A framework for organizing cancer-related variations from existing databases, publications and NGS data using a High-performance Integrated Virtual Environment (HIVE)

Years of sequence feature curation by UniProtKB/Swiss-Prot, PIR-PSD, NCBI-CDD, RefSeq and other database biocurators has led to a rich repository of information on functional sites of genes and proteins. This information along with variation-related annotation can be used to scan human short sequence reads from next-generation sequencing (NGS) pipelines for presence of non-synonymous single-nucleotide variations (nsSNVs) that affect functional sites. This and similar workflows are becoming more important because thousands of NGS data sets are being made available through projects such as The Cancer Genome Atlas (TCGA), and researchers want to evaluate their biomarkers in genomic data. BioMuta, an integrated sequence feature database, provides a framework for automated and manual curation and integration of cancer-related sequence features so that they can be used in NGS analysis pipelines. Sequence feature information in BioMuta is collected from the Catalogue of Somatic Mutations in Cancer (COSMIC), ClinVar, UniProtKB and through biocuration of information available from publications. Additionally, nsSNVs identified through automated analysis of NGS data from TCGA are also included in the database. Because of the petabytes of data and information present in NGS primary repositories, a platform HIVE (High-performance Integrated Virtual Environment) for storing, analyzing, computing and curating NGS data and associated metadata has been developed. Using HIVE, 31 979 nsSNVs were identified in TCGA-derived NGS data from breast cancer patients. All variations identified through this process are stored in a Curated Short Read archive, and the nsSNVs from the tumor samples are included in BioMuta. Currently, BioMuta has 26 cancer types with 13 896 small-scale and 308 986 large-scale study-derived variations. Integration of variation data allows identifications of novel or common nsSNVs that can be prioritized in validation studies

Human germline and pan-cancer variomes and their distinct functional profiles

Identification of non-synonymous single nucleotide variations (nsSNVs) has exponentially increased due to advances in Next-Generation Sequencing technologies. The functional impacts of these variations have been difficult to ascertain because the corresponding knowledge about sequence functional sites is quite fragmented. It is clear that mapping of variations to sequence functional features can help us better understand the pathophysiological role of variations. In this study, we investigated the effect of nsSNVs on more than 17 common types of post-translational modification (PTM) sites, active sites and binding sites. Out of 1 705 285 distinct nsSNVs on 259 216 functional sites we identified 38 549 variations that significantly affect 10 major functional sites. Furthermore, we found distinct patterns of site disruptions due to germline and somatic nsSNVs. Pan-cancer analysis across 12 different cancer types led to the identification of 51 genes with 106 nsSNV affected functional sites found in 3 or more cancer types. 13 of the 51 genes overlap with previously identified Significantly Mutated Genes (Nature. 2013 Oct 17;502(7471)). 62 mutations in these 13 genes affecting functional sites such as DNA, ATP binding and various PTM sites occur across several cancers and can be prioritized for additional validation and investigations

BiomMiner: An advanced exploratory microbiome analysis and visualization pipeline.

Current microbiome applications require substantial bioinformatics expertise to execute. As microbiome clinical diagnostics are being developed, there is a critical need to implement computational tools and applications that are user-friendly for the medical community to understand microbiome correlation with the health. To address this need, we have developed BiomMiner (pronounced as "biominer"), an automated pipeline that provides a comprehensive analysis of microbiome data. The pipeline finds taxonomic signatures of microbiome data and compiles actionable clinical report that allows clinicians and biomedical scientists to efficiently perform statistical analysis and data mining on the large microbiome datasets. BiomMiner generates web-enabled visualization of the analysis results and is specifically designed to facilitate the use of microbiome datasets in clinical applications

Census-based rapid and accurate metagenome taxonomic profiling

Background: Understanding the taxonomic composition of a sample, whether from patient, food or environment, is important to several types of studies including pathogen diagnostics, epidemiological studies, biodiversity analysis and food quality regulation. With the decreasing costs of sequencing, metagenomic data is quickly becoming the preferred typed of data for such analysis. Results: Rapidly defining the taxonomic composition (both taxonomic profile and relative frequency) in a metagenomic sequence dataset is challenging because the task of mapping millions of sequence reads from a metagenomic study to a non-redundant nucleotide database such as the NCBI non-redundant nucleotide database (nt) is a computationally intensive task. We have developed a robust subsampling-based algorithm implemented in a tool called CensuScope meant to take a ‘sneak peak’ into the population distribution and estimate taxonomic composition as if a census was taken of the metagenomic landscape. CensuScope is a rapid and accurate metagenome taxonomic profiling tool that randomly extracts a small number of reads (based on user input) and maps them to NCBI’s nt database. This process is repeated multiple times to ascertain the taxonomic composition that is found in majority of the iterations, thereby providing a robust estimate of the population and measures of the accuracy for the results. Conclusion: CensuScope can be run on a laptop or on a high-performance computer. Based on our analysis we are able to provide some recommendations in terms of the number of sequence reads to analyze and the number of iterations to use. For example, to quantify taxonomic groups present in the sample at a level of 1% or higher a subsampling size of 250 random reads with 50 iterations yields a statistical power of >99%.12 page(s

Expression of Inflammation-Related Genes Is Altered in Gastric Tissue of Patients with Advanced Stages of NAFLD

Obesity is associated with chronic low-grade inflammation perpetuated by visceral adipose. Other organs, particularly stomach and intestine, may also overproduce proinflammatory molecules. We examined the gene expression patterns in gastric tissue of morbidly obese patients with nonalcoholic fatty liver disease (NAFLD) and compared the changes in gene expression in different histological forms of NAFLD. Stomach tissue samples from 20 morbidly obese NAFLD patients who were undergoing sleeve gastrectomy were profiled using qPCR for 84 genes encoding inflammatory cytokines, chemokines, their receptors, and other components of inflammatory cascades. Interleukin 8 receptor-beta (IL8RB) gene overexpression in gastric tissue was correlated with the presence of hepatic steatosis, hepatic fibrosis, and histologic diagnosis of nonalcoholic steatohepatitis (NASH). Expression levels of soluble interleukin 1 receptor antagonist (IL1RN) were correlated with the presence of NASH and hepatic fibrosis. mRNA levels of interleukin 8 (IL8), chemokine (C-C motif) ligand 4 (CCL4), and its receptor chemokine (C-C motif) receptor type 5 (CCR5) showed a significant increase in patients with advanced hepatic inflammation and were correlated with the severity of the hepatic inflammation. The results of our study suggest that changes in expression patterns for inflammatory molecule encoding genes within gastric tissue may contribute to the pathogenesis of obesity-related NAFLD

Gut Microbial Signature of Hepatocellular Cancer in Men With Cirrhosis.

The gut microbiome is altered in cirrhosis. Recent evidence has suggested a key role for the gut microbiota in the progression of cirrhosis and the development of hepatocellular carcinoma (HCC). We studied the differences in the microbial composition in patients with cirrhosis with prior and future HCC in the context of other complications (eg, infections, hepatic encephalopathy). The following 2 cohorts were recruited prospectively: the prior HCC cohort, in which outpatients with HCC within 2 years were age-matched, sex-matched, and Model for End-Stage Liver Disease (MELD) score-matched with those without HCC; and the future HCC cohort, in which patients were followed for 2 years and divided into future HCC versus no HCC after age, sex, and MELD-score matching and other complications were also recorded. Microbiota composition and predicted function were analyzed with ribosomal RNA sequencing and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PiCRUST)and compared between (1) prior HCC versus none and (2) future HCC versus none, and in the future cohort, comparisons were also made between those patients who developed (1) HCC only versus without complications, (2) HCC only versus non-HCC complications only, and (3) HCC + other complications versus non-HCC complications only. A total of 142 men (76 total in the prior cohort [38 with/38 without HCC] and 66 total in the future cohort [33 with/33 without future HCC]) were included. The groups had similar etiology, lactulose/rifaximin/proton pump inhibitor use, diabetes mellitus, and non-HCC complications. Microbial diversity was similar between prior HCC/not or future HCC/not. On DESeq2 higher Clostridium sensu stricto and Anaerotruncus were significantly associated with protection from HCC, whereas the reverse was seen with Raoultella and Haemophilus regardless of prior/future HCC comparisons. Functions focused on urea cycle, bioenergetics, tryptophan, and toluene metabolism were different between the groups. Rothia was specific for other complications. Despite age, sex, and MELD-score matching and accounting for other complications, gut microbiota composition and the predicted function are different in men with cirrhosis with and without prior HCC and can be extended toward future HCC development

Genetically diverse mouse models of SARS-CoV-2 infection reproduce clinical variation in type I interferon and cytokine responses in COVID-19

Abstract Inflammation in response to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection drives severity of coronavirus disease 2019 (COVID-19) and is influenced by host genetics. To understand mechanisms of inflammation, animal models that reflect genetic diversity and clinical outcomes observed in humans are needed. We report a mouse panel comprising the genetically diverse Collaborative Cross (CC) founder strains crossed to human ACE2 transgenic mice (K18-hACE2) that confers susceptibility to SARS-CoV-2. Infection of CC x K18-hACE2 resulted in a spectrum of survival, viral replication kinetics, and immune profiles. Importantly, in contrast to the K18-hACE2 model, early type I interferon (IFN-I) and regulated proinflammatory responses were required for control of SARS-CoV-2 replication in PWK x K18-hACE2 mice that were highly resistant to disease. Thus, virus dynamics and inflammation observed in COVID-19 can be modeled in diverse mouse strains that provide a genetically tractable platform for understanding anti-coronavirus immunity