijessen, 1993, Hybridization With Nucleic Acid
Probes, Elsevier
Science Publishers B.V. and I~ricka, 1992, Nonisotopic DNA Probe Techniques,
Academic
Press San Diego, CA.
5.7.1.5. SIGNAL DETECTION AND DATA ANALYSIS
When fluorescently labeled probes are used, the fluorescence emissions at each
site
of a transcript array can be, preferably, detected by scanning confocal laser
microscopy. In
one embodiment, a separate scan, using the appropriate excitation line, is
corned out for
each of the tvvo fluorophores used. Alternatively, a laser can be used that
allows
simultaneous specimen illumination at wavelengths specific to the two
fluorophores and
emissions from the two fluorophores can be analyzed simultaneously (see Shalon
et al.,
1996, A DNA, microarray sys~~tem for analyzing complex DNA samples using two-
color
fluorescent probe hybridization, Genome Research 6:639-645, which is
incorporated by
reference in its entirety for all purposes). In a preferred embodiment, the
arrays are scanned
with a laser fluorescent scanner with a computer controlled X-Y stage and a
microscope
objective. Sequential excitation of the two fluorophores is achieved with a
mufti-line, mixed
gas laser and the emitted light is split by wavelength and detected with two
photomultiplier
tubes. Fluorescence laser scanning devices are described in Schena et al.,
1996, Genome
Res. 6:639-64.5 and in other rc;ferences cited herein. Alternatively, the fber-
optic bundle
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described by Ferguson et al., 1996, Nature Biotech. 14:1681-1684, may be used
to monitor
mRNA abundance levels at a large number of sites simultaneously.
Signals are recorded and, in a preferred embodiment, analyzed by computer,
e.g.,
using a 12 bit analog to digital board. In one embodiment the scanned image is
despeckled
using a graphics program (e.~,=., Hijaak Graphics Suite) and then analyzed
using an image
gridding pro~am that creates a spreadsheet of the average hybridization at
each wavelength
at each site. If necessary, an experimentally determined correction for "cross
talk" (or
overlap) betvween the channels for the two fluors may be made. For any
particular
hybridization. site on the transcript array, a ratio of the emission of the
two fluorophores can
be calculated. The ratio is independent of the absolute expression level of
the cognate gene,
but is useful ;for genes whose expression is significantly modulated by drug
administration,
gene deletion., or any other tested event.
According to the method of the invention, the relative abundance of an mRNA in
two
biological samples is scored ~~s a perturbation and its magnitude determined
(i.e., the
abundance is different in the two sources of mRNA tested), or as not perturbed
(i.e., the
relative abundance is the same). In various embodiments, a difference between
the two
sources of RNA of at least a i:actor of about 25% (RNA from one source is 25%
more
abundant in one source than the other source}, more usually about 50%, even
more often by
a factor of abut 2 (twice as abundant), 3 (three times as abundant) or 5 (five
times as
abundant) is scored as a perturbation.
Preferably, in addition to identifying a perturbation as positive or negative,
it is
advantageous to determine the magnitude of the perturbation. This can be
carried out, as
noted above, by calculating the ratio of the emission of the two fluorophores
used for
differential labeling, or by analogous methods that will be readily apparent
to those of skill
in the art.
5.7.2. PATHWAY RESPONSE AND GENE~ETS
In one. embodiment of the present invention, genesets are determined by
observing
the gene expression response of perturbation to a particular pathway. In one
embodiment of
the invention, transcript arrays reflecting the transcriptional state of a
biological sample of
interest are made by hybridizing a mixture of two differently labeled probes
each
corresponding (i.e., complementary) to the mRNA of a different sample of
interest, to the
microarray.. According to the present. invention, the two samples are.af
the.same type, i.e.,_
of the same species and strain, but may differ genetically at a small number
(e.g., one, two,
three, or five, preferably one) of loci. Alternatively, they are isogeneic and
differ in their
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environmental history (e.g., exposed to a drug versus not exposed). The genes
whose
expression ~~re highly correlated may belong to a geneset.
In one aspect of the :invention, gene expression change in response to a large
number
of perturbations is used to construct a clustering tree for the purpose of
defining genesets.
Preferably, the perturbations should target different pathways. In order to
measure
expression responses to the ;pathway perturbation, biological samples are
subjected to graded
perturbations to pathways of interest. The samples exposed to the perturbation
and samples
not exposed to the perturbation are used to construct transcript arrays, which
are measured to
find the mRLVAs with modified expression and the degree of modification due to
exposure to
the perturbation. Thereby, t;he perturbation-response relationship is
obtained.
The density of levels of the graded drug exposure and graded perturbation
control
parameter is governed by the sharpness and structure in the individual gene
responses - the
steeper the steepest part of the response, the denser the levels needed to
properly resolve the
response.
Further, it is preferable in order to reduce experimental error to reverse the
fluorescent labels in two-color differential hybridization experiments to
reduce biases
peculiar to individual genes or array spot locations. In other words, it is
preferable to first
measure gene expression with one labeling (e.g., labeling perturbed cells with
a first
fluorochrome and unperturbed cells with a second fluorochrome) of the mRNA
from the two
cells being measured, and then to measure gene expression from the two cells
with reversed
labeling (e.g., labeling perturbed cells with the second fluorochrome and
unperturbed cells
with the first fluorochrome). Multiple measurements over exposure levels and
perturbation
control parameter levels provide additional experimental error control. With
adequate
sampling a trade-off may be made when choosing the width of the spline
function S used to
interpolate response data between averaging of errors and loss of structure in
the response
functions.
5.7.3. MEASUREMENT OF GRADED PERTURBATION RESPONSE DATA
To measure graded response data, the cells are exposed to graded levels of the
drug,
drug candidate of interest or ;grade strength of other perturbation. When the
cells are grown
in vitro, the compound is usually added to their nutrient medium. In the case
of yeast, it is
preferable to harvest the yeast in early log phase, since expression patterns
are relatively
insensitive. to time of harvest at that time. Several levels of the drug or
other compounds are
added. The particular level employed depends on the particular characteristics
of the drug,
but usually will be between about 1 ng/ml and 100 mg/ml. In some cases a drug
will be
solubilized in a solvent such as DMSO.
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The c:ells exposed to the drug and cells not exposed to the drug are used to
construct
transcript arrays, which are measured to find the mRNAs with altered
expression due to
exposure to the drug. Thereby, the drug response is obtained.
Similarly for measurements of pathway responses, it is preferable also for
drug
responses, in the case of two-color differential hybridization, to measure
also with reversed
labeling. Also, it is preferable that the levels of drug exposure used proved
sufficient
resolution (e.g., by using approximately 10 levels of drug exposure) of
rapidly changing
regions of the drug response.
5.7.4. OTHER METHODS OF TRANSCRIPTIONAL STATE MEASUREMENT
The transcriptional state of a cell may be measured by other gene expression
technologies known in the art. Several such technologies produce pools of
restriction
fragments of limited complexity for electrophoretic analysis, such as methods
combining
double restriction enzyme digestion with phasing primers (see, e.g., European
Patent O
534858 A1, filed September :24, 1992, by Zabeau et al.), or methods selecting
restriction
fragments with sites closest to a defined mRNA end (see, e.g., Prashar et al.,
1996, Proc.
Natl. Aced. Sci. USA 93:659-663). Other methods statistically sample cDNA
pools, such as
by sequencing sufficient bases (e.g., 20-50 bases) in each of multiple cDNAs
to identify
each cDNA, or by sequencing; short tags (e.g., 9-10 bases) which are generated
at known
positions relative to a defined mRNA end (see, e.g., Velculescu, 1995, Science
270:484-
487).
5.7.5. MEASUREMENT OF OTHER ASPECTS OF BIOLOGICAL STATE
In various embodiments of the present invention, aspects of the biological
state other
than the transcriptional state, such as the translational state, the activity
state, or mixed
aspects can be measured in order to obtain drug and pathway responses. Details
of these
embodiments are described in this section.
5.7.5.1. EMBODIMENTS BASED ON TRANSLATIONAL STATE MEASUREME
Measurement of the translational state may be performed according to several
methods. For example, whole; genome monitoring ofprotein (i.e., the
"proteome," Goffeau
et al., supra) c;an be carried out by constructing a microarray in which
binding sites comprise
immobilized, preferably monoclonal,.antibodies specific to a plurality-of
protein species .
encoded by flee cell genome. Preferably, antibodies are present for a
substantial fraction of
~e encoded proteins, or at least for those proteins relevant to the action of
a drug of interest.
Methods for making monoclonal antibodies are well known (see, e.g., Harlow and
Lane,
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1988, Antibodies: A Laboratory Manual, Cold Spring Harbor, New York, which is
incorporated in its entirety for all purposes). In a preferred embodiment,
monoclonal
antibodies are raised against synthetic peptide fragments designed based on
genomic
sequence of the cell. With such an antibody array, proteins from the cell are
contacted to the
array and their binding is assayed with assays known in the art.
Alternatively, proteins can be separated by two-dimensional gel
electrophoresis
systems. Two-dimensional gel electrophoresis is well-known in the art and
typically
involves iso-electric focusing along a first dimension followed by SDS-PAGE
electrophoresis along a second dimension. See, e.g., Hames et al., 1990, Gel
Electrophoresis
of Proteins: A Practical Approach, IRL Press, New York; Shevchenko et al.,
1996, Proc.
Naf1 Acad. Sci. USA 93:1440-1445; Sagliocco et al., 1996, Yeast 12:1519-1533;
Larder,
1996, Science 274:536-539. The resulting electropherograms can be analyzed by
numerous
techniques, including mass spectrometric techniques, western blotting and
immunoblot
analysis using polyclonal and monoclonal antibodies, and internal and N-
terminal micro-
sequencing. Using these tecrmiques, it is possible to identify a substantial
fraction of all the
proteins produced under given physiological conditions, including in cells
(e.g., in yeast)
exposed to a drug, or in cells modified by, e.g., deletion or over-expression
of a specific
gene.
5.7.5.2. EEMBODIMENTS BASED ON OTHER ASPECTS OF THE
BIOLOGICAL STATE
Even though methods of this invention are illustrated by embodiments involving
gene expression profiles, the methods of the invention are applicable to any
cellular
constituent that can be monitored.
In particular, where activities of proteins relevant to the characterization
of a
perturbation, ouch as drug action, can be measured, embodiments of this
invention can be
based on such measurements. Activity measurements can be performed by any
functional,
biochemical, ~or physical mea~zs appropriate to the particular activity being
characterized.
Where the activity involves a chemical transformation, the cellular protein
can be contacted
with the natural substrate(s), amd the rate of transformation measured. Where
the activity
involves association in multimeric units, for example association of an
activated DNA
binding complex with DNA, f:he amount of associated protein or secondary
consequences of
the association, such as amounts of m.RNA transcribed, can be measured. Also,
where only
a functional activity is known, for example, as in cell cycle control,
performance of the
action can be observed. However known and measured, the changes in protein
activities
form the response data analyzed by the foregoing methods of this invention.
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In alternative and non-limiting embodiments, response data may be formed of
mixed
aspects of the; biological state; of a cell. Response data can be constructed
from, e.g.,
changes in certain mRNA abundances, changes in certain protein abundances, and
changes
in certain protein activities.
5.8. METHOD FOR PROBING CELLULAR STATES
One aspect of the invc,ntion provides methods for the analysis of co-varying
cellular
constituents. The methods of this invention are also useful for the analysis
of responses of a
biological sample to perturbations designed to probe cellular state. This
section provides
some illustrative methods fox probing cellular states.
Methods for targeted perturbation of cellular states at various levels of a
cell are
increasingly widely known and applied in the art. Any such methods that are
capable of
specifically t~~rgeting and controllably modifying (e.g., either by a graded
increase or
activation or by a graded decrease or inhibition) specific cellular
constituents (e.g., gene
expression, RNA concentrations, protein abundances, protein activities, or so
forth) can be
employed in performing cellular state perturbations. Controllable
modifications of cellular
constituents consequentially controllably perturb cellular states originating
at the modified
cellular constituents. Preferable modification methods are capable of
individually targeting
each of a plurality of cellular constituents and most preferably a substantial
fraction of such
cellular constituents.
The following methods are exemplary of those that can be used to modify
cellular
constituents and thereby to produce cellular state perturbations which
generate the cellular
state responses used in the steps of the methods of this invention as
previously described.
This invention is adaptable to other methods for making controllable
perturbations to
cellular states, and especially to cellular constituents from which cellular
states originate.
Cellular state perturbations are preferably made in cells of cell types
derived from
any organism for which genomic or expressed sequence information is available
and for
which methods are available that permit controllable modification of the
expression of
specific gene... Genome sequencing is currently underway for several
eukaryotic organisms,
including humans, nematodes, Arabidopsis, and flies. In a preferred
embodiment, the
invention is carried out using a yeast, with Saccharomyces cerevisiae most
preferred because
the sequence of the entire genome of a S. cerevisiae strain has been
determined. In addition,
well-established methods are available for controllably modifying expression
of yeast genes.
A preferred strain of yeast is a S. cerevisiae strain for which yeast genomic
sequence is
lmown, such ~~s strain S288C or substantially isogeneic derivatives of it
(see, e.g., Nature
369, 371-8 (1994); P.N.A.S. 92:3809-13 (1995); E.M.B.O. J. 13:5795-5809
(1994), Science
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265:2077-2082 (1994); E.M..B.Q. J. 15:2031-49 (1996), all of which are
incorporated herein.
However, other strains may be used as well. Yeast strains are available from
American Type
Culture Collection, Manassas, Virginia. Standard techniques for manipulating
yeast are
described in C. Kaiser, S. Michaelis, & A. Mitchell, 1994, Methods in Yeast
Genetics: A
S Cold Spring :Harbor Laboratory Course Manual, Cold Spring Harbor Laboratory
Press, New
York; and Sherman et al., 1986, Methods in Yeast Genetics: A Laboratory
Manual, Cold
Spring Harbor Laboratory, Cold Spring Harbor. New York, both of which are
incorporated
by reference in their entirety and for all purposes.
The exemplary methods described in the following include use of titratable
expression systems, use of transfection or viral transduction systems, direct
modifications to
RNA abundances or activities, direct modifications of protein abundances, and
direct
modification of protein activities including use of drugs (or chemical
moieties in general)
with specific known action.
5.8.1. T ITRATABLE EXPRESSION SYSTEMS
Any of the several known titratable, or equivalently controllable, expression
systems
available for use in the budding yeast Saccharomyces cerevisiae are adaptable
to this
invention (Mumberg et al., 1994, Regulatable promoter of Saccharomyces
cerevisiae:
comparison of transcriptional activity and their use for heterologous
expression, Nucl. Acids
Res. 22:5767-5768). Usually, gene expression is controlled by transcriptional
controls, with
the promoter of the gene to bE: controlled replaced on its chromosome by a
controllable,
exogenous promoter. The most commonly used controllable promoter in yeast is
the GAL1
promoter (Jolmston et al., 1984, Sequences that regulate the divergent GALL-
GAL10
promoter in Saccharomyces cerevisiae, Mol Cell. Biol. 8:1440-1448). The GAL1
promoter
is strongly rpressed by the presence of glucose in the growth medium, and is
gradually
switched on in a graded manner to high levels of expression by the decreasing
abundance of
glucose and the presence of galactose. The GALL promoter usually allows a 5-
100 fold
range of expression control on a gene of interest.
Other frequently used promoter systems include the MET25 promoter (Kerjan et
al.,
1986, Nucleotide sequence of the Saccharomyces cerevisiae MET25 gene, Nucl.
Acids. Res.
14:7861-7871 ), which is induced by the absence of methionine in the growth
medium, and
the CUP1 promoter, which is induced by copper (Mascorro-Gallardo et al., 1996,
Construction of a CUP1 promoter-based vector to modulate gene expression in
Saccharomyces cerevisiae, Geyne 172:169-17U). All of these promoter systems
are
controllable in that gene expression can be incrementally controlled by
incremental changes
in the abundances of a controlling moiety in the growth medium.
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One disadvantage of the above listed expression systems is that control of
promoter
activity (effec;ted by, e.g., changes in carbon source, removal of certain
amino acids), often
causes other c;hanges in cellular physiology which independently alter the
expression levels
of other genes. A recently develaped system for yeast, the Tet system,
alleviates this
S problem to a :large extent (Gari et al., 1997, A set of vectors with a
tetracycline-regulatable
promoter system for modulated gene expression in Saccharomyces cerevisiae,
Yeast 13:837-
848). The Tet promoter, adopted from mammalian expression systems (Gossen et
al., 1995,
Transcriptional activation by tetracyclines in mammalian cells, Proc. Nat.
Acad. Sci. USA
89:5547-5551 ) is modulated by the concentration of the antibiotic
tetracycline or the
structurally related compound doxycycline. Thus, in the absence of
doxycycline, the
promoter induces a high level of expression, and the addition of increasing
levels of
doxycycline causes increased repression of promoter activity. Intermediate
levels of gene
expression can be achieved in the steady state by addition of intermediate
levels of drug.
Furthermore, levels of doxycycline that give maximal repression of promoter
activity (10
1 S micrograms/ml) have no significant effect on the growth rate on wild type
yeast cells (Gari
et al., 1997, A, set of vectors with a tetracycline-regulatable promoter
system for modulated
gene expression in Saccharomyces cerevisiae, Yeast 13:837-848).
In mammalian cells, several means of titrating expression of genes are
available
(Spencer, 1996, Creating conditional mutations in mammals, Trends Genet.
12:181-187).
As mentioned above, the Tet system is widely used, both in its original form,
the "forward"
system, in which addition of doxycycline represses transcription, and in the
newer "reverse"
system, in which doxycycline addition stimulates transcription (Gossen et al.,
1995, Proc.
Natl. Acad. Sc;i. USA 89:5547-SSSI; Hoffmann et at., 1997, Nucl. Acids. Res.
25:1078-
1079; Hofmarm et al., 1996, Proc. Natl. Acad. Sci. USA 83:5185-5190; Paulus et
al., 1996,
Journal of Virology 70:62-67). Another commonly used controllable promoter
system in
mammalian cc;lls is the ecdysone-inducible system developed by Evans and
colleagues (No
et al., 1996, Ecdysone-inducible gene expression in mammalian cella and
transgenic mice,
Proc. Nat. Ac<id. Sci. USA 93::3346-3351), where expression is controlled by
the level of
muristerone added to the cultured cells. Finally, expression can be modulated
using the
"chemical-induced dimerization" {CID) system developed by Schreiber, Crabtree,
and
colleagues (Belshaw et al., 1996, Controlling protein association and
subcellular localization
with a synthetic ligand that induces heterodimerization of proteins, Proc.
Nat. Acad. Sci.
USA 93:4604-4607; Spencer, 1996, Creating conditional mutations in.mammals,
Trends
Genet. 12:181-187) and similar systems in yeast. In this system, the gene of
interest is put
Wider the control of the CID-responsive promoter, and txansfected into cells
expressing two
different hybrid proteins, one comprised of a DNA-binding domain fused to
FKBP12, which
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binds FK506. The other hybrid protein contains a transcriptional activation
domain also
fused to FKBP12. The CID inducing molecule is FK1012, a homodimeric version of
FK506
that is able to bind simultaneously both the DNA binding and transcriptional
activating
hybrid proteins. In the graded presence of FK1012, graded transcription of the
controlled
gene is activ<~ted.
For each of the mammalian expression systems described above, as is widely
known
to those of skill in the art, the; gene of interest is put under the control
of the controllable
promoter, and a plasmid harboring this construct along with an antibiotic
resistance gene is
transfected into cultured mammalian cells. In general, the plasmid DNA
integrates into the
genome, and drug resistant colonies are selected and screened for appropriate
expression of
the regulated gene. Alternatively, the regulated gene can be inserted into an
episomal
plasmid such as pCEP4 (Invitrogen, Inc.), which contains components of the
Epstein-Barr
virus necessary for plasmid replication.
In a preferred embodiment, titratable expression systems, such as the ones
described
1 S above, are introduced for use into cells or organisms lacking the
corresponding endogenous
gene and/or I;ene activity, e.8~., organisms in which the endogenous gene has
been disrupted
or deleted. Methods for producing such "knock outs" are well known to those of
skill in the
art, see e.g., Pettitt et al., 1996, Development 122:4149-4157; Spradling et
al., 1995, Proc.
Natl. Acad. Sci. USA, 92:10824-10830; Ramirez-Solis et al., 1993, Methods
Enzymol.
225:855-878:. and Thomas et al., 1987, Cell S 1:503-512.
~i.8.2. TRANSFECTION SYSTEMS FOR MAMMALIAN CELLS
Transfection or viral transduction of target genes can introduce controllable
perturbations in biological cellular states in mammalian cells. Preferably,
transfection or
transduction ~of a target gene can be used with cells that do not naturally
express the target
gene of interf;st. Such non-expressing cells can be derived from a tissue not
normally
expressing the target gene or the target gene can be specifically mutated in
the cell. The
target gene o:f interest can be cloned into one of many mammalian expression
plasmids, for
example, the pcDNA3.1 +l- system (Invitrogen, Inc.) or retroviral vectors, and
introduced
into the non-expressing host cells. Transfected or transduced cells expressing
the target gene
may be isolated by selection for a drug resistance masker encoded by the
expression vector.
The level of gene transcription is monotonically related to the transfection
dosage. 1n this
way, the effects of varying levels of the target gene may be investigated.
A particular example of the use of this method is the search for drugs that
target the
src-family protein tyrosine kinase, lck, a key component of the T cell
receptor activation
cellular state (Anderson et al.,, 1994, Involvement of the protein tyrosine
kinase p56 (lck) in
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T cell signaling and thymocyte development, Adv. Immunol. 56:171-178).
Inhibitors of this
enzyme are of interest as potential immunosuppressive drugs (Hanke, 1996,
Discovery of a
Novel, Potent, and src family-selective tyrosine kinase inhibitor, J. Biol
Chem 271:695-701).
A specific mutant of the Jurkat T cell line (JcaM 1 ) is available that does
not express lck
kinase (Strau.s et al., 1992, Genetic evidence for the involvement of the lck
tyrosine kinase
in signal transduction through the T cell antigen receptor, Cell 70:585-593).
Therefore,
introduction of the lck gene into JCaM 1 by transfection or transduction
permits specific
perturbation of cellular states of T cell activation regulated by the lck
kinase. The efficiency
of transfection or transduction, and thus the level of perturbation, is dose
related. The
method is generally useful for providing perturbations of gene expression or
protein
abundances in cells not normally expressing the genes to be perturbed.
5.8.3. METHODS Oh MODIFYING RNA ABUNDANCES OR ACTIVITIES
Methods ofmodifying RNA abundances and activities currently fall within three
classes, ribo~:ymes, antisense species, and RNA aptamers (Good et al., 1997,
Gene Therapy
4: 45-54). Controllable application or exposure of a cell to these entities
permits
controllable perturbation of RNA abundances.
Ribozymes are RNAs which are capable of catalyzing RNA cleavage reactions.
(Cech, 1987, Science 236:1532-1539; PCT International Publication WO 90/11364,
published October 4, 1990; Sarver et al., 1990, Science 247: 1222-1225).
"Hairpin" and
"hammerhead" RNA ribozymes can be designed to specifically cleave a particular
target
mRNA. Rules have been established for the design of short RNA molecules with
ribozyme
activity, which are capable o.f cleaving other RNA molecules in a highly
sequence specific
way and can be targeted to virtually all kinds of RNA. (Haseloff et al., 1988,
Nature
334:585-591; Koizumi et al., 1988, FEBS Lett., 228:228-230; Koizumi et al.,
1988, FEBS
Lett., 239:285-288). Ribozyme methods involve exposing a cell to, inducing
expression in a
cell, etc. of such small RNA ribozyme molecules. (Grassi and Marini, 1996,
Annals of
Medicine 28 ~; 499-510; Gibson, 1996, Cancer and Metastasis Reviews 15: 287-
299).
Ribozymes can be routinely expressed in vivo in sufficient number to be
catalytically
effective in cleaving mRNA, and thereby modifying mRNA abundances in a cell.
(Gotten et
al., 1989, Ribozyme mediated destruction ofRNA in vivo, The EMBO J. 8:3861-
3866). In
particular, a ribozyrne coding DNA sequence, designed according to the
previous rules and
synthesized, for example, by standard phosphoramidite chemistry, can be
ligated into a r
restriction enzyme site in the anticodon stem and loop of a gene encoding a
tRNA, which
c~ then be transformed into and expressed in a cell of interest by methods
routine in the art.
Preferably, an inducible promoter (e.g., a glucocorticoid or a tetracycline
response element)
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is also introduced into this construct so that ribozyrne expression can be
selectively
controlled. tIJNA genes (i.e., genes encoding tRNAs) are useful in this
application because
of their small size, high rate of transcription, and ubiquitous expression in
different kinds of
tissues. Therefore, ribozymes can be routinely designed to cleave virtually
any mRNA
sequence, and a cell can be routinely transformed with DNA coding for such
ribozyme
sequences such that a controllable and catalytically effective amount of the
ribozyme is
expressed. Accordingly the abundance of virtually any RNA species in a cell
can be
perturbed.
In another embodiment, activity of a target RNA (preferable mRNA) species,
specifically its rate of translation, can be controllably inhibited by the
controllable
application o:f antisense nucleic acids. An "antisense" nucleic acid as used
herein refers to a
nucleic acid capable of hybridizing to a sequence-specific (e.g., non-poly A)
portion of the
target RNA, for example its translation initiation region, by virtue of some
sequence
complementarity to a coding and/or non-coding region. The antisense nucleic
acids of the
invention can be oligonucleotides that are double-stranded or single-stranded,
RNA or DNA
or a modification or derivative thereof, which can be directly administered in
a controllable
manner to a cell or which can be produced intracellularly by transcription of
exogenous,
introduced sequences in controllable quantities sufficient to perturb
translation of the target
RNA.
Preferably, antisense nucleic acids are of at least six nucleotides and are
preferably
oligonucleotides (ranging from 6 to about 200 oligonucleotides). In specific
aspects, the
oligonucleotide is at least 10 nucleotides, at least 15 nucleotides, at least
100 nucleotides, or
at least 200 nucleotides. The oligonucleotides can be DNA or RNA or chimeric
mixtures or
derivatives or modified versions thereof, single-stranded or double-stranded.
The
oligonucleotide can be modified at the base moiety, sugar moiety, or phosphate
backbone.
The oligonucleotide may include other appending groups such as peptides, or
agents
facilitating transport across the cell membrane (see, e.g., Letsinger et al.,
1989, Proe. Natl.
Acad. Sci. U.S.A. 86: 6553-6556; Lemaitre et al., 1987, Proc. Natl. Acad. Sci.
84: 648-652;
PCT Publication No. WO 88!09810, published December 1 S, 1988), hybridization-
triggered
cleavage agents (see, e.g., Kro1 et al., 1988, BioTechniques 6: 958-976) or
intercalating
agents (see, e~.g., Zon, 1988,1'harm. Res. 5: 539-549).
In a preferred aspect of the invention, an antisense oligonucleotide is
provided,
preferably as single-stranded DNA. The oligonucleotide may be modified at any
positionlon
its structure with constituents generally known in the art.
The antisense oligonucleotides may comprise at least one modified base moiety
which is selected from the group including but not limited to 5-fluorouracil,
5-bromouracil,
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5-chlorouracil, S-iodouracil, hypoxanthine, xanthine, 4-acetylcytosine,
5-(carboxyhydroxylmethyl) uracil, 5-carboxymethylarninomethyl-2-thiouridine,
5-carboxymethylaminomethyluracil, dihydrouracil, beta-D-galactosylqueosine,
inosine,
N6-isopenten_yladenine, 1-me~thylguanine, 1-methylinosine, 2,2-
dimethylguanine,
2-methyladenine, 2-methylguanine, 3-methylcytosine, 5-methylcytosine, N6-
adenine,
7-methylguanine, 5-methylarr~inomethyluracil, 5-methoxyaminomethyl-2-
thiouracil, beta-
D-mannosylqueosine, S'-methaxycarboxyrnethyluracil, 5-methoxyuracil, 2-
methylthio-N6-
isopentenylad.enine, uracil-5-oxyacetic acid (v), wybutoxosine, pseudouracil,
queosine,
2-thiocytosine, S-methyl-2-thiiouracil, 2-thiouracil, 4-thiouracil, 5-
methyluracil, uracil-
5-oxyacetic acid methylester, uracil-S-oxyacetic acid (v), 5-methyl-2-
thiouracil, 3-(3-amino-
3-N-2-carbox,ypropyl) uracil, (acp3)w, and 2,6-diaminopurine.
In another embodiment, the oligonucleotide comprises at least one modified
sugar
moiety selected from the group including, but not limited to, arabinose, 2-
fluoroarabinose,
xylulose, and hexose.
In yet another embodiment, the oligonucleotide comprises at least one modified
phosphate backbone selected from the group consisting of a phosphorothioate, a
phosphoroditlvoate, a phosphoramidothioate, a phosphoramidate, a
phosphordiamidate, a
methylphosphonate, an alkyl phosphotriester, and a formacetal or analog
thereof.
In yet another embodiment, the oligonucleotide is a 2-a-anomeric
oligonucleodde.
~ a-~omeric oligonucleotide forms specific double-stranded hybrids with
complementary
RNA in which, contrary to the usual B-units, the strands run parallel to each
other (Gautier et
al., 1987, Nu<;1. Acids Res. 15: 6625-6641 ).
The oligonucleotide rr~ay be conjugated to another molecule, e.g., a peptide,
hybridization triggered cross-linking agent, transport agent, hybridization-
triggered cleavage
ag~t, etc.
The antisense nucleic acids of the invention comprise a sequence complementary
to
at least a portion of a target RNA species. However, absolute complementarity,
although
preferred, is not required. A sequence "complementary to at least a portion of
an RNA," as
referred to herein, means a sequence having sufficient complementarity to be
able to
hybridize with the RNA, forniing a stable duplex; in the case of double-
stranded antisense
nucleic acids, a single strand of the duplex DNA may thus be tested, or
triplex formation
may be assayed. The ability to hybridize will depend on both the degree of
complementarity
and the length of the antisense nucleic acid. Generally, the longer the
hybridizing nucleic. .
acid, the more base mismatches with a target RNA it may contain and still form
a stable
duplex (or triplex, as the case may be). One skilled in the art can ascertain
a tolerable degree
of mismatch by use of standard procedures to determine the melting point of
the hybridized
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complex. The amount of antisense nucleic acid that will be effective in the
inhibiting
translation of the target RNA can be determined by standard assay techniques.
Oligonucleotides of the invention may be synthesized by standard methods known
in
the art, e.g. b;y use of an automated DNA synthesizer (such as are
commercially available
S from Biosearch, Applied Biosystems, etc.). As examples, phosphorothioate
oligonucleotides
may be synthesized by the method of Stein et al. (1988, Nucl. Acids Res. 16:
3209),
methylphosp:honate oligonucleotides can be prepared by use of controlled pore
glass
polymer supports (Sarin et al., 1988, Proc. Natl. Acad. Sci. U.S.A. 85: 7448-
7451), etc. In
another embodiment, the oligonucleotide is a 2'-0-methylribonucleotide (Inoue
et al., 1987,
Nucl. Acids Res. 15: 6131-61148), or a chimeric RNA-DNA analog (moue et al.,
1987,
FEBS Lett. 215: 327-330).
The synthesized antisense oligonucleotides can then be administered to a cell
in a
controlled manner. For example, the antisense oligonucleotides can be placed
in the growth
environment of the cell at controlled levels where they rnay be taken up by
the cell. The
uptake of the antisense oligonucleotides can be assisted by use of methods
well known in the
art.
In an alternative embodiment, the antisense nucleic acids of the invention are
controllably expressed intracellularly by transcription from an exogenous
sequence. For
example, a vector can be introduced in vivo such that it is taken up by a
cell, within which
cell the vector or a portion thereof is transcribed, producing an antisense
nucleic acid (RNA)
of the invention. Such a vector would contain a sequence encoding the
antisense nucleic
acid. Such a vector can remain episomal or become chromosomally integrated, as
long as it
can be transcribed to produce: the desired antisense RNA. Such vectors can be
constructed
by recombinant DNA technology methods standard in the art. Vectors can be
plasmid, viral,
or others known in the art, used for replication and expression in mammalian
cells.
Expression of the sequences encoding the antisense RNAs can be by any promoter
known in
the art to act in a cell of interest. Such promoters can be inducible or
constitutive. Most
preferably, promoters are controllable or inducible by the administration of
an exogenous
moiety in order to achieve controlled expression of the antisense
oligonucleotide. Such
controllable promoters include the Tet promoter. Less preferably usable
promoters for
mammalian cells include, but are not limited to: the SV40 early promoter
region (Bernoist
and Chambon, 1981, Nature 290: 304-310), the promoter contained in the 3' long
terminal
repeat of Rous -.sarcoma virus (Yamamoto et al., 1980,,Ce1122: 787-797), the
herpes
thymidine kinase promoter (Wagner et al., 1981, Proc. Natl. Acad. Sci. U.S.A.
78: 1441-
1445), the regulatory sequences of the metallothionein gene (Brinster et al.,
1982, Nature
296: 39-42), etc.
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Therefore, antisense nucleic acids can be routinely designed to target
virtually any
mRNA sequence, and a cell c;an be routinely transformed with or exposed to
nucleic acids
coding for such antisense sequences such that an effective and controllable
amount of the
antisense nucleic acid is expressed. Accordingly the translation of virtually
any RNA
species in a cell can be controllably perturbed.
Finally, in a further embodiment, RNA aptamers can be introduced into or
expressed
in a cell. RNA aptamers are specific RNA ligands for proteins, such as for Tat
and Rev
RNA (Good et al., 1997, Gene Therapy 4: 45-54) that can specifically inhibit
their
translation.
5.8.4. METHODS OF MODIFYING PROTEIN ABUNDANCES
Methods of modifying protein abundances include, inter alia, those altering
protein
degradation rates and those using antibodies (which bind to proteins affecting
abundances of
activities of native target protein species). Increasing (or decreasing) the
degradation rates
of a protein species decreases (or increases) the abundance of that species.
Methods for
controllably ;increasing the dc;gradation rate of a target protein in response
to elevated
temperature and/or exposure to a particular drug, which are known in the art,
can be
employed in this invention. lFor example, one such method employs a heat-
inducible or
drug-inducible N-terminal de:gron, which is an N-terminal protein fragment
that exposes a
degradation signal promoting rapid protein degradation at a higher temperature
(e.g., 37° C)
and which is hidden to prevent rapid degradation at a lower temperature (e.g.,
23 ° C)
(Dohmen et. al, 1994, Science 263:1273-1276). Such an exemplary degron is Arg-
DHFR'~,
a variant of marine dihydrofolate reductase in which the N-tenminal Val is
replaced by Arg
and the Pro at position 66 is replaced with Leu. According to this method, for
example, a
gene for a target protein, P, is replaced by standard gene targeting methods
known in the art
(Lodish et al., 1995, Molecular Biology of the Cell, W.H. Freeman and Co., New
York,
especially chap 8) with a gene calling for the fusion protein Ub-Arg-DHFR'~-P
("Ub" stands
for ubiquitin). The N-terminal ubiquitin is rapidly cleaved after translation
exposing the N-
terminal degron. At lower temperatures, lysines internal to Arg-DHFR'~ are not
exposed,
ubiquitination of the fusion protein does not occur, degradation is slow, and
active target
protein levels are high. At higher temperatures (in the absence of
methotrexate), lysines
internal to Arg-DHFR'~ are exposed, ubiquitination of the fusion protein
occurs, degradation
is rapid, and active target protein levels are low. Heat activation of
degradation is .
controllably blocked by exposure methotrexate. This method is adaptable to
other N-
terminal degrees which are responsive to other inducing factors, such as drugs
and
temperature changes.
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Target protein abundances and also, directly or indirectly, their activities
can also be
decreased by (neutralizing) antibodies. By providing for controlled exposure
to such
antibodies, protein abundancea/activities can be controllably modified. For
example,
antibodies to suitable epitopes on protein surfaces may decrease the
abundance, and thereby
indirectly decrease the activity, of the wild-type active form of a target
protein by
aggregating active forms into complexes with less or minimal activity as
compared to the
wild-type unaggregated wild-type fonm. Alternately, antibodies may directly
decrease
protein activity by, e.g., interacting directly with active sites or by
blocking access of
substrates to active sites. Conversely, in certain cases, (activating)
antibodies rnay also
interact with proteins and thei~.r active sites to increase resulting
activity. In either case,
antibodies (of the various types to be described) can be raised against
specific protein
species (by th.e methods to be described) and their effects screened. The
effects of the
antibodies can be assayed and suitable antibodies selected that raise or lower
the target
protein species concentration and/or activity. Such assays involve introducing
antibodies
into a cell (see below), and assaying the concentration of the wild-type
amount or activities
of the target protein by standard means (such as immunoassays) known in the
art. T'he net
activity of the; wild-type form can be assayed by assay means appropriate to
the known
activity of the: target protein.
Antibodies can be introduced into cells in numerous fashions, including, for
example, microinjection of antibodies into a cell (Morgan et al., 1988,
Immunology Today
9:84-86) or transforming hyb~ridoma mRNA encoding a desired antibody into a
cell (Burke
et al., 1984, Cell 36:847-858). In a fiuther technique, recombinant antibodies
can be
engineering and ectopically expressed in a wide variety of non-lymphoid cell
types to bind
to target proteins as well as to block target protein activities (Biocca et
al, 1995, Trends in
Cell Biology 5:248-252). Preferably, expression of the antibody is under
control of a
controllable promoter, such as the Tet promoter. A first step is the selection
of a particular
monoclonal antibody with appropriate specificity to the target protein (see
below). Then
sequences encoding the variable regions of the selected antibody can be cloned
into various
engineered antibody formats, including, for example, whole antibody, Fab
fragments, Fv
fragments, single chain Fv fragments (VH and VL regions united by a peptide
linker) ("ScFv"
fragments), diabodies (two associated ScFv fragments with different
specificities), and so
forth (Hayden et al., 1997, Current Opinion in Immunology 9:210-'~ 12).
Intracellularly
expressed antibodies of the various formats can be targeted into cellular
compartments (e.g.,
the cytoplasm, the nucleus, the mitochondria, etc.) by expressing them as
fizsions with the
various known intracellular leader sequences (Bradbury et al., 1995, Antibody
Engineering
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(vol. 2) (Bonrebaeck ed.), pp 295-361, IRL Press). In particular, the ScFv
format appears to
be particularly suitable for cytoplasmic targeting.
Antibody types include, but are not limited to, polyclonal, monoclonal,
chimeric,
single chain, Fab fragments, and an Fab expression library. Various procedures
known in
the art may be used for the production of polyclonal antibodies to a target
protein. For
production of the antibody, various host animals can be immunized by injection
with the
target protein, such host animals include, but are not limited to, rabbits,
mice, rats, etc.
Various adjuvants can be used to increase the immunological response,
depending on the
host species, and include, but .are not limited to, Freund's (complete and
incomplete),
mineral gels such as aluminum hydroxide, surface active substances such as
lysolecithin,
pluronic polyols, polyanions, peptides, oil emulsions, dinitrophenol, and
potentially useful
human adjuvants such as bacillus Calmette-Guerin (BCG) and corynebacterium
parvum.
For preparation of monoclonal antibodies directed towards a target protein,
any
technique that provides fox the production of antibody molecules by continuous
cell lines in
culture may be used. Such techniques include, but are not restricted to, the
hybridoma
technique originally developed by Kohler and Milstein (1975, Nature 256: 495-
497), the
trioma technique, the human B-cell hybridoma technique (Kozbor et al., 1983,
Immunology
Today 4: 72), and the EBV hybridoma technique to produce human monoclonal
antibodies
(Cole et al., 1!85, in Monoclonal Antibodies and Cancer Therapy, Alan R. Liss,
Inc., pp.
77-96). In an additional embodiment of the invention, monoclonal antibodies
can be
produced in germ-free animals utilizing recent technology (PCT/CTS90/02545).
According
to the invention, human antibodies rnay be used and can be obtained by using
human
hybridomas ((:ote et al., 1983, Proc. Natl. Acad. Sci. USA 80: 2026-2030), or
by
transforming human B cells with EBV virus in vitro (Cole et al., 1985, in
Monoclonal
Antibodies and Cancer Therapy, Alan R. Liss, Inc., pp. 77-96). In fact,
according to the
invention, techniques developed for the production of "chimeric antibodies"
(Morrison et
al., 1984, Proc;. Natl. Acad. Sci. USA 81: 6851-6855; Neuberger et al., 1984,
Nature
312:604-608; Takeda et al., 1185, Nature 314: 452-454) by splicing the genes
from a mouse
antibody molecule specific foo- the target protein together with genes from a
human antibody
molecule of appropriate biological activity can be used; such antibodies are
within the scope
of this invention.
Additionally, where monoclonal antibodies are advantageous, they can be
alternatively. selected from large antibody libraries using the techniquesrof
phage display "
(Marks et al., 1992, J. Biol. Chem. 267:16007-16010). Using this technique,
libraries of up
to 10'2 different antibodies have been expressed on the surface of fd
filamentous phage,
creating a "single pot" in vitro immune system of antibodies available for the
selection of
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monoclonal antibodies (Griffiths et al., 1994, EMBO J. 13:3245-3260).
Selection of
antibodies from such libraries can be done by techniques known in the art,
including
contacting the; phage to immobilized target protein, selecting and cloning
phage bound to the
target, and subcloning the sequences encoding the antibody variable regions
into an
appropriate vector expressing a desired antibody format.
According to the invention, techniques described for the production of single
chain
antibodies (U.S. patent 4,946.,778) can be adapted to produce single chain
antibodies specific
to the target protein. An additional embodiment of the invention utilizes the
techniques
described for the construction of Fab expression libraries (Huse et al., 1989,
Science 246:
1275-1281) to allow rapid and easy identification of monoclonal Fab fragments
with the
desired speciAicity for the target protein.
Antibody fragments that contain the idiotypes of the target protein can be
generated
by techniques known in the art. For example, such fragments include, but are
not limited to:
the F(ab')2 fragment which can be produced by pepsin digestion of the antibody
molecule;
the Fab' fragments that can be generated by reducing the disulfide bridges of
the F(ab')Z
fragment, the Fab fragments that can be generated by txeating the antibody
molecule with
papain and a reducing agent, and Fv fragments.
In the production of antibodies, screening for the desired antibody can be
accomplished by techniques known in the art, e.g., ELISA (enzyme-linked
immunosorbent
assay)- To select antibodies specific to a target protein, one may assay
generated
hybridomas or a phage display antibody library for an antibody that binds to
the target
protein.
5.8.5. METHODS OF MODIFYING PROTEIN ACTIVITIES
Methods of directly modifying protein activities include, inter alia, dominant
negative mutations, specific drugs (used in the sense of this application) or
chemical
moieties genc,~rally, and also t:he use of antibodies, as previously
discussed.
Dominant negative mutations are mutations to endogenous genes or mutant
exogenous gc,nes that when expressed in a cell disrupt the activity of a
targeted protein
species. Depending on the structure and activity of the targeted protein,
general rules exist
that guide thc; selection of an appropriate strategy for constructing dominant
negative
mutations that disrupt activity of that target (Hershkowitz, 1987, Nature
329:219-222). In
the case of active monomeric; forms, over expression of.an inactive farm can
cause
competition :for natural substrates or ligands sufficient to significantly
reduce net activity of
the target protein. Such over expression can be achieved by, for example,
associating a
promoter, preferably a controllable or inducible promoter, of increased
activity with the
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mutant gene. Alternatively, changes to active site residues can be made so
that a virtually
irreversible association occur:> with the target ligand. Such can be achieved
with certain
tyrosine kinases by careful rplacement of active site serine residues
(Perlmutter et al., 1996,
Current Opinion in Immunology 8:285-290).
In the case of active multimeric forms, several strategies can guide selection
of a
dominant negative mutant. lVlultimeric activity can be controllably decreased
by expression
of genes coding exogenous protein fragments that bind to multimeric
association domains
and prevent multimer formation. Alternatively, controllable over expression of
an inactive
protein unit of a particular type can tie up wild-type active units in
inactive multimers, and
thereby decrease multimeric activity (Nocka et al., 1990, The EMBO J. 9:1805-
1813). For
example, in the case of dimeric DNA binding proteins, the DNA binding domain
can be
deleted from the DNA binding unit, or the activation domain deleted from the
activation
unit. Also, in this case, the DNA binding domain unit can be expressed without
the domain
causing association with the activation unit. Thereby, DNA binding sites are
tied up without
~Y possible activation of expression. In the case where a particular type of
unit normally
undergoes a conformational change during activity, expression of a rigid unit
can inactivate
resultant complexes. For a fiirther example, proteins involved in cellular
mechanisms, such
as cellular motility, the mitotic process, cellular architecture, and so
forth, are typically
composed of associations of many subunits of a few types. These structures are
often highly
sensitive to disruption by inclusion of a few monomeric units with structural
defects. Such
mutant monomers disrupt the; relevant protein activities and can be
controllably expressed in
a cell.
In addition to dominant negative mutations, mutant target proteins that are
sensitive
to temperature (or other exogenous factors) can be found by mutagenesis and
screening
procedures chat are well-known in the art.
Also, one of skill in the art will appreciate that expression of antibodies
binding and
inhibiting a target protein can be employed as another dominant negative
strategy.
Finally, activities of certain target proteins can be controllably altered by
exposure to
exogenous drugs or ligands. In a preferable case, a drug is known that
interacts with only
one target protein in the cell and alters the activity of only that one target
protein. Graded
exposure of a cell to varying amounts of that drug thereby causes graded
perturbations of
cellular statea originating at that protein. The alteration can be either a
decrease or an
increase of -activity. Less preferably,. a drug is known and used that alters
the activity of only
a few (e.g., :?-5) target proteins with separate, distinguishable, and non-
overlapping effects.
Graded exposure to such a drug causes graded perturbations to the several
cellular states
originating at the target proteins.
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6. EXAMPLES
The following examples are presented by way of illustration of the previously
described invention and are not limiting of that description.
6.1. EXAMPLE 1: CLUSTERING GENESETS BY COREGULATION
This example illustrates one embodiment of the clustering method of the
invention.
6.1.1. MATERIALS AND METHODS
Transcript measurement:
Yeast (Saccharomyces cerevisiae, Strain YPH499, see, Sikorski and Hieter,
1989, A
system of shuttle vectors and yeast host strains designated for efficient
manipulation of DNA
in Saccharomyces cerevisiae, Genetics 122:19-27) cells were grown in YAPD at
30° C to an
ODboo of 1.0 (t0.2), and total RNA prepared by breaking cells in
phenol/chloroform and
0.1% SDS by standard procedures (Ausubel et al., 1995, Current Protocols in
Molecular
Biology, Greene Publishing and Wiley-Interscience, New York, Ch. 13). Poly(A)+
RNA
was selected by affinity chromatography on oligo-dT cellulose (New England
Biolabs)
essentially as described in Satnbrook et al. (Molecular Cloning - A Laboratory
Manual (2nd
Ed.), Vol. 1, Cold Spring Harbor Laboratory, Cold Spring Harbor, New York,
1989). First
strand cDNA synthesis was carried out with 2 ug poly(A)+ RNA and SuperScriptTM
II
reverse transcriptase (Gibco-~BRL,) according to the manufacturer's
instructions with the
following modifications. Deoxyribonucleotides were present at the following
concentrations: dA, dG, and dC at 500 uM each, dT at 100 uM and either Cy3-
dUTP or
Cy5-dUTP (Amersham) at 100 ~.M. cDNA synthesis reactions were carried out at
42-44° C
for 90 minutes, after which RNA was degraded by the addition of 2 units of
RNAse H, and
the eDNA products were purified by two successive rounds of centrifugation
dialysis using
M~cROCoN-30 microconcen.trators (Amicon) according to the manufacturer's
recommendations.
Double-stranded DNA polynucleotides corresponding in sequence to each ORF in
the S. cerevisiae genome encoding a polypeptide greater than 99 amino acids
(based on the
published yeast genomic sequence, e.g., Goffeau et al., 1996, Science 274:546-
567) are
made by polymerase chain reaction (PCR) amplification of yeast genomic DNA.
Two PCR
primers are chosen internal to each of the ORFs according to two criteria: (i)
the amplified
fragments are 300-800 by and (ii) none of the fragments have a sectien
of.xnore than 10 . .
consecutive. nucleotides of sequence in common. Computer programs are used to
aid in the
design of the PCR primers. Amplification is earned out in 96 well microtitre
plates. The
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resulting DNA fragments are printed onto glass microscope slides using the
method of
Shalon et al., 1996, Genome Research 6:639-645.
Fluorescently-labeled cDNAs (2-6 pg) are resuspended in 4 X SSC plus 1 wg/~,l
tRNA as Garner and filtered using 0.45 pM filters (Millipore, Bedford, MA).
SDS is added
$ to 0.3%, prior to heating to 100° C for 2 minutes. Probes are cooled
and immediately
hybridized to the microarrays produced as described in Example 6.2, for 4
hours at 65 ° C.
Non-hybridized probe is removed by washing in 1 X SSC plus 0.1% SDS at ambient
temperature for 1-2 minutes. Microarrays are scanned with a fluorescence laser-
scanning
device as previously described (Schena et al., 1995, Science 270:467-470;
Schena et al.,
1995, Proc. Natl. Acad. Sci. ZISA 93:10539-11286) and the results (including
the positions
of perturbations) are recorded.
Perturbations: This example involved 18 experiments including different drug
treatments
and genetic mutations related to yeast S. cerevisaie biochemical pathway
homologous to
immunosuppression in humor. Two drugs, FK506 and Cyclosporin were used at the
concentrations of 50 pg/ml or 1 pg/ml in combination with various gene
deletions. Genes
CNA1 and C:lVA2 encode the catalytic subunits of calcineurin. Cardenas et al.,
1994, Yeast
as model T cells, in Perspectives in Drug Discovery and Design, 2:103-126. The
18
different experiment conditions are listed in Table 1:
25
35
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Table 1. Experimental Conditions in 18 Experiments
Experiment No. Experimental conditions/Perturbations
1 +/- FK 506 (50 ~g/ml)
2 +/- FK 506 (1 pglml)
3 -CPH1 +/- FK 506 (50 ~,g/ml)
4 -C:PHI +/- FK 506 (1 ~g/ml)
-FPR +/- FK 506 (50 wg/ml)
6 -FPR -~/- FK 506 ( 1 ug/ml)
-CNA1, CNA2 +/- FK 506 {SO pg/ml)
g -CNA 1, CNA2 +/- FK S 06 ( 1 p.g/ml)
-GCN4 +/- FK 506 (50 ~.g/ml)
ly -CNA1, CNA2, FPR +/- FK 506 (50 pg/ml)
11 -CNA1, CNA2, FPR +/- FK 506 (1 pg/rnl)
12 . -C~GN4 +/- Cyclosporin A (50 ~tg/ml)
13 -FPR +/- Cyclosporin A (50 pg/ml)
14 +/- Cyclosporin A (50 pg/ml)
15 -C'.NA1, CNA2, CPH1 +/- Cyclosporin A (50 pg/ml)
16 -C',NA1, CNA2 +/- Cyclosporin A (50 ug/ml)
1'7 -C',PH1 +/- Cyclosporin A (50 ltg/ml)
lg -/-t-CNAl, CNA2
Cluster analysis: The set of more than 6000 measured mRNA levels was first
reduced to 48
by selecting only those genes which had a response amplitude of at least a
factor of 4 in at
least one of the 18 experiments. The initial selection greatly reduced the
effect of
measurement errors, which domonated the small responses of most genes in most
experiments.
Clustering using the hclust routine was performed on the resulting data table
18
(experiments) x 48 (genes). The code 'hclust'was run using S-plus 4.0 on
Windows NT
workstation. The distance was 1 - r; where the r is the correlation
coefficient (normalized
dot product;l. Statistical significance of each branch node was computed using
the Monte
C~lo procedure described previously herein. One hundred realizations of
permuted data
were clustered to derive an empirical improvement (in compactness) score for
each
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bifucation. T.'he score for the unpermuted data is then expressed in standard
deviations and
values are indicated on the tree of FIG. 6.
6.1.2. RESULTS AND DISCUSSION
FIG. f> shows the clustering tree derived from 'hclust' algorithm operating on
the
18x48 data table. The 48 genes were clustered into various branches. The
vertical
coordinate at the horizontal connector joining two branches indicates the
distance between
branches. Typical values are in the range of 0.2-0.4 where 0 is perfect
correlation and 1 is
zero correlation. The number at the branch is the statistical significance.
Numbers greater
~~ about 2 :indicate that the branching is significant at the 95% confidence
level.
The horizontal line of FIG. 6 is the cut off level for defining genesets. This
level is
arbitrarily set. Those branches with two or fewer members were ignored for
further analysis.
Three genesets with three or more members were defined at this cut off level.
The
significance values (in stand~~rd deviations) shown at the branch notes were
derived as
described, and show that the three branches are truly distinct. The clusters
correspond to the
pathways involving the calci:neurin protein, the PDR gene and the Gcn4
transcription factor,
which indicates that cluster analysis is capable of producing genesets that
have
corresponding genetic regulation pathways. See, Marton et al., Drug Target
validation and
identification of secondary drug target effects using DNA microarrays, Nature
Medicine
4:1293-1301.
6.2. EXAMPLE 2: ENHANCING DETECTION OF RESPONSE PATTERN USING
GENESET AVERAGE RESPONSE
This example illustrates enhanced detection of a particular response pattern
by
g~eset averaging.
Geneset number 3 in the clustering analysis result of FIG. 6 involves genes
regulated
by the Gcn4 transcription factor. This was verified via the literature and via
multiple
sequence alignment analysis of the regulatory regions 5' to the individual
genes (Stormo and
Hartzell, 19:89, Identifying protein binding sites from unaligned DNA
fragments, Proc Natl
Acad ci 86:1183-1187; Hertz and Stormo, 1995, Identification of consensus
patterns in
unaligned DNA and protein sequences: a large-deviation statistical basis for
penalizing gaps,
Proc of 3rd Intl Conf on Bioinformatics and Genome Research, Lim and Cantor,
eds., World
Scientific Publishing Co., L,td. Singapore, pp. 201-216). Twenty-of32 genes in
geneset 3
had a common promoter sequence appropriate to Gcn4. These 20 were used to
define a
8~eset. Response profiles to a titration series of the drug FK506, which is
known to hit this
pathway at higher concentrations, were projected onto this geneset. The
resulting projected
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response is denoted 'Geneset'' in Table 2, where the responses (in standard
deviations of
LoglO(Expression Ratio)) of the individual genes are also shown. NaN means
data not
available. The 'Geneset' response becomes very significant (>3 sigma) at 1.6
~g/ml, and is
much more significant than the individual gene responses at this and higher
concentrations.
Table 2.. Responses of Individual Genes and The Geneset Average Responses
Concentration
(~g/ml)
Gene 0.1 0.31 1.6 7.5 16 50
~R047W 0.0781 0.1553 0.2806 1.1596 3.3107 4.248
YER024W 0.1985 -0.0419 0.4868 1.1526 4.6342 5.8934
ARG5,6 0.1162 0.2722 1.1844 2.7433 6.0457 5.2406
YGL117W' 0.6309 0.6768 1.6276 2.699 4.9827 5.9066
YGL184C 0.0654 -0.0207 -0.0731 -0.4586 2.7166 5.3106
ARG4 0.3585 0.3508 1.6674 3.2973 4.5135 5.8858
YHR029C -0.031 0.2438 0.4421 2.3813 5.0446 5.5781
HISS 0.0292 0.2175 0.9802 2.8414 6.0052 4.9557
CPA2 NaN NaN 1.2429 NaN 4.1093 4.0958
SNO1 -0.2899 0.0244 -0.4772 2.538 5.8877 5.5665
SNZI -0.7223 0.0244 -0.4772 2.538 5.8877 5.5665
YMR195'W 0.7615 0.3323 1.6021 0.8879 4.0983 4.6141
NCE3 0.0371 0.1668 1.2896 1.569 5.5819 3.3928
ARG1 0.2083 0.3436 3.1765 4.2215 4.711 5.7996
HIS3 -0.3719 0.1282 0.71 1.8024 4.6461 5.2637
SSU1 0.6257 0.6655 0.2883 0.5059 4.6461 3.5782
MET16 0.0225 -0.6269 -0.1885 0.1621 3.3857 4.855
ECM13 0.1269 0.2197 0.5226 2.5343 4.8689 3.1882
AR03 NaN -0.1371 0.2684 0.6059 4.0553 5.7035
PCLS 0.1418 0.2767 0.4127 2.2898 ' 5.4688 5.2339
Geneset 0.1728 0.6753 3.3045 7.8209 19.9913 21.3315
Average
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6.3. EXAMPLE 3' M'ROVED CLASSIFICATION OF DRUG ACTIVITY
The 1 ii-experiment data set mentioned in Example 1, supra, was combined with
an
additional 16 experiments using a variety of perturbations including
immunosuppressive
drugs FK506 and Cyclosporin A, and mutations in genes relevant to the activity
of those
drugs; and dnzgs unrelated to immunosuppression -- hydroxyurea, 3-
Aminotriazole, and
methotrexate. The experimental conditions are listed in Table 3:
Table 3. Additional 16 Experiments
Rxneriment No. Experimental conditions/Perturbations
1 3-Aminotriazole (0.01 mM)
2 3-,Aminotriazole (1 mM)
3 3-Aminotriazole ( 10 mM)
4 3-.Aminotriazole (100 mM)
5~ Hydroxyurea ( 1.6 mM)
fi Hydroxyurea (3.1 mM)
H;ydroxyurea (6.2 mM)
g H;ydroxyurea ( 12.5 mM)
g Hydroxyurea (25 mM)
10 Hydroxyurea (SO mM)
11 Methotrexate (3.1 ~.M)
12 Methotrexate (6 uM)
1_ 3 Niethotrexate (25 ~M)
l, 4 Methotrexate (50 ~M)
Methotrexate (100 wM)
'l6 Methotrexate (200 uM)
A cluster analysis was performed with the combined data set. A first down
selection
of genes was done by requiring the genes to have a significant response in 4
or more of the
34 experifnents, where this threshold was defined precisely as greater than
twofold up- or
down-regulation, and a confidence level of 99%, or better. This selection
yielded 194 genes.
Less stringent thresholds would yield more genes and higher incidence of
measurement
errors contaminating the data and confusing the biological identifications of
the genesets;
however, the final results are not very sensitive to this threshold.
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The ':hclust' procedure of S-Plus was used, giving the clustering tree shown
in FIG.
7. There are 16 genesets at the cut level D = 0.4 shown in FIG. 7. Of these
16, 7 consist of
two genes or less. Discarding these small clusters leaves 9 major clusters
marked as shown
in FIG. 7 with numbers 1-9. All the resulting bifurcations above the cut level
are significant
(more than two sigma - see numbers at each node), so the clustexs are truly
distinct.
It is noteworthy that ;genesets defined by the immunosuppressive drug pathways
are
again identified here even though non-immunosuppressive drug response data are
combined
in the analysis.
Geneset 2 contains ttie calcineurin dependent genes from Geneset 1 of FIG. 6,
while
Geneset 4 contains the Gcn4-dependent genes from Geneset 3 of FIG. 6.
The response to FK506 at 16 ~g/ml was obtained and the response profile was
used
as "unknown" profile. The response profile was projected into the genesets
defined using
the cluster analysis of the 34 experiments. The 34 profiles from the
individual experiments
from the clustering set also were projected onto the basis.
The projected profile for FK506 at 16 ug/ml was compared with each of the 34
projected profiles from the clustering set. Five of these comparisons are
illustrated in FIGs
8A-8E, and will be discussed in more detail below.
The correlation between the projected profile of the unknown, and the
projected
profile of each of the 34 training experiments was calculated using the
equation 10 (Section
5.4.2, supra) and is displayed as circles (-0-) in FIG. 9.
Also displayed for comparison are the correlation coefficients computed
without
projection (~-o-), and without projection but with restriction to those genes
that were up- or
down-regulated at the 95% confidence level, and by at least a factor of two,
in one or the
other of the two profiles (-0-).
In general, the projected correlation coefficients track the unprojected ones,
and show
larger values. The larger values are a consequence of the averaging out of
measurement
errors which occurs during projection onto the genesets. These individual
measurement
errors tend to bias the unprojected correlation coefficients low, and this
bias is reduced by
the projection operation.
The correlation coefficient of the projected profiles tends to have large
errors when
the original. profile response was very weak and noise-dominated. Such is the
case at some
of the lower concentrations of drug treatment including Experiments 1,2,7,8.
In Experiment
2, for example, there is a projected correlation coefficient of negative 0.45,
where the
unprojected correlations are;, close to zero. This is a consequence of noise
dominance of the
correlation coefficient. FI(J. 8A shows that treatment with HIJ at 3.1 mM
(gray bars) has a
very weak projected profile;.
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FIG. 8B gives the elements of the projected profiles for the comparison of
FK506 at
161zg/ml (the unknown) with Experiment No. 25 in FIG. 9, FK506 at 50 pg/ml.
The
projected profiles are highly consistent with the very high correlation values
in FIG. 9. The
largest response is in Gemeset 7, which corresponds biologically to an amino
acid starvation
response evidently triggered at large concentrations of the drug. The response
in Geneset 5
is mediated via the primary target of the drug, the calcineurin protein. This
response is still
present at lower concentrations of the drug ( FIG. 8C, gray bars, FK506 at 1
pg/ml), while
the response in Geneset 7 and other Genesets is greatly reduced. This
biological
interpretation is an immediate aid in classification of drug activity. It can
be concluded that
the higher concentration of the drug has triggered secondary, (probably
undesirable),
pathways. One of the primacy mediators of these pathways turns out to be the
transcription
factor Gcn4, as shown by the; grey profile in FIG. 8D from Experiment 34
listed in FIG. 8A.
Here, the activity in Genesets 2,3, and 7 is removed by the deletion of the
GCN4 gene.
However, blind classification using the projected profiles also is improved.
Note that
the projected. correlation coefficients show that the next-nearest neighbor to
the unknown is
the experiment two rows above the best match, '-cph +/- FK506 at 50 pg/ml'.
This is
treatment with the drug of cells genetically deleted for the gene CPH1. This
gene is not
essential to t:he activity of F)<:506, and should not greatly change the
response. Thus the
projected profile correctly shows a high similarity with the unknown, FK506 at
16 p,g/ml.
The unprojected correlation coefficients, however, declare the experiment six
rows above the
best match, '-cna +/- FK506 at SO pg/ml', to be the second best match. This
experiment
involves treatment with the drug of cells genetically deleted for the primary
target,
calcineurin. In this case, the response to Geneset 5, mediated by calcineurin,
has
disappeared (see FIG. 8E) while the other responses remain. This important
biological
difference is reflected in the projected elements of FIG. 8E and in the
projected correlation
coefficients, but not in the unprojected correlation coefficients. Thus
conclusions about
biological similarity would be more reliable in this case based on the
projected correlation
coefficients using the method of the invention than based on unprojected
methods.
6.4 EXPERIMENT 4~ IMPROVED CLASSIFICATION OF
BIQLOGICAL RESPONSE PROFILES
The 34-experiment data set described in Example 3 (Section 6.3, supra) was
also
analyzed by two-dimensional cluster. analysis. In particular, cluster analysis
was first .
performed with the data set to identify genesets as described in Example 3,
supra. Next, the
'hclust' procedure of S-Plus was used again, this time to organize the
biological response
profiles according to the similarity of the biological response.
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The results of this ans~lysis are illustrated in Fig. 16. Fig. 16A shows a
gray scale
display of the plurality of reduced genetic transcripts (horizontal axis)
measured in the 34-
experiments (vertical axis). 'Thus, each row in Fig. 16A indicates the
response of genetic
transcripts to a particular perturbation (e.g., exposure to a particular
drug). The gray scale
represents the logarithm of measured expression ratio as shown in the gray
scale bar on the
top of Fig. 16. Specifically, black denotes up regulation of a transcript (+1
), whereas white
denotes down regulation (-1), and the middle gray scale (0) denotes no change
in expression.
Fig. 16B illustrates co-regulation tree of genetic transcriptions (i.e., the
columns in Fig. 16A)
into genesets described in Ea;ample 3, supra. The column index order
represented in this co-
regulation tree was then used to re-order the column in Fig. 16A to generate
the display
shown in Fig. 16C. The same clustering algorithm was then applied to the row
in Fig. 16C
(i.e., to the response profiles), and the row index was similarly re-ordered
to generate Fig.
16D.
Comparing Figs. 16A and 16D, large structures are readily evident after the
reordering. Not only can genesets be readily identified from vertical striping
in Fig. 16D, but
sets of experiments associated with the activation of particular genesets are
also identified
from horizontal striping in F'ig. 16D. Fig. 17 gives a more detailed view of
Fig. 16D, and
details the experiment assigmnents and some of the geneset assignments in the
reo-ordered
form of Fig. 16D. For example, the 'CNA' vertical stripe indicated in Fig. 17
is the
calcineurin-dependent geneset, which is affected (i.e., transcription
repressed) by all the
experiments involving immunosuppressive drugs in cells except those where the
intermediate
targets of the drug, or calcin.eurin itself, have been removed with mutations.
The experiments
contributing to the large horizontal stripe all activate sets of genesets
which are mostly Gcn4-
dependent. This is particular evident when these experiments are compared with
the top two
rows of Fig. 17 which comprise experiments wherein Gcn4 has been deleted.
6.5. EXAMPLE 5 ~ PROJECTING OUT PROFILE ARTIFACTS
Twcr sets of experiments were performed according to the reverse transcription
procedure described in Example 1 (Section 6.1.1 supra) where the effect of
deletion of the
y~107c gene was measured. In one of the two experiments, RNA concentration in
the
procedure was (intentionally) poorly controlled, thereby generating response
profile data that
was contatr~inated by artifacts. The correlation between the two profiles,
determined by
Equation '7., is shown in Fig. 18. Asterix symbols (*) indicate those
~anscripts which were
up- or down-regulated in either of the two experiments at a confidence level
of 90% or more.
The correlation coefficient between the two experiments is 0.82.
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CA 02348837 2001-04-25
WO 00/24936 PCT/US991Z5025
An artifact template, characterizing the effect of poor control of RNA
concentration in
a reverse transcription procedure, was generated by measuring transcript
levels in S.
cerevisiae wherein the RNA concentration was intentionally varied. Thus, a
response profile
was obtained wherein the "perturbation" was, in fact, the variation of RNA
concentration in
the reverse transcription procedure. This template is plotted in Fig. 19 as
gene expression
ratio vs. mean expression level. Those transcripts which were up- or -down
regulated at the
90% confidence level were labeled with their names and have one-sigma error
bars.
The response profile corresponding to the contaminated YJL107c deletion
experiment
was cleaned using this artifact template. Specifically, best scaling
coefficients were
determined by least squares minimization of Equation 16, and a "cleaned"
response profile
was generated with these coefficients according to Equation 17. The
correlation between the
"cleaned" YfL107c deletion experiment and the corresponding "uncontaminated"
experiment
is shown in Fig. 20. The correlation is improved to 0.87. IN the absence of
significant
artifacts, other sources of random measurement error commonly limit the
correlation between
nominally repeated measurements of profiles to about 0.90. Thus, the
improvement from
0.82 to 0.87 represents nearly the maximum amount of improvement that is
realistically
possible with any artifact removal technique.
7. REFERENCES CITED
All references cited herein are incorporated herein by reference in their
entirety and
for all purposes to the same extent as if each individual publication or
patent or patent
application was specifically and individually indicated to be incorporated by
reference in its
entirety for all purposes.
Many modifications and variations of this invention can be made without
departing
from its spirit and scope, as will be apparent to those skilled in the art.
The specific
embodiments described herein are offered by way of example only, and the
invention is to be
limited only by the terms of the appended claims, along with the full scope of
equivalents to
which such claims are entitled.
35
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