A Rickettsiella endosymbiont is a potential source of essential B-vitamins for the poultry red mite, Dermanyssus gallinae

Obligate blood-sucking arthropods rely on symbiotic bacteria to provision essential B vitamins that are either missing or at sub-optimal amounts in their nutritionally challenging blood diet. The poultry red mite Dermanyssus gallinae, an obligate blood-feeding ectoparasite, is primarily associated with poultry and a serious threat to the hen egg industry. Thus far, the identity and biological role of nutrient provisioning bacterial mutualists from D. gallinae are little understood. Here, we demonstrate that a Rickettsiella Gammaproteobacteria in maternally transmitted in D. gallinae and universally present in D. gallinae mites collected at different sites throughout Europe. In addition, we report the genome sequence of uncultivable endosymbiont “Candidatus Rickettsiella rubrum” from D. gallinae eggs. The endosymbiont has a circular 1. 89 Mbp genome that encodes 1973 protein. Phylogenetic analysis confirms the placement R. rubrum within the Rickettsiella genus, closely related to a facultative endosymbiont from the pea aphid and Coxiella-like endosymbionts from blood feeding ticks. Analysis of the R. rubrum genome reveals many protein-coding sequences are either pseudogenized or lost, but R. rubrum has retained several B vitamin biosynthesis pathways, confirming the importance of these pathways in evolution of its nutritional symbiosis with D. gallinae. In silico metabolic pathway reconstruction revealed that R. rubrum is unable to synthesise protein amino acids and therefore these nutrients are likely provisioned by the host. In contrast R. rubrum retains biosynthetic pathways for B vitamins: thiamine (vitamin B1) via the salvage pathway; riboflavin (vitamin B2) and pyridoxine (vitamin B6) and the cofactors: flavin adenine dinucleotide (FAD) and coenzyme A (CoA) that likely provision these nutrients to the host. We propose that bacterial symbionts which are essential to blood-feeding arthropod survival provide attractive targets for the development of novel control methods.

Leveraging heterogeneous network embedding for metabolic pathway prediction

Motivation Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Results Here, we present pathway2vec, a software package consisting of six representational learning modules used to automatically generate features for pathway inference. Specifically, we build a three-layered network composed of compounds, enzymes and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve prediction outcomes. Availability and implementation The software package and installation instructions are published on http://github.com/pathway2vec. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

A consensus-based ensemble approach to improve de novo transcriptome assembly

Systems-level analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction, depend on the accuracy of the transcriptome. Multiple tools exist to perform transcriptome assembly from RNAseq data. However, assembling high quality transcriptomes is still not a trivial problem. This is especially the case for non-model organisms where adequate reference genomes are often not available. Different methods produce different transcriptome models and there is no easy way to determine which are more accurate. Furthermore, having alternative splicing events could exacerbate such difficult assembly problems. While benchmarking transcriptome assemblies is critical, this is also not trivial due to the general lack of true reference transcriptomes. In this study, we provide a pipeline to generate a set of the benchmark transcriptome and corresponding RNAseq data. Using the simulated benchmarking datasets, we compared the performance of various transcriptome assembly approaches including genome-guided, de novo, and ensemble methods. The results showed that the assembly performance deteriorates significantly when the reference is not available from the same genome (for genome-guided methods) or when alternative transcripts (isoforms) exist. We demonstrated the value of consensus between de novo assemblers in transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we further present ConSemble, a consensus-based de novo ensemble transcriptome assembly pipeline. Without using a reference genome, ConSemble achieved an accuracy up to twice as high as any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms. The RNAseq simulation pipeline, the benchmark transcriptome datasets, and the ConSemble pipeline are all freely available from: http://bioinfolab.unl.edu/emlab/consemble/.Author summaryObtaining the accurate representation of the gene expression is critical in many analyses, such as differential gene expression analysis, co-expression analysis, and metabolic pathway reconstruction. The state of the art high-throughput RNA-sequencing (RNAseq) technologies can be used to sequence the set of all transcripts in a cell, the transcriptome. Although many computational tools are available for transcriptome assembly from RNAseq data, assembling high-quality transcriptomes is difficult especially for non-model organisms. Different methods often produce different transcriptome models and there is no easy way to determine which are more accurate. In this study, we present an approach to evaluate transcriptome assembly performance using simulated benchmarking read sets. The results showed that the assembly performance of genome-guided assembly methods deteriorates significantly when the adequate reference genome is not available. The assembly performance of all methods is affected when alternative transcripts (isoforms) exist. We further demonstrated the value of consensus among assemblers in improving transcriptome assembly. Leveraging the overlapping predictions between the four de novo assemblers, we present ConSemble. Without using a reference genome, ConSemble achieved a much higher accuracy than any de novo assemblers we compared. It matched or exceeded the best performing genome-guided assemblers even when the transcriptomes included isoforms.

Structure and Function of the Arctic and Antarctic Marine Microbiota as Revealed by Metagenomics

Background: The Arctic and Antarctic are the two most geographically distant bioregions on earth. Recent sampling efforts and following metagenomics have shed light on the global ocean microbial diversity and function, yet the microbiota of polar regions has not been included in such global analyses. Results: Here a metagenomic study of seawater samples (n = 60) collected from different depths at 28 locations in the Arctic and Antarctic zones was performed, together with metagenomes from the Tara Oceans. More than 7,500 (19%) polar seawater-derived operational taxonomic units could not be identified in the Tara Oceans datasets, and more than 3,900,000 protein-coding gene orthologs had no hits in the Ocean Microbial Reference Gene Catalog. Analysis of 214 metagenome assembled genomes (MAGs) , recovered from the polar seawater microbiomes, revealed strains that are prevalent in the polar regions while nearly undetectable in temperate seawater. Metabolic pathway reconstruction for these microbes suggested versatility for saccharide and lipids biosynthesis, nitrate and sulfate reduction, and CO2 fixation. Comparison between the Arctic and Antarctic microbiomes revealed that antibiotic resistance genes were enriched in the Arctic while functions like DNA recombination were enriched in the Antarctic. Conclusions: Our data highlight the occurrence of dominant and locally enriched microbes in the Arctic and Antarctic seawater with unique functional traits for environmental adaption, and provide a foundation for analyzing the global ocean microbiome in a more complete perspective.

Leveraging Heterogeneous Network Embedding for Metabolic Pathway Prediction

Metabolic pathway reconstruction from genomic sequence information is a key step in predicting regulatory and functional potential of cells at the individual, population and community levels of organization. Although the most common methods for metabolic pathway reconstruction are gene-centric e.g. mapping annotated proteins onto known pathways using a reference database, pathway-centric methods based on heuristics or machine learning to infer pathway presence provide a powerful engine for hypothesis generation in biological systems. Such methods rely on rule sets or rich feature information that may not be known or readily accessible. Here, we present pathway2vec, a software package consisting of six representational learning based modules used to automatically generate features for pathway inference. Specifically, we build a three layered network composed of compounds, enzymes, and pathways, where nodes within a layer manifest inter-interactions and nodes between layers manifest betweenness interactions. This layered architecture captures relevant relationships used to learn a neural embedding-based low-dimensional space of metabolic features. We benchmark pathway2vec performance based on node-clustering, embedding visualization and pathway prediction using MetaCyc as a trusted source. In the pathway prediction task, results indicate that it is possible to leverage embeddings to improve pathway prediction outcomes.Availability and implementationThe software package, and installation instructions are published on github.com/[email protected]

Structure and Function of the Arctic and Antarctic Marine Microbiota as Revealed by Metagenomics

Background: The Arctic and Antarctic are the two most geographically distant bioregions on earth. Recent sampling efforts and following metagenomics have shed light on the global ocean microbial diversity and function, yet the microbiota of polar regions has not been included in such global analyses.Results: Here a metagenomic study of seawater samples (n = 60) collected from different depths at 28 locations in the Arctic and Antarctic zones was performed, together with metagenomes from with the Tara Oceans datasets. More than 7,500 (19%) polar seawater-derived operational taxonomic units could not be identified in the Tara Oceans datasets, and more than 3,900,000 protein-coding gene orthologs had no hits in the Ocean Microbial Reference Gene Catalog. Analysis of 214 genome bins, recovered from the polar seawater microbiomes, revealed strains that are prevalent in the polar regions while nearly undetectable in temperate seawater. Metabolic pathway reconstruction for these microbes suggested versatility for saccharide and lipids biosynthesis, nitrate and sulfate reduction, and CO2 fixation. Comparison between the Arctic and Antarctic microbiomes revealed that antibiotic resistance genes were enriched in the Arctic while functions like DNA recombination were enriched in the Antarctic.Conclusions: Our data highlight the occurrence of dominant and endemic microbes in the Arctic and Antarctic seawater with unique functional traits for environmental adaption, and provide a foundation for analyzing the global ocean microbiome in a more complete perspective.

Genome-Wide and Functional View of Proteolytic and Lipolytic Bacteria for Efficient Biogas Production through Enhanced Sewage Sludge Hydrolysis

In this study, we used a multifaceted approach to select robust bioaugmentation candidates for enhancing biogas production and to demonstrate the usefulness of a genome-centric approach for strain selection for specific bioaugmentation purposes. We also investigated the influence of the isolation source of bacterial strains on their metabolic potential and their efficiency in enhancing anaerobic digestion. Whole genome sequencing, metabolic pathway reconstruction, and physiological analyses, including phenomics, of phylogenetically diverse strains, Rummeliibacillus sp. POC4, Ochrobactrum sp. POC9 (both isolated from sewage sludge) and Brevundimonas sp. LPMIX5 (isolated from an agricultural biogas plant) showed their diverse enzymatic activities, metabolic versatility and ability to survive under varied growth conditions. All tested strains display proteolytic, lipolytic, cellulolytic, amylolytic, and xylanolytic activities and are able to utilize a wide array of single carbon and energy sources, as well as more complex industrial by-products, such as dairy waste and molasses. The specific enzymatic activity expressed by the three strains studied was related to the type of substrate present in the original isolation source. Bioaugmentation with sewage sludge isolates–POC4 and POC9–was more effective for enhancing biogas production from sewage sludge (22% and 28%, respectively) than an approach based on LPMIX5 strain (biogas production boosted by 7%) that had been isolated from an agricultural biogas plant, where other type of substrate is used.