Systematic identification of functional plant modules through the integration of complementary data sources

A major challenge is to unravel how genes interact and are regulated to exert specific biological functions. The integration of genome-wide functional genomics data, followed by the construction of gene networks, provides a powerful approach to identify functional gene modules. Large-scale expression data, functional gene annotations, experimental protein-protein interactions, and transcription factor-target interactions were integrated to delineate modules in Arabidopsis (Arabidopsis thaliana). The different experimental input data sets showed little overlap, demonstrating the advantage of combining multiple data types to study gene function and regulation. In the set of 1,563 modules covering 13,142 genes, most modules displayed strong coexpression, but functional and cis-regulatory coherence was less prevalent. Highly connected hub genes showed a significant enrichment toward embryo lethality and evidence for cross talk between different biological processes. Comparative analysis revealed that 58% of the modules showed conserved coexpression across multiple plants. Using module-based functional predictions, 5,562 genes were annotated, and an evaluation experiment disclosed that, based on 197 recently experimentally characterized genes, 38.1% of these functions could be inferred through the module context. Examples of confirmed genes of unknown function related to cell wall biogenesis, xylem and phloem pattern formation, cell cycle, hormone stimulus, and circadian rhythm highlight the potential to identify new gene functions. The module-based predictions offer new biological hypotheses for functionally unknown genes in Arabidopsis (1,701 genes) and six other plant species (43,621 genes). Furthermore, the inferred modules provide new insights into the conservation of coexpression and coregulation as well as a starting point for comparative functional annotation

Computational Models for Transplant Biomarker Discovery.

Translational medicine offers a rich promise for improved diagnostics and drug discovery for biomedical research in the field of transplantation, where continued unmet diagnostic and therapeutic needs persist. Current advent of genomics and proteomics profiling called "omics" provides new resources to develop novel biomarkers for clinical routine. Establishing such a marker system heavily depends on appropriate applications of computational algorithms and software, which are basically based on mathematical theories and models. Understanding these theories would help to apply appropriate algorithms to ensure biomarker systems successful. Here, we review the key advances in theories and mathematical models relevant to transplant biomarker developments. Advantages and limitations inherent inside these models are discussed. The principles of key -computational approaches for selecting efficiently the best subset of biomarkers from high--dimensional omics data are highlighted. Prediction models are also introduced, and the integration of multi-microarray data is also discussed. Appreciating these key advances would help to accelerate the development of clinically reliable biomarker systems

Discovering Conserved cis-Regulatory Elements That Regulate Expression in Caenorhabditis elegans

The aim of this dissertation is two-fold:: 1) To catalog all cis-regulatory elements within the intergenic and intronic regions surrounding every gene in C.elegans: i.e. the regulome) and: 2) to determine which cis-regulatory elements are associated with expression under specific conditions. We initially use PhyloNet to predict conserved motifs with instances in about half of the protein-coding genes. This initial first step was valuable as it recovered some known elements and cis-regulatory modules. Yet the results had a lot of redundant motifs and sites, and the approach was not efficiently scalable to the entire regulome of C. elegans or other higher-order eukaryotes. Magma: Multiple Aligner of Genomic Multiple Alignments) overcomes these shortcomings by using efficient clustering and memory management algorithms. Additionally, it implements a fast greedy set-cover solution to significantly reduce redundant motifs. These differences make Magma ~70 times faster than PhyloNet and Magma-based predictions occur near ~99% of all C. elegans protein-coding genes. Furthermore, we show tractable scaling for higher-order eukaryotes with larger regulomes. Finally, we demonstrate that a Magma-predicted motif, which represents the binding specificity for HLH-30, plays a critical role in the host-defense to pathogenic infections. This novel finding shows that hlh-30(-) animals are more susceptible to S. aureus and P. aeruginosa than their wild type counterparts

Integrative modeling of Transcription Factor cooperativity and its effects on phenotypic variability

The regulation of biological processes relies on a complex nucleotide code embedded in our DNA. Its decoding and interpretation is the main task of Transcription Factors (TFs), which altogether enable the recognition and modulation of gene expression. Whenever factors bind to DNA, a set of additional criteria and conditions need to be satisfied, such as TF concentration, DNA openness, and cooperativity with other binding factors. Such combinations of DNA-bound TFs, as well as their structural and functional cooperativity, allow a more fine-grained control of gene expression due to subtle changes in specificity in both DNA recognition and functional outcomes. This thesis explores the prediction of structural TF cooperativity and its biological consequences. Additionally, examples of functional cooperativity are presented and discussed in the context of neuronal activity and reprogramming. Altogether, this dissertation provides an extensive set of insights to better understand the complex interplay between TFs cooperativity and phenotypes

Conserved Motifs and Prediction of Regulatory Modules in Caenorhabditis elegans

Transcriptional regulation, a primary mechanism for controlling the development of multicellular organisms, is carried out by transcription factors (TFs) that recognize and bind to their cognate binding sites. In Caenorhabditis elegans, our knowledge of which genes are regulated by which TFs, through binding to specific sites, is still very limited. To expand our knowledge about the C. elegans regulatory network, we performed a comprehensive analysis of the C. elegans, Caenorhabditis briggsae, and Caenorhabditis remanei genomes to identify regulatory elements that are conserved in all genomes. Our analysis identified 4959 elements that are significantly conserved across the genomes and that each occur multiple times within each genome, both hallmarks of functional regulatory sites. Our motifs show significant matches to known core promoter elements, TF binding sites, splice sites, and poly-A signals as well as many putative regulatory sites. Many of the motifs are significantly correlated with various types of experimental data, including gene expression patterns, tissue-specific expression patterns, and binding site location analysis as well as enrichment in specific functional classes of genes. Many can also be significantly associated with specific TFs. Combinations of motif occurrences allow us to predict the location of cis-regulatory modules and we show that many of them significantly overlap experimentally determined enhancers. We provide access to the predicted binding sites, their associated motifs, and the predicted cis-regulatory modules across the whole genome through a web-accessible database and as tracks for genome browsers

Improving Thermodynamic Models of Transcription by Combining ChIP and Expression Measurements of Synthetic Promoters

Regulation of gene expression is a fundamental process in biology. Accurate mathematical models of the relationship between regulatory sequence and observed expression would advance our understanding of biology. I developed ReLoS, a regulatory logic simulator, to explore mathematical frameworks for describing the relationship between regulatory sequence and observed expression and to explore methods of learning combinatorial regulatory rules from expression data. ReLoS is a flexible simulator allowing a variety of formalisms to be applied. ReLoS was used to explore the question of how complex rules of combinatorial transcriptional regulation must be to explain the complexity of transcriptional regulation observed in biology. A previously published dataset was analyzed for regulatory elements that explained the behavior of regulatory modules for 254 genes in 255 conditions. I found that ReLoS was able to recapitulate a reasonable fraction of the variation: mean gene-wise correlation of 0.7) with only twelve combinatorial rules comprising 13 cis-regulatory elements. This result suggested that learning the combinatorial rules of transcriptional regulation should be possible. State ensemble statistical thermodynamic models are a class of models used to describe combinatorial transcriptional regulation. One way to parameterize these models is measuring the expression of a reporter gene driven by many similar promoters . Models parameterized in this fashion do better at explaining the sequence to expression relationship, but fail to distinguish between multiple biological mechanisms that give rise to equivalent expression results in the synthetic promoters, thus limiting the generalizability of the models. I developed a ChIP-based strategy for quantitatively measuring the relative occupancy of transcription factors on synthetic promoters. This data complements existing methods for obtaining expression data from the same promoters. Comparison of models parameterized with only expression, only occupancy, or expression and occupancy reveals specific biological details that are missed when considering only expression data. In particular, the occupancy data suggests that differential regulatory effects of Cbf1 in glucose versus amino acid are a function of how it interacts with polymerase rather than changes in concentration or binding affinity. Additionally, the occupancy data suggests that Gcn4 binds in a cooperative manner and that Gcn4 occupancy is adversely affected by the presence of a nearby Nrg1 site. Finally, the occupancy data and expression data taken together suggest that Gcn4 binds in competition with another transcription factor. Synthesizing disparate sources of information resulted in an improved understanding of the mechanics of transcriptional regulation of the synthetic promoters and was ultimately largely successful in decoupling the DNA binding energies from the TF interactions with polymerase. However, it suggests that more sophisticated models of the relationship between occupancy and expression may be required in at least some cases. Incorporating different sources of data into models of regulation will continue to be important for learning the biological specifics that drive expression changes

Mapping genetic interactions in cancer: a road to rational combination therapies.

The discovery of synthetic lethal interactions between poly (ADP-ribose) polymerase (PARP) inhibitors and BRCA genes, which are involved in homologous recombination, led to the approval of PARP inhibition as a monotherapy for patients with BRCA1/2-mutated breast or ovarian cancer. Studies following the initial observation of synthetic lethality demonstrated that the reach of PARP inhibitors is well beyond just BRCA1/2 mutants. Insights into the mechanisms of action of anticancer drugs are fundamental for the development of targeted monotherapies or rational combination treatments that will synergize to promote cancer cell death and overcome mechanisms of resistance. The development of targeted therapeutic agents is premised on mapping the physical and functional dependencies of mutated genes in cancer. An important part of this effort is the systematic screening of genetic interactions in a variety of cancer types. Until recently, genetic-interaction screens have relied either on the pairwise perturbations of two genes or on the perturbation of genes of interest combined with inhibition by commonly used anticancer drugs. Here, we summarize recent advances in mapping genetic interactions using targeted, genome-wide, and high-throughput genetic screens, and we discuss the therapeutic insights obtained through such screens. We further focus on factors that should be considered in order to develop a robust analysis pipeline. Finally, we discuss the integration of functional interaction data with orthogonal methods and suggest that such approaches will increase the reach of genetic-interaction screens for the development of rational combination therapies