Roger P. Alexander

The West Nile virus (WNV) is an emerging infection of biodefense concern and there are no available treatments or vaccines. Here we used a high-throughput method based on a novel gene expression analysis, RNA-Seq, to give a global picture of differential gene expression by primary human macrophages of 10 healthy donors infected in vitro with WNV. From a total of 28 million reads per sample, we identified 1,514 transcripts that were differentially expressed after infection. Both predicted and novel gene changes were detected, as were gene isoforms, and while many of the genes were expressed by all donors, some were unique. Knock-down of genes not previously known to be associated with WNV resistance identified their critical role in control of viral infection. Our study distinguishes both common gene pathways as well as novel cellular responses. Such analyses will be valuable for translational studies of susceptible and resistant individuals—and for targeting therapeutics—in multiple biological settings.

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.

From Genome to Regulatory Networks: For biologists, having a genome in hand is only the beginning—much more investigation is still needed to characterize how the genome is used to help to produce a functional organism (see the Perspective by Blaxter ). In this vein, Gerstein et al. (p. 1775 ) summarize for the Caenorhabditis elegans genome, and The modENCODE Consortium (p. 1787 ) summarize for the Drosophila melanogaster genome, full transcriptome analyses over developmental stages, genome-wide identification of transcription factor binding sites, and high-resolution maps of chromatin organization. Both studies identified regions of the nematode and fly genomes that show highly occupied targets (or HOT) regions where DNA was bound by more than 15 of the transcription factors analyzed and the expression of related genes were characterized. Overall, the studies provide insights into the organization, structure, and function of the two genomes and provide basic information needed to guide and correlate both focused and genome-wide studies.

Most of the human genome consists of DNA that does not code for proteins. Annotating functional regions in the non-coding genome involves two complementary analysis techniques: comparative analysis, which involves examining DNA sequences, and functional analysis, which involves examining the output of functional genomics experiments. With the exponential increase in DNA sequence data, it is now possible to compare sequences within a single human haplotype, between cell types in a single person, across the human population and between species. Integrating the analysis across all these scales is useful. There are two main methods of sequence comparison: scanning for regions of high sequence similarity above some operational threshold, and building statistical models of sequence families. Model-based sequence analysis can incorporate more biological knowledge than sequence similarity scans and provide more refined results. The output of most high-throughput functional genomics experiments can be treated as a continuous signal mapped onto the genome and analysed with a standardized signal processing approach. Signal processing involves smoothing the raw signal, then thresholding and segmenting the signal into discrete annotated blocks. Integration of multiple types of signals generates a progression of more and more complex annotations; these smaller annotations are clustered into groups and then into functional networks that begin to represent the state of biological knowledge about the genome. A chronic problem with annotation based on functional genomics data is the lack of sufficient validation by more low-throughput methods. Techniques such as paired-end sequencing and chromosome conformation capture (and its descendants) enable annotation of connectivity between elements and necessitate a move beyond the one-dimensional signal approach to annotation.

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.

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.

Relating structure and dynamics of molecular networks remains very challenging.

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 organism's 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.