MetaCerberus: distributed highly parallelized HMM-based processing for robust functional annotation across the tree of life.

MOTIVATION: MetaCerberus is a massively parallel, fast, low memory, scalable annotation tool for inference gene function across genomes to metacommunities. MetaCerberus provides an elusive HMM/HMMER-based tool at a rapid scale with low memory. It offers scalable gene elucidation to major public databases, including KEGG (KO), COGs, CAZy, FOAM, and specific databases for viruses, including VOGs and PHROGs, from single genomes to metacommunities. RESULTS: MetaCerberus is 1.3× as fast on a single node than eggNOG-mapper v2 on 5× less memory using an exclusively HMM/HMMER mode. In a direct comparison, MetaCerberus provides better annotation of viruses, phages, and archaeal viruses than DRAM, Prokka, or InterProScan. MetaCerberus annotates more KOs across domains when compared to DRAM, with a 186× smaller database, and with 63× less memory. MetaCerberus is fully integrated for automatic analysis of statistics and pathways using differential statistic tools (i.e. DESeq2 and edgeR), pathway enrichment (GAGE R), and pathview R. MetaCerberus provides a novel tool for unlocking the biosphere across the tree of life at scale. AVAILABILITY AND IMPLEMENTATION: MetaCerberus is written in Python and distributed under a BSD-3 license. The source code of MetaCerberus is freely available at https://github.com/raw-lab/metacerberus compatible with Python 3 and works on both Mac OS X and Linux. MetaCerberus can also be easily installed using bioconda: mamba create -n metacerberus -c bioconda -c conda-forge metacerberus.

Functional recovery following traumatic brain injury in rats is enhanced by oral supplementation with bovine thymus extract.

Traumatic brain injury (TBI) is one of the leading causes of death worldwide. There are currently no effective treatments for TBI, and trauma survivors suffer from a variety of long-lasting health consequences. With nutritional support recently emerging as a vital step in improving TBI patients' outcomes, we sought to evaluate the potential therapeutic benefits of nutritional supplements derived from bovine thymus gland, which can deliver a variety of nutrients and bioactive molecules. In a rat model of controlled cortical impact (CCI), we determined that animals supplemented with a nuclear fraction of bovine thymus (TNF) display greatly improved performance on beam balance and spatial memory tests following CCI. Using RNA-Seq, we identified an array of signaling pathways that are modulated by TNF supplementation in rat hippocampus, including those involved in the process of autophagy. We further show that bovine thymus-derived extracts contain antigens found in neural tissues and that supplementation of rats with thymus extracts induces production of serum IgG antibodies against neuronal and glial antigens, which may explain the enhanced animal recovery following CCI through possible oral tolerance mechanism. Collectively, our data demonstrate, for the first time, the potency of a nutritional supplement containing nuclear fraction of bovine thymus in enhancing the functional recovery from TBI.

Repression of MUC1 Promotes Expansion and Suppressive Function of Myeloid-Derived Suppressor Cells in Pancreatic and Breast Cancer Murine Models.

Myeloid-derived suppressor cells (MDSCs) are immature myeloid cells that are responsible for immunosuppression in tumor microenvironment. Here we report the impact of mucin 1 (MUC1), a transmembrane glycoprotein, on proliferation and functional activity of MDSCs. To determine the role of MUC1 in MDSC phenotype, we analyzed MDSCs derived from wild type (WT) and MUC1-knockout (MUC1KO) mice bearing syngeneic pancreatic (KCKO) or breast (C57MG) tumors. We observed enhanced tumor growth of pancreatic and breast tumors in the MUC1KO mice compared to the WT mice. Enhanced tumor growth in the MUC1KO mice was associated with increased numbers of suppressive MDSCs and T regulatory (Tregs) cells in the tumor microenvironment. Compared to the WT host, MUC1KO host showed higher levels of iNOS, ARG1, and TGF-ß, thus promoting proliferation of MDSCs with an immature and immune suppressive phenotype. When co-cultured with effector T cells, MDSCs from MUC1KO mice led to higher repression of IL-2 and IFN-γ production by T cells as compared to MDSCs from WT mice. Lastly, MDSCs from MUC1KO mice showed higher levels of c-Myc and activated pSTAT3 as compared to MDSCs from WT mice, suggesting increased survival, proliferation, and prevention of maturation of MDSCs in the MUC1KO host. We report diminished T cell function in the KO versus WT mice. In summary, the data suggest that MUC1 may regulate signaling pathways that are critical to maintain the immunosuppressive properties of MDSCs.

Genomic insights from the first chromosome-scale assemblies of oat (Avena spp.) diploid species.

BACKGROUND: Cultivated hexaploid oat (Common oat; Avena sativa) has held a significant place within the global crop community for centuries; although its cultivation has decreased over the past century, its nutritional benefits have garnered increased interest for human consumption. We report the development of fully annotated, chromosome-scale assemblies for the extant progenitor species of the As- and Cp-subgenomes, Avena atlantica and Avena eriantha respectively. The diploid Avena species serve as important genetic resources for improving common oat's adaptive and food quality characteristics. RESULTS: The A. atlantica and A. eriantha genome assemblies span 3.69 and 3.78 Gb with an N50 of 513 and 535 Mb, respectively. Annotation of the genomes, using sequenced transcriptomes, identified ~ 50,000 gene models in each species-including 2965 resistance gene analogs across both species. Analysis of these assemblies classified much of each genome as repetitive sequence (~ 83%), including species-specific, centromeric-specific, and telomeric-specific repeats. LTR retrotransposons make up most of the classified elements. Genome-wide syntenic comparisons with other members of the Pooideae revealed orthologous relationships, while comparisons with genetic maps from common oat clarified subgenome origins for each of the 21 hexaploid linkage groups. The utility of the diploid genomes was demonstrated by identifying putative candidate genes for flowering time (HD3A) and crown rust resistance (Pc91). We also investigate the phylogenetic relationships among other A- and C-genome Avena species. CONCLUSIONS: The genomes we report here are the first chromosome-scale assemblies for the tribe Poeae, subtribe Aveninae. Our analyses provide important insight into the evolution and complexity of common hexaploid oat, including subgenome origin, homoeologous relationships, and major intra- and intergenomic rearrangements. They also provide the annotation framework needed to accelerate gene discovery and plant breeding.

Yersiniabactin-Producing Adherent/Invasive Escherichia coli Promotes Inflammation-Associated Fibrosis in Gnotobiotic Il10-/- Mice.

Fibrosis is a significant complication of intestinal disorders associated with microbial dysbiosis and pathobiont expansion, notably Crohn's disease (CD). Mechanisms that favor fibrosis are not well understood, and therapeutic strategies are limited. Here we demonstrate that colitis-susceptible Il10-deficient mice develop inflammation-associated fibrosis when monoassociated with adherent/invasive Escherichia coli (AIEC) that harbors the yersiniabactin (Ybt) pathogenicity island. Inactivation of Ybt siderophore production in AIEC nearly abrogated fibrosis development in inflamed mice. In contrast, inactivation of Ybt import through its cognate receptor FyuA enhanced fibrosis severity. This corresponded with increased colonic expression of profibrogenic genes prior to the development of histological disease, therefore suggesting causality. fyuA-deficient AIEC also exhibited greater localization within subepithelial tissues and fibrotic lesions that was dependent on Ybt biosynthesis and corresponded with increased fibroblast activation in vitro Together, these findings suggest that Ybt establishes a profibrotic environment in the host in the absence of binding to its cognate receptor and indicate a direct link between intestinal AIEC and the induction of inflammation-associated fibrosis.

LinkageMapView-rendering high-resolution linkage and QTL maps.

MOTIVATION: Linkage and quantitative trait loci (QTL) maps are critical tools for the study of the genetic basis of complex traits. With the advances in sequencing technology over the past decade, linkage map densities have been increasing dramatically, while the visualization tools have not kept pace. LinkageMapView is a free add-on package written in R that produces high resolution, publication-ready visualizations of linkage and QTL maps. While there is software available to generate linkage map graphics, none are freely available, produce publication quality figures, are open source and can run on all platforms. LinkageMapView can be integrated into map building pipelines as it seamlessly incorporates output from R/qtl and also accepts simple text or comma delimited files. There are numerous options within the package to build highly customizable maps, allow for linkage group comparisons, and annotate QTL regions. AVAILABILITY AND IMPLEMENTATION: https://cran.r-project.org/web/packages/LinkageMapView/.

A novel catechol-O-methyltransferase variant associated with human disc degeneration.

BACKGROUND: Disc degeneration and its associated low back pain are a major health care concern causing disability with a prominent role in this country's medical, social and economic structure. Low back pain is devastating and influences the quality of life for millions. Low back pain lifetime prevalence approximates 80% with an estimated direct cost burden of $86 billion per year. Back pain patients incur higher costs, greater health care utilization, and greater work loss than patients without back pain. METHODS: Research was performed following approval of our Institutional Review Board. DNA was isolated, processed and amplified using routine techniques. Amplified DNA was hybridized to Affymetrix Genome-Wide Human SNP Arrays. Quality control and genotyping analysis were performed using Affymetrix Genotyping Console. The Birdseed v2 algorithm was used for genotyping analysis. 2589 SNPs were selected a priori to enter statistical analysis using lotistic regression in SAS. RESULTS: Our objective was to search for novel single nucleotide polymorphisms (SNPs) associated with disc degeneration. Four SNPs were found to have a significant relationship to disc degeneration; three are novel. Rs165656, a new SNP found to be associated with disc degeneration, was in catechol-O-methyltransferase (COMT), a gene with well-recognized pain involvement, especially in female subjects (p=0.01). Analysis confirmed the previously association between COMT SNP rs4633 and disc degeneration. We also report two novel disc degeneration-related SNPs (rs2095019 and rs470859) located in intergenic regions upstream to thrombospondin 2. CONCLUSIONS: Findings contribute to the challenging field of disc degeneration and pain, and are important in light of the high clinical relevance of low back pain and the need for improved understanding of its fundamental basis.

Genetic basis and selection of glyceollin elicitation in wild soybean.

Glyceollins, a family of phytoalexins elicited in legume species, play crucial roles in environmental stress response (e.g., defending against pathogens) and human health. However, little is known about the genetic basis of glyceollin elicitation. In the present study, we employed a metabolite-based genome-wide association (mGWA) approach to identify candidate genes involved in glyceollin elicitation in genetically diverse and understudied wild soybeans subjected to soybean cyst nematode. In total, eight SNPs on chromosomes 3, 9, 13, 15, and 20 showed significant associations with glyceollin elicitation. Six genes fell into two gene clusters that encode glycosyltransferases in the phenylpropanoid pathway and were physically close to one of the significant SNPs (ss715603454) on chromosome 9. Additionally, transcription factors (TFs) genes such as MYB and WRKY were also found as promising candidate genes within close linkage to significant SNPs on chromosome 9. Notably, four significant SNPs on chromosome 9 show epistasis and a strong signal for selection. The findings describe the genetic foundation of glyceollin biosynthesis in wild soybeans; the identified genes are predicted to play a significant role in glyceollin elicitation regulation in wild soybeans. Additionally, how the epistatic interactions and selection influence glyceollin variation in natural populations deserves further investigation to elucidate the molecular mechanism of glyceollin biosynthesis.

A universal deep neural network for in-depth cleaning of single-cell RNA-Seq data.

Single cell RNA sequencing (scRNA-Seq) is being widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts, which need thorough cleaning. Existing denoising and imputation methods largely focus on a single type of noise (i.e., dropouts) and have strong distribution assumptions which greatly limit their performance and application. Here we design and develop the AutoClass model, integrating two deep neural network components, an autoencoder, and a classifier, as to maximize both noise removal and signal retention. AutoClass is distribution agnostic as it makes no assumption on specific data distributions, hence can effectively clean a wide range of noise and artifacts. AutoClass outperforms the state-of-art methods in multiple types of scRNA-Seq data analyses, including data recovery, differential expression analysis, clustering analysis, and batch effect removal. Importantly, AutoClass is robust on key hyperparameter settings including bottleneck layer size, pre-clustering number and classifier weight. We have made AutoClass open source at: https://github.com/datapplab/AutoClass .

Identification of potential biomarkers and novel therapeutic targets through genomic analysis of small cell bladder carcinoma and associated clinical outcomes.

OBJECTIVES: Small cell bladder carcinoma (SCBC) represents a rare histologic variant with a poor prognosis and for which no routine biomarkers exist. Limited reports of genomic sequencing in SCBC have demonstrated a high prevalence of TP53 and RB1 gene mutations, though the prognostic value of these and other gene variants in SCBC remains undefined. In this study, we performed targeted genomic sequencing on a cohort of SCBC patients and correlated genomic findings with clinical outcomes to identify potential novel biomarkers. MATERIALS AND METHODS: Thirty-one patients with SCBC and available treatment-naïve tumor specimens were identified from an institutional database (23 limited stage [LS], 8 extensive stage [ES]). Small cell carcinoma specimens were microdissected and subjected to tumor next-generation whole-exon sequencing with a 592 gene panel. Kaplan-Meier techniques and Cox proportional hazards models were used to evaluate genomic aberration association with relapse-free survival (RFS) and overall survival (OS) in the limited stage cohort. RESULTS: The most common pathogenic gene variants included ARID1A (48%), TP53 (48%) and RB1 (48%). Mutations in genes with potential therapeutic targets not routinely evaluated in SCBC included BRCA1/2 (16%), POLE (13%), JAK2 (13%), PDGFB (13%) and FGFR3 (3%). Multiple novel biomarker candidates showed trends for improvements in OS in the LS subset including ERCC2 (HR 0.322, P = 0.122) and RB1 (HR 0.481, P = 0.182), while LS patients with TP53 mutations (HR 2.730, P = 0.056), and MCL1 gene amplification (HR 4.183, P = 0.018) suggested inferior OS. Additionally, gene or copy number variants with potential prognostic benefit included UBR5 and DAXX (P = 0.02, [hazard ratios nonestimable due to zero events in biomarker positive groups]). CONCLUSIONS: These results support the role for tumor genomic profiling in SCBC and identify multiple potential novel biomarkers and therapeutic targets in this rare disease. Efforts to validate these findings should lead to improved decision-making and treatment outcomes in SCBC.

Genomic analysis of response to bacillus Calmette-Guérin (BCG) treatment in high-grade stage 1 bladder cancer patients.

BACKGROUND: Intravesical bacillus Calmette-Guérin (BCG) therapy is standard treatment for high-risk non-muscle invasive bladder cancer (NMIBC) but overall efficacy is low, and no reliable predictive biomarkers currently exist to refine patient selection. We performed genomic analysis on high-grade (HG) T1 NMIBCs to determine if response to therapy is predicted by certain mutational and/or expressional changes. METHODS: Patients with HG T1 NMIBC treated with induction BCG were stratified by response into durable and non-durable responders. Baseline tumor samples were subjected to targeted DNA sequencing and whole-exome RNAseq. Genomic variants differing significantly between response groups were analyzed using Ingenuity Pathway Analysis (IPA) software. Variant selection was refined to target potential biomarker candidates for responsiveness to BCG. RESULTS: Among 42 patients, the median follow-up was 51.7 months and 40.5% (n=17) were durable BCG responders. Deleterious mutations in the RNA sequence of JCHAIN, S100A7, CLEC2B, and ANXA10 were more common in non-durable responders. Mutations in MCL1 and MSH6 detected on targeted sequencing were more commonly found in durable responders. Of all deleterious DNA and RNA mutations identified, only MCL1 was significantly associated with longer recurrence free survival (RFS) (P=0.031). CONCLUSIONS: Differences in the genomic profiles of HG T1 NMIBC tumors exist between those who show durable response to BCG and those who do not. Using pathway analysis, those differences imply upregulation of several interconnected inflammatory pathways among responders. Specific variants identified here, namely MCL1, are candidates for further study and, if clinically validated, may serve as useful biomarkers in the future.

Dietary iron variably modulates assembly of the intestinal microbiota in colitis-resistant and colitis-susceptible mice.

Iron deficiency, a common comorbidity of gastrointestinal inflammatory disorders such as inflammatory bowel diseases (IBD), is often treated with oral iron supplementation. However, the safety of oral iron supplementation remains controversial because of its association with exacerbated disease activity in a subset of IBD patients. Because iron modulates bacterial growth and function, one possible mechanism by which iron may exacerbate inflammation in susceptible hosts is by modulating the intestinal microbiota. We, therefore, investigated the impact of dietary iron on the intestinal microbiota, utilizing the conventionalization of germ-free mice as a model of a microbial community in compositional flux to recapitulate the instability of the IBD-associated intestinal microbiota. Our findings demonstrate that altering intestinal iron availability during community assembly modulated the microbiota in non-inflamed wild type (WT) and colitis-susceptible interleukin-10-deficient (Il10-/-) mice. Depletion of luminal iron availability promoted luminal compositional changes associated with dysbiotic states irrespective of host genotype, including an expansion of Enterobacteriaceae such as Escherichia coli. Mechanistic in vitro growth competitions confirmed that high-affinity iron acquisition systems in E. coli enhance its abundance over other bacteria in iron-restricted conditions, thereby enabling pathobiont iron scavenging during dietary iron restriction. In contrast, distinct luminal community assembly was observed with dietary iron supplementation in WT versus Il10-/- mice, suggesting that the effects of increased iron on the microbiota differ with host inflammation status. Taken together, shifts in dietary iron intake during community assembly modulate the ecological structure of the intestinal microbiota and is dependent on host genotype and inflammation status.

Connecting nutrition composition measures to biomedical research.

OBJECTIVES: Biomedical research is gaining ground on human disease through many types of "omics", which is leading to increasingly effective treatments and broad applications for precision medicine. The majority of disease treatments still revolve around drugs and biologics. Although food is consumed in much higher quantities, we understand very little about how the human body metabolizes and uses the full range of nutrients, or how these processes affect human health and disease risk. Nutrient composition databases are used by dietitians to describe common consumer food products, but these fail to identify chemicals with the same nomenclature as metabolic pathways in basic life sciences research and with far less precision. Consumer-oriented nutrient compositions often describe generic substances (e.g. Sugars) while scientific reporting is often much more specific (e.g. Dextrose, Fructose, etc.). Integrating these two fields of research presents a difficult challenge for novel applications of precision nutrition. DATA DESCRIPTION: This data set provides a manually curated collection of nutrient identifiers from the USDA's Nutrition Data Bases and maps them to PubChem (a resource for cheminformatics and drug discovery research), biomedical literature records in PubMed using Medical Subject Headings, biological pathways using the Chemical Entities of Biological Interest ontology.

Systematic reconstruction of autism biology from massive genetic mutation profiles.

Autism spectrum disorder (ASD) affects 1% of world population and has become a pressing medical and social problem worldwide. As a paradigmatic complex genetic disease, ASD has been intensively studied and thousands of gene mutations have been reported. Because these mutations rarely recur, it is difficult to (i) pinpoint the fewer disease-causing versus majority random events and (ii) replicate or verify independent studies. A coherent and systematic understanding of autism biology has not been achieved. We analyzed 3392 and 4792 autism-related mutations from two large-scale whole-exome studies across multiple resolution levels, that is, variants (single-nucleotide), genes (protein-coding unit), and pathways (molecular module). These mutations do not recur or replicate at the variant level, but significantly and increasingly do so at gene and pathway levels. Genetic association reveals a novel gene + pathway dual-hit model, where the mutation burden becomes less relevant. In multiple independent analyses, hundreds of variants or genes repeatedly converge to several canonical pathways, either novel or literature-supported. These pathways define recurrent and systematic ASD biology, distinct from previously reported gene groups or networks. They also present a catalog of novel ASD risk factors including 118 variants and 72 genes. At a subpathway level, most variants disrupt the pathway-related gene functions, and in the same gene, they tend to hit residues extremely close to each other and in the same domain. Multiple interacting variants spotlight key modules, including the cAMP (adenosine 3',5'-monophosphate) second-messenger system and mGluR (metabotropic glutamate receptor) signaling regulation by GRKs (G protein-coupled receptor kinases). At a superpathway level, distinct pathways further interconnect and converge to three biology themes: synaptic function, morphology, and plasticity.

A Consensus Map in Cultivated Hexaploid Oat Reveals Conserved Grass Synteny with Substantial Subgenome Rearrangement.

Hexaploid oat ( L., 2 = 6 = 42) is a member of the Poaceae family and has a large genome (∼12.5 Gb) containing 21 chromosome pairs from three ancestral genomes. Physical rearrangements among parental genomes have hindered the development of linkage maps in this species. The objective of this work was to develop a single high-density consensus linkage map that is representative of the majority of commonly grown oat varieties. Data from a cDNA-derived single-nucleotide polymorphism (SNP) array and genotyping-by-sequencing (GBS) were collected from the progeny of 12 biparental recombinant inbred line populations derived from 19 parents representing oat germplasm cultivated primarily in North America. Linkage groups from all mapping populations were compared to identify 21 clusters of conserved collinearity. Linkage groups within each cluster were then merged into 21 consensus chromosomes, generating a framework consensus map of 7202 markers spanning 2843 cM. An additional 9678 markers were placed on this map with a lower degree of certainty. Assignment to physical chromosomes with high confidence was made for nine chromosomes. Comparison of homeologous regions among oat chromosomes and matches to orthologous regions of rice ( L.) reveal that the hexaploid oat genome has been highly rearranged relative to its ancestral diploid genomes as a result of frequent translocations among chromosomes. Heterogeneous chromosome rearrangements among populations were also evident, probably accounting for the failure of some linkage groups to match the consensus. This work contributes to a further understanding of the organization and evolution of hexaploid grass genomes.

A need for a transdisciplinary environment: the Plant Pathways Elucidation Project.

The Plant Pathways Elucidation Project (P2EP) is a multi-institutional project that utilizes cutting-edge genomics research and related disciplines to provide greater understanding of the relation between plant-pathway products and human health. P2EP includes an educational focus to expose student scholars to the rigors of research, while harnessing open collaborations between academia and industry.

Lowering industry firewalls: pre-competitive informatics initiatives in drug discovery.

Pharmaceutical research and development is facing substantial challenges that have prompted the industry to shift funding from early- to late-stage projects. Among the effects is a major change in the attitude of many companies to their internal bioinformatics resources: the focus has moved from the vigorous pursuit of intellectual property towards exploration of pre-competitive cross-industry collaborations and engagement with the public domain. High-quality, open and accessible data are the foundation of pre-competitive research, and strong public-private partnerships have considerable potential to enhance public data resources, which would benefit everyone engaged in drug discovery. In this article, we discuss the background to these changes and propose new areas of collaboration in computational biology and chemistry between the public domain and the pharmaceutical industry.