Roger P. Alexander

Architecture of the human regulatory network derived from ENCODE data

Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the principles of the human transcriptional regulatory network, we determined the genomic binding information of 119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations. In particular, there are significant differences in the binding proximal and distal to genes. We organized all the transcription factor binding into a hierarchy and integrated it with other genomic information (for example, microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome sequences and understanding basic principles of human biology and disease.

Evolutionary genomics reveals conserved structural determinants of signaling and adaptation in microbial chemoreceptors.

As an important model for transmembrane signaling, methyl-accepting chemotaxis proteins (MCPs) have been extensively studied by using genetic, biochemical, and structural techniques. However, details of the molecular mechanism of signaling are still not well understood. The availability of genomic information for hundreds of species enables the identification of features in protein sequences that are conserved over long evolutionary distances and thus are critically important for function. We carried out a large-scale comparative genomic analysis of the MCP signaling and adaptation domain family and identified features that appear to be critical for receptor structure and function. Based on domain length and sequence conservation, we identified seven major MCP classes and three distinct structural regions within the cytoplasmic domain: signaling, methylation, and flexible bundle subdomains. The flexible bundle subdomain, not previously recognized in MCPs, is a conserved element that appears to be important for signal transduction. Remarkably, the N- and C-terminal helical arms of the cytoplasmic domain maintain symmetry in length and register despite dramatic variation, from 24 to 64 7-aa heptads in overall domain length. Loss of symmetry is observed in some MCPs, where it is concomitant with specific changes in the sensory module. Each major MCP class has a distinct pattern of predicted methylation sites that is well supported by experimental data. Our findings indicate that signaling and adaptation functions within the MCP cytoplasmic domain are tightly coupled, and that their coevolution has contributed to the significant diversity in chemotaxis mechanisms among different organisms.

Comparative analysis of the transcriptome across distant species.

The transcriptome is the readout of the genome. Identifying common features in it across distant species can reveal fundamental principles. To this end, the ENCODE and modENCODE consortia have generated large amounts of matched RNA-sequencing data for human, worm and fly. Uniform processing and comprehensive annotation of these data allow comparison across metazoan phyla, extending beyond earlier within-phylum transcriptome comparisons and revealing ancient, conserved features. Specifically, we discover co-expression modules shared across animals, many of which are enriched in developmental genes. Moreover, we use expression patterns to align the stages in worm and fly development and find a novel pairing between worm embryo and fly pupae, in addition to the embryo-to-embryo and larvae-to-larvae pairings. Furthermore, we find that the extent of non-canonical, non-coding transcription is similar in each organism, per base pair. Finally, we find in all three organisms that the gene-expression levels, both coding and non-coding, can be quantitatively predicted from chromatin features at the promoter using a ‘universal model’ based on a single set of organism-independent parameters.

Understanding transcriptional regulation by integrative analysis of transcription factor binding data

Statistical models have been used to quantify the relationship between gene expression and transcription factor (TF) binding signals. Here we apply the models to the large-scale data generated by the ENCODE project to study transcriptional regulation by TFs. Our results reveal a notable difference in the prediction accuracy of expression levels of transcription start sites (TSSs) captured by different technologies and RNA extraction protocols. In general, the expression levels of TSSs with high CpG content are more predictable than those with low CpG content. For genes with alternative TSSs, the expression levels of downstream TSSs are more predictable than those of the upstream ones. Different TF categories and specific TFs vary substantially in their contributions to predicting expression. Between two cell lines, the differential expression of TSS can be precisely reflected by the difference of TF-binding signals in a quantitative manner, arguing against the conventional on-and-off model of TF binding. Finally, we explore the relationships between TF-binding signals and other chromatin features such as histone modifications and DNase hypersensitivity for determining expression. The models imply that these features regulate transcription in a highly coordinated manner.

RodZ, a component of the bacterial core morphogenic apparatus

The molecular basis of bacterial cell morphogenesis remains largely an open question. Here we discover a morphogenic protein, RodZ, which is widely conserved across the bacterial kingdom. In Caulobacter crescentus, RodZ is essential for viability and is involved in all aspects of this organisms complex morphology. Depletion or over-production of RodZ results in grossly misshapen cells with stalk defects. RodZ exhibits a localization pattern during the cell cycle corresponding to sites of active peptidoglycan synthesis. The temporal transition of RodZ between patchy/helical and mid-cell localization mimics and depends on the actin-like MreB cytoskeleton. In Escherichia coli, an organism with a distinct mode of growth and MreB localization dynamics, RodZ follows MreB and retains its crucial role in cell morphogenesis, demonstrating conservation of function. Genomic analysis shows that RodZ represents an ancient function unique to bacteria. Multiple sequence alignment of 143 RodZ sequences from species across bacterial phyla identifies an N-terminal cytoplasmic domain with a helix-turn-helix motif, a transmembrane sequence, and a previously unidentified, conserved periplasmic or extracellular C-terminal domain. Both the N- and C-terminal domains are important for function, with the N-terminal domain containing localization determinants. This study uncovers a key missing player in the cytoskeleton-based growth machinery enabling heritable and defined cellular forms in bacteria.

Comparative Genomic and Protein Sequence Analyses of a Complex System Controlling Bacterial Chemotaxis

Molecular machinery governing bacterial chemotaxis consists of the CheA-CheY two-component system, an array of specialized chemoreceptors, and several auxiliary proteins. It has been studied extensively in Escherichia coli and, to a significantly lesser extent, in several other microbial species. Emerging evidence suggests that homologous signal transduction pathways regulate not only chemotaxis, but several other cellular functions in various bacterial species. The availability of genome sequence data for hundreds of organisms enables productive study of this system using comparative genomics and protein sequence analysis. This chapter describes advances in genomics of the chemotaxis signal transduction system, provides information on relevant bioinformatics tools and resources, and outlines approaches toward developing a computational framework for predicting important biological functions from raw genomic data based on available experimental evidence.

Improved reconstruction of in silico gene regulatory networks by integrating knockout and perturbation data.

We performed computational reconstruction of the in silico gene regulatory networks in the DREAM3 Challenges. Our task was to learn the networks from two types of data, namely gene expression profiles in deletion strains (the ‘deletion data’) and time series trajectories of gene expression after some initial perturbation (the ‘perturbation data’). In the course of developing the prediction method, we observed that the two types of data contained different and complementary information about the underlying network. In particular, deletion data allow for the detection of direct regulatory activities with strong responses upon the deletion of the regulator while perturbation data provide richer information for the identification of weaker and more complex types of regulation. We applied different techniques to learn the regulation from the two types of data. For deletion data, we learned a noise model to distinguish real signals from random fluctuations using an iterative method. For perturbation data, we used differential equations to model the change of expression levels of a gene along the trajectories due to the regulation of other genes. We tried different models, and combined their predictions. The final predictions were obtained by merging the results from the two types of data. A comparison with the actual regulatory networks suggests that our approach is effective for networks with a range of different sizes. The success of the approach demonstrates the importance of integrating heterogeneous data in network reconstruction.

A statistical framework for modeling gene expression using chromatin features and application to modENCODE datasets

We develop a statistical framework to study the relationship between chromatin features and gene expression. This can be used to predict gene expression of protein coding genes, as well as microRNAs. We demonstrate the prediction in a variety of contexts, focusing particularly on the modENCODE worm datasets. Moreover, our framework reveals the positional contribution around genes (upstream or downstream) of distinct chromatin features to the overall prediction of expression levels.

Understanding modularity in molecular networks requires dynamics.

Relating structure and dynamics of molecular networks remains very challenging. The era of genome sequencing has produced long lists of the molecular parts from which cellular machines are constructed. A fundamental goal in systems biology is to understand how cellular behavior emerges from the interaction in time and space of genetically encoded molecular parts, as well as nongenetically encoded small molecules. Networks provide a natural framework for the organization and quantitative representation of all the available data about molecular interactions. The structural and dynamic properties of molecular networks have been the subject of intense research. Despite major advances, bridging network structure to dynamics—and therefore to behavior—remains challenging. A key concept of modern engineering that recurs in the functional analysis of biological networks is modularity. Most approaches to molecular network analysis rely to some extent on the assumption that molecular networks are modular—that is, they are separable and can be studied to some degree in isolation. We describe recent advances in the analysis of modularity in biological networks, focusing on the increasing realization that a dynamic perspective is essential to grouping molecules into modules and determining their collective function.

Comparing genomes to computer operating systems in terms of the topology and evolution of their regulatory control networks

The genome has often been called the operating system (OS) for a living organism. A computer OS is described by a regulatory control network termed the call graph, which is analogous to the transcriptional regulatory network in a cell. To apply our firsthand knowledge of the architecture of software systems to understand cellular design principles, we present a comparison between the transcriptional regulatory network of a well-studied bacterium (Escherichia coli) and the call graph of a canonical OS (Linux) in terms of topology and evolution. We show that both networks have a fundamentally hierarchical layout, but there is a key difference: The transcriptional regulatory network possesses a few global regulators at the top and many targets at the bottom; conversely, the call graph has many regulators controlling a small set of generic functions. This top-heavy organization leads to highly overlapping functional modules in the call graph, in contrast to the relatively independent modules in the regulatory network. We further develop a way to measure evolutionary rates comparably between the two networks and explain this difference in terms of network evolution. The process of biological evolution via random mutation and subsequent selection tightly constrains the evolution of regulatory network hubs. The call graph, however, exhibits rapid evolution of its highly connected generic components, made possible by designers’ continual fine-tuning. These findings stem from the design principles of the two systems: robustness for biological systems and cost effectiveness (reuse) for software systems.