Drug target prediction and prioritization: using orthology to predict essentiality in parasite genomes

<p>Abstract</p> <p>Background</p> <p>New drug targets are urgently needed for parasites of socio-economic importance. Genes that are essential for parasite survival are highly desirable targets, but information on these genes is lacking, as gene knockouts or knockdowns are difficult to perform in many species of parasites. We examined the applicability of large-scale essentiality information from four model eukaryotes, <it>Caenorhabditis elegans, Drosophila melanogaster, Mus musculus </it>and <it>Saccharomyces cerevisiae</it>, to discover essential genes in each of their genomes. Parasite genes that lack orthologues in their host are desirable as selective targets, so we also examined prediction of essential genes within this subset.</p> <p>Results</p> <p>Cross-species analyses showed that the evolutionary conservation of genes and the presence of essential orthologues are each strong predictors of essentiality in eukaryotes. Absence of paralogues was also found to be a general predictor of increased relative essentiality. By combining several orthology and essentiality criteria one can select gene sets with up to a five-fold enrichment in essential genes compared with a random selection. We show how quantitative application of such criteria can be used to predict a ranked list of potential drug targets from <it>Ancylostoma caninum </it>and <it>Haemonchus contortus </it>- two blood-feeding strongylid nematodes, for which there are presently limited sequence data but no functional genomic tools.</p> <p>Conclusions</p> <p>The present study demonstrates the utility of using orthology information from multiple, diverse eukaryotes to predict essential genes. The data also emphasize the challenge of identifying essential genes among those in a parasite that are absent from its host.</p

Phylogenetic reconciliation : making the most of genomes to understand microbial ecology and evolution

Peer reviewe

Phylogenetic reconciliation: making the most of genomes to understand microbial ecology and evolution

In recent years, phylogenetic reconciliation has emerged as a promising approach for studying microbial ecology and evolution. The core idea is to model how gene trees evolve along a species tree and to explain differences between them via evolutionary events including gene duplications, transfers, and losses. Here, we describe how phylogenetic reconciliation provides a natural framework for studying genome evolution and highlight recent applications including ancestral gene content inference, the rooting of species trees, and the insights into metabolic evolution and ecological transitions they yield. Reconciliation analyses have elucidated the evolution of diverse microbial lineages, from Chlamydiae to Asgard archaea, shedding light on ecological adaptation, host–microbe interactions, and symbiotic relationships. However, there are many opportunities for broader application of the approach in microbiology. Continuing improvements to make reconciliation models more realistic and scalable, and integration of ecological metadata such as habitat, pH, temperature, and oxygen use offer enormous potential for understanding the rich tapestry of microbial life

Host-linked soil viral ecology along a permafrost thaw gradient

Climate change threatens to release abundant carbon that is sequestered at high latitudes, but the constraints on microbial metabolisms that mediate the release of methane and carbon dioxide are poorly understood1,2,3,4,5,6,7. The role of viruses, which are known to affect microbial dynamics, metabolism and biogeochemistry in the oceans8,9,10, remains largely unexplored in soil. Here, we aimed to investigate how viruses influence microbial ecology and carbon metabolism in peatland soils along a permafrost thaw gradient in Sweden. We recovered 1,907 viral populations (genomes and large genome fragments) from 197 bulk soil and size-fractionated metagenomes, 58% of which were detected in metatranscriptomes and presumed to be active. In silico predictions linked 35% of the viruses to microbial host populations, highlighting likely viral predators of key carbon-cycling microorganisms, including methanogens and methanotrophs. Lineage-specific virus/host ratios varied, suggesting that viral infection dynamics may differentially impact microbial responses to a changing climate. Virus-encoded glycoside hydrolases, including an endomannanase with confirmed functional activity, indicated that viruses influence complex carbon degradation and that viral abundances were significant predictors of methane dynamics. These findings suggest that viruses may impact ecosystem function in climate-critical, terrestrial habitats and identify multiple potential viral contributions to soil carbon cycling

Diverse sediment microbiota shape methane emission temperature sensitivity in Arctic lakes

Northern post-glacial lakes are significant, increasing sources of atmospheric carbon through ebullition (bubbling) of microbially-produced methane (CH4) from sediments. Ebullitive CH4 flux correlates strongly with temperature, reflecting that solar radiation drives emissions. However, here we show that the slope of the temperature-CH4 flux relationship differs spatially across two post-glacial lakes in Sweden. We compared these CH4 emission patterns with sediment microbial (metagenomic and amplicon), isotopic, and geochemical data. The temperature-associated increase in CH4 emissions was greater in lake middles—where methanogens were more abundant—than edges, and sediment communities were distinct between edges and middles. Microbial abundances, including those of CH4-cycling microorganisms and syntrophs, were predictive of porewater CH4 concentrations. Results suggest that deeper lake regions, which currently emit less CH4 than shallower edges, could add substantially to CH4 emissions in a warmer Arctic and that CH4 emission predictions may be improved by accounting for spatial variations in sediment microbiota

Divergent methyl-coenzyme M reductase genes in a deep-subseafloor Archaeoglobi

The methyl-coenzyme M reductase (MCR) complex is a key enzyme in archaeal methane generation and has recently been proposed to also be involved in the oxidation of short-chain hydrocarbons including methane, butane, and potentially propane. The number of archaeal clades encoding the MCR continues to grow, suggesting that this complex was inherited from an ancient ancestor, or has undergone extensive horizontal gene transfer. Expanding the representation of MCR-encoding lineages through metagenomic approaches will help resolve the evolutionary history of this complex. Here, a near-complete Archaeoglobi metagenome-assembled genome (MAG; Ca. Polytropus marinifundus gen. nov. sp. nov.) was recovered from the deep subseafloor along the Juan de Fuca Ridge flank that encodes two divergent McrABG operons similar to those found in Ca. Bathyarchaeota and Ca. Syntrophoarchaeum MAGs. Ca. P. marinifundus is basal to members of the class Archaeoglobi, and encodes the genes for β-oxidation, potentially allowing an alkanotrophic metabolism similar to that proposed for Ca. Syntrophoarchaeum. Ca. P. marinifundus also encodes a respiratory electron transport chain that can potentially utilize nitrate, iron, and sulfur compounds as electron acceptors. Phylogenetic analysis suggests that the Ca. P. marinifundus MCR operons were horizontally transferred, changing our understanding of the evolution and distribution of this complex in the Archaea

Sequenceserver: A Modern Graphical User Interface for Custom BLAST Databases

Comparing newly obtained and previously known nucleotide and amino-acid sequences underpins modern biological research. BLAST is a well-established tool for such comparisons but is challenging to use on new data sets. We combined a user-centric design philosophy with sustainable software development approaches to create Sequenceserver, a tool for running BLAST and visually inspecting BLAST results for biological interpretation. Sequenceserver uses simple algorithms to prevent potential analysis errors and provides flexible text-based and visual outputs to support researcher productivity. Our software can be rapidly installed for use by individuals or on shared servers