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Mark Gerstein

Publications and source records attributed to Mark Gerstein.

At least 37 records · Page 2Linked to original sources
PubMed2003

Ontologies for proteomics: towards a systematic definition of structure and function that scales to the genome level.

A principal aim of post-genomic biology is elucidating the structures, functions and biochemical properties of all gene products in a genome. However, to adequately comprehend such a large amount of information we need new descriptions of proteins that scale to the genomic level. In short, we need a unified ontology for proteomics. Much progress has been made towards this end, including a variety of approaches to systematic structural and functional classification and initial work towards developing standardized, unified descriptions for protein properties. In relation to function, there is a particularly great diversity of approaches, involving placing a protein in structured hierarchies or more-generalized networks and a recent approach based on circumscribing a protein's function through systematic enumeration of molecular interactions.

Computational Biology↗
PubMed2003

Spectral biclustering of microarray data: coclustering genes and conditions.

Global analyses of RNA expression levels are useful for classifying genes and overall phenotypes. Often these classification problems are linked, and one wants to find "marker genes" that are differentially expressed in particular sets of "conditions." We have developed a method that simultaneously clusters genes and conditions, finding distinctive "checkerboard" patterns in matrices of gene expression data, if they exist. In a cancer context, these checkerboards correspond to genes that are markedly up- or downregulated in patients with particular types of tumors. Our method, spectral biclustering, is based on the observation that checkerboard structures in matrices of expression data can be found in eigenvectors corresponding to characteristic expression patterns across genes or conditions. In addition, these eigenvectors can be readily identified by commonly used linear algebra approaches, in particular the singular value decomposition (SVD), coupled with closely integrated normalization steps. We present a number of variants of the approach, depending on whether the normalization over genes and conditions is done independently or in a coupled fashion. We then apply spectral biclustering to a selection of publicly available cancer expression data sets, and examine the degree to which the approach is able to identify checkerboard structures. Furthermore, we compare the performance of our biclustering methods against a number of reasonable benchmarks (e.g., direct application of SVD or normalized cuts to raw data).

Breast Neoplasms↗
PubMed2002

Thermostability of membrane protein helix-helix interaction elucidated by statistical analysis.

A prerequisite for the survival of (micro)organisms at high temperatures is an adaptation of protein stability to extreme environmental conditions. In contrast to soluble proteins, where many factors have already been identified, the mechanisms by which the thermostability of membrane proteins is enhanced are almost unknown. The hydrophobic membrane environment constrains possible stabilizing factors for transmembrane domains, so that a difference might be expected between soluble and membrane proteins. Here we present sequence analysis of predicted transmembrane helices of the genomes from eight thermophilic and 12 mesophilic organisms. A comparison of the amino acid compositions indicates that more polar residues can be found in the transmembrane helices of thermophilic organisms. Particularly, the amino acids aspartic acid and glutamic acid replace the corresponding amides. Cysteine residues are found to be significantly decreased by about 70% in thermophilic membrane domains suggesting a non-specific function of most cysteine residues in transmembrane domains of mesophilic organisms. By a pair-motif analysis of the two sets of transmembrane helices, we found that the small residues glycine and serine contribute more to transmembrane helix-helix interactions in thermophilic organisms. This may result in a tighter packing of the helices allowing more hydrogen bond formation.

Archaeal Proteins↗
PubMed2002

Complex transcriptional circuitry at the G1/S transition in Saccharomyces cerevisiae.

In the yeast Saccharomyces cerevisiae, SBF (Swi4-Swi6 cell cycle box binding factor) and MBF (MluI binding factor) are the major transcription factors regulating the START of the cell cycle, a time just before DNA replication, bud growth initiation, and spindle pole body (SPB) duplication. These two factors bind to the promoters of 235 genes, but bind less than a quarter of the promoters upstream of genes with peak transcript levels at the G1 phase of the cell cycle. Several functional categories, which are known to be crucial for G1/S events, such as SPB duplication/migration and DNA synthesis, are under-represented in the list of SBF and MBF gene targets. SBF binds the promoters of several other transcription factors, including HCM1, PLM2, POG1, TOS4, TOS8, TYE7, YAP5, YHP1, and YOX1. Here, we demonstrate that these factors are targets of SBF using an independent assay. To further elucidate the transcriptional circuitry that regulates the G1-to-S-phase progression, these factors were epitope-tagged and their binding targets were identified by chIp-chip analysis. These factors bind the promoters of genes with roles in G1/S events including DNA replication, bud growth, and spindle pole complex formation, as well as the general activities of mitochondrial function, transcription, and protein synthesis. Although functional overlap exists between these factors and MBF and SBF, each of these factors has distinct functional roles. Most of these factors bind the promoters of other transcription factors known to be cell cycle regulated or known to be important for cell cycle progression and differentiation processes indicating that a complex network of transcription factors coordinates the diverse activities that initiate a new cell cycle.

Binding Sites↗
PubMed2002

Digging deep for ancient relics: a survey of protein motifs in the intergenic sequences of four eukaryotic genomes.

We have examined conserved protein motifs in the non-coding, intergenic regions ("pseudomotif patterns") and surveyed their occurrence in the fly, worm, yeast and human genomes (chromosomes 21 and 22 only). To identify these patterns, we masked out annotated genes, pseudogenes and repeat regions from the raw genomic sequence and then compared the remaining sequence, in six-frame translation, against 1319 patterns from the PROSITE database. For each pseudomotif pattern, the absolute number of occurrences is not very informative unless compared against a statistical expectation; consequently, we calculated the expected occurrence of each pattern using a Poisson model and verified this with simulations. Using a p-value cut-off of 0.01, we found 67 pseudomotif patterns over-represented in fly intergenic regions, 34 in worm, 21 in human and six in yeast. These include the zinc finger, leucine zipper, nucleotide-binding motif and EGF domain. Many of the over-represented patterns were common to two or more organisms, but there were a few that were unique to specific ones. Furthermore, we found more over-represented patterns in the fly than in the worm, although the fly has fewer pseudogenes. This puzzling observation can be explained by a higher deletion rate in the fly genome. We also surveyed under-represented patterns, finding 23 in the fly, 12 in the worm, 18 in human and two in yeast. If intergenic sequences were truly random, we would expect an equal number of over and under-represented patterns. The fact that for each organism the number of over-represented patterns is greater than the number of under-represented ones implies that a fraction of the intergenic regions consist of ancient protein fragments that, due to accumulated disablements, have become unrecognizable by conventional techniques for gene and pseudogene identification. Moreover, we find that in aggregate the over-represented pseudomotif patterns occupy a substantial fraction of the intergenic regions. Further information is available at http://pseudogene.org

Amino Acid Motifs↗
PubMed2002

Genomic and proteomic analysis of the myeloid differentiation program: global analysis of gene expression during induced differentiation in the MPRO cell line.

We have used an approach using 2-dimensional gel electrophoresis with mass spectrometry analysis combined with oligonucleotide chip hybridization for a comprehensive and quantitative study of the temporal patterns of protein and mRNA expression during myeloid development in the MPRO murine cell line. This global analysis detected 123 known proteins and 29 "new" proteins out of 220 protein spots identified by tandem mass spectroscopy, including proteins in 12 functional categories such as transcription factors and cytokines. Bioinformatic analysis of these proteins revealed clusters with functional importance to myeloid differentiation. Previous analyses have found that for a substantial number of genes the absolute amount of protein in the cell is not strongly correlated to the amount of mRNA. These conclusions were based on simultaneous measurement of mRNA and protein at just a single time point. Here, however, we are able to investigate the relationship between mRNA and protein in terms of simultaneous changes in their levels over multiple time points. This is the first time such a relationship has been studied, and we find that it gives a much stronger correlation, consistent with the hypothesis that a substantial proportion of protein change is a consequence of changed mRNA levels, rather than posttranscriptional effects. Cycloheximide inhibition also showed that most of the proteins detected by gel electrophoresis were relatively stable. Specific investigation of transcription factor mRNA representation showed considerable similarity to those of mature human neutrophils and highlighted several transcription factors and other functional nuclear proteins whose mRNA levels change prominently during MPRO differentiation but which have not been investigated previously in the context of myeloid development. Data are available online at http://bioinfo.mbb.yale.edu/expression/myelopoiesis.

Animals↗
PubMed2002

Genomic analysis of membrane protein families: abundance and conserved motifs.

BACKGROUND: Polytopic membrane proteins can be related to each other on the basis of the number of transmembrane helices and sequence similarities. Building on the Pfam classification of protein domain families, and using transmembrane-helix prediction and sequence-similarity searching, we identified a total of 526 well-characterized membrane protein families in 26 recently sequenced genomes. To this we added a clustering of a number of predicted but unclassified membrane proteins, resulting in a total of 637 membrane protein families. RESULTS: Analysis of the occurrence and composition of these families revealed several interesting trends. The number of assigned membrane protein domains has an approximately linear relationship to the total number of open reading frames (ORFs) in 26 genomes studied. Caenorhabditis elegans is an apparent outlier, because of its high representation of seven-span transmembrane (7-TM) chemoreceptor families. In all genomes, including that of C. elegans, the number of distinct membrane protein families has a logarithmic relation to the number of ORFs. Glycine, proline, and tyrosine locations tend to be conserved in transmembrane regions within families, whereas isoleucine, valine, and methionine locations are relatively mutable. Analysis of motifs in putative transmembrane helices reveals that GxxxG and GxxxxxxG (which can be written GG4 and GG7, respectively; see Materials and methods) are among the most prevalent. This was noted in earlier studies; we now find these motifs are particularly well conserved in families, however, especially those corresponding to transporters, symporters, and channels. CONCLUSIONS: We carried out a genome-wide analysis on patterns of the classified polytopic membrane protein families and analyzed the distribution of conserved amino acids and motifs in the transmembrane helix regions in these families.

Amino Acid Motifs↗
PubMed2002

Normal mode analysis of macromolecular motions in a database framework: developing mode concentration as a useful classifying statistic.

We investigated protein motions using normal modes within a database framework, determining on a large sample the degree to which normal modes anticipate the direction of the observed motion and were useful for motions classification. As a starting point for our analysis, we identified a large number of examples of protein flexibility from a comprehensive set of structural alignments of the proteins in the PDB. Each example consisted of a pair of proteins that were considerably different in structure given their sequence similarity. On each pair, we performed geometric comparisons and adiabatic-mapping interpolations in a high-throughput pipeline, arriving at a final list of 3,814 putative motions and standardized statistics for each. We then computed the normal modes of each motion in this list, determining the linear combination of modes that best approximated the direction of the observed motion. We integrated our new motions and normal mode calculations in the Macromolecular Motions Database, through a new ranking interface at http://molmovdb.org. Based on the normal mode calculations and the interpolations, we identified a new statistic, mode concentration, related to the mathematical concept of information content, which describes the degree to which the direction of the observed motion can be summarized by a few modes. Using this statistic, we were able to determine the fraction of the 3,814 motions where one could anticipate the direction of the actual motion from only a few modes. We also investigated mode concentration in comparison to related statistics on combinations of normal modes and correlated it with quantities characterizing protein flexibility (e.g., maximum backbone displacement or number of mobile atoms). Finally, we evaluated the ability of mode concentration to automatically classify motions into a variety of simple categories (e.g., whether or not they are "fragment-like"), in comparison to motion statistics. This involved the application of decision trees and feature selection (particular machine-learning techniques) to training and testing sets derived from merging the "list" of motions with manually classified ones.

Databases, Protein↗
PubMed2002

Functional profiling of the Saccharomyces cerevisiae genome.

Determining the effect of gene deletion is a fundamental approach to understanding gene function. Conventional genetic screens exhibit biases, and genes contributing to a phenotype are often missed. We systematically constructed a nearly complete collection of gene-deletion mutants (96% of annotated open reading frames, or ORFs) of the yeast Saccharomyces cerevisiae. DNA sequences dubbed 'molecular bar codes' uniquely identify each strain, enabling their growth to be analysed in parallel and the fitness contribution of each gene to be quantitatively assessed by hybridization to high-density oligonucleotide arrays. We show that previously known and new genes are necessary for optimal growth under six well-studied conditions: high salt, sorbitol, galactose, pH 8, minimal medium and nystatin treatment. Less than 7% of genes that exhibit a significant increase in messenger RNA expression are also required for optimal growth in four of the tested conditions. Our results validate the yeast gene-deletion collection as a valuable resource for functional genomics.

Cell Size↗
PubMed2002

The dominance of the population by a selected few: power-law behaviour applies to a wide variety of genomic properties.

BACKGROUND: The sequencing of genomes provides us with an inventory of the 'molecular parts' in nature, such as protein families and folds, and their functions in living organisms. Through the analysis of such inventories, it has been shown that different genomes have very different usage of parts; for example, the common folds in the worm are very different from those in Escherichia coli. RESULTS: Despite these differences, we find that the genomic occurrence of generalized parts follows a well-known mathematical framework called the power law, with a few parts occurring many times and most occurring only a few times. This observation is true in a wide variety of genomic contexts. Earlier studies found power laws in a few specific cases, such as the occurrence of protein families. Here, we find many further cases of power-law behavior, for example in the occurrence of pseudogenes and in levels of gene expression. We show comprehensively that this behavior applies across many different genomes, for many different types of parts (DNA words, InterPro families, protein superfamilies and folds, pseudogene families and pseudomotifs), and for the many disparate attributes associated with these parts (their functions, interactions and expression levels). CONCLUSIONS: Power-law behavior provides a concise mathematical description of an important biological feature: the sheer dominance of a few members over the overall population. We present this behavior in a unified framework and propose that all these observations are connected to an underlying DNA duplication process as genomes evolved to their current state.

Animals↗
PubMed2002

Comprehensive analysis of amino acid and nucleotide composition in eukaryotic genomes, comparing genes and pseudogenes.

Based on searches for disabled homologs to known proteins, we have identified a large population of pseudogenes in four sequenced eukaryotic genomes-the worm, yeast, fly and human (chromosomes 21 and 22 only). Each of our nearly 2500 pseudogenes is characterized by one or more disablements mid-domain, such as premature stops and frameshifts. Here, we perform a comprehensive survey of the amino acid and nucleotide composition of these pseudogenes in comparison to that of functional genes and intergenic DNA. We show that pseudogenes invariably have an amino acid composition intermediate between genes and translated intergenic DNA. Although the degree of intermediacy varies among the four organisms, in all cases, it is most evident for amino acid types that differ most in occurrence between genes and intergenic regions. The same intermediacy also applies to codon frequencies, especially in the worm and human. Moreover, the intermediate composition of pseudogenes applies even though the composition of the genes in the four organisms is markedly different, showing a strong correlation with the overall A/T content of the genomic sequence. Pseudogenes can be divided into 'ancient' and 'modern' subsets, based on the level of sequence identity with their closest matching homolog (within the same genome). Modern pseudogenes usually have a much closer sequence composition to genes than ancient pseudogenes. Collectively, our results indicate that the composition of pseudogenes that are under no selective constraints progressively drifts from that of coding DNA towards non-coding DNA. Therefore, we propose that the degree to which pseudogenes approach a random sequence composition may be useful in dating different sets of pseudogenes, as well as to assess the rate at which intergenic DNA accumulates mutations. Our compositional analyses with the interactive viewer are available over the web at http://genecensus.org/pseudogene.

Amino Acid Sequence↗
PubMed2002

Studying genomes through the aeons: protein families, pseudogenes and proteome evolution.

Protein families can be used to understand many aspects of genomes, both their "live" and their "dead" parts (i.e. genes and pseudogenes). Surveys of genomes have revealed that, in every organism, there are always a few large families and many small ones, with the overall distribution following a power-law. This commonality is equally true for both genes and pseudogenes, and exists despite the fact that the specific families that are enlarged differ greatly between organisms. Furthermore, because of family structure there is great redundancy in proteomes, a fact linked to the large number of dispensable genes for each organism and the small size of the minimal, indispensable sub-proteome. Pseudogenes in prokaryotes represent families that are in the process of being dispensed with. In particular, the genome sequences of certain pathogenic bacteria (Mycobacterium leprae, Yersinia pestis and Rickettsia prowazekii) show how an organism can undergo reductive evolution on a large scale (i.e. the dying out of families) as a result of niche change. There appears to be less pressure to delete pseudogenes in eukaryotes. These can be divided into two varieties, duplicated and processed, where the latter involves reverse transcription from an mRNA intermediate. We discuss these collectively in yeast, worm, fly, and human. The fly has few pseudogenes apparently because of its high rate of genomic DNA deletion. In the other three organisms, the distribution of pseudogenes on the chromosome and amongst different families is highly non-uniform. Pseudogenes tend not to occur in the middle of chromosome arms, and tend to be associated with lineage-specific (as opposed to highly conserved) families that have environmental-response functions. This may be because, rather than being dead, they may form a reservoir of diverse "extra parts" that can be resurrected to help an organism adapt to its surroundings. In yeast, there may be a novel mechanism involving the [PSI+] prion that potentially enables this resurrection. In worm, the pseudogenes tend to arise out of families (e.g. chemoreceptors) that are greatly expanded in it compared to the fly. The human genome stands out in having many processed pseudogenes. These have a character very different from those of the duplicated variety, to a large extent just representing random insertions. Thus, their occurrence tends to be roughly in proportion to the amount of mRNA for a particular protein and to reflect the extent of the intergenic sequences. Further information about pseudogenes is available at http://genecensus.org/pseudogene

Animals↗
PubMed2002

Structural genomics analysis: characteristics of atypical, common, and horizontally transferred folds.

We conducted a structural genomics analysis of the folds and structural superfamilies in the first 20 completely sequenced genomes by focusing on the patterns of fold usage and trying to identify structural characteristics of typical and atypical folds. We assigned folds to sequences using PSI-blast, run with a systematic protocol to reduce the amount of computational overhead. On average, folds could be assigned to about a fourth of the ORFs in the genomes and about a fifth of the amino acids in the proteomes. More than 80% of all the folds in the SCOP structural classification were identified in one of the 20 organisms, with worm and E. coli having the largest number of distinct folds. Folds are particularly effective at comprehensively measuring levels of gene duplication, because they group together even very remote homologues. Using folds, we find the average level of duplication varies depending on the complexity of the organism, ranging from 2.4 in M. genitalium to 32 for the worm, values significantly higher than those observed based purely on sequence similarity. We rank the common folds in the 20 organisms, finding that the top three are the P-loop NTP hydrolase, the ferrodoxin fold, and the TIM-barrel, and discuss in detail the many factors that affect and bias these rankings. We also identify atypical folds that are "unique" to one of the organisms in our study and compare the characteristics of these folds with the most common ones. We find that common folds tend be more multifunctional and associated with more regular, "symmetrical" structures than the unique ones. In addition, many of the unique folds are associated with proteins involved in cell defense (e.g., toxins). We analyze specific patterns of fold occurrence in the genomes by associating some of them with instances of horizontal transfer and others with gene loss. In particular, we find three possible examples of transfer between archaea and bacteria and six between eukarya and bacteria. We make available our detailed results at http://genecensus.org/20.

Animals↗
PubMed2002

Subcellular localization of the yeast proteome.

Protein localization data are a valuable information resource helpful in elucidating eukaryotic protein function. Here, we report the first proteome-scale analysis of protein localization within any eukaryote. Using directed topoisomerase I-mediated cloning strategies and genome-wide transposon mutagenesis, we have epitope-tagged 60% of the Saccharomyces cerevisiae proteome. By high-throughput immunolocalization of tagged gene products, we have determined the subcellular localization of 2744 yeast proteins. Extrapolating these data through a computational algorithm employing Bayesian formalism, we define the yeast localizome (the subcellular distribution of all 6100 yeast proteins). We estimate the yeast proteome to encompass approximately 5100 soluble proteins and >1000 transmembrane proteins. Our results indicate that 47% of yeast proteins are cytoplasmic, 13% mitochondrial, 13% exocytic (including proteins of the endoplasmic reticulum and secretory vesicles), and 27% nuclear/nucleolar. A subset of nuclear proteins was further analyzed by immunolocalization using surface-spread preparations of meiotic chromosomes. Of these proteins, 38% were found associated with chromosomal DNA. As determined from phenotypic analyses of nuclear proteins, 34% are essential for spore viability--a percentage nearly twice as great as that observed for the proteome as a whole. In total, this study presents experimentally derived localization data for 955 proteins of previously unknown function: nearly half of all functionally uncharacterized proteins in yeast. To facilitate access to these data, we provide a searchable database featuring 2900 fluorescent micrographs at http://ygac.med.yale.edu.

Algorithms↗
PubMed2002

A question of size: the eukaryotic proteome and the problems in defining it.

We discuss the problems in defining the extent of the proteomes for completely sequenced eukaryotic organisms (i.e. the total number of protein-coding sequences), focusing on yeast, worm, fly and human. (i) Six years after completion of its genome sequence, the true size of the yeast proteome is still not defined. New small genes are still being discovered, and a large number of existing annotations are being called into question, with these questionable ORFs (qORFs) comprising up to one-fifth of the 'current' proteome. We discuss these in the context of an ideal genome-annotation strategy that considers the proteome as a rigorously defined subset of all possible coding sequences ('the orfome'). (ii) Despite the greater apparent complexity of the fly (more cells, more complex physiology, longer lifespan), the nematode worm appears to have more genes. To explain this, we compare the annotated proteomes of worm and fly, relating to both genome-annotation and genome evolution issues. (iii) The unexpectedly small size of the gene complement estimated for the complete human genome provoked much public debate about the nature of biological complexity. However, in the first instance, for the human genome, the relationship between gene number and proteome size is far from simple. We survey the current estimates for the numbers of human genes and, from this, we estimate a range for the size of the human proteome. The determination of this is substantially hampered by the unknown extent of the cohort of pseudogenes ('dead' genes), in combination with the prevalence of alternative splicing. (Further information relating to yeast is available at http://genecensus.org/yeast/orfome)

Animals↗
PubMed2002

GATA-1 binding sites mapped in the beta-globin locus by using mammalian chIp-chip analysis.

The expression of the beta-like globin genes is intricately regulated by a series of both general and tissue-restricted transcription factors. The hemapoietic lineage-specific transcription factor GATA-1 is important for erythroid differentiation and has been implicated in regulating the expression of the erythroid-specific genes including the genes of the beta-globin locus. In the human erythroleukemic K562 cell line, only one DNA region has been identified previously as a putative site of GATA-1 interaction by in vivo footprinting studies. We mapped GATA-1 binding throughout the beta-globin locus by using chIp-chip analysis of K562 cells. We found that GATA-1 binds in a region encompassing the HS2 core element, as was previously identified, and an additional region of GATA-1 binding upstream of the gammaG gene. This approach will be of general utility for mapping transcription factor binding sites within the beta-globin locus and throughout the genome.

Animals↗