Stratification of co-evolving genomic groups using ranked phylogenetic profiles

<p>Abstract</p> <p>Background</p> <p>Previous methods of detecting the taxonomic origins of arbitrary sequence collections, with a significant impact to genome analysis and in particular metagenomics, have primarily focused on compositional features of genomes. The evolutionary patterns of phylogenetic distribution of genes or proteins, represented by phylogenetic profiles, provide an alternative approach for the detection of taxonomic origins, but typically suffer from low accuracy. Herein, we present <it>rank-BLAST</it>, a novel approach for the assignment of protein sequences into genomic groups of the same taxonomic origin, based on the ranking order of phylogenetic profiles of target genes or proteins across the reference database.</p> <p>Results</p> <p>The rank-BLAST approach is validated by computing the phylogenetic profiles of all sequences for five distinct microbial species of varying degrees of phylogenetic proximity, against a reference database of 243 fully sequenced genomes. The approach - a combination of sequence searches, statistical estimation and clustering - analyses the degree of sequence divergence between sets of protein sequences and allows the classification of protein sequences according to the species of origin with high accuracy, allowing taxonomic classification of 64% of the proteins studied. In most cases, a main cluster is detected, representing the corresponding species. Secondary, functionally distinct and species-specific clusters exhibit different patterns of phylogenetic distribution, thus flagging gene groups of interest. Detailed analyses of such cases are provided as examples.</p> <p>Conclusion</p> <p>Our results indicate that the rank-BLAST approach can capture the taxonomic origins of sequence collections in an accurate and efficient manner. The approach can be useful both for the analysis of genome evolution and the detection of species groups in metagenomics samples.</p

The genome sequence of <i>Trypanosoma brucei gambiense</i>, causative agent of chronic Human African Trypanosomiasis

&lt;p&gt;&lt;b&gt;Background:&lt;/b&gt; &lt;i&gt;Trypanosoma brucei gambiense&lt;/i&gt; is the causative agent of chronic Human African Trypanosomiasis or sleeping sickness, a disease endemic across often poor and rural areas of Western and Central Africa. We have previously published the genome sequence of a &lt;i&gt;T. b. brucei&lt;/i&gt; isolate, and have now employed a comparative genomics approach to understand the scale of genomic variation between &lt;i&gt;T. b. gambiense&lt;/i&gt; and the reference genome. We sought to identify features that were uniquely associated with &lt;i&gt;T. b. gambiense&lt;/i&gt; and its ability to infect humans.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Methods and findings:&lt;/b&gt; An improved high-quality draft genome sequence for the group 1 &lt;i&gt;T. b. gambiense&lt;/i&gt; DAL 972 isolate was produced using a whole-genome shotgun strategy. Comparison with &lt;i&gt;T. b. brucei&lt;/i&gt; showed that sequence identity averages 99.2% in coding regions, and gene order is largely collinear. However, variation associated with segmental duplications and tandem gene arrays suggests some reduction of functional repertoire in &lt;i&gt;T. b. gambiense&lt;/i&gt; DAL 972. A comparison of the variant surface glycoproteins (VSG) in &lt;i&gt;T. b. brucei&lt;/i&gt; with all &lt;i&gt;T. b. gambiense&lt;/i&gt; sequence reads showed that the essential structural repertoire of VSG domains is conserved across &lt;i&gt;T. brucei&lt;/i&gt;.&lt;/p&gt; &lt;p&gt;&lt;b&gt;Conclusions:&lt;/b&gt; This study provides the first estimate of intraspecific genomic variation within &lt;i&gt;T. brucei&lt;/i&gt;, and so has important consequences for future population genomics studies. We have shown that the &lt;i&gt;T. b. gambiense&lt;/i&gt; genome corresponds closely with the reference, which should therefore be an effective scaffold for any &lt;i&gt;T. brucei&lt;/i&gt; genome sequence data. As VSG repertoire is also well conserved, it may be feasible to describe the total diversity of variant antigens. While we describe several as yet uncharacterized gene families with predicted cell surface roles that were expanded in number in &lt;i&gt;T. b. brucei&lt;/i&gt;, no &lt;i&gt;T. b. gambiense&lt;/i&gt;-specific gene was identified outside of the subtelomeres that could explain the ability to infect humans.&lt;/p&gt

Protein coalitions in a core mammalian biochemical network linked by rapidly evolving proteins

<p>Abstract</p> <p>Background</p> <p>Cellular ATP levels are generated by glucose-stimulated mitochondrial metabolism and determine metabolic responses, such as glucose-stimulated insulin secretion (GSIS) from the β-cells of pancreatic islets. We describe an analysis of the evolutionary processes affecting the core enzymes involved in glucose-stimulated insulin secretion in mammals. The proteins involved in this system belong to ancient enzymatic pathways: glycolysis, the TCA cycle and oxidative phosphorylation.</p> <p>Results</p> <p>We identify two sets of proteins, or protein coalitions, in this group of 77 enzymes with distinct evolutionary patterns. Members of the glycolysis, TCA cycle, metabolite transport, pyruvate and NADH shuttles have low rates of protein sequence evolution, as inferred from a human-mouse comparison, and relatively high rates of evolutionary gene duplication. Respiratory chain and glutathione pathway proteins evolve faster, exhibiting lower rates of gene duplication. A small number of proteins in the system evolve significantly faster than co-pathway members and may serve as rapidly evolving adapters, linking groups of co-evolving genes.</p> <p>Conclusions</p> <p>Our results provide insights into the evolution of the involved proteins. We find evidence for two coalitions of proteins and the role of co-adaptation in protein evolution is identified and could be used in future research within a functional context.</p

Towards resolving the transcription factor network controlling myelin gene expression

In the central nervous system (CNS), myelin is produced from spirally-wrapped oligodendrocyte plasma membrane and, as exemplified by the debilitating effects of inherited or acquired myelin abnormalities in diseases such as multiple sclerosis, it plays a critical role in nervous system function. Myelin sheath production coincides with rapid up-regulation of numerous genes. The complexity of their subsequent expression patterns, along with recently recognized heterogeneity within the oligodendrocyte lineage, suggest that the regulatory networks controlling such genes drive multiple context-specific transcriptional programs. Conferring this nuanced level of control likely involves a large repertoire of interacting transcription factors (TFs). Here, we combined novel strategies of computational sequence analyses with in vivo functional analysis to establish a TF network model of coordinate myelin-associated gene transcription. Notably, the network model captures regulatory DNA elements and TFs known to regulate oligodendrocyte myelin gene transcription and/or oligodendrocyte development, thereby validating our approach. Further, it links to numerous TFs with previously unsuspected roles in CNS myelination and suggests collaborative relationships amongst both known and novel TFs, thus providing deeper insight into the myelin gene transcriptional network

Metabolic innovations towards the human lineage

Background: We describe a function-driven approach to the analysis of metabolism which takes into account the phylogenetic origin of biochemical reactions to reveal subtle lineage-specific metabolic innovations, undetectable by more traditional methods based on sequence comparison. The origins of reactions and thus entire pathways are inferred using a simple taxonomic classification scheme that describes the evolutionary course of events towards the lineage of interest. We investigate the evolutionary history of the human metabolic network extracted from a metabolic database, construct a network of interconnected pathways and classify this network according to the taxonomic categories representing eukaryotes, metazoa and vertebrates. Results: It is demonstrated that lineage-specific innovations correspond to reactions and pathways associated with key phenotypic changes during evolution, such as the emergence of cellular organelles in eukaryotes, cell adhesion cascades in metazoa and the biosynthesis of complex cell-specific biomolecules in vertebrates. Conclusion: This phylogenetic view of metabolic networks puts gene innovations within an evolutionary context, demonstrating how the emergence of a phenotype in a lineage provides a platform for the development of specialized traits

COmplete GENome Tracking (COGENT): a flexible data environment for computational genomics

Summary: We present a database of fully sequenced and published genomes to facilitate the re-distribution of data and ensure reproducibility of results in the field of compu-tational genomics. For its design we have implemented an extremely simple yet powerful schema to allow linking of genome sequence data to other resources