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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 . Single cell RNA sequencing (scRNA-Seq) is widely used in biomedical research. Here the authors develop a novel AI model-AutoClass, which effectively cleans a wide range of noise and artifacts in scRNA-Seq data and improves downstream analyses.

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 A s - and C p -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 ( Pc 91). 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.

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.

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.

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-β, 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.

Core Ideas We constructed a hexaploid oat consensus map from 12 populations representing 19 parents. The map represents the most common physical chromosome arrangements in oat. Deviations from the consensus map may indicate physical rearrangements. Large chromosomal translocations vary among different varieties. There is regional synteny with rice but considerable subgenome rearrangement. Hexaploid oat (Avena sativa L., 2n = 6x = 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 (Oryza sativa 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.

The focus for the use of bioinformatics resources in the pharmaceutical industry is increasingly moving from the vigorous pursuit of intellectual property towards exploration of pre-competitive collaborations and engagement with the public domain. In this article, we discuss the rationale for these changes and the associated challenges, and also propose new areas of public–private collaboration in computational biology and chemistry that could enhance drug discovery in academia and industry. 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.

Abstract Single cell RNA sequencing (scRNA-Seq) has been widely used in biomedical research and generated enormous volume and diversity of data. The raw data contain multiple types of noise and technical artifacts and need thorough cleaning. The 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. We designed and developed 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 free of distribution assumptions, hence can effectively clean a wide range of noises 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. Competing Interest Statement The authors have declared no competing interest. Footnotes * github URL

Glyceollins, a family of phytoalexin induced in legume species, play essential roles in responding to environmental stresses and in human health. However, little is known about the genetic basis and selection of glyceollin induction. We employed a metabolite-based genome-wide association (mGWA) approach to identify candidate genes involved in glyceollin induction from genetically diverse and understudied wild soybeans subjected to soybean cyst nematode stress. Eight SNPs on chromosomes 3, 9, 13, 15, and 20 showed significant association with glyceollin induction. Six genes close to one of the significant SNPs (ss715603454) on chromosome 9 fell into two clusters, and they encode enzymes in the glycosyltransferase class within the phenylpropanoid pathway. Transcription factors (TFs) genes, such as MYB and WRKY were also found within the linkage disequilibrium of the significant SNPs on chromosome 9. Epistasis and a strong selection signal were detected on the four significant SNPs on chromosome 9. Gene clusters and transcription factors may play important roles in regulating glyceollin induction in wild soybeans. Additionally, as major evolutionary factors, epistatic interactions and selection may influence glyceollin variation in natural populations.Competing Interest StatementThe authors have declared no competing interest.

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 mono-associated with adherent/invasive Escherichia coli (AIEC) that harbor 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 sub-epithelial 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 pro-fibrotic environment in the host in the absence of binding to its cognate receptor and indicates a direct link between intestinal AIEC and the induction of inflammation-associated fibrosis.