Crowdsourcing temporal transcriptomic coronavirus host infection data: Resources, guide, and novel insights.

The emergence of severe acute respiratory syndrome-related coronavirus 2 (SARS-CoV-2) reawakened the need to rapidly understand the molecular etiologies, pandemic potential, and prospective treatments of infectious agents. The lack of existing data on SARS-CoV-2 hampered early attempts to treat severe forms of coronavirus disease-2019 (COVID-19) during the pandemic. This study coupled existing transcriptomic data from severe acute respiratory syndrome-related coronavirus 1 (SARS-CoV-1) lung infection animal studies with crowdsourcing statistical approaches to derive temporal meta-signatures of host responses during early viral accumulation and subsequent clearance stages. Unsupervised and supervised machine learning approaches identified top dysregulated genes and potential biomarkers (e.g. CXCL10, BEX2, and ADM). Temporal meta-signatures revealed distinct gene expression programs with biological implications to a series of host responses underlying sustained Cxcl10 expression and Stat signaling. Cell cycle switched from G1/G0 phase genes, early in infection, to a G2/M gene signature during late infection that correlated with the enrichment of DNA damage response and repair genes. The SARS-CoV-1 meta-signatures were shown to closely emulate human SARS-CoV-2 host responses from emerging RNAseq, single cell, and proteomics data with early monocyte-macrophage activation followed by lymphocyte proliferation. The circulatory hormone adrenomedullin was observed as maximally elevated in elderly patients who died from COVID-19. Stage-specific correlations to compounds with potential to treat COVID-19 and future coronavirus infections were in part validated by a subset of twenty-four that are in clinical trials to treat COVID-19. This study represents a roadmap to leverage existing data in the public domain to derive novel molecular and biological insights and potential treatments to emerging human pathogens.

Renal cell neoplasms contain shared tumor type-specific copy number variations.

Copy number variant (CNV) analysis was performed on renal cell carcinoma (RCC) specimens (chromophobe, clear cell, oncocytoma, papillary type 1, and papillary type 2) using high-resolution arrays (1.85 million probes). The RCC samples exhibited diverse genomic changes within and across tumor types, ranging from 106 to 2238 CNV segments in a clear-cell specimen and in a papillary type 2 specimen, respectively. Despite this heterogeneity, distinct CNV segments were common within each tumor classification: chromophobe (seven segments), clear cell (three segments), oncocytoma (nine segments), and papillary type 2 (two segments). Shared segments ranged from a 6.1-kb deletion (oncocytomas) to a 208.3-kb deletion (chromophobes). Among common tumor type-specific variations, chromophobes, clear-cell tumors, and oncocytomas were composed exclusively of noncoding DNA. No CNV regions were common to papillary type 1 specimens, although there were 12 amplifications and 12 deletions in five of six samples. Three microRNAs and 12 mRNA genes had a ≥98% coding region contained within CNV regions, including multiple gene families (chromophobe: amylases 1A, 1B, and 1C; oncocytoma: general transcription factors 2H2, 2B, 2C, and 2D). Gene deletions involved in histone modification and chromatin remodeling affected individual subtypes (clear cell: SFMBT and SETD2; papillary type 2: BAZ1A) and the collective RCC group (KDM4C). The genomic amplifications/deletions identified herein represent potential diagnostic and/or prognostic biomarkers.

Microevolution of group A streptococci in vivo: capturing regulatory networks engaged in sociomicrobiology, niche adaptation, and hypervirulence.

The onset of infection and the switch from primary to secondary niches are dramatic environmental changes that not only alter bacterial transcriptional programs, but also perturb their sociomicrobiology, often driving minor subpopulations with mutant phenotypes to prevail in specific niches. Having previously reported that M1T1 Streptococcus pyogenes become hypervirulent in mice due to selection of mutants in the covRS regulatory genes, we set out to dissect the impact of these mutations in vitro and in vivo from the impact of other adaptive events. Using a murine subcutaneous chamber model to sample the bacteria prior to selection or expansion of mutants, we compared gene expression dynamics of wild type (WT) and previously isolated animal-passaged (AP) covS mutant bacteria both in vitro and in vivo, and we found extensive transcriptional alterations of pathoadaptive and metabolic gene sets associated with invasion, immune evasion, tissue-dissemination, and metabolic reprogramming. In contrast to the virulence-associated differences between WT and AP bacteria, Phenotype Microarray analysis showed minor in vitro phenotypic differences between the two isogenic variants. Additionally, our results reflect that WT bacteria's rapid host-adaptive transcriptional reprogramming was not sufficient for their survival, and they were outnumbered by hypervirulent covS mutants with SpeB(-)/Sda(high) phenotype, which survived up to 14 days in mice chambers. Our findings demonstrate the engagement of unique regulatory modules in niche adaptation, implicate a critical role for bacterial genetic heterogeneity that surpasses transcriptional in vivo adaptation, and portray the dynamics underlying the selection of hypervirulent covS mutants over their parental WT cells.

The RAST Server: rapid annotations using subsystems technology.

BACKGROUND: The number of prokaryotic genome sequences becoming available is growing steadily and is growing faster than our ability to accurately annotate them. DESCRIPTION: We describe a fully automated service for annotating bacterial and archaeal genomes. The service identifies protein-encoding, rRNA and tRNA genes, assigns functions to the genes, predicts which subsystems are represented in the genome, uses this information to reconstruct the metabolic network and makes the output easily downloadable for the user. In addition, the annotated genome can be browsed in an environment that supports comparative analysis with the annotated genomes maintained in the SEED environment. The service normally makes the annotated genome available within 12-24 hours of submission, but ultimately the quality of such a service will be judged in terms of accuracy, consistency, and completeness of the produced annotations. We summarize our attempts to address these issues and discuss plans for incrementally enhancing the service. CONCLUSION: By providing accurate, rapid annotation freely to the community we have created an important community resource. The service has now been utilized by over 120 external users annotating over 350 distinct genomes.

The National Microbial Pathogen Database Resource (NMPDR): a genomics platform based on subsystem annotation.

The National Microbial Pathogen Data Resource (NMPDR) (http://www.nmpdr.org) is a National Institute of Allergy and Infections Disease (NIAID)-funded Bioinformatics Resource Center that supports research in selected Category B pathogens. NMPDR contains the complete genomes of approximately 50 strains of pathogenic bacteria that are the focus of our curators, as well as >400 other genomes that provide a broad context for comparative analysis across the three phylogenetic Domains. NMPDR integrates complete, public genomes with expertly curated biological subsystems to provide the most consistent genome annotations. Subsystems are sets of functional roles related by a biologically meaningful organizing principle, which are built over large collections of genomes; they provide researchers with consistent functional assignments in a biologically structured context. Investigators can browse subsystems and reactions to develop accurate reconstructions of the metabolic networks of any sequenced organism. NMPDR provides a comprehensive bioinformatics platform, with tools and viewers for genome analysis. Results of precomputed gene clustering analyses can be retrieved in tabular or graphic format with one-click tools. NMPDR tools include Signature Genes, which finds the set of genes in common or that differentiates two groups of organisms. Essentiality data collated from genome-wide studies have been curated. Drug target identification and high-throughput, in silico, compound screening are in development.

Essential genes on metabolic maps.

Within the past five years genome-scale gene essentiality data sets have been published for ten diverse bacterial species. These data are a rich source of information about cellular networks that we are only beginning to explore. The analysis of these data, very heterogeneous in nature, is a challenging task. Even the definition of 'essential genes' in various genome-scale studies varies from genes 'absolutely required for survival' to those 'strongly contributing to fitness' and robust competitive growth. A comparative analysis of gene essentiality across multiple organisms based on projection of experimentally observed essential genes to functional roles in a collection of metabolic pathways and subsystems is emerging as a powerful tool of systems biology.

The subsystems approach to genome annotation and its use in the project to annotate 1000 genomes.

The release of the 1000th complete microbial genome will occur in the next two to three years. In anticipation of this milestone, the Fellowship for Interpretation of Genomes (FIG) launched the Project to Annotate 1000 Genomes. The project is built around the principle that the key to improved accuracy in high-throughput annotation technology is to have experts annotate single subsystems over the complete collection of genomes, rather than having an annotation expert attempt to annotate all of the genes in a single genome. Using the subsystems approach, all of the genes implementing the subsystem are analyzed by an expert in that subsystem. An annotation environment was created where populated subsystems are curated and projected to new genomes. A portable notion of a populated subsystem was defined, and tools developed for exchanging and curating these objects. Tools were also developed to resolve conflicts between populated subsystems. The SEED is the first annotation environment that supports this model of annotation. Here, we describe the subsystem approach, and offer the first release of our growing library of populated subsystems. The initial release of data includes 180 177 distinct proteins with 2133 distinct functional roles. This data comes from 173 subsystems and 383 different organisms.