Note: Descriptions are shown in the official language in which they were submitted.
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CHARACTERIZATION OF THE YEAST TRANSCRIPTOME
TECH1VICAL FIEhD OF THE INVENTION
This invention is related to the characterization of the expressed genes
of the yeast genome. More particularly, it is related to the identification
and
use of previously unrecognized genes.
BACKGROUND OF THE ~~'VENTION
It is by now axiomatic that the phenotype of an organism is largely
determined by the genes expressed within it. These expressed genes can be
represented by a "transcriptome", conveying the identity of each expressed
gene and its level of expression for a defined population of cells. Unlike the
genome, which is essentially a static entity, the transcriptome can be
modulated by both external and internal factors. The transcriptome thereby
serves as a dynamic link between an organism's genome and its physical
characteristics.
The transcriptome as defined above has not been characterized in any
eukaryotic or prokaryotic organism, largely because of technological
limitations. However, some general features of gene expression patterns
were elucidated two decades ago through RNA-DNA hybridization
measurements (Bishop et ai., 1974; Hereford and Rosbash, 1977). In many
organisms, it was thus found that at least three classes of transcripts could
be
identified, with either high, medium, or low levels of expression, and the
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number of transcripts per cell were estimated (Lewin, 1980). These data of
course provided little information about the specific genes that were members
of each class. Data on the expression levels of individual genes have
accumulated as new genes were discovered. However, in only a few
instances have the absolute levels of expression of particular genes been
measured and compared to other genes in the same cell type.
Description of any cell's transcriptome would therefore provide new
information useful for understanding numerous aspects of cell biology and
biochemistry.
gTTMNLARY OF THE INVENTION
It is an object of the present invention to provide genes which are
involved in cell cycle progression.
It is another object of the present invention to provide methods of using
the genes to affect the cell cycle.
It is an object of the present invention to provide methods for screening
candidate antifungal drugs.
Another object of the invention is to provide a method for obtaining
human homologs of the yeast genes which are involved in cell cycle
progression.
Another object of the invention is to provide probes for ascertaining
phase in the cell cycle of a cell.
These and other objects of the invention are achieved by providing the
art with one or more of the embodiments described below. According to one
embodiment of the invention an isolated DNA molecule is provided. It
comprises a yeast gene which is involved in cell cycle progression selected
from the group of NORF genes identified in Table 3 or 4.
According to another embodiment of the invention a method of using
yeast genes is provided. The method is for affecting the cell cycle of a cell.
The method comprises the step of
administering to a cell an isolated DNA molecule comprising a
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yeast gene which is involved in cell cycle progression selected from the
differentially expressed genes identified in Tables 1, 2, 3 and 4.
In yet another embodiment of the invention a method for screening
candidate antifungal drugs is provided. The method comprises the steps of
S contacting a test substance with a yeast cell;
monitoring expression of a yeast gene which is involved in cell
cycle progression selected from the group of yeast genes identified in Tables
1, 2, 3 and 4, wherein a test substance which modifies the expression of the
yeast gene is a candidate antifungal drug.
In still another embodiment of the invention a method for identifying
human genes which are involved in cell cycle progression is provided. The
method comprises the step of
hybridizing a probe comprising at least 14 contiguous
nucleotides of a yeast gene which is differentially expressed between at least
two phases selected from the group consisting of log phase, S phase, and
G2/M phase, wherein the yeast gene is identified in Table 1, 2, 3, or 4.
Also provided by the present invention are isolated DNA molecules,
which comprise probes for ascertaining phase in the cell cycle of a cell,
wherein the probe comprises at least 14 contiguous nucleotides of a NORF
gene as identified in Table 3 or 4.
These and other embodiments of the invention which will be apparent
to those of skill in the art upon reading the detailed disclosure provided
below, make available to the art hitherto unrecognized genes, and information
about the expression of genes globally at the organismal level. We provide
the first description of a transcriptome, deterniined in S. cerevisiae cells.
This
organism was chosen because it is widely used to clarify the biochemical and
physiologic parameters underlying eukaryotic cellular functions and because
it is the only eukaryote in which the entire genome has been defined at the
nucleotide level (Goffeau, et al., 1996).
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RR1EF DE SCIZTPTION OF TH N 1.~ lt~ w 11r c~~
Figure 1. Schematic of SAGE Method and Genome Analysis.
In applying SAGE to the analysis of yeast gene expression patterns, the 3'
most NIaIII site was used to define a unique position in each transcript and
to provide a site for ligation of a linker with a BsmFI site. The type Its
enzyme BsmFI, which cleaves a defined distance from its non-palindromic
recognition site, was then used to generate a l5bp SAGE tag (designated by
the black arrows), which includes the NIaIII site. Automated sequencing of
concatenated SAGE tags allowed the routine identification of about a
thousand tags per sequencing gel. Once sequenced, the abundance of each
SAGE tag was calculated, and each tag was used to search the entire yeast
genome to identify its corresponding gene. The lower panel shows a small
region of Chromosome 15. Gray arrows indicate all potential SAGE tags
(l~Tla~ sites) and black arrows indicate 3' most SAGE tags. The total number
of tags observed for each potential tag is indicated above (+ strand) or below
(- strand) the tag. As expected, the observed SAGE tags were associated
with the 3' end of expressed genes.
Figure 2. Sampling of Yeast Gene Expression.
Analysis of increasing amounts of ascertained tags reveals a plateau in the
number of unique expressed genes. Triangles represent genes with known
functions, squares represent genes predicted on the basis of sequence
information, and circles represent total genes.
Figure 3. Virtual Rot.
(a) Abundance Classes in the Yeast Transcriptome. The transcript abundance
is plotted in reverse order on the abscissa, whereas the fraction of total
transcripts with at least that abundance is plotted on the ordinate. The
dotted
lines identify the three components of the curve, 1, 2, and 3. This is
analogous to a Rot curve derived from reassociation kinetics where the
product of initial RNA concentration and time is plotted on the abscissa, and
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the percent of labeled cDNA that hybridizes to excess mRNA is plotted on
the ordinate.
(b) Comparison of Virtual Rot and Rot Components. Transitions and data
from virtual Rot components were calculated from the data in Figure 3A,
while data for Rot components were obtained from Hereford and Rosbash,
1977.
Figure 4. Chromosomal Expression Map for S. cerevisiae. Individual yeast
genes were positioned on each chromosome according to their open reading
frame (ORF) start coordinates. Abundance levels of tags corresponding to
each gene are displayed on the vertical axis, with transcription from the +
strand indicated above the abscissa and that from the - strand indicated
below.
Yellow bands at ends of the expanded chromosome represent telomeric
regions that are undertranscribed (see text for details).
Figure 5. Northern Blot Analysis of Representative Genes. TDH2/3,
TEF1/2 and NORF1, are expressed relatively equally in all three states (lane
1, G2/M arrested; lane 2, S phase arrested; lane 3, log phase), while RNR4,
RNR2 , and NORFS are highly expressed in S-phase arrested cells. The
expression level observed by SAGE (number of tags) is noted below each
lane and was highly correlated with quantitation of the Northern blot by
PhosphorImager analysis (rz=0.97).
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Table Legends
Table 1. Highly Expressed Genes
Tag represents the 10 by SAGE tag adjacent to the NIaIII site; Gene
represents the gene or genes corresponding to a particular tag (multiple genes
that match unique tags are from related families, with an average identity of
93%); Locus and Description denote the locus name, and functional
description of each ORF, respectively; Copies/cell represents the abundance
of each transcript in the SAGE library, assuming 15,000 total transcripts per
cell and 60,633 ascertained transcripts.
Table 2. Expression of Putative Coding Sequences
Table columns are the same as for Table 1.
Table 3. Expression of NORF genes
SAGE Tag, Locus, and Copies/cell are the same as for Table 1; Chr and Tag
Pos denote the chromosome and position of each tag; ORF Size denotes the
size of the ORF corresponding to the indicated tag. In each case, the tag was
located within or less than 250 by 3' of the NORF.
pETAILED DESCRIPTION
It is a discovery of the present invention that certain hitherto unknown
genes (the NORFs) exist and are expressed in yeast. These genes, as well as
other previously identified and previously postulated genes, can be used to
study, monitor, and affect phase of cell cycle. The present invention provides
information on which genes are differentially expressed during the cell cycle.
Differentially expressed genes can be used as markers of phases of the cell
cycle. They can also be used to affect a change in the phase of the cell
cycle.
In addition, they can be used to screen for drugs which affect the cell cycle,
by affecting expression of the genes. Human homologs of these eukaryotic
genes are also presumed to exist, and can be identified using the yeast genes
as probes or primers to identify the human homologs.
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New genes termed NORFs (not previously assigned open reading
frames) have been found. They are uniquely identified by their SAGE tags.
In addition their entire nucleotide sequence is known and publicly available.
In general, these were not previously identified as genes due to their small
size. However, they have now been found to be expressed.
Differentially expressed yeast genes are those whose expression varies
by a statistically significant difference (to greater than 95% confidence
level)
within different growth phases, particularly log phase, S phase, and G2/M.
Preferably the difference is greater than 10%, 25%, 50%, or 100%. The
genes which have been found to have such differential expression
characteristics are: NORF N° 1, 2, 4, 5, 6, 17, 25, 27, TEF1/TEF2,
EN02,
ADH1, ADH2, PGK1, CUP1A/CUP1B, PYK1, YKL056C, YMR116C,
YEL033W, YOR182C, YCR013C, ribonucleotide reductase 2 and 4, and
YJR085C.
The DNA molecules according to the invention can be genomic or
cDNA. Preferably they are isolated free of other cellular components such
as membrane components, proteins, and lipids. They can be made by a cell
and isolated, or synthesized using PCR or an automatic synthesizer. Any
technique for obtaining a DNA of known sequence may be used. Methods
for purifying and isolating DNA are routine and are known in the art.
To administer yeast genes to cells, any DNA delivery techniques known
in the art may be used, without limitation. These include liposomes,
transfection, transduction, transformation, viral infection, electroporation.
Vectors for particular purposes and characteristics can be selected by the
skilled artisan for their known properties. Cells which can be used as gene
recipients are yeast and other fungi, mammalian cells, including humans, and
bacterial cells.
Antifungal drugs can be identified using yeast cells as described herein.
Expression of a differentially expressed gene can be monitored by any means
known in the art. When a test substance affects the expression of such a
differentially expressed gene, it is a candidate drug for affecting the growth
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properties of fungi, and may be useful as an antifungal agent.
Because differentially expressed genes are likely to be involved in cell
cycle progression, it is likely that these genes are conserved among species.
The differentially expressed genes identified by the present invention can be
used to identify homologs in humans and other mammals. Means for
identifying homologous genes among different species are well known in the
art. Briefly, stringency of hybridization can be reduced so that imperfectly
matching sequences hybridize. This can be in the context of inter alia
Southern blots, Northern blots, colony hybridization or PCR. Any
hybridization technique which is known in the art can be used.
Probes according to the present invention are isolated DNA molecules
which have at least 10, and preferably at least 12, 14, 16, 18, 20, or 25
contiguous nucleotides of a particular NORF gene or other differentially
expressed gene. The probes may or may not be labeled. They may be used
as primers for PCR or for Southern or Northern blots. Preferably the probes
are anchored to a sofid support. More preferably they are present on an array
so that multiple probes can simultaneously hybridize to a single biological
sample. The probes can be spotted onto the array or synthesized in situ on
the array. See Lockhart et. al., Nature Biotechnology, Vol. 14, December
i 996, "Expression monitoring by hybridization to high-density
oligonucleotide arrays." A single array can contain more than 100, 500 or
even 1,000 different probes in discrete locations.
The above disclosure generally describes the present invention. A more
complete understanding can be obtained by reference to the following specific
examples which are provided herein for purposes of illustration only, and are
not intended to limit the scope of the invention.
Summary
We have analyzed the set of genes expressed from the yeast genome, herein
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called the transcriptome, using serial analysis of gene expression (SAGE).
Analysis of 60,633 transcripts revealed 4,665 genes, with expression levels
ranging from 0.3 to over 200 transcripts per cell. Of these genes, 1,981 had
known functions, while 2,684 were previously uncharacterized. Integration
of positional information with gene expression data allowed the generation
of chromosomal expression maps, identifying physical regions of
transcriptional activity, and identified genes that had not been predicted by
sequence information alone. These studies provide insight into global
patterns of gene expression in yeast and demonstrate the feasibility of
genome-wide expression studies in eukaryotes.
Results
Characteristics and Rationale of SAGE Approach
Several methods have recently been described for the high throughput
evaluation of gene expression (Nguyen et al., 1995; Schena et al., 1995;
Velculescu et al., 1995). We used SAGE (Serial Analysis of Gene
Expression) because it can provide quantitative gene expression data without
the prerequisite of a hybridization probe for each transcript. The SAGE
technology is based on two basic principles (Figure 1). First, a .short
sequence tag (9-11 bp) contains sufficient information to uniquely identify a
transcript, provided that it is derived from a defined location within that
transcript. Second, many transcript tags can be concatenated into a single
molecule and then sequenced, revealing the identity of multiple tags
simultaneously. The expression pattern of any population of transcripts can
be quantitatively evaluated by determining the abundance of individual tags
and identifying the gene corresponding to each tag.
Genome-wide expression
In order to maximize representation of genes involved in normal growth and
cell-cycle progression, SAGE libraries were generated from yeast cells in
three states: log phase, S phase arrested and G2/M phase arrested. In total,
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SAGE tags corresponding to 60,633 total transcripts were identified
(including 20,184 from log phase, 20,034 from S phase arrested, and 20,415
from GZ/M phase arrested cells). Of these tags, 56,291 tags (93%) precisely
matched the yeast genome, 88 tags matched the mitochondria) genome, and
91 tags matched the 2 micron plasmid.
The number of SAGE tags required to define a yeast transcriptome
depends on the confidence level desired for detecting low abundance mRNA
molecules. Assuming the previously derived estimate of 15,000 mRNA
molecules per cell (Hereford and Rosbash, 1977), 20,000 tags would
represent a 1.3 fold coverage even for mRNA molecules present at a single
copy per cell, and would provide a 72% probability of detecting such
transcripts (as determined by Monte Carlo simulations). Analysis of 20,184
tags from log phase cells identified 3,298 unique genes. As an independent
confirmation of mRNA copy number per cell, we compared the expression
level of SUP44/RPS4, one of the few genes whose absolute mRNA levels
have been reliably determined by quantitative hybridization experiments (Iyer
and Struhl, 1996), with expression levels determined by SAGE.
SUP44/RPS4 was measured by hybridization at 75 +/- 10 copies/cell (Iyer
and Struhl, 1996), in good accord with the SAGE data of 63 copies/cell,
suggesting that the estimate of 15,000 mRNA molecules per cell was
reasonably accurate. Analysis of SAGE tags from S phase arrested and G2/M
phase arrested cells revealed similar expression levels for this gene (range
52
to 55 wpies/cell), as well as for the vast majority of expressed genes. As
less
than 1% of the genes were expressed at dramatically different levels among
these three states (see below), SAGE tags obtained from all libraries were
combined and used to analyze global patterns of gene expression.
Analysis of ascertained tags at increasing increments revealed that the
number of unique transcripts plateaued at 60,000 tags (Figure 2). This
suggested that generation of fi~rther SAGE tags would yield few additional
genes, consistent with the fact that sixty thousand transcripts represented a
four-fold redundancy for genes expressed as low as 1 transcript per cell.
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Likewise, Monte Carlo simulations indicated that analysis of 60,000 tags
would identify at least one tag for a given transcript 97% of the time if its
expression level was one copy per cell.
The 56,291 tags that precisely matched the yeast genome represented
4,665 different genes. This number is in agreement with the estimate of
3,000 to 4,000 expressed genes obtained by RNA DNA reassociation kinetics
(Hereford and Rosbash, 1977). These expressed genes included 85% of the
genes with characterized functions (1,981 of 2,340), and 76% of the total
genes predicted from analysis of the yeast genome (4,665 of 6,121). These
numbers are consistent with a relatively complete sampling of the yeast
transcriptome given the limited number of physiological states examined and
the large number of genes predicted solely on the basis of genomic sequence
analysis.
The transcript expression per gene was observed to vary from 0.3 to
over 200 copies per cell. Analysis of the distribution of gene expression
levels revealed several abundance classes that were similar to those observed
in previous studies using reassociation kinetics. A "virtual Rot" of the genes
observed by SAGE (Figure 3A) identified three main components of the
transcriptome with abundances ranging over three orders of magnitude. A
Rot curve derived from RNA-cDNA reassociation kinetics also contained
three main components distributed over a similar range of abundances
{Hereford and Rosbash, 1977). Although the kinetics of reassociation of a
particular class of RNA and cDNA may be affected by numerous
experimental variables, there were striking similarities between Rot and
virtual Rot analyses (Figure 3B). Because Rot analysis may not detect all
transcripts of low abundance (Lewin, 1980), it is not surprising that SAGE
revealed both a larger total number of expressed genes and a higher fraction
of the transcriptome belonging to the low abundance transcript class.
Integration of Expression Information with the Genomic Map
The SAGE expression data could be integrated with existing positional
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information to generate chromosomal expression maps (Figure 4). These
maps were generated using the sequence of the yeast genome and the position
coordinates of ORFs obtained from the Stanford Yeast Genome Database.
Although there were a few genes that were noted to be physically proximal
and have similarly high levels of expression, there did not appear to be any
clusters of particularly high or low expression on any chromosome. Genes
like histones H3 and H4, which are known to have coregulated divergent
promoters and are immediately adjacent on chromosome 14 (Smith and
Murray, 1983), had very similar expression levels (5 and 6 copies per cell,
respectively). The distribution of transcripts among the chromosomes
suggested that overall transcription was evenly dispersed, with total
transcript
levels being roughly linearly related to chromosome size (rz =0.85, data not
shown). However, regions within 10 kb of telomeres appeared to be
uniformly undertranscribed, containing on average 3.2 tags per gene as
compared with 12.4 tags per gene for non-telomeric regions. (Figure 4). This
is consistent with the previously described observations of "telomeric
silencing" in yeast (Gottschling et al., 1990). Recent studies have reported
telomeric position effects as far as 4 kb from telomere ends (Renauld et al.,
1993).
Gene Expression Patterns
Table 1 lists the 30 most highly expressed genes, all of which are expressed
at greater than 60 mRNA copies per cell. . As expected, these genes mostly
correspond to well characterized enzymes involved in energy metabolism and
protein synthesis and were expressed at similar levels in all three growth
states (Examples in Figure 5). Some of these genes, including EN02
(McAlister and Holland, 1982), PDCI (Schmitt et aL, 1983), PGKI
(Chambers et al., 1989), PYKI (Nishizawa et al., 1989), and ADHl (penis et
al., 1983), are known to be dramatically induced in the glucose-rich growth
conditions used in this study. In contrast, glucose repressible genes such as
the GALIlGAL7/GAL10 cluster (St John and Davis, 1979), and GAL3 (Bajwa
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et al., 1988) were observed to be expressed at very low levels (0.3 or fewer
copies per cell). As expected for the yeast strain used in this study, mating
type a specific genes, such as the a factor genes (MFAl, MFA2) (Michaelis
and Herskowitz, 1988), and alpha factor receptor (STE2) (Burkholder and
Harlwell, 1985) were all observed to be expressed at significant levels (range
2 to 10 copies per cell), while mating type alpha specific genes (MFaI,
MFa2, STE3) (Hagen et al., 1986; Kurjan and Herskowitz, 1982; Singh et al.,
1983) were observed to be expressed at very low levels (<0.3 copies/cell).
Three of the highly expressed genes in Table 1 had not been previously
characterized. One contained an ORF with predicted ribosomal function,
previously identified only by genomic sequence analysis. Analyses of all
SAGE data suggested that there were 2,684 such genes corresponding to
uncharacterized ORFs which were transcribed at detectable levels. The 30
most abundant of these transcripts were observed more than 30 times,
corresponding to at least 8 transcripts per cell {Table 2). The other two
highly expressed uncharacterized genes corresponded to ORFs not predicted
by analysis of the yeast genome sequence (NORF = ~onannotated ~).
Analyses of SAGE data suggested that there were approximately 160 NORF
genes transcribed at detectable levels. The 30 most abundant of these
transcripts were observed at least 9 times (Table 3 and examples in Figure 5).
Interestingly, one of the NORF genes (NORFS) was only expressed in
S phase arrested cells and corresponded to the transcript whose abundance
varied the most in the three states analyzed (> 49 fold, Figure 5).
Comparison of S phase arrested cells to the other states also identified
greater
than 9 fold elevation of the RNR2 and RNR4 transcripts (Figure 5). Induction
of these ribonucleoside reductase genes is likely to be due to the hydroxyurea
treatment used to arrest cells in S phase (Elledge and Davis, 1989).
Likewise, comparison of G2/M arrested cells identified elevation of RBL2
and dynein light chain, both microtubule associated proteins (Archer et al.,
1995; Dick et al., 1996). As with the RNR inductions, these elevated levels
seem likely to be related to the nocodazole treatment used to arrest cells in
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the G2/M phase. While there were many relatively small differences between
the states (for example, NORFI, Figure 5), overall comparison of the three
states revealed surprisingly few dramatic differences; there were only 29
transcripts whose abundance varied more than 10 fold among the three
different states analyzed.
Discussion
Analysis of a yeast transcriptome affords a unique view of the RNA
components defining cellular life. We observed gene expression levels to vary
over three orders of magnitude, with the transcripts involved in energy
metabolism and protein synthesis the most highly expressed. Key transcripts,
such as those encoding enzymes required for DNA replication (e.g. POLI and
POL3), kinetochore proteins (NDC10 and SKPI), and many other interesting
proteins, were present at 1 or fewer copies per cell on average. These
1 S abundances are consistent with previous qualitative data from
reassociation
kinetics which suggested that the largest number of expressed genes was
present at 1 or 2 copies per cell. These observations indicate that low
transcript copy numbers are sufficient for gene expression in yeast, and
suggest that yeast possess a mechanism for rigid control of RNA abundance.
The synthesis of chromosomal expression maps presents a cataloging
of the expression level of genes, organized by their genomic positions. It is
not surprising that gene expression is well balanced throughout the 16
chromosomes of S. cerevisiae. As most genes have independent regulatory
elements, it would have been surprising to find a large number of physically
adjacent genes that had similar high levels of expression. Of the few genes
that were known to have coregulated divergent promoters, like the H3/H4
pair, SAGE data confirmed concordant levels of expression. For areas like
telomere ends that are known to be transcriptionally suppressed, SAGE data
corroborated low levels of expression. Other expected expression patterns
such as high levels of glucose induced glycolytic enzymes, low levels of
glucose repressed GAL genes, expression of mating type a specific genes, and
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low of expression of mating type alpha genes, were observed. Finally,
identification of tags corresponding to NORF genes suggests that there is a
significant number of small proteins encoded by the yeast genome that were
undetected by the criteria used for systematic sequence analysis. The yeast
genome sequence has been annotated for all ORES larger than 300bp,
(encoding proteins 100 amino acids or greater). Genes encoding proteins
below this cut off are therefore commonly unannotated. This class of genes
might also be underrepresented in mutational collections because of the small
target size for mutagenesis, and given their small size, may encode proteins
with novel functions. The systematic knockout of these NORF genes will
therefore be of great interest.
Comparison of gene expression patterns from altered physiologic states
can provide insight into genes that are important in a variety of processes.
Comparison of transcriptomes from a variety of physiologic states should
1 S provide a minimum set of genes whose expression is required for normal
vegetative growth, and another set composed of genes that will be expressed
only in response to specific environmental stimuli, or during specialized
processes. For example, recent work has defined a minimal set of 250 genes
required for prokaryotic cellular life (Mushegian and Koonin, 1996).
Examination of the yeast genome readily identified homologous genes for 196
of these, over 90% of which were observed to be expressed in the SAGE
analysis. Detailed analyses of yeast transcriptomes, as well as transcriptomes
from other organisms, should ultimately allow the generation of a minimal set
of genes required for eukaryotic life.
Like other genome-wide analyses, SAGE analysis of yeast
transcriptomes has several potential limitations. First, a small number of
transcripts would be expected to lack an NIaIII site and therefore would not
be detected by our analysis. Second, our analysis was limited to transcripts
found at least as frequently as 0.3 copies per cell. Transcripts expressed in
only a minute fraction of the cell cycle, or transcripts expressed in only a
fraction of the cell population, would not be reliably detected by our
analysis.
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Finally, mRNA sequence data are practically unavailable for yeast, and
consequently, some SAGE tags cannot be unambiguously matched to
corresponding genes. Tags which were derived from overlapping genes, or
genes which have unusually long 3' untranslated regions may be misassigned.
Increased availability of 3' UTR sequences in yeast mRNA molecules should
help to resolve the ambiguities.
Despite these potential limitations, it is clear that the analyses described
here furnish both global and local pictures of gene expression, precisely
defined at the nucleotide level. These data, like the sequence of the yeast
genome itselt; provide simple, basic information integral to the
interpretation
of many experiments in the future. The availability of mRNA sequence
information from EST sequencing as well as various genome projects, will
soon allow definition of transcriptomes from a variety of organisms, including
human. The data recorded here suggest that a reasonably complete picture
of a human cell transcriptome will require only about 10 - 20 fold more tags
than evaluated here, a number well within the practical realm achievable with
a small number of automated sequencers. The analysis of global expression
patterns in higher eukaryotes is expected, in general, to be similar to those
reported here for S. cerevisiae. However, the analysis of the transcriptome
in different cells and from different individuals should yield a wealth of
information regarding gene function in normal, developmental, and disease
states.
Experimental Procedures
Yeast cell culture
The source of transcripts for all experiments was S. cerevisiae strain YPH499
{MATa ura3-52 lys2-801 ade2-101 leu2-dl his3-d200 trill-d63) (Sikorski
and ITieter, 1989). Logarithmically growing cells were obtained by growing
yeast cells to early log phase (3 x 106 cells/ml) in YPD (Rose et al., 1990)
rich medium (YPD supplemented with 6mM uracil, 4.8 mM adenine and 24
mM tryptophan) at 30°C. For arrest in the G1/S phase of the cell cycle,
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hydroxyurea (0.1M) was added to early log phase cells, and the culture was
incubated an additional 3 .5 hours at 3 0 ° C. For arrest in the G2/M
phase of
the cell cycle, nocodazole (l5ug/ml) was added to early log phase cells and
the culture was incubated for an additional 100 minutes at 30 ° C.
Harvested
cells were washed once with water prior to freezing at -70 ° C. The
growth
states of the harvested cells were confirmed by microscopic and flow
cytometric analyses (Basrai et al., 1996).
RNA isolation and Northern Blot Analysis
Total yeast RNA was prepared using the hot phenol method as described
(Leeds et al., 1991). mRNA was obtained using the MessageMaker Kit
(GibcoBRL) following the manufacturer's protocol. Northern blot analysis
was performed as described (El-Deiry et al., 1993), using probes PCR
amplified from yeast genomic DNA.
SAGE protocol
The SAGE method was performed as previously described (Velculescu et al.,
1995), with exceptions noted below. PolyA RNA was converted to double-
stranded cDNA with a BRL synthesis kit using the manufacturer's protocol
except for the inclusion of primer biotin-5'-Tlg-3'. The cDNA was cleaved
with MaIII (Anchoring Enzyme). As NIaIII sites were observed to occur
once every 309 base pairs in three arbitrarily chosen yeast chromosomes (1,
5, 10), 95% of yeast transcripts were predicted to be detectable with a NIaIII-
based SAGE approach. After capture of the 3' cDNA fragments on
streptavidin coated magnetic beads (Dynal), the bound cDNA was divided
into two pools, and one of the following linkers containing recognition sites
for BsmFI was ligated to each pool: Linker 1, 5'-
TTTGGATTTGCTGGTGCAGTACAACTAGGCTTAATAGGGACATG-3'
( S E D I D N O : 1 ) . 5 ' -
TCCCTATTAAGCCTAGTTGTACTGCACCAGCAAATCC
[amino mod. C7]-3'(SED ID N0:2).; Linker 2,5'-
17
CA 02278645 1999-07-22
~WO 98/32847 PCT/US98/01216
TTTCTGCTCGAATTCAAGCTTCTAACGATGTACGGGGACATG-3'
( S E D I D N O : 3 ) 5 ' -
TCCCCGTACATCGTTAGAAGCTTGAATTCGAGCAG[amino mod. C7]-
3' (SED ID N0:4).
As BsmFI (Tagging Enzyme) cleaves 14 by away from its recognition
site, and the MaIli site overlaps the BsmFI site by 1 bp, a 15 by SAGE tag
was released with BsmFI. SAGE tag overhangs were filled-in with Klenow,
and tags from the two pools were combined and ligated to each other. The
ligation product was diluted and then amplified with PCR for 28 cycles with
5'-GGATTTGCTGGTGCAGTACA-3' (SED ID NO:S) and 5'-
CTGCTCGAATTCAAGCTTCT-3' (SED ID N0:6), as primers. The PCR
product was analyzed by polyacrylamide gel electrophoresis (PAGE), and the
PCR product containing two tags ligated tail to tail (ditag) was excised. The
PCR product was then cleaved with NlalB, and the band containing the ditags
was excised and self ligated. After ligation, the concatenated products were
separated by PAGE and products between 500 by and 2 kb were excised.
These products were cloned into the SphI site of pZero (Invitrogen).
Colonies were screened for inserts by PCR with M13 forward and M13
reverse sequences located outside the cloning site as primers.
PCR products from selected clones were sequenced with the TaqFS
DyePrimer kits (Perkin Elmer) and analyzed using a 377 ABI automated
sequences (Perkin Elmer), following the manufacturer's protocol. Each
successfi~i sequencing reaction identified an average of 26 tags; given a 90%
sequencing reaction success rate, this corresponded to an average of about
850 tags per sequencing gel.
SAGE data analysis
Sequence files were analyzed by means of the SAGE program group
(Velculescu et al., 1995), which identifies the anchoring enzyme site with the
proper spacing and extracts the two intervening tags and records them in a
database. The 68,691 tags obtained contained 62,965 tags from unique
18
CA 02278645 1999-07-22
WO 98/32847 PCT/US98/a1216
ditags and 5,726 tags from repeated ditags. The latter were counted only
once to eliminate potential PCR bias of the quantitation, as described
(Velculescu et al., 1995). Of 62,965 tags, 2,332 tags corresponded to linker
sequences, and were excluded from further analysis. Of the remaining tags,
4,342 tags could not be assigned, and were likely due to sequencing errors (in
the tags or in the yeast genomic sequence). If all of these were due to tag
sequencing errors, this corresponds to a sequencing error rate of about 0.7%
per base pair (for a lObp tag), not far from what we would have expected
under our automated sequencing conditions. However, some unassigned tags
had a much higher than expected frequency of A's as the last five base pairs
of the tag (5 of the 52 most abundant unassigned tags), suggesting that these
tags were derived from transcripts containing anchoring enzyme sites within
several base pairs from their polyA tails. Given the frequency ofNIaTII sites
in the genome (one in 309 base pairs), approximately 3% of transcripts were
predicted to contain NIaIII sites within 10 by of their polyA tails.
As very sparse data are available for yeast mRNA sequences and efforts
to date have not been able to identify a highly conserved polyadenylation
signal (Irniger and Braus, 1994; Zaret and Sherman, 1982), we used 14 by of
SAGE tags (i.e. the NIaTII site plus the adjacent 10 bp) to search the yeast
genome directly (yeast genome sequence obtained from the Stanford yeast
genome ftp site (genome-ftp.stanford.edu) on August 7, 1996). Because only
coding regions are annotated in the yeast genome, and SAGE tags can be
derived from 3' untranslated regions of genes, a SAGE tag was considered to
correspond to a particular gene if it matched the ORF or the region 500 by
3' of the ORF (locus names, gene names and ORF chromosomal coordinates
were obtained from Stanford yeast genome ftp site, and ORF descriptions
were obtained fromMIPS www site (http://www.mips.biochem. mpg.de~ on
August 14, 1996). ORFs were considered genes with known functions if they
were associated with a three letter gene name, while ORFs without such
designations were considered uncharacterized.
As expected, SAGE tags matched transcribed portions of the genome
19
CA 02278645 1999-07-22
WO 98132847 PCT/LTS98/01216
in a highly non-random fashion, with 88% matching ORFs or their adjacent
3' regions in the correct orientation (chi-squared P value < 10''~. In
instances
when more than one tag matched a particular ORF in the correct orientation,
the abundance was calculated to be the sum of the matched tags (for Figure
2, Figure 3, and Figure 4). Tags that matched ORFs in the incorrect
orientation were not used in abundance calculations. In instances when a tag
matched more than one region of the genome (for example an ORF and non-
ORF region) only the matched ORF was considered. In some cases the 15th
base of the tag could also be used to resolve ambiguities. For Figure 4, only
tags that matched the genome once were used.
For the identification of NORF genes, only tags were considered that
matched portions of the genome that were further than 500 by 3' of a
previously identified ORF, and were observed at least two times in the SAGE
libraries.
CA 02278645 1999-07-22
WO 98/32847 PCT/US98/0121b
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21
CA 02278645 1999-07-22
WO 98/32847 PCT/(1598/01216
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22
?. .
CA 02278645 1999-07-22
WO 98/32847 PCT/US98/01216
t OOD ~ O l~ ~ ~ (O In ~ ~ OMO V ~ 01 r t(~ m O) 'd' V' ~ O Or0 OM7 N ~ N N
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LL lL lL LL LL LL LL ll. LL LL Ll LL LL LL LL LL LL LL LL LL
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23
CA 02278645 1999-07-22
WO 98132847 PCT/US98/01216
TABLE 4
Additional NORFs
::::
:.:....:::::::::::::::.:,...::::...:::....::.:...:.::..::.::>::::.::.::....:::.
::<...:,:;.::.::.::.:....:::::::....::.'.:.::..:::..''<...:.....:::.::::.......
:::>..-.::::::':<.....::~~.::.::....::::....:..::::.:.:;'..::....::....::
......:................'
..~::.....'...:.:.,.::::..,.::
...:".....'...'.
. .' ...'..."
....:....:.::::::
..:.....:'
....... .:':,.
....
:. .. .. .,'.
...".'....
...... . .
. ........
. .. ......'
.. . ...:
:::'.,',:::..:.:.,.:::
.: .
::i:::.: .:
'
:: > , ' :...,
: 'st2o-::a':-'.'::::".';.'..;
' . ,................
. ' : : :
. _:;_:_:y
w ..' . '
.
:.~'::'.'-.'.''.'.:::.,.::.'.
..
.. . . .:.:.:..:.~:::.::::.,::..":::.:~...............'.::..:..
. ::::::.:::..:....
... .. .........
... .. . .
..............'.
...... ...
..............
.. ..... ...
.,~ ..' :.",......
.........'...,.....'......
.... .. .
......................,.,..,;~.;:::::~::::::::::;:::....::;:;ox;;;;;
::.,.::a:.:;..:;:;t.:::;::".
:.'.:.::'..:":",:..:,.....":.........",....:..::......,.."...."..,...
~
~
GGCGCAATTT 4 1108395 2
TAAGTGATGA 7 593382 2
TTGTTGAATT 10 608373 2
GAAGCAGTAA 3 155607 2
ACATATGTTA 4 916112 2
CCCTACACGG 6 223289 2
GTAATTGGAC 10 392099 2
ATCAGACAAA 14 687272 2
TTATGAAAGA 15 81263 2
ATTCGTTCTA 15 841970 2
AGCAGGAGTT 16 188350 2
TTCTATTAGG 2 418749 2
TGGATTTCAG 4 1224930 2
CAGATATAAT 5 52488 2
CTGTTTTGGG 11 374761 2
CATTTTTAGT 11 508212 2
TTGAAAAGAT 13 104160 2
TAAGCCCATC 13 251273 2
AGCGTCCTCA 15 832420 2
TTTAGTTAAT 2 477623 _ 2
ATGGTAGCCA 3 56961 2
AATTAGACTA 3 162589 2
AGTGACTCTT 4 1490879 2
GGACTATAAG 5 251266 2
ACTTTTTCAG 10 159213 2
GTCATATAGT 13 158765 2
CAACAAAGTG 13 171166 2
GTGGGAAAGG 13 804600 2
TACTTTATAT 16 366449 2
AATACCAGCG 3 175540 1
GCCTTGTATA 4 372624 1
GGTACATTCA 5 67152 1
GATTTCTCTG 5 187462 1
TAGTTGCTCC 7 . 317108 1
GTAAGAAATC 7 836202 1
CTTGGGCTAT 8 107992 1
AAATGGTGAT 11 558686 1
ATCATTTGGG 12 199358 1
CTGAACTTTA 12 283720 1
CCAGAAGGAG 13 652873 1
CCGGTTACTA 15 803663 1
CGATGAGAAG 15 1004369 1
AAACCGTCCC 16 199141 1
TCATTCATAC 2 164728 1
TATCTTTTTG 4 169784 1
TTAGAATAAT 4 603508 1
GTACGCTGTG 5 118089 1
TATATTAATT 6 64228 1
24
CA 02278645 1999-07-22
1~V0 98/32847 PCT/US98/01216
GTTCTTGCCT 7 939579 1
ATATAGCTGC 10 181144 1
CCAAAAAAAA 11 91785 1
GAACTCCACA 11 94125 1
CCTTCACTGC 11 374172 1
CACATCATAA 11 625896 1
GAAGTATTGA 12 603999 1
TGCGCGTATA 13 206410 1
GGGTAGTACT 13 671730 1
TAGTTTTGTC 15 33475 1
CAATTCCTAC 1 172182 0.8
TTTGATTTGA 2 46431 0.8
GGCTCTGGTT 2 414510 0.8
CAGAAATAGC 2 565130 0.8
CTGTTATTTT 2 616054 0.8
CGAAGTCAAA 2 680605 0.8
CTCTAGATAA 3 171584 0.8
AGTCAAAATG 4 192750 0.8
GCGAGTTTAG 4 691301 0.8
GCTCCAATAG 4 1131020 0.8
TTTATTTGAG 4 1237501 0.8
GTTATATTGA 4 1401803 0.8
TGGGTTGAAG 5 251266 0.8
ATTTTATTTG 5 447729 0.8
ATCATAAAAA 5 548612 0.8
TTATATAAAA 6 223182 0.8
CTACTTCTGC 8 34653 0.8
ATAAGACAGT 10 227802 0.8
TTCATAAGTT 10 471894 0.8
TAAATCTGAG 11 145617 0.8
CTGGTAGAAA 11 151174 0.8
CACGTACACA 11 403208 0.8
CCAAGATCAA 11 425882 0.8
AGCTTGTTCC 12 234966 0.8
CACATTCGTT 12 759953 0.8
CTTACATATA 12 789781 0.8
TCTATAGCAA 13 228936 0.8
CCTTTCTGAA 13 ~ 297985 0.8
CCTTTAGAAT 13 777999 0.8
AATTAACACC 13 842122 0.8
GCGCAGGGGC 14 440984 0.8
TGTTTATAAA 14 661710 0.8
AAAAGTCATT 15 32081 0.8
TTCGTAAACT 15 680625 0.8
TTTTTGGAGT 15 888343 0.8
AGGCATCTTG 16 250284 0.8
AAATCAAAAC 16 453890 0.8
AATTGACGAA 16 560169 0.8
TTGATGATTT 16 582360 0.8
CCTGTTTTTG 16 643476 0.8
TTTTTAAAAA 1 101436 0.5
CA 02278645 1999-07-22
WO 98/32847 PCT/US98/01216
AAGTTTGATC 1 199848 0.5
AGCACCTATG 2 46913 0.5
TGATTTATCC 2 418946 0.5
ACTGCATCTG 2 680860 0.5
CAAGTTAGGA 2 744770 0.5
ATACCCAATT 3 29939 0.5
AACTTTGTAT 3 30056 0.5
GCGGCGGGTG 3 41645 0.5
AAAATTGTTC 3 57108 0.5
TCAAGTACTC 3 157855 0.5
AACTGTATGC 3 223882 0.5
CTATCGGCCA 3 278840 0.5
ACAAGCCCAA 3 289917 0.5
GTACAGGGCT 4 93873 0.5
AAGATCATCG 4 254851 0.5
GAACTCCTGG 4 340891 0.5
GAACGAGAAG 4 371850 0.5
TZTTTAATAC 4 372058 0.5
TCTCCAGTTG 4 381712 0.5
AATACGTTAC 4 471791 0.5
ACGATTGGCT 4 509158 0.5
TGTTTATAAG 4 521709 0.5
CGTTTTCGTC 4 538839 0.5
TCGAACCTCT 4 578702 0.5
TCCACACACA 4 930972 0.5
CCGTGCGTGC 4 1324367 0.5
TTTCTTCAAC 5 116099 0.5
CCAAGTCTCG 5 159320 0.5
AGAGCGAATT 5 207517 0.5
TGTAGATTAT 5 280465 0.5
AAAAGTAGTT 5 286387 0.5
ACTTGGTATG 5 422942 0.5
TTAATGTTAT 5 544523 0.5
TACACGCGCG 5 544555 0.5
GGTCACTCCT 6 62983 0.5
AAGTGATGAA 6 76141 0.5
TTTATCTTGT 6 130327 0.5
AGTGATTGTT 6 256223 0.5
~
GCTTTGTTGT 7 72577 0.5
TCATTGATTC 7 110590 0.5
TTCACCGGAA 7 323655 0.5
ACTATTCTGT 7 423957 0.5
GGGCCAACCC 7 433787 0.5
AAAATATCTT 7 559397 0.5
TAGTAGTAAC 7 622201 0.5
AAGCGCACAA 7 735909 0.5
TCGCTGTTTT 7 800300 0.5
TGTATTTTTG 7 836202 0.5
CTAAACAAAG 7 836587 0.5
TAGGAAGAAA 7 905046 0.5
GGAAAAATTA 7 958839 0.5
26
r
CA 02278645 1999-07-22
WO 98/32847 PCT/US98/01216
TTTGGATAGT 7 974754 0.5
CGTTTGTGTA 8 202655 0.5
AGAAAAAAAC 8 386651 0.5
TAAAGTCCAG 8 518998 0.5
TAAGCAGATT 8 529129 0.5
ATGAGCATTT 9 97114 0.5
AGGTGCAAAA 9 229077 0.5
TAACAAAGAG 10 628227 0.5
CAATTGGCAA 10 721781 0.5
ACTCCCTGTA 11 93528 0.5
CTCTATTGAT 11 144281 0.5
GCTTTCCTTT 11 146665 0.5
ACCGCAAAGA 11 231872 0.5
CTTGTTCAAA 12 230972 0.5
AATGTGCTGT 12 320426 0.5
GCAGATAGCG 12 341324 0.5
TCTGACTTAG 12 368780 0.5
CCCGGATGTT 12 433912 0.5
GTAACGATTG 12 449917 0.5
GAATAACGAA 12 673851 0.5
ACTGCTATTT 12 712476 0.5
GTTCTCTAGC 12 712712 0.5
CATCACCATC 12 794710 0.5
TTGCACTTCT 12 806833 0.5
ACTGTTTATG 12 867350 . 0.5
TTGCTATATA 12 1017911 0.5
TACATTCTAA 13 95707 0.5
CTCTTAGTTG 13 158970 0.5
ACGAACACTT 13 278341 0.5
TGCGCAAGTC 13 283795 0.5
TTTTTCTTAA 13 363037 0.5
CAAATGCATT 13 390802 0.5
CAAATTGTGT 13 395599 0.5
GCAATACTAT 13 826521 0.5
AGTGACGATG 14 60143 0.5
TACTGGTTTA 14 118854 0.5
GTTTGACCTA 14 335512 0.5
AGCGTTTGAT 14 478481 0.5
CTCTGTTGCG 14 ~ 728251 0.5
,A 15 35952 0.5
TTTGCTTGGT 15 242742 0.5
AGTTTTCCTG 15 304813 0.5
TTTAAAGATA 15 331453 0.5
AAGGAGACAC 15 448624 0.5
CTATATATCA 15 544530 0.5
GATGGAATAG 15 571210 0.5
TCGAGTCGAA 15 758202 0.5
15 882567 0.5
TTTCCAGAAT 15 969884 0.5
TGGACAATGT 15 970607 0.5
GGAATTAAGA 15 979894 0.5
27
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ACTATATGTT 16 582230 0.5
GATATATCAT 16 589647 0.5
AGAATTGATT 16 744406 0.5
CACTGTCTCC 16 824649 0.5
28
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