Core Biological Processes Research Articles

Targeting the mRNA-binding protein HuR impairs malignant characteristics of pancreatic ductal adenocarcinoma cells.

Post-transcriptional regulation is a powerful mediator of gene expression, and can rapidly alter the expression of numerous transcripts involved in tumorigenesis. We have previously shown that the mRNA-binding protein HuR (ELAVL1) is elevated in human pancreatic ductal adenocarcinoma (PDA) specimens compared to normal pancreatic tissues, and its cytoplasmic localization is associated with increased tumor stage. To gain a better insight into HuR's role in PDA biology and to assess it as a candidate therapeutic target, we altered HuR expression in PDA cell lines and characterized the resulting phenotype in preclinical models. HuR silencing by short hairpin and small interfering RNAs significantly decreased cell proliferation and anchorage-independent growth, as well as impaired migration and invasion. In comparison, HuR overexpression increased migration and invasion, but had no significant effects on cell proliferation and anchorage-independent growth. Importantly, two distinct targeted approaches to HuR silencing showed marked impairment in tumor growth in mouse xenografts. NanoString nCounter® analyses demonstrated that HuR regulates core biological processes, highlighting that HuR inhibition likely thwarts PDA viability through post-transcriptional regulation of diverse signaling pathways (e.g. cell cycle, apoptosis, DNA repair). Taken together, our study suggests that targeted inhibition of HuR may be a novel, promising approach to the treatment of PDA.

MicroRNA-199a induces differentiation of induced pluripotent stem cells into endothelial cells by targeting sirtuin 1.

The ability to reprogram induced pluripotent stem (iPS) cells from somatic cells may facilitate significant advances in regenerative medicine. MicroRNAs (miRNAs) are involved in a number of core biological processes, including cardiogenesis, hematopoietic lineage differentiation and oncogenesis. An improved understanding of the complex molecular signals that are required for the differentiation of iPS cells into endothelial cells (ECs) may allow specific targeting of their activity in order to enhance cell differentiation and promote tissue regeneration. The present study reports that miR‑199a is involved in EC differentiation from iPS cells. Augmented expression of miR‑199a was detected during EC differentiation, and reached higher levels during the later stages of this process. Furthermore, miR‑199a inhibited the differentiation of iPS cells into smooth muscle cells. Notably, sirtuin 1 was identified as a target of miR‑199a . Finally, the ability of miR‑199a to induce angiogenesis was evaluated in vitro, using Matrigel plugs assays. This may indicate a novel function for miR‑199a as a regulator of the phenotypic switch during vascular cell differentiation. The present study provides support to the notion that with an understanding of the molecular mechanisms underlying vascular cell differentiation, stem cell regenerative therapy may ultimately be developed as an effective treatment for cardiovascular disease.

Systems Genetics Analysis of Genome-Wide Association Study Reveals Novel Associations Between Key Biological Processes and Coronary Artery Disease.

Genome-wide association studies have identified multiple genetic variants affecting the risk of coronary artery disease (CAD). However, individually these explain only a small fraction of the heritability of CAD and for most, the causal biological mechanisms remain unclear. We sought to obtain further insights into potential causal processes of CAD by integrating large-scale GWA data with expertly curated databases of core human pathways and functional networks. Using pathways (gene sets) from Reactome, we carried out a 2-stage gene set enrichment analysis strategy. From a meta-analyzed discovery cohort of 7 CAD genome-wide association study data sets (9889 cases/11 089 controls), nominally significant gene sets were tested for replication in a meta-analysis of 9 additional studies (15 502 cases/55 730 controls) from the Coronary ARtery DIsease Genome wide Replication and Meta-analysis (CARDIoGRAM) Consortium. A total of 32 of 639 Reactome pathways tested showed convincing association with CAD (replication P<0.05). These pathways resided in 9 of 21 core biological processes represented in Reactome, and included pathways relevant to extracellular matrix (ECM) integrity, innate immunity, axon guidance, and signaling by PDRF (platelet-derived growth factor), NOTCH, and the transforming growth factor-β/SMAD receptor complex. Many of these pathways had strengths of association comparable to those observed in lipid transport pathways. Network analysis of unique genes within the replicated pathways further revealed several interconnected functional and topologically interacting modules representing novel associations (eg, semaphoring-regulated axonal guidance pathway) besides confirming known processes (lipid metabolism). The connectivity in the observed networks was statistically significant compared with random networks (P<0.001). Network centrality analysis (degree and betweenness) further identified genes (eg, NCAM1, FYN, FURIN, etc) likely to play critical roles in the maintenance and functioning of several of the replicated pathways. These findings provide novel insights into how genetic variation, interpreted in the context of biological processes and functional interactions among genes, may help define the genetic architecture of CAD.

Genome-wide survey of the seagrass Zostera muelleri suggests modification of the ethylene signalling network.

Seagrasses are flowering plants which grow fully submerged in the marine environment. They have evolved a range of adaptations to environmental challenges including light attenuation through water, the physical stress of wave action and tidal currents, high concentrations of salt, oxygen deficiency in marine sediment, and water-borne pollination. Although, seagrasses are a key stone species of the costal ecosystems, many questions regarding seagrass biology and evolution remain unanswered. Genome sequence data for the widespread Australian seagrass species Zostera muelleri were generated and the unassembled data were compared with the annotated genes of five sequenced plant species (Arabidopsis thaliana, Oryza sativa, Phoenix dactylifera, Musa acuminata, and Spirodela polyrhiza). Genes which are conserved between Z. muelleri and the five plant species were identified, together with genes that have been lost in Z. muelleri. The effect of gene loss on biological processes was assessed on the gene ontology classification level. Gene loss in Z. muelleri appears to influence some core biological processes such as ethylene biosynthesis. This study provides a foundation for further studies of seagrass evolution as well as the hormonal regulation of plant growth and development.

Co-expressional conservation in virulence and stress related genes of three Gammaproteobacterial species: Escherichia coli, Salmonella enterica and Pseudomonas aeruginosa.

Gene co-expression analysis is one of the main aspects of systems biology that uses high-throughput gene expression data. In the present study we applied cross-species co-expressional analysis on a module of biofilm and stress response associated genes. We addressed different kinds of stresses in three most intensively studied members of Gammaproteobacteria including Escherichia coli K12, Pseudomonas aeruginosa PAO1 and Salmonella enterica for which large sets of gene expression data are available. Our aim was to evaluate the presence of common stress response strategies adopted by these microorganisms that may be assigned to the other members of Gammaproteobacteria. Results of functional annotation analysis revealed distinct categories among co-expressed genes, most of which concerned biological processes associated with virulence and stress response. Transcriptional regulatory analysis of genes present in co-expressed modules showed that the global stress sigma factor, RpoS, besides several local transcription factors accounts for the observed co-expressional response, and that several cases of feed-forward loops exist between global regulators, local transcription factors and their targets. Our results lend partial support to our underlying assumption of the conservation of core biological processes and regulatory interactions among these related Gammaproteobacteria members. This has led to the implementation of transferring gene function annotations from well-studied Gammaproteobacterial species to less-characterized members. These findings can shed light on the discovery of new drug targets capable of controlling severe infections caused by these groups of bacteria.

Scaling Laws Governing Growth and Division of Single Bacterial Cells

Are there simple quantitative relationships that govern the behaviors of whole organisms? If so, what form do they take? To address these questions, we study growth and division of Caulobacter crescentus, a paradigm for polar cell development and cell cycle control. Using a unique combination of technologies that yields unprecedented statistical precision, we find that their mean sizes grow exponentially in time and that their size distributions collapse to a single curve when rescaled by their means. An analogous result holds for the division-time distributions. We account for these observations with a minimal stochastic model that is based on an autocatalytic cycle. It predicts the scalings, as well as specific functional forms for the universal curves. Our experimental and theoretical analysis reveals a simple physical principle governing these core biological processes: a single timescale governs noisy bacterial growth and division despite the complexity of underlying molecular mechanisms.

Whole genome association study identifies regions of the bovine genome and biological pathways involved in carcass trait performance in Holstein-Friesian cattle.

BackgroundFour traits related to carcass performance have been identified as economically important in beef production: carcass weight, carcass fat, carcass conformation of progeny and cull cow carcass weight. Although Holstein-Friesian cattle are primarily utilized for milk production, they are also an important source of meat for beef production and export. Because of this, there is great interest in understanding the underlying genomic structure influencing these traits. Several genome-wide association studies have identified regions of the bovine genome associated with growth or carcass traits, however, little is known about the mechanisms or underlying biological pathways involved. This study aims to detect regions of the bovine genome associated with carcass performance traits (employing a panel of 54,001 SNPs) using measures of genetic merit (as predicted transmitting abilities) for 5,705 Irish Holstein-Friesian animals. Candidate genes and biological pathways were then identified for each trait under investigation.ResultsFollowing adjustment for false discovery (q-value < 0.05), 479 quantitative trait loci (QTL) were associated with at least one of the four carcass traits using a single SNP regression approach. Using a Bayesian approach, 46 QTL were associated (posterior probability > 0.5) with at least one of the four traits. In total, 557 unique bovine genes, which mapped to 426 human orthologs, were within 500kbs of QTL found associated with a trait using the Bayesian approach. Using this information, 24 significantly over-represented pathways were identified across all traits. The most significantly over-represented biological pathway was the peroxisome proliferator-activated receptor (PPAR) signaling pathway.ConclusionsA large number of genomic regions putatively associated with bovine carcass traits were detected using two different statistical approaches. Notably, several significant associations were detected in close proximity to genes with a known role in animal growth such as glucagon and leptin. Several biological pathways, including PPAR signaling, were shown to be involved in various aspects of bovine carcass performance. These core genes and biological processes may form the foundation for further investigation to identify causative mutations involved in each trait. Results reported here support previous findings suggesting conservation of key biological processes involved in growth and metabolism.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-837) contains supplementary material, which is available to authorized users.

Fibroblast Growth Factor Receptor 3 Interacts with and Activates TGFβ-Activated Kinase 1 Tyrosine Phosphorylation and NFκB Signaling in Multiple Myeloma and Bladder Cancer

Cancer is a major public health problem worldwide. In the United States alone, 1 in 4 deaths is due to cancer and for 2013 a total of 1,660,290 new cancer cases and 580,350 cancer-related deaths are projected. Comprehensive profiling of multiple cancer genomes has revealed a highly complex genetic landscape in which a large number of altered genes, varying from tumor to tumor, impact core biological pathways and processes. This has implications for therapeutic targeting of signaling networks in the development of treatments for specific cancers. The NFκB transcription factor is constitutively active in a number of hematologic and solid tumors, and many signaling pathways implicated in cancer are likely connected to NFκB activation. A critical mediator of NFκB activity is TGFβ-activated kinase 1 (TAK1). Here, we identify TAK1 as a novel interacting protein and target of fibroblast growth factor receptor 3 (FGFR3) tyrosine kinase activity. We further demonstrate that activating mutations in FGFR3 associated with both multiple myeloma and bladder cancer can modulate expression of genes that regulate NFκB signaling, and promote both NFκB transcriptional activity and cell adhesion in a manner dependent on TAK1 expression in both cancer cell types. Our findings suggest TAK1 as a potential therapeutic target for FGFR3-associated cancers, and other malignancies in which TAK1 contributes to constitutive NFκB activation.

CDKN2B expression in adipose tissue of familial combined hyperlipidemia patients

The purpose of this study was to determine the core biological processes perturbed in the subcutaneous adipose tissue of familial combined hyperlipidemia (FCHL) patients. Annotation of FCHL and control microarray datasets revealed a distinctive FCHL transcriptome, characterized by gene expression changes regulating five overlapping systems: the cytoskeleton, cell adhesion and extracellular matrix; vesicular trafficking; lipid homeostasis; and cell cycle and apoptosis. Expression values for the cell-cycle inhibitor CDKN2B were increased, replicating data from an independent FCHL cohort. In 3T3-L1 cells, CDKN2B knockdown induced C/EBPα expression and lipid accumulation. The minor allele at SNP site rs1063192 (C) was predicted to create a perfect seed for the human miRNA-323b-5p. A miR-323b-5p mimic significantly reduced endogenous CDKN2B protein levels and the activity of a CDKN2B 3'UTR luciferase reporter carrying the rs1063192 C allele. Although the allele displayed suggestive evidence of association with reduced CDKN2B mRNA in the MuTHER adipose tissue dataset, family studies suggest the association between increased CDKN2B expression and FCHL-lipid abnormalities is driven by factors external to this gene locus. In conclusion, from a comparative annotation analysis of two separate FCHL adipose tissue transcriptomes and a subsequent focus on CDKN2B, we propose that dysfunctional adipogenesis forms an integral part of FCHL pathogenesis.

Layered Signaling Regulatory Networks Analysis of Gene Expression Involved in Malignant Tumorigenesis of Non-Resolving Ulcerative Colitis via Integration of Cross-Study Microarray Profiles

BackgroundUlcerative colitis (UC) was the most frequently diagnosed inflammatory bowel disease (IBD) and closely linked to colorectal carcinogenesis. By far, the underlying mechanisms associated with the disease are still unclear. With the increasing accumulation of microarray gene expression profiles, it is profitable to gain a systematic perspective based on gene regulatory networks to better elucidate the roles of genes associated with disorders. However, a major challenge for microarray data analysis is the integration of multiple-studies generated by different groups.Methodology/Principal FindingsIn this study, firstly, we modeled a signaling regulatory network associated with colorectal cancer (CRC) initiation via integration of cross-study microarray expression data sets using Empirical Bayes (EB) algorithm. Secondly, a manually curated human cancer signaling map was established via comprehensive retrieval of the publicly available repositories. Finally, the co-differently-expressed genes were manually curated to portray the layered signaling regulatory networks.ResultsOverall, the remodeled signaling regulatory networks were separated into four major layers including extracellular, membrane, cytoplasm and nucleus, which led to the identification of five core biological processes and four signaling pathways associated with colorectal carcinogenesis. As a result, our biological interpretation highlighted the importance of EGF/EGFR signaling pathway, EPO signaling pathway, T cell signal transduction and members of the BCR signaling pathway, which were responsible for the malignant transition of CRC from the benign UC to the aggressive one.ConclusionsThe present study illustrated a standardized normalization approach for cross-study microarray expression data sets. Our model for signaling networks construction was based on the experimentally-supported interaction and microarray co-expression modeling. Pathway-based signaling regulatory networks analysis sketched a directive insight into colorectal carcinogenesis, which was of significant importance to monitor disease progression and improve therapeutic interventions.

Molecular Pathways Involved in Prostate Carcinogenesis: Insights from Public Microarray Datasets

BackgroundProstate cancer is currently the most frequently diagnosed malignancy in men and the second leading cause of cancer-related deaths in industrialized countries. Worldwide, an increase in prostate cancer incidence is expected due to an increased life-expectancy, aging of the population and improved diagnosis. Although the specific underlying mechanisms of prostate carcinogenesis remain unknown, prostate cancer is thought to result from a combination of genetic and environmental factors altering key cellular processes. To elucidate these complex interactions and to contribute to the understanding of prostate cancer progression and metastasis, analysis of large scale gene expression studies using bioinformatics approaches is used to decipher regulation of core processes.Methodology/Principal FindingsIn this study, a standardized quality control procedure and statistical analysis (http://www.arrayanalysis.org/) were applied to multiple prostate cancer datasets retrieved from the ArrayExpress data repository and pathway analysis using PathVisio (http://www.pathvisio.org/) was performed. The results led to the identification of three core biological processes that are strongly affected during prostate carcinogenesis: cholesterol biosynthesis, the process of epithelial-to-mesenchymal transition and an increased metabolic activity.ConclusionsThis study illustrates how a standardized bioinformatics evaluation of existing microarray data and subsequent pathway analysis can quickly and cost-effectively provide essential information about important molecular pathways and cellular processes involved in prostate cancer development and disease progression. The presented results may assist in biomarker profiling and the development of novel treatment approaches.

A Census of Human Soluble Protein Complexes

Cellular processes often depend on stable physical associations between proteins. Despite recent progress, knowledge of the composition of human protein complexes remains limited. To close this gap, we applied an integrative global proteomic profiling approach, based on chromatographic separation of cultured human cell extracts into more than one thousand biochemical fractions that were subsequently analyzed by quantitative tandem mass spectrometry, to systematically identify a network of 13,993 high-confidence physical interactions among 3,006 stably associated soluble human proteins. Most of the 622 putative protein complexes we report are linked to core biological processes and encompass both candidate disease genes and unannotated proteins to inform on mechanism. Strikingly, whereas larger multiprotein assemblies tend to be more extensively annotated and evolutionarily conserved, human protein complexes with five or fewer subunits are far more likely to be functionally unannotated or restricted to vertebrates, suggesting more recent functional innovations.

The mRNA-Bound Proteome and Its Global Occupancy Profile on Protein-Coding Transcripts

Protein-RNA interactions are fundamental to core biological processes, such as mRNA splicing, localization, degradation, and translation. We developed a photoreactive nucleotide-enhanced UV crosslinking and oligo(dT) purification approach to identify the mRNA-bound proteome using quantitative proteomics and to display the protein occupancy on mRNA transcripts by next-generation sequencing. Application to a human embryonic kidney cell line identified close to 800 proteins. To our knowledge, nearly one-third were not previously annotated as RNA binding, and about 15% were not predictable by computational methods to interact with RNA. Protein occupancy profiling provides a transcriptome-wide catalog of potential cis-regulatory regions on mammalian mRNAs and showed that large stretches in 3' UTRs can be contacted by the mRNA-bound proteome, with numerous putative binding sites in regions harboring disease-associated nucleotide polymorphisms. Our observations indicate the presence of a large number of mRNA binders with diverse molecular functions participating in combinatorial posttranscriptional gene-expression networks.

Stitching together Multiple Data Dimensions Reveals Interacting Metabolomic and Transcriptomic Networks That Modulate Cell Regulation

Author SummaryIt is now possible to score variations in DNA across whole genomes, RNA levels and alternative isoforms, metabolite levels, protein levels and protein state information, protein–protein interactions, and protein–DNA interactions, in a comprehensive fashion in populations of individuals. Interactions among these molecular entities define the complex web of biological processes that give rise to all higher order phenotypes, including disease. The development of analytical approaches that simultaneously integrate different dimensions of data is essential if we are to extract the meaning from large-scale data to elucidate the complexity of living systems. Here, we use a novel Bayesian network reconstruction algorithm that simultaneously integrates DNA variation, RNA levels, metabolite levels, protein–protein interaction data, protein–DNA binding data, and protein–small-molecule interaction data to construct molecular networks in yeast. We demonstrate that these networks can be used to infer causal relationships among genes, enabling the identification of novel genes that modulate cellular regulation. We show that our network predictions either recapitulate known biology or can be prospectively validated, demonstrating a high degree of accuracy in the predicted network.

Development of Cures in the Era of Genomic Medicine

Cancer, in contrast to many other somatic disorders of mankind, is a semi-autonomous from its human host deregulation of cells where complex genetic, epigenetic and metabolic changes lead to a cancer phenotype- incessant growth, resistance to apoptosis, and perpetual mutations and clonal evolution. In depth understanding of biology of tumors has lead not only to spectacular cures and major advancements in the well-being of cancer patients, but it also propelled forward other fields of Medicine where similar pathological processes are at play. In fact, genetic and regulatory changes in cancer cells tend to involve pathways that are usually spared from germline alterations due to incompatibility with normal tissue and organ development [1]. Conversely, non-neoplastic disorders tend to affect genes and metabolic pathways that are more regulatory in nature and more peripheral to the life-sustaining pathways in mammalian cells [2]. Despite these differences, cancer medicine has been offering itself as a unique testing ground for manipulation of these core biological processes in humans. From perturbation of DNA replication with chemotherapy DNA poisons, the field of oncology moved on to develop a new class of anticancer agents targeting components of cellular signaling systems, usually a kinase of a cell surface receptor that are functionally upregulated or are products of an amplified gene. This approach is meeting with some success in specific tumor types: for example, trastuzumab against human epidermal growth factor receptor 2 (HER2) in breast cancer cells, bevacizumab against vascular endothelial growth factor (VEGF), cetuximab against epidermal growth factor receptor (EGFR), sorafenib against B-RAF. The lessons and challenges are instructive to other areas of drug development in several ways.

GOing Bayesian: model-based gene set analysis of genome-scale data

The interpretation of data-driven experiments in genomics often involves a search for biological categories that are enriched for the responder genes identified by the experiments. However, knowledge bases such as the Gene Ontology (GO) contain hundreds or thousands of categories with very high overlap between categories. Thus, enrichment analysis performed on one category at a time frequently returns large numbers of correlated categories, leaving the choice of the most relevant ones to the user's; interpretation.Here we present model-based gene set analysis (MGSA) that analyzes all categories at once by embedding them in a Bayesian network, in which gene response is modeled as a function of the activation of biological categories. Probabilistic inference is used to identify the active categories. The Bayesian modeling approach naturally takes category overlap into account and avoids the need for multiple testing corrections met in single-category enrichment analysis. On simulated data, MGSA identifies active categories with up to 95% precision at a recall of 20% for moderate settings of noise, leading to a 10-fold precision improvement over single-category statistical enrichment analysis. Application to a gene expression data set in yeast demonstrates that the method provides high-level, summarized views of core biological processes and correctly eliminates confounding associations.

Folding kinetics of recognition loop peptides from a photolyase and cryptochrome-DASH

Cryptochromes (CRY) and photolyases (PL) use a common flavin adenine dinucleotide cofactor and homologous protein scaffold to accomplish numerous, seemingly dissimilar functions. PL repairs UV-damaged DNA in a mechanism requiring light and DNA base flipping. CRY cannot repair DNA, and instead function in core biological processes including plant photomorphogenesis, circadian rhythm, and magnetoreception. One subclass, CRY-DASH, does catalyze repair of single-stranded DNA; compromised base flipping may deactivate its tight binding to duplex DNA substrates. We recently demonstrated that the a “recognition loop” involved in DNA binding by both PL and CRY-DASH is among the most flexible regions in the two proteins, and exhibits especially heightened dynamics in CRY-DASH. Here, we establish that these distinct dynamics are encoded by the loop sequences: we quantify the flexibility of the isolated loop peptides through the kinetics and activation parameters for their folding. Mirroring the dynamics within the proteins, the CRY-DASH recognition loop peptide folds 2.5-fold faster than its counterpart in PL, predominantly due to a lower enthalpy of activation. We propose that these distinct dynamics are functionally significant in DNA recognition. Binding duplex DNA in the catalytically-active base-flipped conformation imposes significant order on the recognition loop, and a corresponding entropic penalty. This may be surmounted by the more preorganized PL recognition loop, but may impose too large a barrier for the more dynamic loop in CRY-DASH. These results suggest that evolution of protein dynamics, through local sequence tuning in the recognition loop, may be an important mechanism for functional diversification in PL and CRY.

Epigenetic regulation of the honey bee transcriptome: unravelling the nature of methylated genes

BackgroundEpigenetic modification of DNA via methylation is one of the key inventions in eukaryotic evolution. It provides a source for the switching of gene activities, the maintenance of stable phenotypes and the integration of environmental and genomic signals. Although this process is widespread among eukaryotes, both the patterns of methylation and their relevant biological roles not only vary noticeably in different lineages, but often are poorly understood. In addition, the evolutionary origins of DNA methylation in multicellular organisms remain enigmatic. Here we used a new 'epigenetic' model, the social honey bee Apis mellifera, to gain insights into the significance of methylated genes.ResultsWe combined microarray profiling of several tissues with genome-scale bioinformatics and bisulfite sequencing of selected genes to study the honey bee methylome. We find that around 35% of the annotated honey bee genes are expected to be methylated at the CpG dinucleotides by a highly conserved DNA methylation system. We show that one unifying feature of the methylated genes in this species is their broad pattern of expression and the associated 'housekeeping' roles. In contrast, genes involved in more stringently regulated spatial or temporal functions are predicted to be un-methylated.ConclusionOur data suggest that honey bees use CpG methylation of intragenic regions as an epigenetic mechanism to control the levels of activity of the genes that are broadly expressed and might be needed for conserved core biological processes in virtually every type of cell. We discuss the implications of our findings for genome-scale regulatory network structures and the evolution of the role(s) of DNA methylation in eukaryotes. Our findings are particularly important in the context of the emerging evidence that environmental factors can influence the epigenetic settings of some genes and lead to serious metabolic and behavioural disorders.

Estimates of the Effect of Natural Selection on Protein-Coding Content

Analysis of natural selection is key to understanding many core biological processes, including the emergence of competition, cooperation, and complexity, and has important applications in the targeted development of vaccines. Selection is hard to observe directly but can be inferred from molecular sequence variation. For protein-coding nucleotide sequences, the ratio of nonsynonymous to synonymous substitutions (ω) distinguishes neutrally evolving sequences (ω = 1) from those subjected to purifying (ω < 1) or positive Darwinian (ω > 1) selection. We show that current models used to estimate ω are substantially biased by naturally occurring sequence compositions. We present a novel model that weights substitutions by conditional nucleotide frequencies and which escapes these artifacts. Applying it to the genomes of pathogens causing malaria, leprosy, tuberculosis, and Lyme disease gave significant discrepancies in estimates with ∼10–30% of genes affected. Our work has substantial implications for how vaccine targets are chosen and for studying the molecular basis of adaptive evolution.

Protein Occupancy Landscape of a Bacterial Genome

Protein-DNA interactions are fundamental to core biological processes, including transcription, DNA replication, and chromosomal organization. We have developed in vivo protein occupancy display (IPOD), a technology that reveals protein occupancy across an entire bacterial chromosome at the resolution of individual binding sites. Application to Escherichia coli reveals thousands of protein occupancy peaks, highly enriched within and in close proximity to noncoding regulatory regions. In addition, we discovered extensive (>1 kilobase) protein occupancy domains (EPODs), some of which are localized to highly expressed genes, enriched in RNA-polymerase occupancy. However, the majority are localized to transcriptionally silent loci dominated by conserved hypothetical ORFs. These regions are highly enriched in both predicted and experimentally determined binding sites of nucleoid proteins and exhibit extreme biophysical characteristics such as high intrinsic curvature. Our observations implicate these transcriptionally silent EPODs as the elusive organizing centers, long proposed to topologically isolate chromosomal domains.