proGenomes: a resource for consistent functional and taxonomic annotations of prokaryotic genomes.

The availability of microbial genomes has opened many new avenues of research within microbiology. This has been driven primarily by comparative genomics approaches, which rely on accurate and consistent characterization of genomic sequences. It is nevertheless difficult to obtain consistent taxonomic and integrated functional annotations for defined prokaryotic clades. Thus, we developed proGenomes, a resource that provides user-friendly access to currently 25 038 high-quality genomes whose sequences and consistent annotations can be retrieved individually or by taxonomic clade. These genomes are assigned to 5306 consistent and accurate taxonomic species clusters based on previously established methodology. proGenomes also contains functional information for almost 80 million protein-coding genes, including a comprehensive set of general annotations and more focused annotations for carbohydrate-active enzymes and antibiotic resistance genes. Additionally, broad habitat information is provided for many genomes. All genomes and associated information can be downloaded by user-selected clade or multiple habitat-specific sets of representative genomes. We expect that the availability of high-quality genomes with comprehensive functional annotations will promote advances in clinical microbial genomics, functional evolution and other subfields of microbiology. proGenomes is available at http://progenomes.embl.de.

MOCAT2: a metagenomic assembly, annotation and profiling framework.

UNLABELLED: MOCAT2 is a software pipeline for metagenomic sequence assembly and gene prediction with novel features for taxonomic and functional abundance profiling. The automated generation and efficient annotation of non-redundant reference catalogs by propagating pre-computed assignments from 18 databases covering various functional categories allows for fast and comprehensive functional characterization of metagenomes. AVAILABILITY AND IMPLEMENTATION: MOCAT2 is implemented in Perl 5 and Python 2.7, designed for 64-bit UNIX systems and offers support for high-performance computer usage via LSF, PBS or SGE queuing systems; source code is freely available under the GPL3 license at http://mocat.embl.de CONTACT: : bork@embl.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Strain dynamics of contaminating bacteria modulate the yield of ethanol biorefineries.

Bioethanol is a sustainable energy alternative and can contribute to global greenhouse-gas emission reductions by over 60%. Its industrial production faces various bottlenecks, including sub-optimal efficiency resulting from bacteria. Broad-spectrum removal of these contaminants results in negligible gains, suggesting that the process is shaped by ecological interactions within the microbial community. Here, we survey the microbiome across all process steps at two biorefineries, over three timepoints in a production season. Leveraging shotgun metagenomics and cultivation-based approaches, we identify beneficial bacteria and find improved outcome when yeast-to-bacteria ratios increase during fermentation. We provide a microbial gene catalogue which reveals bacteria-specific pathways associated with performance. We also show that Limosilactobacillus fermentum overgrowth lowers production, with one strain reducing yield by ~5% in laboratory fermentations, potentially due to its metabolite profile. Temperature is found to be a major driver for strain-level dynamics. Improved microbial management strategies could unlock environmental and economic gains in this US $ 60 billion industry enabling its wider adoption.

Molecular interaction networks for the analysis of human disease: utility, limitations, and considerations.

High-throughput '-omics' data can be combined with large-scale molecular interaction networks, for example, protein-protein interaction networks, to provide a unique framework for the investigation of human molecular biology. Interest in these integrative '-omics' methods is growing rapidly because of their potential to understand complexity and association with disease; such approaches have a focus on associations between phenotype and "network-type." The potential of this research is enticing, yet there remain a series of important considerations. Here, we discuss interaction data selection, data quality, the relative merits of using data from large high-throughput studies versus a meta-database of smaller literature-curated studies, and possible issues of sociological or inspection bias in interaction data. Other work underway, especially international consortia to establish data formats, quality standards and address data redundancy, and the improvements these efforts are making to the field, is also evaluated. We present options for researchers intending to use large-scale molecular interaction networks as a functional context for protein or gene expression data, including microRNAs, especially in the context of human disease.

Visualization of the interactome: what are we looking at?

Network visualization of the interactome has been become routine in systems biology research. Not only does it serve as an illustration on the cellular organization of protein-protein interactions, it also serves as a biological context for gaining insights from high-throughput data. However, the challenges to produce an effective visualization have been great owing to the fact that the scale, biological context and dynamics of any given interactome are too large and complex to be captured by a single visualization. Visualization design therefore requires a pragmatic trade-off between capturing biological concept and being comprehensible. In this review, we focus on the biological interpretation of different network visualizations. We will draw on examples predominantly from our experiences but elaborate them in the context of the broader field. A rich variety of networks will be introduced including interactomes and the complexome in 2D, interactomes in 2.5D and 3D and dynamic networks.

A multidimensional matrix for systems biology research and its application to interaction networks.

A multidimensional matrix containing 76 parameters from 21 transcriptomics, proteomics, interactomics, phenotypic and sequence-based data sets, in which each data set covered most of the Saccharomyces cerevisiae proteome, was compiled for systems biology research. The maximal information coefficient (MIC) was used to measure correlations between every pair of parameters. Out of 2850 possible comparisons, 340 pairs of variables (12%) showed statistically significant MIC scores. There were 321 relationships that were expected; these included relationships within physicochemical parameters of proteins, between abundance levels of genes/proteins and expression noise, and between different types of intracellular networks. We found 19 potentially novel relationships between different types of "-omics" data. The strongest of these involved genetic interaction networks, which were correlated with pleiotropy and cell-to-cell variability in protein expression. Protein disorder also showed a number of significant relationships with protein abundance, signaling and regulatory networks. Significant cross-talk was seen between the signaling and kinase interaction networks. Investigation of this revealed densely connected kinase clusters and significant signaling between them, along with signaling centers that act as integrators or broadcasters of intracellular information. These centers may allow for redundancy and a means of dampening noise in networks under a variety of genetic or environmental perturbations.

Drivers and determinants of strain dynamics following fecal microbiota transplantation.

Fecal microbiota transplantation (FMT) is a therapeutic intervention for inflammatory diseases of the gastrointestinal tract, but its clinical mode of action and subsequent microbiome dynamics remain poorly understood. Here we analyzed metagenomes from 316 FMTs, sampled pre and post intervention, for the treatment of ten different disease indications. We quantified strain-level dynamics of 1,089 microbial species, complemented by 47,548 newly constructed metagenome-assembled genomes. Donor strain colonization and recipient strain resilience were mostly independent of clinical outcomes, but accurately predictable using LASSO-regularized regression models that accounted for host, microbiome and procedural variables. Recipient factors and donor-recipient complementarity, encompassing entire microbial communities to individual strains, were the main determinants of strain population dynamics, providing insights into the underlying processes that shape the post-FMT gut microbiome. Applying an ecology-based framework to our findings indicated parameters that may inform the development of more effective, targeted microbiome therapies in the future, and suggested how patient stratification can be used to enhance donor microbiota colonization or the displacement of recipient microbes in clinical practice.

Four-dimensional visualisation and analysis of protein-protein interaction networks.

Protein-protein interaction networks are typically built with interactions collated from many experiments. These networks are thus composite and show all interactions that are currently known to occur in a cell. However, these representations are static and ignore the constant changes in protein-protein interactions. Here we present software for the generation and analysis of dynamic, four-dimensional (4-D) protein interaction networks. In this, time-course-derived abundance data are mapped onto three-dimensional networks to generate network movies. These networks can be navigated, manipulated and queried in real time. Two types of dynamic networks can be generated: a 4-D network that maps expression data onto protein nodes and one that employs 'real-time rendering' by which protein nodes and their interactions appear and disappear in association with temporal changes in expression data. We illustrate the utility of this software by the analysis of singlish interface date hub interactions during the yeast cell cycle. In this, we show that proteins MLC1 and YPT52 show strict temporal control of when their interaction partners are expressed. Since these proteins have one and two interaction interfaces, respectively, it suggests that temporal control of gene expression may be used to limit competition at the interaction interfaces of some hub proteins. The software and movies of the 4-D networks are available at http://www.systemsbiology.org.au/downloads_geomi.html.

Visualization and analysis of the complexome network of Saccharomyces cerevisiae.

Most processes in the cell are delivered by protein complexes, rather than individual proteins. While the association of proteins has been studied extensively in protein-protein interaction networks (the interactome), an intuitive and effective representation of complex-complex connections (the complexome) is not yet available. Here, we describe a new representation of the complexome of Saccharomyces cerevisiae. Using the core-module-attachment data of Gavin et al. ( Nature 2006 , 440 , 631 - 6 ), protein complexes in the network are represented as nodes; these are connected by edges that represent shared core and/or module protein subunits. To validate this network, we examined the network topology and its distribution of biological processes. The complexome network showed scale-free characteristics, with a power law-like node degree distribution and clustering coefficient independent of node degree. Connected complexes in the network showed similarities in biological process that were nonrandom. Furthermore, clusters of interacting complexes reflected a higher-level organization of many cellular functions. The strong functional relationships seen in these clusters, along with literature evidence, allowed 44 uncharacterized complexes to be assigned putative functions using guilt-by-association. We demonstrate our network model using the GEOMI visualization platform, on which we have developed capabilities to integrate and visualize complexome data.

Bacterial resistance to CRISPR-Cas antimicrobials.

In the age of antibiotic resistance and precise microbiome engineering, CRISPR-Cas antimicrobials promise to have a substantial impact on the way we treat diseases in the future. However, the efficacy of these antimicrobials and their mechanisms of resistance remain to be elucidated. We systematically investigated how a target E. coli strain can escape killing by episomally-encoded CRISPR-Cas9 antimicrobials. Using Cas9 from Streptococcus pyogenes (SpCas9) we studied the killing efficiency and resistance mutation rate towards CRISPR-Cas9 antimicrobials and elucidated the underlying genetic alterations. We find that killing efficiency is not correlated with the number of cutting sites or the type of target. While the number of targets did not significantly affect efficiency of killing, it did reduce the emergence of chromosomal mutations conferring resistance. The most frequent target of resistance mutations was the plasmid-encoded SpCas9 that was inactivated by bacterial genome rearrangements involving translocation of mobile genetic elements such as insertion elements. This resistance mechanism can be overcome by re-introduction of an intact copy of SpCas9. The work presented here provides a guide to design strategies that reduce resistance and improve the activity of CRISPR-Cas antimicrobials.

Proteins deleterious on overexpression are associated with high intrinsic disorder, specific interaction domains, and low abundance.

In proteomics, there is a major challenge in how the functional significance of overexpressed proteins can be interpreted. This is particularly the case when examining proteins in cells or tissues. Here we have analyzed the physicochemical parameters, abundance level, half-life and degree of intrinsic disorder of proteins previously overexpressed in the yeast Saccharomyces cerevisiae. We also examined the interaction domains present and the manner in which overexpressed proteins are, or are not, associated with known complexes. We found a number of protein characteristics were strongly associated with deleterious phenotypes. These included protein abundance (where low-abundance proteins tend to be deleterious on overexpression), intrinsic disorder (where a striking association was seen between percent disorder and degree of deleterious effect), and the number of likely domain-domain interactions. Furthermore, we found a number of domain types, for example, DUF221 and the ubiquitin interaction motif, that were present predominantly in proteins that are deleterious on overexpression. Together, these results provide strong evidence that particular types of proteins are deleterious on overexpression whereas others are not. These factors can be considered in the interpretation of protein expression differences in proteomic experiments.

Disentangling the impact of environmental and phylogenetic constraints on prokaryotic within-species diversity.

Microbial organisms inhabit virtually all environments and encompass a vast biological diversity. The pangenome concept aims to facilitate an understanding of diversity within defined phylogenetic groups. Hence, pangenomes are increasingly used to characterize the strain diversity of prokaryotic species. To understand the interdependence of pangenome features (such as the number of core and accessory genes) and to study the impact of environmental and phylogenetic constraints on the evolution of conspecific strains, we computed pangenomes for 155 phylogenetically diverse species (from ten phyla) using 7,000 high-quality genomes to each of which the respective habitats were assigned. Species habitat ubiquity was associated with several pangenome features. In particular, core-genome size was more important for ubiquity than accessory genome size. In general, environmental preferences had a stronger impact on pangenome evolution than phylogenetic inertia. Environmental preferences explained up to 49% of the variance for pangenome features, compared with 18% by phylogenetic inertia. This observation was robust when the dataset was extended to 10,100 species (59 phyla). The importance of environmental preferences was further accentuated by convergent evolution of pangenome features in a given habitat type across different phylogenetic clades. For example, the soil environment promotes expansion of pangenome size, while host-associated habitats lead to its reduction. Taken together, we explored the global principles of pangenome evolution, quantified the influence of habitat, and phylogenetic inertia on the evolution of pangenomes and identified criteria governing species ubiquity and habitat specificity.

The Interactorium: visualising proteins, complexes and interaction networks in a virtual 3-D cell.

Here, we describe the Interactorium, a tool in which a Virtual Cell is used as the context for the seamless visualisation of the yeast protein interaction network, protein complexes and protein 3-D structures. The tool has been designed to display very complex networks of up to 40 000 proteins or 6000 multiprotein complexes and has a series of toolboxes and menus to allow real-time data manipulation and control the manner in which data are displayed. It incorporates new algorithms that reduce the complexity of the visualisation by the generation of putative new complexes from existing data and by the reduction of edges through the use of protein "twins" when they occur in multiple locations. Since the Interactorium permits multi-level viewing of the molecular biology of the cell, it is a considerable advance over existing approaches. We illustrate its use for Saccharomyces cerevisiae but note that it will also be useful for the analysis of data from simpler prokaryotes and higher eukaryotes, including humans. The Interactorium is available for download at http://www.interactorium.net.

Extensive transmission of microbes along the gastrointestinal tract.

The gastrointestinal tract is abundantly colonized by microbes, yet the translocation of oral species to the intestine is considered a rare aberrant event, and a hallmark of disease. By studying salivary and fecal microbial strain populations of 310 species in 470 individuals from five countries, we found that transmission to, and subsequent colonization of, the large intestine by oral microbes is common and extensive among healthy individuals. We found evidence for a vast majority of oral species to be transferable, with increased levels of transmission in colorectal cancer and rheumatoid arthritis patients and, more generally, for species described as opportunistic pathogens. This establishes the oral cavity as an endogenous reservoir for gut microbial strains, and oral-fecal transmission as an important process that shapes the gastrointestinal microbiome in health and disease.

Durable coexistence of donor and recipient strains after fecal microbiota transplantation.

Fecal microbiota transplantation (FMT) has shown efficacy in treating recurrent Clostridium difficile infection and is increasingly being applied to other gastrointestinal disorders, yet the fate of native and introduced microbial strains remains largely unknown. To quantify the extent of donor microbiota colonization, we monitored strain populations in fecal samples from a recent FMT study on metabolic syndrome patients using single-nucleotide variants in metagenomes. We found extensive coexistence of donor and recipient strains, persisting 3 months after treatment. Colonization success was greater for conspecific strains than for new species, the latter falling within fluctuation levels observed in healthy individuals over a similar time frame. Furthermore, same-donor recipients displayed varying degrees of microbiota transfer, indicating individual patterns of microbiome resistance and donor-recipient compatibilities.

Temporal and technical variability of human gut metagenomes.

BACKGROUND: Metagenomics has become a prominent approach for exploring the role of the gut microbiota in human health. However, the temporal variability of the healthy gut microbiome has not yet been studied in depth using metagenomics and little is known about the effects of different sampling and preservation approaches. We performed metagenomic analysis on fecal samples from seven subjects collected over a period of up to two years to investigate temporal variability and assess preservation-induced variation, specifically, fresh frozen compared to RNALater. We also monitored short-term disturbances caused by antibiotic treatment and bowel cleansing in one subject. RESULTS: We find that the human gut microbiome is temporally stable and highly personalized at both taxonomic and functional levels. Over multiple time points, samples from the same subject clustered together, even in the context of a large dataset of 888 European and American fecal metagenomes. One exception was observed in an antibiotic intervention case where, more than one year after the treatment, samples did not resemble the pre-treatment state. Clustering was not affected by the preservation method. No species differed significantly in abundance, and only 0.36% of gene families were differentially abundant between preservation methods. CONCLUSIONS: Technical variability is small compared to the temporal variability of an unperturbed gut microbiome, which in turn is much smaller than the observed between-subject variability. Thus, short-term preservation of fecal samples in RNALater is an appropriate and cost-effective alternative to freezing of fecal samples for metagenomic studies.

Disturbed protein-protein interaction networks in metastatic melanoma are associated with worse prognosis and increased functional mutation burden.

For disseminated melanoma, new prognostic biomarkers and therapeutic targets are urgently needed. The organization of protein-protein interaction networks was assessed via the transcriptomes of four independent studies of metastatic melanoma and related to clinical outcome and MAP-kinase pathway mutations (BRAF/NRAS). We also examined patient outcome-related differences in a predicted network of microRNAs and their targets. The 32 hub genes with the most reproducible survival-related disturbances in co-expression with their protein partner genes included oncogenes and tumor suppressors, previously known correlates of prognosis, and other proteins not previously associated with melanoma outcome. Notably, this network-based gene set could classify patients according to clinical outcomes with 67-80% accuracy among cohorts. Reproducibly disturbed networks were also more likely to have a higher functional mutation burden than would be expected by chance. The disturbed regions of networks are therefore markers of clinically relevant, selectable tumor evolution in melanoma which may carry driver mutations.

Time-course proteome analysis reveals the dynamic response of Cryptococcus gattii cells to fluconazole.

Cryptococcus gattii is an encapsulated fungus capable of causing fatal disease in immunocompetent humans and animals. As current antifungal therapies are few and limited in efficacy, and resistance is an emerging issue, the development of new treatment strategies is urgently required. The current study undertook a time-course analysis of the proteome of C. gattii during treatment with fluconazole (FLC), which is used widely in prophylactic and maintenance therapies. The aims were to analyze the overall cellular response to FLC, and to find fungal proteins involved in this response that might be useful targets in therapies that augment the antifungal activity of FLC. During FLC treatment, an increase in stress response, ATP synthesis and mitochondrial respiratory chain proteins, and a decrease in most ribosomal proteins was observed, suggesting that ATP-dependent efflux pumps had been initiated for survival and that the maintenance of ribosome synthesis was differentially expressed. Two proteins involved in fungal specific pathways were responsive to FLC. An integrative network analysis revealed co-ordinated processes involved in drug response, and highlighted hubs in the network representing essential proteins that are required for cell viability. This work demonstrates the dynamic cellular response of a typical susceptible isolate of C. gattii to FLC, and identified a number of proteins and pathways that could be targeted to augment the activity of FLC.

A genome-wide screen in yeast identifies specific oxidative stress genes required for the maintenance of sub-cellular redox homeostasis.

Maintenance of an optimal redox environment is critical for appropriate functioning of cellular processes and cell survival. Despite the importance of maintaining redox homeostasis, it is not clear how the optimal redox potential is sensed and set, and the processes that impact redox on a cellular/organellar level are poorly understood. The genetic bases of cellular redox homeostasis were investigated using a green fluorescent protein (GFP) based redox probe, roGFP2 and a pH sensitive GFP-based probe, pHluorin. The use of roGFP2, in conjunction with pHluorin, enabled determination of pH-adjusted sub-cellular redox potential in a non-invasive and real-time manner. A genome-wide screen using both the non-essential and essential gene collections was carried out in Saccharomyces cerevisiae using cytosolic-roGFP2 to identify factors essential for maintenance of cytosolic redox state under steady-state conditions. 102 genes of diverse function were identified that are required for maintenance of cytosolic redox state. Mutations in these genes led to shifts in the half-cell glutathione redox potential by 75-10 mV. Interestingly, some specific oxidative stress-response processes were identified as over-represented in the data set. Further investigation of the role of oxidative stress-responsive systems in sub-cellular redox homeostasis was conducted using roGFP2 constructs targeted to the mitochondrial matrix and peroxisome and E(GSH) was measured in cells in exponential and stationary phase. Analyses allowed for the identification of key redox systems on a sub-cellular level and the identification of novel genes involved in the regulation of cellular redox homeostasis.

Sequencing and validation of the genome of a Campylobacter concisus reveals intra-species diversity.

Campylobacter concisus is an emerging pathogen of the human gastrointestinal tract. Its role in different diseases remains a subject of debate; this may be due to strain to strain genetic variation. Here, we sequence and analyze the genome of a C. concisus from a biopsy of a child with Crohn's disease (UNSWCD); the second such genome for this species. A 1.8 Mb genome was assembled with paired-end reads from a next-generation sequencer. This genome is smaller than the 2.1 Mb C. concisus reference BAA-1457. While 1593 genes were conserved across UNSWCD and BAA-1457, 138 genes from UNSWCD and 281 from BAA-1457 were unique when compared against the other. To further validate the genome assembly and annotation, comprehensive shotgun proteomics was performed. This confirmed 78% of open reading frames in UNSWCD and, importantly, provided evidence of expression for 217 proteins previously defined as 'hypothetical' in Campylobacter. Substantial functional differences were observed between the UNSWCD and the reference strain. Enrichment analysis revealed differences in membrane proteins, response to stimulus, molecular transport and electron carriers. Synteny maps for the 281 genes not present in UNSWCD identified seven functionally associated gene clusters. These included one associated with the CRISPR family and another which encoded multiple restriction endonucleases; these genes are all involved in resistance to phage attack. Many of the observed differences are consistent with UNSWCD having adapted to greater surface interaction with host cells, as opposed to BAA-1457 which may prefer a free-living environment.