Saeed Tavazoie

Predicting Gene Expression from Sequence

We describe a systematic genome-wide approach for learning the complex combinatorial code underlying gene expression. Our probabilistic approach identifies local DNA-sequence elements and the positional and combinatorial constraints that determine their context-dependent role in transcriptional regulation. The inferred regulatory rules correctly predict expression patterns for 73% of genes in Saccharomyces cerevisiae, utilizing microarray expression data and sequences in the 800 bp upstream of genes. Application to Caenorhabditis elegans identifies predictive regulatory elements and combinatorial rules that control the phased temporal expression of transcription factors, histones, and germline specific genes. Successful prediction requires diverse and complex rules utilizing AND, OR, and NOT logic, with significant constraints on motif strength, orientation, and relative position. This system generates a large number of mechanistic hypotheses for focused experimental validation, and establishes a predictive dynamical framework for understanding cellular behavior from genomic sequence.

Mapping Global Histone Acetylation Patterns to Gene Expression

Histone acetyltransferases and deacetylases with specificities for different sites of acetylation affect common chromatin regions. This could generate unique patterns of acetylation that may specify downstream biological processes. To search for existence of these patterns and their relationship to gene activity, we analyzed the genome-wide acetylation profiles for eleven lysines in the four core histones of Saccharomyces cerevisiae. We find that both hyper- and hypoacetylation of individual lysines are associated with transcription, generating distinct patterns of acetylation that define groups of biologically related genes. The genes within these groups are significantly coexpressed, mediate similar physiological processes, share unique cis-regulatory DNA motifs, and are enriched for binding of specific transcription factors. Our data also indicate that the in vivo binding of the transcription factor Bdf1 is associated with acetylation on most lysines but relative deacetylation on H4 lysine 16. Thus, certain acetylation patterns may be used as surfaces for specific protein-histone interactions, providing one mechanism for coordinate regulation of chromatin processes that are biologically related.

Genome-wide binding map of the histone deacetylase Rpd3 in yeast

We describe the genome-wide distribution of the histone deacetylase and repressor Rpd3 and its associated proteins Ume1 and Ume6 in Saccharomyces cerevisiae. Using a new cross-linking protocol, we found that Rpd3 binds upstream of many individual genes and upstream of members of gene classes with similar functions in anabolic processes. In addition, Rpd3 is preferentially associated with promoters that direct high transcriptional activity. We also found that Rpd3 was absent from large sub-telomeric domains. We show by co-immunoprecipitation and by the high similarity of their binding maps that Ume1 interacts with Rpd3. In contrast, despite the known role of Ume6 in Rpd3 recruitment, only a limited number of the genes targeted by Rpd3 are also enriched for (or targeted by) Ume6. This suggests that Rpd3 is brought to many promoters by alternative recruiters, some of which may bind the putative cis-regulatory DNA elements that we have identified in sets of Rpd3 target genes. Finally, we show that comparing the genome-wide pattern of Rpd3 binding with gene expression and histone acetylation in the rpd3Δ mutant strain reveals new sites of Rpd3 function.

Predictive Behavior Within Microbial Genetic Networks

The homeostatic framework has dominated our understanding of cellular physiology. We question whether homeostasis alone adequately explains microbial responses to environmental stimuli, and explore the capacity of intracellular networks for predictive behavior in a fashion similar to metazoan nervous systems. We show that in silico biochemical networks, evolving randomly under precisely defined complex habitats, capture the dynamical, multidimensional structure of diverse environments by forming internal representations that allow prediction of environmental change. We provide evidence for such anticipatory behavior by revealing striking correlations of Escherichia coli transcriptional responses to temperature and oxygen perturbations—precisely mirroring the covariation of these parameters upon transitions between the outside world and the mammalian gastrointestinal tract. We further show that these internal correlations reflect a true associative learning paradigm, because they show rapid decoupling upon exposure to novel environments.

HNRNPA2B1 Is a Mediator of m6A-Dependent Nuclear RNA Processing Events

N(6)-methyladenosine (m(6)A) is the most abundant internal modification of messenger RNA. While the presence of m(6)A on transcripts can impact nuclear RNA fates, a reader of this mark that mediates processing of nuclear transcripts has not been identified. We find that the RNA-binding protein HNRNPA2B1 binds m(6)A-bearing RNAs in vivo and in vitro and its biochemical footprint matches the m(6)A consensus motif. HNRNPA2B1 directly binds a set of nuclear transcripts and elicits similar alternative splicing effects as the m(6)A writer METTL3. Moreover, HNRNPA2B1 binds to m(6)A marks in a subset of primary miRNA transcripts, interacts with the microRNA Microprocessor complex protein DGCR8, and promotes primary miRNA processing. Also, HNRNPA2B1 loss and METTL3 depletion cause similar processing defects for these pri-miRNA precursors. We propose HNRNPA2B1 to be a nuclear reader of the m(6)A mark and to mediate, in part, this marks effects on primary microRNA processing and alternative splicing. PAPERCLIP.

Selection analyses of insertional mutants using subgenic-resolution arrays

We describe a method of genome-wide analysis of quantitative growth phenotypes using insertional mutagenesis and DNA microarrays. We applied the method to assess the fitness contributions of Escherichia coli gene domains under specific growth conditions. A transposon library was subjected to competitive growth selection in Luria–Bertani (LB) and in glucose minimal media. Transposon-containing genomic DNA fragments from the selected libraries were compared with the initial unselected transposon insertion library on DNA microarrays to identify insertions that affect fitness. Genes involved in the biosynthesis of nutrients not provided in the growth medium were found to be significantly enriched in the set of genes containing negatively selected insertions. The data also identify fitness contributions of several uncharacterized genes, including putative transcriptional regulators and enzymes. The applicability of this high-resolution array selection in other species is discussed.

Ras and Gpa2 mediate one branch of a redundant glucose signaling pathway in yeast.

Addition of glucose to starved yeast cells elicits a dramatic restructuring of the transcriptional and metabolic state of the cell. While many components of the signaling network responsible for this response have been identified, a comprehensive view of this network is lacking. We have used global analysis of gene expression to assess the roles of the small GTP-binding proteins, Ras2 and Gpa2, in mediating the transcriptional response to glucose. We find that 90% of the transcriptional changes in the cell attendant on glucose addition are recapitulated by activation of Ras2 or Gpa2. In addition, we find that protein kinase A (PKA) mediates all of the Ras2 and Gpa2 transcriptional effects. However, we also find that most of the transcriptional effects of glucose addition to wild-type cells are retained in strains containing a PKA unresponsive to changes in cAMP levels. Thus, most glucose-responsive genes are regulated redundantly by a Ras/PKA-dependent pathway and by one or more PKA-independent pathways. Computational analysis extracted RRPE/PAC as the major response element for Ras and glucose regulation and revealed additional response elements mediating glucose and Ras regulation. These studies provide a paradigm for extracting the topology of signal transduction pathways from expression data.

Fast and systematic genome-wide discovery of conserved regulatory elements using a non-alignment based approach

We describe a powerful new approach for discovering globally conserved regulatory elements between two genomes. The method is fast, simple and comprehensive, without requiring alignments. Its application to pairs of yeasts, worms, flies and mammals yields a large number of known and novel putative regulatory elements. Many of these are validated by independent biological observations, have spatial and/or orientation biases, are co-conserved with other elements and show surprising conservation across large phylogenetic distances.

Regulatory and metabolic rewiring during laboratory evolution of ethanol tolerance in E. coli

Understanding the genetic basis of adaptation is a central problem in biology. However, revealing the underlying molecular mechanisms has been challenging as changes in fitness may result from perturbations to many pathways, any of which may contribute relatively little. We have developed a combined experimental/computational framework to address this problem and used it to understand the genetic basis of ethanol tolerance in Escherichia coli. We used fitness profiling to measure the consequences of single‐locus perturbations in the context of ethanol exposure. A module‐level computational analysis was then used to reveal the organization of the contributing loci into cellular processes and regulatory pathways (e.g. osmoregulation and cell‐wall biogenesis) whose modifications significantly affect ethanol tolerance. Strikingly, we discovered that a dominant component of adaptation involves metabolic rewiring that boosts intracellular ethanol degradation and assimilation. Through phenotypic and metabolomic analysis of laboratory‐evolved ethanol‐tolerant strains, we investigated naturally accessible pathways of ethanol tolerance. Remarkably, these laboratory‐evolved strains, by and large, follow the same adaptive paths as inferred from our coarse‐grained search of the fitness landscape.

Revealing Global Regulatory Perturbations across Human Cancers

The discovery of pathways and regulatory networks whose perturbation contributes to neoplastic transformation remains a fundamental challenge for cancer biology. We show that such pathway perturbations, and the cis-regulatory elements through which they operate, can be efficiently extracted from global gene expression profiles. Our approach utilizes information-theoretic analysis of expression levels, pathways, and genomic sequences. Analysis across a diverse set of human cancers reveals the majority of previously known cancer pathways. Through de novo motif discovery we associate these pathways with transcription-factor binding sites and miRNA targets, including those of E2F, NF-Y, p53, and let-7. Follow-up experiments confirmed that these predictions correspond to functional in vivo regulatory interactions. Strikingly, the majority of the perturbations, associated with putative cis-regulatory elements, fall outside of known cancer pathways. Our study provides a systems-level dissection of regulatory perturbations in cancer-an essential component of a rational strategy for therapeutic intervention and drug-target discovery.