An approach for collaborative development of a federated biomedical knowledge graph-based question-answering system: Question-of-the-Month challenges.

Knowledge graphs have become a common approach for knowledge representation. Yet, the application of graph methodology is elusive due to the sheer number and complexity of knowledge sources. In addition, semantic incompatibilities hinder efforts to harmonize and integrate across these diverse sources. As part of The Biomedical Translator Consortium, we have developed a knowledge graph-based question-answering system designed to augment human reasoning and accelerate translational scientific discovery: the Translator system. We have applied the Translator system to answer biomedical questions in the context of a broad array of diseases and syndromes, including Fanconi anemia, primary ciliary dyskinesia, multiple sclerosis, and others. A variety of collaborative approaches have been used to research and develop the Translator system. One recent approach involved the establishment of a monthly "Question-of-the-Month (QotM) Challenge" series. Herein, we describe the structure of the QotM Challenge; the six challenges that have been conducted to date on drug-induced liver injury, cannabidiol toxicity, coronavirus infection, diabetes, psoriatic arthritis, and ATP1A3-related phenotypes; the scientific insights that have been gleaned during the challenges; and the technical issues that were identified over the course of the challenges and that can now be addressed to foster further development of the prototype Translator system. We close with a discussion on Large Language Models such as ChatGPT and highlight differences between those models and the Translator system.

ARAX: a graph-based modular reasoning tool for translational biomedicine.

MOTIVATION: With the rapidly growing volume of knowledge and data in biomedical databases, improved methods for knowledge-graph-based computational reasoning are needed in order to answer translational questions. Previous efforts to solve such challenging computational reasoning problems have contributed tools and approaches, but progress has been hindered by the lack of an expressive analysis workflow language for translational reasoning and by the lack of a reasoning engine-supporting that language-that federates semantically integrated knowledge-bases. RESULTS: We introduce ARAX, a new reasoning system for translational biomedicine that provides a web browser user interface and an application programming interface (API). ARAX enables users to encode translational biomedical questions and to integrate knowledge across sources to answer the user's query and facilitate exploration of results. For ARAX, we developed new approaches to query planning, knowledge-gathering, reasoning and result ranking and dynamically integrate knowledge providers for answering biomedical questions. To illustrate ARAX's application and utility in specific disease contexts, we present several use-case examples. AVAILABILITY AND IMPLEMENTATION: The source code and technical documentation for building the ARAX server-side software and its built-in knowledge database are freely available online (https://github.com/RTXteam/RTX). We provide a hosted ARAX service with a web browser interface at arax.rtx.ai and a web API endpoint at arax.rtx.ai/api/arax/v1.3/ui/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomic Analysis of MSM Rectal Chlamydia trachomatis Isolates Identifies Predicted Tissue-Tropic Lineages Generated by Intraspecies Lateral Gene Transfer-Mediated Evolution.

Chlamydia trachomatis is an obligate intracellular bacterium that causes serious diseases in humans. Rectal infection and disease caused by this pathogen are important yet understudied aspects of C. trachomatis natural history. The University of Washington Chlamydia Repository has a large collection of male-rectal-sourced strains (MSM rectal strains) isolated in Seattle, USA and Lima, Peru. Initial characterization of strains collected over 30 years in both Seattle and Lima led to an association of serovars G and J with male rectal infections. Serovar D, E, and F strains were also collected from MSM patients. Genome sequence analysis of a subset of MSM rectal strains identified a clade of serovar G and J strains that had high overall genomic identity. A genome-wide association study was then used to identify genomic loci that were correlated with tissue tropism in a collection of serovar-matched male rectal and female cervical strains. The polymorphic membrane protein PmpE had the strongest correlation, and amino acid sequence alignments identified a set of PmpE variable regions (VRs) that were correlated with host or tissue tropism. Examination of the positions of VRs by the protein structure-predicting Alphafold2 algorithm demonstrated that the VRs were often present in predicted surface-exposed loops in both PmpE and PmpH protein structure. Collectively, these studies identify possible tropism-predictive loci for MSM rectal C. trachomatis infections and identify predicted surface-exposed variable regions of Pmp proteins that may function in MSM rectal versus cervical tropism differences.

The precision medicine process for treating rare disease using the artificial intelligence tool mediKanren.

There are over 6,000 different rare diseases estimated to impact 300 million people worldwide. As genetic testing becomes more common practice in the clinical setting, the number of rare disease diagnoses will continue to increase, resulting in the need for novel treatment options. Identifying treatments for these disorders is challenging due to a limited understanding of disease mechanisms, small cohort sizes, interindividual symptom variability, and little commercial incentive to develop new treatments. A promising avenue for treatment is drug repurposing, where FDA-approved drugs are repositioned as novel treatments. However, linking disease mechanisms to drug action can be extraordinarily difficult and requires a depth of knowledge across multiple fields, which is complicated by the rapid pace of biomedical knowledge discovery. To address these challenges, The Hugh Kaul Precision Medicine Institute developed an artificial intelligence tool, mediKanren, that leverages the mechanistic insight of genetic disorders to identify therapeutic options. Using knowledge graphs, mediKanren enables an efficient way to link all relevant literature and databases. This tool has allowed for a scalable process that has been used to help over 500 rare disease families. Here, we provide a description of our process, the advantages of mediKanren, and its impact on rare disease patients.

Biolink Model: A universal schema for knowledge graphs in clinical, biomedical, and translational science.

Within clinical, biomedical, and translational science, an increasing number of projects are adopting graphs for knowledge representation. Graph-based data models elucidate the interconnectedness among core biomedical concepts, enable data structures to be easily updated, and support intuitive queries, visualizations, and inference algorithms. However, knowledge discovery across these "knowledge graphs" (KGs) has remained difficult. Data set heterogeneity and complexity; the proliferation of ad hoc data formats; poor compliance with guidelines on findability, accessibility, interoperability, and reusability; and, in particular, the lack of a universally accepted, open-access model for standardization across biomedical KGs has left the task of reconciling data sources to downstream consumers. Biolink Model is an open-source data model that can be used to formalize the relationships between data structures in translational science. It incorporates object-oriented classification and graph-oriented features. The core of the model is a set of hierarchical, interconnected classes (or categories) and relationships between them (or predicates) representing biomedical entities such as gene, disease, chemical, anatomic structure, and phenotype. The model provides class and edge attributes and associations that guide how entities should relate to one another. Here, we highlight the need for a standardized data model for KGs, describe Biolink Model, and compare it with other models. We demonstrate the utility of Biolink Model in various initiatives, including the Biomedical Data Translator Consortium and the Monarch Initiative, and show how it has supported easier integration and interoperability of biomedical KGs, bringing together knowledge from multiple sources and helping to realize the goals of translational science.

RTX-KG2: a system for building a semantically standardized knowledge graph for translational biomedicine.

BACKGROUND: Biomedical translational science is increasingly using computational reasoning on repositories of structured knowledge (such as UMLS, SemMedDB, ChEMBL, Reactome, DrugBank, and SMPDB in order to facilitate discovery of new therapeutic targets and modalities. The NCATS Biomedical Data Translator project is working to federate autonomous reasoning agents and knowledge providers within a distributed system for answering translational questions. Within that project and the broader field, there is a need for a framework that can efficiently and reproducibly build an integrated, standards-compliant, and comprehensive biomedical knowledge graph that can be downloaded in standard serialized form or queried via a public application programming interface (API). RESULTS: To create a knowledge provider system within the Translator project, we have developed RTX-KG2, an open-source software system for building-and hosting a web API for querying-a biomedical knowledge graph that uses an Extract-Transform-Load approach to integrate 70 knowledge sources (including the aforementioned core six sources) into a knowledge graph with provenance information including (where available) citations. The semantic layer and schema for RTX-KG2 follow the standard Biolink model to maximize interoperability. RTX-KG2 is currently being used by multiple Translator reasoning agents, both in its downloadable form and via its SmartAPI-registered interface. Serializations of RTX-KG2 are available for download in both the pre-canonicalized form and in canonicalized form (in which synonyms are merged). The current canonicalized version (KG2.7.3) of RTX-KG2 contains 6.4M nodes and 39.3M edges with a hierarchy of 77 relationship types from Biolink. CONCLUSION: RTX-KG2 is the first knowledge graph that integrates UMLS, SemMedDB, ChEMBL, DrugBank, Reactome, SMPDB, and 64 additional knowledge sources within a knowledge graph that conforms to the Biolink standard for its semantic layer and schema. RTX-KG2 is publicly available for querying via its API at arax.rtx.ai/api/rtxkg2/v1.2/openapi.json . The code to build RTX-KG2 is publicly available at github:RTXteam/RTX-KG2 .

Predicting chemotherapy response using a variational autoencoder approach.

BACKGROUND: Multiple studies have shown the utility of transcriptome-wide RNA-seq profiles as features for machine learning-based prediction of response to chemotherapy in cancer. While tumor transcriptome profiles are publicly available for thousands of tumors for many cancer types, a relatively modest number of tumor profiles are clinically annotated for response to chemotherapy. The paucity of labeled examples and the high dimension of the feature data limit performance for predicting therapeutic response using fully-supervised classification methods. Recently, multiple studies have established the utility of a deep neural network approach, the variational autoencoder (VAE), for generating meaningful latent features from original data. Here, we report the first study of a semi-supervised approach using VAE-encoded tumor transcriptome features and regularized gradient boosted decision trees (XGBoost) to predict chemotherapy drug response for five cancer types: colon, pancreatic, bladder, breast, and sarcoma. RESULTS: We found: (1) VAE-encoding of the tumor transcriptome preserves the cancer type identity of the tumor, suggesting preservation of biologically relevant information; and (2) as a feature-set for supervised classification to predict response-to-chemotherapy, the unsupervised VAE encoding of the tumor's gene expression profile leads to better area under the receiver operating characteristic curve and area under the precision-recall curve classification performance than the original gene expression profile or the PCA principal components or the ICA components of the gene expression profile, in four out of five cancer types that we tested. CONCLUSIONS: Given high-dimensional "omics" data, the VAE is a powerful tool for obtaining a nonlinear low-dimensional embedding; it yields features that retain biological patterns that distinguish between different types of cancer and that enable more accurate tumor transcriptome-based prediction of response to chemotherapy than would be possible using the original data or their principal components.

Exosomal proteomic signatures correlate with drug resistance and carboplatin treatment outcome in a spontaneous model of canine osteosarcoma.

BACKGROUND: Osteosarcoma patients often experience poor outcomes despite chemotherapy treatment, likely due in part to various mechanisms of tumor cell innate and/or acquired drug resistance. Exosomes, microvesicles secreted by cells, have been shown to play a role in drug resistance, but a comprehensive protein signature relating to osteosarcoma carboplatin resistance has not been fully characterized. METHODS: In this study, cell lysates and exosomes from two derivatives (HMPOS-2.5R and HMPOS-10R) of the HMPOS osteosarcoma cell line generated by repeated carboplatin treatment and recovery, were characterized proteomically by mass spectrometry. Protein cargos of circulating serum exosomes from dogs with naturally occurring osteosarcoma, were also assessed by mass spectrometry, to identify biomarkers that discriminate between good and poor responders to carboplatin therapy. RESULTS: Both cell lysates and exosomes exhibited distinct protein signatures related to drug resistance. Furthermore, exosomes from the resistant HMPOS-2.5R cell line were found to transfer drug resistance to drug-sensitive HMPOS cells. The comparison of serum exosomes from dogs with a favorable disease-free interval [DFI] of > 300 days, and dogs with < 100 days DFI revealed a proteomic signature that could discriminate between the two cohorts with high accuracy. Furthermore, when the patient's exosomes were compared to exosomes isolated from carboplatin resistant cell lines, several putative biomarkers were found to be shared. CONCLUSIONS: The findings of this study highlight the significance of exosomes in the potential transfer of drug resistance, and the discovery of novel biomarkers for the development of liquid biopsies to better guide personalized chemotherapy treatment.

Wnt signaling enhances macrophage responses to IL-4 and promotes resolution of atherosclerosis.

Atherosclerosis is a disease of chronic inflammation. We investigated the roles of the cytokines IL-4 and IL-13, the classical activators of STAT6, in the resolution of atherosclerosis inflammation. Using Il4-/-Il13-/- mice, resolution was impaired, and in control mice, in both progressing and resolving plaques, levels of IL-4 were stably low and IL-13 was undetectable. This suggested that IL-4 is required for atherosclerosis resolution, but collaborates with other factors. We had observed increased Wnt signaling in macrophages in resolving plaques, and human genetic data from others showed that a loss-of-function Wnt mutation was associated with premature atherosclerosis. We now find an inverse association between activation of Wnt signaling and disease severity in mice and humans. Wnt enhanced the expression of inflammation resolving factors after treatment with plaque-relevant low concentrations of IL-4. Mechanistically, activation of the Wnt pathway following lipid lowering potentiates IL-4 responsiveness in macrophages via a PGE2/STAT3 axis.

Voting-based integration algorithm improves causal network learning from interventional and observational data: An application to cell signaling network inference.

In order to increase statistical power for learning a causal network, data are often pooled from multiple observational and interventional experiments. However, if the direct effects of interventions are uncertain, multi-experiment data pooling can result in false causal discoveries. We present a new method, "Learn and Vote," for inferring causal interactions from multi-experiment datasets. In our method, experiment-specific networks are learned from the data and then combined by weighted averaging to construct a consensus network. Through empirical studies on synthetic and real-world datasets, we found that for most of the larger-sized network datasets that we analyzed, our method is more accurate than state-of-the-art network inference approaches.

Vitamin E Deficiency Disrupts Gene Expression Networks during Zebrafish Development.

Vitamin E (VitE) is essential for vertebrate embryogenesis, but the mechanisms involved remain unknown. To study embryonic development, we fed zebrafish adults (>55 days) either VitE sufficient (E+) or deficient (E-) diets for >80 days, then the fish were spawned to generate E+ and E- embryos. To evaluate the transcriptional basis of the metabolic and phenotypic outcomes, E+ and E- embryos at 12, 18 and 24 h post-fertilization (hpf) were subjected to gene expression profiling by RNASeq. Hierarchical clustering, over-representation analyses and gene set enrichment analyses were performed with differentially expressed genes. E- embryos experienced overall disruption to gene expression associated with gene transcription, carbohydrate and energy metabolism, intracellular signaling and the formation of embryonic structures. mTOR was apparently a major controller of these changes. Thus, embryonic VitE deficiency results in genetic and transcriptional dysregulation as early as 12 hpf, leading to metabolic dysfunction and ultimately lethal outcomes.

An approach for open multivariate analysis of integrated clinical and environmental exposures data.

The Integrated Clinical and Environmental Exposures Service (ICEES) provides regulatory-compliant open access to sensitive patient data that have been integrated with public exposures data. ICEES was designed initially to support dynamic cohort creation and bivariate contingency tests. The objective of the present study was to develop an open approach to support multivariate analyses using existing ICEES functionalities and abiding by all regulatory constraints. We first developed an open approach for generating a multivariate table that maintains contingencies between clinical and environmental variables using programmatic calls to the open ICEES application programming interface. We then applied the approach to data on a large cohort (N = 22,365) of patients with asthma or related conditions and generated an eight-feature table. Due to regulatory constraints, data loss was incurred with the incorporation of each successive feature variable, from a starting sample size of N = 22,365 to a final sample size of N = 4,556 (20.4%), but data loss was < 10% until the addition of the final two feature variables. We then applied a generalized linear model to the subsequent dataset and focused on the impact of seven select feature variables on asthma exacerbations, defined as annual emergency department or inpatient visits for respiratory issues. We identified five feature variables-sex, race, obesity, prednisone, and airborne particulate exposure-as significant predictors of asthma exacerbations. We discuss the advantages and disadvantages of ICEES open multivariate analysis and conclude that, despite limitations, ICEES can provide a valuable resource for open multivariate analysis and can serve as an exemplar for regulatory-compliant informatic solutions to open patient data, with capabilities to explore the impact of environmental exposures on health outcomes.

Prospective evaluation of the lymph node proteome in dogs with multicentric lymphoma supplemented with sulforaphane.

BACKGROUND: Lymphoma (LSA) is a common malignancy in dogs. Epigenetic changes are linked to LSA pathogenesis and poor prognosis in humans, and LSA pathogenesis in dogs. Sulforaphane (SFN), an epigenetic-targeting compound, has recently gained interest in relation to cancer prevention and therapy. OBJECTIVE: Examine the impact of oral supplementation with SFN on the lymph node proteome of dogs with multicentric LSA. ANIMALS: Seven client-owned dogs with multicentric LSA. METHODS: Prospective, nonrandomized, noncontrolled study in treatment-naïve dogs with intermediate or large cell multicentric LSA. Lymph node cell aspirates were obtained before and after 7 days of oral supplementation with SFN, and analyzed via label-free mass spectrometry, immunoblots, and Gene Set Enrichment Analysis. RESULTS: There was no clinical response and no adverse events attributed to SFN. For individual dogs, the expression of up to 650 proteins changed by at least 2-fold (range, 2-100) after supplementation with SFN. When all dogs where analyzed together, 14 proteins were significantly downregulated, and 10 proteins were significantly upregulated after supplementation with SFN (P < .05). Proteins and gene sets impacted by SFN were commonly involved in immunity, response to oxidative stress, gene transcription, apoptosis, protein transport, maturation and ubiquitination. CONCLUSIONS AND CLINICAL IMPORTANCE: Sulforaphane is associated with major changes in the proteome of neoplastic lymphocytes in dogs.

Pan-tissue transcriptome analysis of long noncoding RNAs in the American beaver Castor canadensis.

BACKGROUND: Long noncoding RNAs (lncRNAs) have roles in gene regulation, epigenetics, and molecular scaffolding and it is hypothesized that they underlie some mammalian evolutionary adaptations. However, for many mammalian species, the absence of a genome assembly precludes the comprehensive identification of lncRNAs. The genome of the American beaver (Castor canadensis) has recently been sequenced, setting the stage for the systematic identification of beaver lncRNAs and the characterization of their expression in various tissues. The objective of this study was to discover and profile polyadenylated lncRNAs in the beaver using high-throughput short-read sequencing of RNA from sixteen beaver tissues and to annotate the resulting lncRNAs based on their potential for orthology with known lncRNAs in other species. RESULTS: Using de novo transcriptome assembly, we found 9528 potential lncRNA contigs and 187 high-confidence lncRNA contigs. Of the high-confidence lncRNA contigs, 147 have no known orthologs (and thus are putative novel lncRNAs) and 40 have mammalian orthologs. The novel lncRNAs mapped to the Oregon State University (OSU) reference beaver genome with greater than 90% sequence identity. While the novel lncRNAs were on average shorter than their annotated counterparts, they were similar to the annotated lncRNAs in terms of the relationships between contig length and minimum free energy (MFE) and between coverage and contig length. We identified beaver orthologs of known lncRNAs such as XIST, MEG3, TINCR, and NIPBL-DT. We profiled the expression of the 187 high-confidence lncRNAs across 16 beaver tissues (whole blood, brain, lung, liver, heart, stomach, intestine, skeletal muscle, kidney, spleen, ovary, placenta, castor gland, tail, toe-webbing, and tongue) and identified both tissue-specific and ubiquitous lncRNAs. CONCLUSIONS: To our knowledge this is the first report of systematic identification of lncRNAs and their expression atlas in beaver. LncRNAs-both novel and those with known orthologs-are expressed in each of the beaver tissues that we analyzed. For some beaver lncRNAs with known orthologs, the tissue-specific expression patterns were phylogenetically conserved. The lncRNA sequence data files and raw sequence files are available via the web supplement and the NCBI Sequence Read Archive, respectively.

Combining eQTL and SNP Annotation Data to Identify Functional Noncoding SNPs in GWAS Trait-Associated Regions.

We describe a statistical method for prioritizing candidate causal noncoding single nucleotide polymorphisms (SNPs) in regions of the genome that are detected as trait-associated in a population-based genome-wide association study (GWAS). Our method's key step is to combine, within a naïve Bayes-like framework, three quantities for each SNP: (1) the p-value for the association test between the SNP's genotype and the trait; (2) the p-value for the SNP's cis-expression quantitative trait locus (cis-eQTL) association test; and (3) a model-based prediction score for the SNP's potential to be a regulatory SNP (rSNP). The method is flexible with respect to the source of the model-based rSNP prediction score; we demonstrate the method using scores obtained using the previously published machine-learning-based rSNP prediction method, CERENKOV2. Because it requires only the GWAS trait association test p-value for each SNP and not full genotype information, our method is applicable for GWAS secondary analysis in the common situation where only summary data (and not full genotype data) are readily available. We illustrate how the method works in step-by-step fashion.

CERENKOV3: Clustering and molecular network-derived features improve computational prediction of functional noncoding SNPs.

Identification of causal noncoding single nucleotide polymorphisms (SNPs) is important for maximizing the knowledge dividend from human genome-wide association studies (GWAS). Recently, diverse machine learning-based methods have been used for functional SNP identification; however, this task remains a fundamental challenge in computational biology. We report CERENKOV3, a machine learning pipeline that leverages clustering-derived and molecular network-derived features to improve prediction accuracy of regulatory SNPs (rSNPs) in the context of post-GWAS analysis. The clustering-derived feature, locus size (number of SNPs in the locus), derives from our locus partitioning procedure and represents the sizes of clusters based on SNP locations. We generated two molecular network-derived features from representation learning on a network representing SNP-gene and gene-gene relations. Based on empirical studies using a ground-truth SNP dataset, CERENKOV3 significantly improves rSNP recognition performance in AUPRC, AUROC, and AVGRANK (a locus-wise rank-based measure of classification accuracy we previously proposed).

Analysis of Staphylococcus infections in a veterinary teaching hospital from 2012 to 2015.

Records of all Diagnostic laboratory submissions from 2012 to 2015 were examined and subjected to analysis according to species, location of infection, species of bacteria, and antibiotic resistance/susceptibility. A total of 23.8% of all culture isolates were Staphylococcus sp. Of those Staphylococcus, 43% were isolated from surgical site infections. Staphylococcus pseudintermedius accounted for approximately 28% of all staphylococcus cultures, while methicillin-resistant (MR) S. pseudintermedius accounted for 8% of all staphylococcus cultures. Environmental samples were also collected by swabbing surfaces in the intensive care unit (ICU) and anesthesia prep room at the OSU VTH. Isolated bacterial colonies were subjected to PCR for species identification and for the presence of the mecA gene. Ability of horizontal transfer in vitro of the mecA gene was evaluated by incubating the mecA positive bacterium, with the mecA negative bacterium, and then plated onto agar plates infused with known concentration of oxacillin. Colonies were then subjected to PCR for species and mecA identification. Horizontal transfer of the mecA gene was demonstrated and confirmed via PCR from MR S. epidermidis to MS S. pseudintermedius in an in vitro model that mimicked the veterinary hospital environment. Biofilms were established using four Staphylococcus species isolated from swabbing the Intensive Care Unit (ICU) and anesthesia prep room and were resistant when exposed to the current cleaning agent. Staphylococcus species makeup nearly » of all infections at OSU VDL during the four years of the study, and MS S. pseudintermedius was shown to acquire the mecA gene from an environmental strain.

Elucidating the transcriptional program of feline injection-site sarcoma using a cross-species mRNA-sequencing approach.

BACKGROUND: Feline injection-site sarcoma (FISS), an aggressive iatrogenic subcutaneous malignancy, is challenging to manage clinically and little is known about the molecular basis of its pathogenesis. Tumor transcriptome profiling has proved valuable for gaining insights into the molecular basis of cancers and for identifying new therapeutic targets. Here, we report the first study of the FISS transcriptome and the first cross-species comparison of the FISS transcriptome with those of anatomically similar soft-tissue sarcomas in dogs and humans. METHODS: Using high-throughput short-read paired-end sequencing, we comparatively profiled FISS tumors vs. normal tissue samples as well as cultured FISS-derived cell lines vs. skin-derived fibroblasts. We analyzed the mRNA-seq data to compare cancer/normal gene expression level, identify biological processes and molecular pathways that are associated with the pathogenesis of FISS, and identify multimegabase genomic regions of potential somatic copy number alteration (SCNA) in FISS. We additionally conducted cross-species analyses to compare the transcriptome of FISS to those of soft-tissue sarcomas in dogs and humans, at the level of cancer/normal gene expression ratios. RESULTS: We found: (1) substantial differential expression biases in feline orthologs of human oncogenes and tumor suppressor genes suggesting conserved functions in FISS; (2) a genomic region with recurrent SCNA in human sarcomas that is syntenic to a feline genomic region of probable SCNA in FISS; and (3) significant overlap of the pattern of transcriptional alterations in FISS with the patterns of transcriptional alterations in soft-tissue sarcomas in humans and in dogs. We demonstrated that a protein, BarH-like homeobox 1 (BARX1), has increased expression in FISS cells at the protein level. We identified 11 drugs and four target proteins as potential new therapies for FISS, and validated that one of them (GSK-1059615) inhibits growth of FISS-derived cells in vitro. CONCLUSIONS: (1) Window-based analysis of mRNA-seq data can uncover SCNAs. (2) The transcriptome of FISS-derived cells is highly consistent with that of FISS tumors. (3) FISS is highly similar to soft-tissue sarcomas in dogs and humans, at the level of gene expression. This work underscores the potential utility of comparative oncology in improving understanding and treatment of FISS.

Single-cell analysis of fate-mapped macrophages reveals heterogeneity, including stem-like properties, during atherosclerosis progression and regression.

Atherosclerosis is a leading cause of death worldwide in industrialized countries. Disease progression and regression are associated with different activation states of macrophages derived from inflammatory monocytes entering the plaques. The features of monocyte-to-macrophage transition and the full spectrum of macrophage activation states during either plaque progression or regression, however, are incompletely established. Here, we use a combination of single-cell RNA sequencing and genetic fate mapping to profile, for the first time to our knowledge, plaque cells derived from CX3CR1+ precursors in mice during both progression and regression of atherosclerosis. The analyses revealed a spectrum of macrophage activation states with greater complexity than the traditional M1 and M2 polarization states, with progression associated with differentiation of CXC3R1+ monocytes into more distinct states than during regression. We also identified an unexpected cluster of proliferating monocytes with a stem cell-like signature, suggesting that monocytes may persist in a proliferating self-renewal state in inflamed tissue, rather than differentiating immediately into macrophages after entering the tissue.

Res2s2aM: Deep residual network-based model for identifying functional noncoding SNPs in trait-associated regions.

Noncoding single nucleotide polymorphisms (SNPs) and their target genes are important components of the heritability of diseases and other polygenic traits. Identifying these SNPs and target genes could potentially reveal new molecular mechanisms and advance precision medicine. For polygenic traits, genome-wide association studies (GWAS) are preferred tools for identifying trait-associated regions. However, identifying causal noncoding SNPs within such regions is a difficult problem in computational biology. The DNA sequence context of a noncoding SNP is well-established as an important source of information that is beneficial for discriminating functional from nonfunctional noncoding SNPs. We describe the use of a deep residual network (ResNet)-based model-entitled Res2s2aM-that fuses anking DNA sequence information with additional SNP annotation information to discriminate functional from nonfunctional noncoding SNPs. On a ground-truth set of disease-associated SNPs compiled from the Genome-wide Repository of Associations between SNPs and Phenotypes (GRASP) database, Res2s2aM improves the prediction accuracy of functional SNPs significantly in comparison to models based only on sequence information as well as a leading tool for post-GWAS noncoding SNP prioritization (RegulomeDB).