Revealing modular organization in the yeast transcriptional network.

Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.

Single-cell proteomic analysis of S. cerevisiae reveals the architecture of biological noise.

A major goal of biology is to provide a quantitative description of cellular behaviour. This task, however, has been hampered by the difficulty in measuring protein abundances and their variation. Here we present a strategy that pairs high-throughput flow cytometry and a library of GFP-tagged yeast strains to monitor rapidly and precisely protein levels at single-cell resolution. Bulk protein abundance measurements of >2,500 proteins in rich and minimal media provide a detailed view of the cellular response to these conditions, and capture many changes not observed by DNA microarray analyses. Our single-cell data argue that noise in protein expression is dominated by the stochastic production/destruction of messenger RNAs. Beyond this global trend, there are dramatic protein-specific differences in noise that are strongly correlated with a protein's mode of transcription and its function. For example, proteins that respond to environmental changes are noisy whereas those involved in protein synthesis are quiet. Thus, these studies reveal a remarkable structure to biological noise and suggest that protein noise levels have been selected to reflect the costs and potential benefits of this variation.

Backup without redundancy: genetic interactions reveal the cost of duplicate gene loss.

Many genes can be deleted with little phenotypic consequences. By what mechanism and to what extent the presence of duplicate genes in the genome contributes to this robustness against deletions has been the subject of considerable interest. Here, we exploit the availability of high-density genetic interaction maps to provide direct support for the role of backup compensation, where functionally overlapping duplicates cover for the loss of their paralog. However, we find that the overall contribution of duplicates to robustness against null mutations is low ( approximately 25%). The ability to directly identify buffering paralogs allowed us to further study their properties, and how they differ from non-buffering duplicates. Using environmental sensitivity profiles as well as quantitative genetic interaction spectra as high-resolution phenotypes, we establish that even duplicate pairs with compensation capacity exhibit rich and typically non-overlapping deletion phenotypes, and are thus unable to comprehensively cover against loss of their paralog. Our findings reconcile the fact that duplicates can compensate for each other's loss under a limited number of conditions with the evolutionary instability of genes whose loss is not associated with a phenotypic penalty.

Comparative gene expression analysis by differential clustering approach: application to the Candida albicans transcription program.

Differences in gene expression underlie many of the phenotypic variations between related organisms, yet approaches to characterize such differences on a genome-wide scale are not well developed. Here, we introduce the "differential clustering algorithm" for revealing conserved and diverged co-expression patterns. Our approach is applied at different levels of organization, ranging from pair-wise correlations within specific groups of functionally linked genes, to higher-order correlations between such groups. Using the differential clustering algorithm, we systematically compared the transcription program of the fungal pathogen Candida albicans with that of the model organism Saccharomyces cerevisiae. Many of the identified differences are related to the differential requirement for mitochondrial function in the two yeasts. Distinct regulation patterns of cell cycle genes and of amino acid metabolic genes were also revealed and, in some cases, could be linked to the differential appearance of cis-regulatory elements in the gene promoter regions. Our study provides a comprehensive framework for comparative gene expression analysis and a rich source of hypotheses for uncharacterized open reading frames and putative cis-regulatory elements in C. albicans.

Similarities and differences in genome-wide expression data of six organisms.

Comparing genomic properties of different organisms is of fundamental importance in the study of biological and evolutionary principles. Although differences among organisms are often attributed to differential gene expression, genome-wide comparative analysis thus far has been based primarily on genomic sequence information. We present a comparative study of large datasets of expression profiles from six evolutionarily distant organisms: S. cerevisiae, C. elegans, E. coli, A. thaliana, D. melanogaster, and H. sapiens. We use genomic sequence information to connect these data and compare global and modular properties of the transcription programs. Linking genes whose expression profiles are similar, we find that for all organisms the connectivity distribution follows a power-law, highly connected genes tend to be essential and conserved, and the expression program is highly modular. We reveal the modular structure by decomposing each set of expression data into coexpressed modules. Functionally related sets of genes are frequently coexpressed in multiple organisms. Yet their relative importance to the transcription program and their regulatory relationships vary among organisms. Our results demonstrate the potential of combining sequence and expression data for improving functional gene annotation and expanding our understanding of how gene expression and diversity evolved.

Principles of transcriptional control in the metabolic network of Saccharomyces cerevisiae.

Cellular networks are subject to extensive regulation, which modifies the availability and efficiency of connections between components in response to external conditions. Thus far, studies of large-scale networks have focused on their connectivity, but have not considered how the modulation of this connectivity might also determine network properties. To address this issue, we analyzed how the coordinated expression of enzymes shapes the metabolic network of Saccharomyces cerevisiae. By integrating large-scale expression data with the structural description of the metabolic network, we systematically characterized the transcriptional regulation of metabolic pathways. The analysis revealed recurrent patterns, which may represent design principles of metabolic gene regulation. First, we find that transcription regulation biases metabolic flow toward linearity by coexpressing only distinct branches at metabolic branchpoints. Second, individual isozymes were often separately coregulated with distinct processes, providing a means of reducing crosstalk between pathways using a common reaction. Finally, transcriptional regulation defined a hierarchical organization of metabolic pathways into groups of varying expression coherence. These results emphasize the utility of incorporating regulatory information when analyzing properties of large-scale cellular networks.

Expression Profiler: next generation--an online platform for analysis of microarray data.

Expression Profiler (EP, http://www.ebi.ac.uk/expressionprofiler) is a web-based platform for microarray gene expression and other functional genomics-related data analysis. The new architecture, Expression Profiler: next generation (EP:NG), modularizes the original design and allows individual analysis-task-related components to be developed by different groups and yet still seamlessly to work together and share the same user interface look and feel. Data analysis components for gene expression data preprocessing, missing value imputation, filtering, clustering methods, visualization, significant gene finding, between group analysis and other statistical components are available from the EBI (European Bioinformatics Institute) web site. The web-based design of Expression Profiler supports data sharing and collaborative analysis in a secure environment. Developed tools are integrated with the microarray gene expression database ArrayExpress and form the exploratory analytical front-end to those data. EP:NG is an open-source project, encouraging broad distribution and further extensions from the scientific community.

Iterative signature algorithm for the analysis of large-scale gene expression data.

We present an approach for the analysis of genome-wide expression data. Our method is designed to overcome the limitations of traditional techniques, when applied to large-scale data. Rather than alloting each gene to a single cluster, we assign both genes and conditions to context-dependent and potentially overlapping transcription modules. We provide a rigorous definition of a transcription module as the object to be retrieved from the expression data. An efficient algorithm, which searches for the modules encoded in the data by iteratively refining sets of genes and conditions until they match this definition, is established. Each iteration involves a linear map, induced by the normalized expression matrix, followed by the application of a threshold function. We argue that our method is in fact a generalization of singular value decomposition, which corresponds to the special case where no threshold is applied. We show analytically that for noisy expression data our approach leads to better classification due to the implementation of the threshold. This result is confirmed by numerical analyses based on in silico expression data. We discuss briefly results obtained by applying our algorithm to expression data from the yeast Saccharomyces cerevisiae.

Strategy of transcription regulation in the budding yeast.

Cells must adjust their gene expression in order to compete in a constantly changing environment. Two alternative strategies could in principle ensure optimal coordination of gene expression with physiological requirements. First, characters of the internal physiological state, such as growth rate, metabolite levels, or energy availability, could be feedback to tune gene expression. Second, internal needs could be inferred from the external environment, using evolutionary-tuned signaling pathways. Coordination of ribosomal biogenesis with the requirement for protein synthesis is of particular importance, since cells devote a large fraction of their biosynthetic capacity for ribosomal biogenesis. To define the relative contribution of internal vs. external sensing to the regulation of ribosomal biogenesis gene expression in yeast, we subjected S. cerevisiae cells to conditions which decoupled the actual vs. environmentally-expected growth rate. Gene expression followed the environmental signal according to the expected, but not the actual, growth rate. Simultaneous monitoring of gene expression and growth rate in continuous cultures further confirmed that ribosome biogenesis genes responded rapidly to changes in the environments but were oblivious to longer-term changes in growth rate. Our results suggest that the capacity to anticipate and prepare for environmentally-mediated changes in cell growth presented a major selection force during yeast evolution.

Rewiring of the yeast transcriptional network through the evolution of motif usage.

Recent experiments revealed large-scale differences in the transcription programs of related species, yet little is known about the genetic basis underlying the evolution of gene expression and its contribution to phenotypic diversity. Here we describe a large-scale modulation of the yeast transcription program that is connected to the emergence of the capacity for rapid anaerobic growth. Genes coding for mitochondrial and cytoplasmic ribosomal proteins display a strongly correlated expression pattern in Candida albicans, but this correlation is lost in the fermentative yeast Saccharomyces cerevisiae. We provide evidence that this change in gene expression is connected to the loss of a specific cis-regulatory element from dozens of genes following the apparent whole-genome duplication event. Our results shed new light on the genetic mechanisms underlying the large-scale evolution of transcriptional networks.

Exploration of the function and organization of the yeast early secretory pathway through an epistatic miniarray profile.

We present a strategy for generating and analyzing comprehensive genetic-interaction maps, termed E-MAPs (epistatic miniarray profiles), comprising quantitative measures of aggravating or alleviating interactions between gene pairs. Crucial to the interpretation of E-MAPs is their high-density nature made possible by focusing on logically connected gene subsets and including essential genes. Described here is the analysis of an E-MAP of genes acting in the yeast early secretory pathway. Hierarchical clustering, together with novel analytical strategies and experimental verification, revealed or clarified the role of many proteins involved in extensively studied processes such as sphingolipid metabolism and retention of HDEL proteins. At a broader level, analysis of the E-MAP delineated pathway organization and components of physical complexes and illustrated the interconnection between the various secretory processes. Extension of this strategy to other logically connected gene subsets in yeast and higher eukaryotes should provide critical insights into the functional/organizational principles of biological systems.

A human-curated annotation of the Candida albicans genome.

Recent sequencing and assembly of the genome for the fungal pathogen Candida albicans used simple automated procedures for the identification of putative genes. We have reviewed the entire assembly, both by hand and with additional bioinformatic resources, to accurately map and describe 6,354 genes and to identify 246 genes whose original database entries contained sequencing errors (or possibly mutations) that affect their reading frame. Comparison with other fungal genomes permitted the identification of numerous fungus-specific genes that might be targeted for antifungal therapy. We also observed that, compared to other fungi, the protein-coding sequences in the C. albicans genome are especially rich in short sequence repeats. Finally, our improved annotation permitted a detailed analysis of several multigene families, and comparative genomic studies showed that C. albicans has a far greater catabolic range, encoding respiratory Complex 1, several novel oxidoreductases and ketone body degrading enzymes, malonyl-CoA and enoyl-CoA carriers, several novel amino acid degrading enzymes, a variety of secreted catabolic lipases and proteases, and numerous transporters to assimilate the resulting nutrients. The results of these efforts will ensure that the Candida research community has uniform and comprehensive genomic information for medical research as well as for future diagnostic and therapeutic applications.

Challenges and prospects in the analysis of large-scale gene expression data.

Large heterogeneous expression data comprising a variety of cellular conditions hold the promise of a global view of transcriptional regulation. While standard analysis methods have been successfully applied to smaller data sets, large-scale data pose specific challenges that have prompted the development of new and more sophisticated approaches. This paper focuses on one such approach (the Signature Algorithm) and discusses the central challenges in the analysis of large data sets, and how they might be overcome. Biological questions that have been addressed using the Signature Algorithm are highlighted and a summary of other important methods from the literature is provided.

Defining transcription modules using large-scale gene expression data.

MOTIVATION: Large-scale gene expression data comprising a variety of cellular conditions hold the promise of a global view on the transcription program. While conventional clustering algorithms have been successfully applied to smaller datasets, the utility of many algorithms for the analysis of large-scale data is limited by their inability to capture combinatorial and condition-specific co-regulation. In addition, there is an increasing need to integrate the rapidly accumulating body of other high-throughput biological data with the expression analysis. In a previous work, we introduced the signature algorithm, which overcomes the problems of conventional clustering and allows for intuitive integration of additional biological data. However, this approach is constrained by the comprehensiveness of relevant external data and its lacking ability to capture hierarchical modularity. METHODS: We present a novel method for the analysis of large-scale expression data, which assigns genes into context-dependent and potentially overlapping regulatory units. We introduce the notion of a transcription module as a self-consistent regulatory unit consisting of a set of co-regulated genes as well as the experimental conditions that induce their co-regulation. Self-consistency is defined by a rigorous mathematical criterion. We propose an efficient algorithm to identify such modules, which is based on the iterative application of the signature algorithm. A threshold parameter that determines the resolution of the modular decomposition is introduced. RESULTS: The method is applied systematically to over 1000 expression profiles of the yeast Saccharomyces cerevisiae, and the results are presented using two complementary visualization schemes we developed. The average biological coherence, as measured by the conservation of putative cis-regulatory motifs between four related yeast species, is higher for transcription modules than for clusters identified by other methods applied to the same dataset. Our method is related to singular value decomposition (SVD) and to the pairwise average linkage clustering algorithm. It extends SVD by filtering out noise in the expression data and offering variable resolution to reveal hierarchical organization. It furthermore has the advantage over both methods of capturing overlapping modules in the presence of combinatorial regulation. SUPPLEMENTARY INFORMATION: http://www.weizmann.ac.il/~barkai/modules