AnnoView enables large-scale analysis, comparison, and visualization of microbial gene neighborhoods.

The analysis and comparison of gene neighborhoods is a powerful approach for exploring microbial genome structure, function, and evolution. Although numerous tools exist for genome visualization and comparison, genome exploration across large genomic databases or user-generated datasets remains a challenge. Here, we introduce AnnoView, a web server designed for interactive exploration of gene neighborhoods across the bacterial and archaeal tree of life. Our server offers users the ability to identify, compare, and visualize gene neighborhoods of interest from 30 238 bacterial genomes and 1672 archaeal genomes, through integration with the comprehensive Genome Taxonomy Database and AnnoTree databases. Identified gene neighborhoods can be visualized using pre-computed functional annotations from different sources such as KEGG, Pfam and TIGRFAM, or clustered based on similarity. Alternatively, users can upload and explore their own custom genomic datasets in GBK, GFF or CSV format, or use AnnoView as a genome browser for relatively small genomes (e.g. viruses and plasmids). Ultimately, we anticipate that AnnoView will catalyze biological discovery by enabling user-friendly search, comparison, and visualization of genomic data. AnnoView is available at http://annoview.uwaterloo.ca.

COVID-19 severity gradient differentially dysregulates clinically relevant drug processing genes in nasopharyngeal swab samples.

AIM: Understanding how COVID-19 impacts the expression of clinically relevant drug metabolizing enzymes and membrane transporters (DMETs) is vital for addressing potential safety and efficacy concerns related to systemic and peripheral drug concentrations. This study investigates the impact of COVID-19 severity on DMETs expression and the underlying mechanisms to inform the design of precise clinical dosing regimens for affected patients. METHODS: Transcriptomics analysis of 102 DMETs, 10 inflammatory markers, and 12 xenosensing regulatory genes was conducted on nasopharyngeal swabs from 50 SARS-CoV-2 positive (17 outpatients, 16 non-ICU, and 17 ICU) and 13 SARS-CoV-2 negative individuals, clinically tested through qPCR, in the Greater Toronto area from October 2020 to October 2021. RESULTS: We observed a significant differential gene expression for 42 DMETs, 6 inflammatory markers, and 9 xenosensing regulatory genes. COVID-19 severity was associated with the upregulation of AKR1C1, MGST1, and SULT1E1, and downregulation of ABCC10, CYP3A43, and SLC29A4 expressions. Altogether, SARS-CoV-2-positive patients showed an upregulation in CYP2C9, CYP2C19, AKR1C1, SULT1B1, SULT2B1, and SLCO4A1 and downregulation in FMO5, MGST3, ABCC5, and SLCO4C1 compared with SARS-CoV-2 negative individuals. These dysregulations were associated with significant changes in the expression of inflammatory and xenosensing regulatory genes driven by the disease. GSTM3, PPARA, and AKR1C1 are potential biomarkers of the observed DMETs dysregulation pattern in nasopharyngeal swabs of outpatients, non-ICU, and ICU patients, respectively. CONCLUSION: The severity of COVID-19 is associated with the dysregulation of DMETs involved in processing commonly prescribed drugs, suggesting potential disease-drug interactions, especially for narrow therapeutic index drugs.

Genomic classification and antimicrobial resistance profiling of Streptococcus pneumoniae and Haemophilus influenza isolates associated with paediatric otitis media and upper respiratory infection.

Acute otitis media (AOM) is the most common childhood bacterial infectious disease requiring antimicrobial therapy. Most cases of AOM are caused by translocation of Streptococcus pneumoniae or Haemophilus influenzae from the nasopharynx to the middle ear during an upper respiratory tract infection (URI). Ongoing genomic surveillance of these pathogens is important for vaccine design and tracking of emerging variants, as well as for monitoring patterns of antibiotic resistance to inform treatment strategies and stewardship.In this work, we examined the ability of a genomics-based workflow to determine microbiological and clinically relevant information from cultured bacterial isolates obtained from patients with AOM or an URI. We performed whole genome sequencing (WGS) and analysis of 148 bacterial isolates cultured from the nasopharynx (N = 124, 94 AOM and 30 URI) and ear (N = 24, all AOM) of 101 children aged 6-35 months presenting with AOM or an URI. We then performed WGS-based sequence typing and antimicrobial resistance profiling of each strain and compared results to those obtained from traditional microbiological phenotyping.WGS of clinical isolates resulted in 71 S. pneumoniae genomes and 76 H. influenzae genomes. Multilocus sequencing typing (MSLT) identified 33 sequence types for S. pneumoniae and 19 predicted serotypes including the most frequent serotypes 35B and 3. Genome analysis predicted 30% of S. pneumoniae isolates to have complete or intermediate penicillin resistance. AMR predictions for S. pneumoniae isolates had strong agreement with clinical susceptibility testing results for beta-lactam and non beta-lactam antibiotics, with a mean sensitivity of 93% (86-100%) and a mean specificity of 98% (94-100%). MLST identified 29 H. influenzae sequence types. Genome analysis identified beta-lactamase genes in 30% of H. influenzae strains, which was 100% in agreement with clinical beta-lactamase testing. We also identified a divergent highly antibiotic-resistant strain of S. pneumoniae, and found its closest sequenced strains, also isolated from nasopharyngeal samples from over 15 years ago.Ultimately, our work provides the groundwork for clinical WGS-based workflows to aid in detection and analysis of H. influenzae and S. pneumoniae isolates.

PhyloCorrelate: inferring bacterial gene-gene functional associations through large-scale phylogenetic profiling.

MOTIVATION: Statistical detection of co-occurring genes across genomes, known as 'phylogenetic profiling', is a powerful bioinformatic technique for inferring gene-gene functional associations. However, this can be a challenging task given the size and complexity of phylogenomic databases, difficulty in accounting for phylogenetic structure, inconsistencies in genome annotation and substantial computational requirements. RESULTS: We introduce PhyloCorrelate-a computational framework for gene co-occurrence analysis across large phylogenomic datasets. PhyloCorrelate implements a variety of co-occurrence metrics including standard correlation metrics and model-based metrics that account for phylogenetic history. By combining multiple metrics, we developed an optimized score that exhibits a superior ability to link genes with overlapping GO terms and KEGG pathways, enabling gene function prediction. Using genomic and functional annotation data from the Genome Taxonomy Database and AnnoTree, we performed all-by-all comparisons of gene occurrence profiles across the bacterial tree of life, totaling 154 217 052 comparisons for 28 315 genes across 27 372 bacterial genomes. All predictions are available in an online database, which instantaneously returns the top correlated genes for any PFAM, TIGRFAM or KEGG query. In total, PhyloCorrelate detected 29 762 high confidence associations between bacterial gene/protein pairs, and generated functional predictions for 834 DUFs and proteins of unknown function. AVAILABILITYAND IMPLEMENTATION: PhyloCorrelate is available as a web-server at phylocorrelate.uwaterloo.ca as well as an R package for analysis of custom datasets. We anticipate that PhyloCorrelate will be broadly useful as a tool for predicting function and interactions for gene families. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Phylogenomics of 8,839 Clostridioides difficile genomes reveals recombination-driven evolution and diversification of toxin A and B.

Clostridioides difficile is the major worldwide cause of antibiotic-associated gastrointestinal infection. A pathogenicity locus (PaLoc) encoding one or two homologous toxins, toxin A (TcdA) and toxin B (TcdB), is essential for C. difficile pathogenicity. However, toxin sequence variation poses major challenges for the development of diagnostic assays, therapeutics, and vaccines. Here, we present a comprehensive phylogenomic analysis of 8,839 C. difficile strains and their toxins including 6,492 genomes that we assembled from the NCBI short read archive. A total of 5,175 tcdA and 8,022 tcdB genes clustered into 7 (A1-A7) and 12 (B1-B12) distinct subtypes, which form the basis of a new method for toxin-based subtyping of C. difficile. We developed a haplotype coloring algorithm to visualize amino acid variation across all toxin sequences, which revealed that TcdB has diversified through extensive homologous recombination throughout its entire sequence, and formed new subtypes through distinct recombination events. In contrast, TcdA varies mainly in the number of repeats in its C-terminal repetitive region, suggesting that recombination-mediated diversification of TcdB provides a selective advantage in C. difficile evolution. The application of toxin subtyping is then validated by classifying 351 C. difficile clinical isolates from Brigham and Women's Hospital in Boston, demonstrating its clinical utility. Subtyping partitions TcdB into binary functional and antigenic groups generated by intragenic recombinations, including two distinct cell-rounding phenotypes, whether recognizing frizzled proteins as receptors, and whether it can be efficiently neutralized by monoclonal antibody bezlotoxumab, the only FDA-approved therapeutic antibody. Our analysis also identifies eight universally conserved surface patches across the TcdB structure, representing ideal targets for developing broad-spectrum therapeutics. Finally, we established an open online database (DiffBase) as a central hub for collection and classification of C. difficile toxins, which will help clinicians decide on therapeutic strategies targeting specific toxin variants, and allow researchers to monitor the ongoing evolution and diversification of C. difficile.

"From Protein Toxins to Applied Toxicological Testing" virtual workshop identifies the need for a bioinformatic framework to assess novel food protein safety.

On October 21-22, 2020 the HESI (Health and Environmental Sciences Institute) Protein Allergens, Toxins, and Bioinformatics Committee, and the Society of Toxicology Food Safety Specialty Section co-hosted a virtual workshop titled "From Protein Toxins to Applied Toxicological Testing". The workshop focused on the safety assessment of novel proteins contained in foods and feeds, was globally represented by over 200 stakeholder attendees, and featured contributions from experts in academia, government and non-government organizations, and agricultural biotechnology developers from the private sector. A range of topics relevant to novel protein safety were discussed, including: the state of protein toxin biology, modes and mechanisms of action, structures and activity, use of bioinformatic analyses to assess the safety of a protein, and ways to leverage computational biology with in silico approaches for protein toxin identification/characterization. Key outcomes of the workshop included the appreciation of the complexity of developing a definition for a protein toxin when viewed from the perspective of food and feed safety, confirming the need for a case-by-case hypothesis-driven interpretation of bioinformatic results that leverages additional metadata rather than an alignment threshold-driven interpretation, and agreement that a "toxin protein database" is not necessary, as the bioinformatic needs for toxin detection may be accomplished by existing databases such as Pfam and UniProtKB/Swiss-Prot. In this paper, a path forward is proposed.

PathFams: statistical detection of pathogen-associated protein domains.

BACKGROUND: A substantial fraction of genes identified within bacterial genomes encode proteins of unknown function. Identifying which of these proteins represent potential virulence factors, and mapping their key virulence determinants, is a challenging but important goal. RESULTS: To facilitate virulence factor discovery, we performed a comprehensive analysis of 17,929 protein domain families within the Pfam database, and scored them based on their overrepresentation in pathogenic versus non-pathogenic species, taxonomic distribution, relative abundance in metagenomic datasets, and other factors. CONCLUSIONS: We identify pathogen-associated domain families, candidate virulence factors in the human gut, and eukaryotic-like mimicry domains with likely roles in virulence. Furthermore, we provide an interactive database called PathFams to allow users to explore pathogen-associated domains as well as identify pathogen-associated domains and domain architectures in user-uploaded sequences of interest. PathFams is freely available at https://pathfams.uwaterloo.ca .

Slr4, a newly identified S-layer protein from marine Gammaproteobacteria, is a major biofilm matrix component.

S-layers are paracrystalline proteinaceous lattices that surround prokaryotic cells, forming a critical interface between the cells and their extracellular environment. Here, we report the discovery of a novel S-layer protein present in the Gram-negative marine organism, Pseudoalteromonas tunicata D2. An uncharacterized protein (EAR28894) was identified as the most abundant protein in planktonic cultures and biofilms. Bioinformatic methods predicted a beta-helical structure for EAR28894 similar to the Caulobacter S-layer protein, RsaA, despite sharing less than 20% sequence identity. Transmission electron microscopy revealed that purified EAR28894 protein assembled into paracrystalline sheets with a unique square lattice symmetry and a unit cell spacing of ~9.1 nm. An S-layer was found surrounding the outer membrane in wild-type cells and completely removed from cells in an EAR28894 deletion mutant. S-layer material also appeared to be "shed" from wild-type cells and was highly abundant in the extracellular matrix where it is associated with outer membrane vesicles and other matrix components. EAR28894 and its homologs form a new family of S-layer proteins that are widely distributed in Gammaproteobacteria including species of Pseudoalteromonas and Vibrio, and found exclusively in marine metagenomes. We propose the name Slr4 for this novel protein family.

Potentiation of long-acting ß2-agonist and glucocorticoid responses in human airway epithelial cells by modulation of intracellular cAMP.

INTRODUCTION: Over 300 million people in the world live with asthma, resulting in 500,000 annual global deaths with future increases expected. It is estimated that around 50-80% of asthma exacerbations are due to viral infections. Currently, a combination of long-acting beta agonists (LABA) for bronchodilation and glucocorticoids (GCS) to control lung inflammation represent the dominant strategy for the management of asthma, however, it is still sub-optimal in 35-50% of moderate-severe asthmatics resulting in persistent lung inflammation, impairment of lung function, and risk of mortality. Mechanistically, LABA/GCS combination therapy results in synergistic efficacy mediated by intracellular cyclic adenosine monophosphate (cAMP). HYPOTHESIS: Increasing intracellular cAMP during LABA/GCS combination therapy via inhibiting phosphodiesterase 4 (PDE4) and/or blocking the export of cAMP by ATP Binding Cassette Transporter C4 (ABCC4), will potentiate anti-inflammatory responses of mainstay LABA/GCS therapy. METHODS: Expression and localization experiments were performed using in situ hybridization and immunohistochemistry in human lung tissue from healthy subjects, while confirmatory transcript and protein expression analyses were performed in primary human airway epithelial cells and cell lines. Intervention experiments were performed on the human airway epithelial cell line, HBEC-6KT, by pre-treatment with combinations of LABA/GCS with PDE4 and/or ABCC4 inhibitors followed by Poly I:C or imiquimod challenge as a model for viral stimuli. Cytokine readouts for IL-6, IL-8, CXCL10/IP-10, and CCL5/RANTES were quantified by ELISA. RESULTS: Using archived human lung and human airway epithelial cells, ABCC4 gene and protein expression were confirmed in vitro and in situ. LABA/GCS attenuation of Poly I:C or imiquimod-induced IL-6 and IL-8 were potentiated with ABCC4 and PDE4 inhibition, which was greater when ABCC4 and PDE4 inhibition was combined. Modulation of cAMP levels had no impact on LABA/GCS modulation of Poly I:C-induced CXCL10/IP-10 or CCL5/RANTES. CONCLUSION: Modulation of intracellular cAMP levels by PDE4 or ABCC4 inhibition potentiates LABA/GCS efficacy in human airway epithelial cells challenged with viral stimuli. The data suggest further exploration of the value of adding cAMP modulators to mainstay LABA/GCS therapy in asthma for potentiated anti-inflammatory efficacy.

AnnoTree: visualization and exploration of a functionally annotated microbial tree of life.

Bacterial genomics has revolutionized our understanding of the microbial tree of life; however, mapping and visualizing the distribution of functional traits across bacteria remains a challenge. Here, we introduce AnnoTree-an interactive, functionally annotated bacterial tree of life that integrates taxonomic, phylogenetic and functional annotation data from over 27 000 bacterial and 1500 archaeal genomes. AnnoTree enables visualization of millions of precomputed genome annotations across the bacterial and archaeal phylogenies, thereby allowing users to explore gene distributions as well as patterns of gene gain and loss in prokaryotes. Using AnnoTree, we examined the phylogenomic distributions of 28 311 gene/protein families, and measured their phylogenetic conservation, patchiness, and lineage-specificity within bacteria. Our analyses revealed widespread phylogenetic patchiness among bacterial gene families, reflecting the dynamic evolution of prokaryotic genomes. Genes involved in phage infection/defense, mobile elements, and antibiotic resistance dominated the list of most patchy traits, as well as numerous intriguing metabolic enzymes that appear to have undergone frequent horizontal transfer. We anticipate that AnnoTree will be a valuable resource for exploring prokaryotic gene histories, and will act as a catalyst for biological and evolutionary hypothesis generation. AnnoTree is freely available at http://annotree.uwaterloo.ca.

Predicting the recombination potential of severe acute respiratory syndrome coronavirus 2 and Middle East respiratory syndrome coronavirus.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) recently emerged to cause widespread infections in humans. SARS-CoV-2 infections have been reported in the Kingdom of Saudi Arabia, where Middle East respiratory syndrome coronavirus (MERS-CoV) causes seasonal outbreaks with a case fatality rate of ~37 %. Here we show that there exists a theoretical possibility of future recombination events between SARS-CoV-2 and MERS-CoV RNA. Through computational analyses, we have identified homologous genomic regions within the ORF1ab and S genes that could facilitate recombination, and have analysed co-expression patterns of the cellular receptors for SARS-CoV-2 and MERS-CoV, ACE2 and DPP4, respectively, to identify human anatomical sites that could facilitate co-infection. Furthermore, we have investigated the likely susceptibility of various animal species to MERS-CoV and SARS-CoV-2 infection by comparing known virus spike protein-receptor interacting residues. In conclusion, we suggest that a recombination between SARS-CoV-2 and MERS-CoV RNA is possible and urge public health laboratories in high-risk areas to develop diagnostic capability for the detection of recombined coronaviruses in patient samples.

Gene expression and in situ protein profiling of candidate SARS-CoV-2 receptors in human airway epithelial cells and lung tissue.

In December 2019, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged, causing the coronavirus disease 2019 (COVID-19) pandemic. SARS-CoV, the agent responsible for the 2003 SARS outbreak, utilises angiotensin-converting enzyme 2 (ACE2) and transmembrane serine protease 2 (TMPRSS2) host molecules for viral entry. ACE2 and TMPRSS2 have recently been implicated in SARS-CoV-2 viral infection. Additional host molecules including ADAM17, cathepsin L, CD147 and GRP78 may also function as receptors for SARS-CoV-2.To determine the expression and in situ localisation of candidate SARS-CoV-2 receptors in the respiratory mucosa, we analysed gene expression datasets from airway epithelial cells of 515 healthy subjects, gene promoter activity analysis using the FANTOM5 dataset containing 120 distinct sample types, single cell RNA sequencing (scRNAseq) of 10 healthy subjects, proteomic datasets, immunoblots on multiple airway epithelial cell types, and immunohistochemistry on 98 human lung samples.We demonstrate absent to low ACE2 promoter activity in a variety of lung epithelial cell samples and low ACE2 gene expression in both microarray and scRNAseq datasets of epithelial cell populations. Consistent with gene expression, rare ACE2 protein expression was observed in the airway epithelium and alveoli of human lung, confirmed with proteomics. We present confirmatory evidence for the presence of TMPRSS2, CD147 and GRP78 protein in vitro in airway epithelial cells and confirm broad in situ protein expression of CD147 and GRP78 in the respiratory mucosa.Collectively, our data suggest the presence of a mechanism dynamically regulating ACE2 expression in human lung, perhaps in periods of SARS-CoV-2 infection, and also suggest that alternative receptors for SARS-CoV-2 exist to facilitate initial host cell infection.

Pygenprop: a Python library for programmatic exploration and comparison of organism genome properties.

SUMMARY: A critical step in comparative genomics is the identification of differences in the presence/absence of encoded biochemical pathways among organisms. Our library, Pygenprop, facilitates these comparisons using data from the Genome Properties database. Pygenprop is written in Python and, unlike existing libraries, it is compatible with a variety of tools in the Python data science ecosystem, such as Jupyter Notebooks for interactive analyses and scikit-learn for machine learning. Pygenprop assigns YES, NO, or PARTIAL support for each property based on InterProScan annotations of open reading frames from an organism's genome. The library contains classes for representing the Genome Properties database as a whole and methods for detecting differences in property assignments between organisms. As the Genome Properties database grows, we anticipate widespread adoption of Pygenprop for routine genome analyses and integration within third-party bioinformatics software. AVAILABILITY AND IMPLEMENTATION: Pygenprop is written in Python and is compatible with versions 3.6 or higher. Source code is available under Apache Licence Version 2 at https://github.com/Micromeda/pygenprop. The package can be installed from both PyPi (https://pypi.org/project/pygenprop) and Anaconda (https://anaconda.org/lbergstrand/pygenprop). Documentation is available on Read the Docs (http://pygenprop.rtfd.io/).

A study of nitric oxide dynamics in a growing biofilm using a density dependent reaction-diffusion model.

One of a number of critical roles played by NO· as a chemical weapon (generated by the immune system) is to neutralize pathogens. However, the virulence of pathogens depends on the production activity of reductants to detoxify NO·. Broad reactivity of NO· makes it complicated to predict the fate of NO· inside bacteria and its effects on the treatment of any infection. Here, we present a mathematical model of biofilm response to NO·, as a stressor. The model is comprised of a PDE system of highly nonlinear reaction-diffusion equations that we study in computer simulations to determine the positive and negative effects of key parameters on bacterial defenses against NO·. From the reported results, we conjecture that the oscillatory behavior of NO· under a microaerobic regime is a temporal phenomenon and does not give rise to a spatial pattern. It is also shown computationally that decreasing the initial size of the biofilm colony negatively impacts the functionality of reducing agents that deactivate NO·. Whereas nutrient deprivation results in the development of biofilms with heterogeneous structure, its effect on the activity of NO· reductants depends on the oxygen availability, biofilm size, and the amount of NO·.

Lineage-specific mutational clustering in protein structures predicts evolutionary shifts in function.

Motivation: Spatially clustered mutations within specific regions of protein structure are thought to result from strong positive selection for altered protein functions and are a common feature of oncoproteins in cancer. Although previous studies have used spatial substitution clustering to identify positive selection between pairs of proteins, the ability of this approach to identify functional shifts in protein phylogenies has not been explored. Results: We implemented a previous measure of spatial substitution clustering (the P3D statistic) and extended it to detect spatially clustered substitutions at specific branches of phylogenetic trees. We then applied the analysis to 423 690 phylogenetic branches from 9261 vertebrate protein families, and examined its ability to detect historical shifts in protein function. Our analysis identified 19 607 lineages from 5362 protein families in which substitutions were spatially clustered on protein structures at P3D < 0.01. Spatially clustered substitutions were overrepresented among ligand-binding residues and were significantly enriched among particular protein families and functions including C2H2 transcription factors and protein kinases. A small but significant proportion of branches with spatially clustered substitution also were under positive selection according to the branch-site test. Lastly, exploration of the top-scoring candidates revealed historical substitution events in vertebrate protein families that have generated new functions and protein interactions, including ancient adaptations in SLC7A2, PTEN, and SNAP25 . Ultimately, our work shows that lineage-specific, spatially clustered substitutions are a useful feature for identifying functional shifts in protein families, and reveal new candidates for future experimental study. Availability and Implementation: Source code and predictions for analyses performed in this study are available at: https://github.com/doxeylab/evoclust3d. Contact: acdoxey@uwaterloo.ca. Supplementary information: Supplementary data are available at Bioinformatics online.

Agricultural soil denitrifiers possess extensive nitrite reductase gene diversity.

Denitrification transforms nitrogen applied as fertilizer and emits N2 O, which is a potent greenhouse gas. Very little is known about the identities of abundant and active denitrifiers in agricultural soils. We coupled DNA stable-isotope probing (DNA-SIP) with flow-through reactors (FTRs) to detect active agricultural soil denitrifiers. The FTRs were incubated with nitrate and 13 C6 -glucose under anoxic conditions and sampled at multiple time points. Labelled DNA from active microorganisms was analyzed by 16S rRNA gene fingerprinting, amplicon and shotgun metagenomic sequencing. Taxonomic representation of heavy fractions was consistent across sites and time points, including Betaproteobacteria (71%; Janthinobacterium, Acidovorax, Azoarcus and Dechloromonas), Alphaproteobacteria (8%; Rhizobium), Gammaproteobacteria (4%; Pseudomonas) and Actinobacteria (4%; Streptomycetaceae). Most nitrite-reductase reads from heavy DNA annotated to the copper-containing form (nirK). Assigned taxonomies of active denitrifiers based on reads matching the nirK gene were comparable to those obtained through nitric oxide (norB) and RNA polymerase (rpoB) annotations but not the nitrous oxide reductase gene (nosZ). Analysis of recovered metagenomes from heavy DNA demonstrated extensive nirK sequence family diversity, including novel taxonomic groups that are not captured by existing primers.

mimicMe: a web server for prediction and analysis of host-like proteins in microbial pathogens.

SUMMARY: mimicMe is a web server for prediction and analysis of host-like proteins (mimics) encoded by microbial pathogens. Users select a host species and any set of pathogen and control proteomes (bacterial, fungal, protozoan or viral) and mimicMe reports host-like proteins that are unique to or enriched among pathogens. Additional server features include visualization of structural similarities between pathogen and host proteins as well as function-enrichment analysis. AVAILABILITY AND IMPLEMENTATION: mimicMe is available at http://mimicme.uwaterloo.ca CONTACT: acdoxey@uwaterloo.ca.

MetAnnotate: function-specific taxonomic profiling and comparison of metagenomes.

BACKGROUND: Metagenomes provide access to the taxonomic composition and functional capabilities of microbial communities. Although metagenomic analysis methods exist for estimating overall community composition or metabolic potential, identifying specific taxa that encode specific functions or pathways of interest can be more challenging. Here we present MetAnnotate, which addresses the common question: "which organisms perform my function of interest within my metagenome(s) of interest?" MetAnnotate uses profile hidden Markov models to analyze shotgun metagenomes for genes and pathways of interest, classifies retrieved sequences either through a phylogenetic placement or best hit approach, and enables comparison of these profiles between metagenomes. RESULTS: Based on a simulated metagenome dataset, the tool achieves high taxonomic classification accuracy for a broad range of genes, including both markers of community abundance and specific biological pathways. Lastly, we demonstrate MetAnnotate by analyzing for cobalamin (vitamin B12) synthesis genes across hundreds of aquatic metagenomes in a fraction of the time required by the commonly used Basic Local Alignment Search Tool top hit approach. CONCLUSIONS: MetAnnotate is multi-threaded and installable as a local web application or command-line tool on Linux systems. Metannotate is a useful framework for general and/or function-specific taxonomic profiling and comparison of metagenomes.

Evaluation of multiple displacement amplification for metagenomic analysis of low biomass samples.

Combining multiple displacement amplification (MDA) with metagenomics enables the analysis of samples with extremely low DNA concentrations, making them suitable for high-throughput sequencing. Although amplification bias and nonspecific amplification have been reported from MDA-amplified samples, the impact of MDA on metagenomic datasets is not well understood. We compared three MDA methods (i.e. bulk MDA, emulsion MDA, and primase MDA) for metagenomic analysis of two DNA template concentrations (approx. 1 and 100 pg) derived from a microbial community standard "mock community" and two low biomass environmental samples (i.e. borehole fluid and groundwater). We assessed the impact of MDA on metagenome-based community composition, assembly quality, functional profiles, and binning. We found amplification bias against high GC content genomes but relatively low nonspecific amplification such as chimeras, artifacts, or contamination for all MDA methods. We observed MDA-associated representational bias for microbial community profiles, especially for low-input DNA and with the primase MDA method. Nevertheless, similar taxa were represented in MDA-amplified libraries to those of unamplified samples. The MDA libraries were highly fragmented, but similar functional profiles to the unamplified libraries were obtained for bulk MDA and emulsion MDA at higher DNA input and across these MDA libraries for the groundwater sample. Medium to low-quality bins were possible for the high input bulk MDA metagenomes for the most simple microbial communities, borehole fluid, and mock community. Although MDA-based amplification should be avoided, it can still reveal meaningful taxonomic and functional information from samples with extremely low DNA concentration where direct metagenomics is otherwise impossible.

Metatranscriptomic RNA-Seq Data Analysis of Virus-Infected Host Cells.

RNA sequencing (RNA-seq) analysis of virus-infected host cells enables researchers to study a wide range of phenomena involving host-virus interactions. This includes genomic analysis of the viral population itself, as well as analysis of the transcriptional dynamics of the virus and host during infection. In this chapter, we provide a guide for researchers interested in performing RNA-seq data analysis of virus-infected host cells or cell lines. We outline several bioinformatic protocols for quantifying viral abundance, assembling viral genomes from mixed samples, and performing differential expression analysis, among other common workflows. These workflows can be used as starting points for researchers aiming to analyze RNA-seq datasets of mixed samples containing both host and viral RNA, such as virus-infected cell lines or clinical samples.