Mouse Genome Informatics (MGI): Resources for Mining Mouse Genetic, Genomic, and Biological Data in Support of Primary and Translational Research

The genomic landscape of Ménière's disease: a path to endolymphatic hydrops

BACKGROUND

Ménière's disease (MD) is a disorder of the inner ear that causes episodic bouts of severe dizziness, roaring tinnitus, and fluctuating hearing loss. To date, no targeted therapy exists. As such, we have undertaken a large whole genome sequencing study on carefully phenotyped unilateral MD patients with the goal of gene/pathway discovery and a move towards targeted intervention. This study was a retrospective review of patients with a history of Ménière's disease. Genomic DNA, acquired from saliva samples, was purified and subjected to whole genome sequencing.

RESULTS

Stringent variant calling, performed on 511 samples passing quality checks, followed by gene-based filtering by recurrence and proximity in molecular interaction networks, led to 481 high priority MD genes. These high priority genes, including MPHOSPH8, MYO18A, TRIOBP, OTOGL, TNC, and MYO6, were previously implicated in hearing loss, balance, and cochlear function, and were significantly enriched in common variant studies of hearing loss. Validation in an independent MD cohort confirmed 82 recurrent genes. Pathway analysis pointed to cell-cell adhesion, extracellular matrix, and cellular energy maintenance as key mediators of MD. Furthermore, the MD-prioritized genes were highly expressed in human inner ear hair cells and dark/vestibular cells, and were differentially expressed in a mouse model of hearing loss.

CONCLUSION

By enabling the development of model systems that may lead to targeted therapies and MD screening panels, the genes and variants identified in this study will inform diagnosis and treatment of MD.

Computational identification of disease models through cross-species phenotype comparison

The use of standardised phenotyping screens to identify abnormal phenotypes in mouse knockouts, together with the use of ontologies to describe such phenotypic features, allows the implementation of an automated and unbiased pipeline to identify new models of disease by performing phenotype comparisons across species. Using data from the International Mouse Phenotyping Consortium (IMPC), approximately half of mouse mutants are able to mimic, at least partially, the human ortholog disease phenotypes as computed by the PhenoDigm algorithm. We found the number of phenotypic abnormalities in the mouse and the corresponding Mendelian disorder, the pleiotropy and severity of the disease, and the viability and zygosity status of the mouse knockout to be associated with the ability of mouse models to recapitulate the human disorder. An analysis of the IMPC impact on disease gene discovery through a publication-tracking system revealed that the resource has been implicated in at least 109 validated rare disease-gene associations over the last decade.

Differential Correlation of Transcriptome Data Reveals Gene Pairs and Pathways Involved in Treatment of Citrobacter rodentium Infection with Bioactive Punicalagin

This study is part of the work investigating bioactive fruit enzymes as sustainable alternatives to parasite anthelmintics that can help reverse the trend of lost efficacy. The study looked to define biological and molecular interactions that demonstrate the ability of the pomegranate extract punicalagin against intracellular parasites. The study compared transcriptomic reads of two distinct conditions. Condition A was treated with punicalagin (PA) and challenged with Citrobacter rodentium, while condition B (CM) consisted of a group that was challenged and given mock treatment of PBS. To understand the effect of punicalagin on transcriptomic changes between conditions, a differential correlation analysis was conducted. The analysis examined the regulatory connections of genes expressed between different treatment conditions by statistically querying the relationship between correlated gene pairs and modules in differing conditions. The results indicated that punicalagin treatment had strong positive correlations with the over-enriched gene ontology (GO) terms related to oxidoreductase activity and lipid metabolism. However, the GO terms for immune and cytokine responses were strongly correlated with no punicalagin treatment. The results matched previous studies that showed punicalagin to have potent antioxidant and antiparasitic effects when used to treat parasitic infections in mice and livestock. Overall, the results indicated that punicalagin enhanced the effect of tissue-resident genes.

Improving the classification of cardinality phenotypes using collections

MOTIVATION

Phenotypes are observable characteristics of an organism and they can be highly variable. Information about phenotypes is collected in a clinical context to characterize disease, and is also collected in model organisms and stored in model organism databases where they are used to understand gene functions. Phenotype data is also used in computational data analysis and machine learning methods to provide novel insights into disease mechanisms and support personalized diagnosis of disease. For mammalian organisms and in a clinical context, ontologies such as the Human Phenotype Ontology and the Mammalian Phenotype Ontology are widely used to formally and precisely describe phenotypes. We specifically analyze axioms pertaining to phenotypes of collections of entities within a body, and we find that some of the axioms in phenotype ontologies lead to inferences that may not accurately reflect the underlying biological phenomena.

RESULTS

We reformulate the phenotypes of collections of entities using an ontological theory of collections. By reformulating phenotypes of collections in phenotypes ontologies, we avoid potentially incorrect inferences pertaining to the cardinality of these collections. We apply our method to two phenotype ontologies and show that the reformulation not only removes some problematic inferences but also quantitatively improves biological data analysis.

Loss-of-function variants in KCTD19 cause non-obstructive azoospermia in humans

Azoospermia is a significant cause of male infertility, with non-obstructive azoospermia (NOA) being the most severe type of spermatogenic failure. NOA is mostly caused by congenital factors, but our understanding of its genetic causes is very limited. Here, we identified a frameshift variant (c.201_202insAC, p.Tyr68Thrfs∗17) and two nonsense variants (c.1897C>T, p.Gln633∗; c.2005C>T, p.Gln669∗) in KCTD19 (potassium channel tetramerization domain containing 19) from two unrelated infertile Chinese men and a consanguineous Pakistani family with three infertile brothers. Testicular histological analyses revealed meiotic metaphase I (MMI) arrest in the affected individuals. Mice modeling KCTD19 variants recapitulated the same MMI arrest phenotype due to severe disrupted individualization of MMI chromosomes. Further analysis showed a complete loss of KCTD19 protein in both Kctd19 mutant mouse testes and affected individual testes. Collectively, our findings demonstrate the pathogenicity of the identified KCTD19 variants and highlight an essential role of KCTD19 in MMI chromosome individualization.

Repurposing ketamine to treat cocaine use disorder: integration of artificial intelligence-based prediction, expert evaluation, clinical corroboration and mechanism of action analyses

BACKGROUND AND AIMS

Cocaine use disorder (CUD) is a significant public health issue for which there is no Food and Drug Administration (FDA) approved medication. Drug repurposing looks for new cost-effective uses of approved drugs. This study presents an integrated strategy to identify repurposed FDA-approved drugs for CUD treatment.

DESIGN

Our drug repurposing strategy combines artificial intelligence (AI)-based drug prediction, expert panel review, clinical corroboration and mechanisms of action analysis being implemented in the National Drug Abuse Treatment Clinical Trials Network (CTN). Based on AI-based prediction and expert knowledge, ketamine was ranked as the top candidate for clinical corroboration via electronic health record (EHR) evaluation of CUD patient cohorts prescribed ketamine for anesthesia or depression compared with matched controls who received non-ketamine anesthesia or antidepressants/midazolam. Genetic and pathway enrichment analyses were performed to understand ketamine's potential mechanisms of action in the context of CUD.

SETTING

The study utilized TriNetX to access EHRs from more than 90 million patients world-wide. Genetic- and functional-level analyses used DisGeNet, Search Tool for Interactions of Chemicals and Kyoto Encyclopedia of Genes and Genomes databases.

PARTICIPANTS

A total of 7742 CUD patients who received anesthesia (3871 ketamine-exposed and 3871 anesthetic-controlled) and 7910 CUD patients with depression (3955 ketamine-exposed and 3955 antidepressant-controlled) were identified after propensity score-matching.

MEASUREMENTS

EHR analysis outcome was a CUD remission diagnosis within 1 year of drug prescription.

FINDINGS

Patients with CUD prescribed ketamine for anesthesia displayed a significantly higher rate of CUD remission compared with matched individuals prescribed other anesthetics [hazard ratio (HR) = 1.98, 95% confidence interval (CI) = 1.42-2.78]. Similarly, CUD patients prescribed ketamine for depression evidenced a significantly higher CUD remission ratio compared with matched patients prescribed antidepressants or midazolam (HR = 4.39, 95% CI = 2.89-6.68). The mechanism of action analysis revealed that ketamine directly targets multiple CUD-associated genes (BDNF, CNR1, DRD2, GABRA2, GABRB3, GAD1, OPRK1, OPRM1, SLC6A3, SLC6A4) and pathways implicated in neuroactive ligand-receptor interaction, cAMP signaling and cocaine abuse/dependence.

CONCLUSIONS

Ketamine appears to be a potential repurposed drug for treatment of cocaine use disorder.

Drug repurposing for reducing the risk of cataract extraction in patients with diabetes mellitus: integration of artificial intelligence-based drug prediction and clinical corroboration

Diabetes mellitus (DM) increases the incidence of age-related cataracts. Currently, no medication is approved or known to delay clinical cataract progression. Using a novel approach based on AI, we searched for drugs with potential cataract surgery-suppressing effects. We developed a drug discovery strategy that combines AI-based potential candidate prediction among 2650 Food and Drug Administration (FDA)-approved drugs with clinical corroboration leveraging multicenter electronic health records (EHRs) of approximately 800,000 cataract patients from the TriNetX platform. Among the top-10 AI-predicted repurposed candidate drugs, we identified three DM diagnostic ICD code groups, such as cataract patients with type 1 diabetes mellitus (T1DM), type 2 diabetes mellitus (T2DM), or hyperglycemia, and conducted retrospective cohort analyses to evaluate the efficacy of these candidate drugs in reducing the risk of cataract extraction. Aspirin, melatonin, and ibuprofen were associated with a reduced 5-, 10-, and 20-year cataract extraction risk in all types of diabetes. Acetylcysteine was associated with a reduced 5-, 10-, and 20-year cataract extraction risk in T2DM and hyperglycemia but not in T1DM patient groups. The suppressive effects of aspirin, acetylcysteine, and ibuprofen waned over time, while those of melatonin became stronger in both genders. Thus, the four repositioned drugs have the potential to delay cataract progression in both genders. All four drugs share the ability to directly or indirectly inhibit cyclooxygenase-2 (COX-2), an enzyme that is increased by multiple cataractogenic stimuli.

The Type 2 Diabetes Knowledge Portal: An open access genetic resource dedicated to type 2 diabetes and related traits

Associations between human genetic variation and clinical phenotypes have become a foundation of biomedical research. Most repositories of these data seek to be disease-agnostic and therefore lack disease-focused views. The Type 2 Diabetes Knowledge Portal (T2DKP) is a public resource of genetic datasets and genomic annotations dedicated to type 2 diabetes (T2D) and related traits. Here, we seek to make the T2DKP more accessible to prospective users and more useful to existing users. First, we evaluate the T2DKP's comprehensiveness by comparing its datasets with those of other repositories. Second, we describe how researchers unfamiliar with human genetic data can begin using and correctly interpreting them via the T2DKP. Third, we describe how existing users can extend their current workflows to use the full suite of tools offered by the T2DKP. We finally discuss the lessons offered by the T2DKP toward the goal of democratizing access to complex disease genetic results.

Integrin β3-Mediated Cell Senescence Associates with Gut Inflammation and Intestinal Degeneration in Models of Alzheimer's Disease

Alzheimer's disease (AD) is a neurodegenerative disorder characterized by memory loss and personality changes that ultimately lead to dementia. Currently, 50 million people worldwide suffer from dementia related to AD, and the pathogenesis underlying AD pathology and cognitive decline is unknown. While AD is primarily a neurological disease of the brain, individuals with AD often experience intestinal disorders, and gut abnormalities have been implicated as a major risk factor in the development of AD and relevant dementia. However, the mechanisms that mediate gut injury and contribute to the vicious cycle between gut abnormalities and brain injury in AD remain unknown. In the present study, a bioinformatics analysis was performed on the proteomics data of variously aged AD mouse colon tissues. We found that levels of integrin β3 and β-galactosidase (β-gal), two markers of cellular senescence, increased with age in the colonic tissue of mice with AD. The advanced artificial intelligence (AI)-based prediction of AD risk also demonstrated the association between integrin β3 and β-gal and AD phenotypes. Moreover, we showed that elevated integrin β3 levels were accompanied by senescence phenotypes and immune cell accumulation in AD mouse colonic tissue. Further, integrin β3 genetic downregulation abolished upregulated senescence markers and inflammatory responses in colonic epithelial cells in conditions associated with AD. We provide a new understanding of the molecular actions underpinning inflammatory responses during AD and suggest integrin β3 may function as novel target mediating gut abnormalities in this disease.

Mapping the common gene networks that underlie related diseases

A longstanding goal of biomedicine is to understand how alterations in molecular and cellular networks give rise to the spectrum of human diseases. For diseases with shared etiology, understanding the common causes allows for improved diagnosis of each disease, development of new therapies and more comprehensive identification of disease genes. Accordingly, this protocol describes how to evaluate the extent to which two diseases, each characterized by a set of mapped genes, are colocalized in a reference gene interaction network. This procedure uses network propagation to measure the network 'distance' between gene sets. For colocalized diseases, the network can be further analyzed to extract common gene communities at progressive granularities. In particular, we show how to: (1) obtain input gene sets and a reference gene interaction network; (2) identify common subnetworks of genes that encompass or are in close proximity to all gene sets; (3) use multiscale community detection to identify systems and pathways represented by each common subnetwork to generate a network colocalized systems map; (4) validate identified genes and systems using a mouse variant database; and (5) visualize and further investigate select genes, interactions and systems for relevance to phenotype(s) of interest. We demonstrate the utility of this approach by identifying shared biological mechanisms underlying autism and congenital heart disease. However, this protocol is general and can be applied to any gene sets attributed to diseases or other phenotypes with suspected joint association. A typical NetColoc run takes less than an hour. Software and documentation are available at https://github.com/ucsd-ccbb/NetColoc .

Genetic architecture of innate and adaptive immune cells in pigs

Pig industry is facing new challenges that make necessary to reorient breeding programs to produce more robust and resilient pig populations. The aim of the present work was to study the genetic determinism of lymphocyte subpopulations in the peripheral blood of pigs and identify genomic regions and biomarkers associated to them. For this purpose, we stained peripheral blood mononuclear cells to measure ten immune-cell-related traits including the relative abundance of different populations of lymphocytes, the proportions of CD4⁺ T cells and CD8⁺ T cells, and the ratio of CD4⁺/CD8⁺ T cells from 391 healthy Duroc piglets aged 8 weeks. Medium to high heritabilities were observed for the ten immune-cell-related traits and significant genetic correlations were obtained between the proportion of some lymphocytes populations. A genome-wide association study pointed out 32 SNPs located at four chromosomal regions on pig chromosomes SSC3, SSC5, SSC8, and SSCX as significantly associated to T-helper cells, memory T-helper cells and γδ T cells. Several genes previously identified in human association studies for the same or related traits were located in the associated regions, and were proposed as candidate genes to explain the variation of T cell populations such as CD4, CD8A, CD8B, KLRC2, RMND5A and VPS24. The transcriptome analysis of whole blood samples from animals with extreme proportions of γδ T, T-helper and memory T-helper cells identified differentially expressed genes (CAPG, TCF7L1, KLRD1 and CD4) located into the associated regions. In addition, differentially expressed genes specific of different T cells subpopulations were identified such as SOX13 and WC1 genes for γδ T cells. Our results enhance the knowledge about the genetic control of lymphocyte traits that could be considered to optimize the induction of immune responses to vaccines against pathogens. Furthermore, they open the possibility of applying effective selection programs for improving immunocompetence in pigs and support the use of the pig as a very reliable human biomedical model.

A homozygous KASH5 frameshift mutation causes diminished ovarian reserve, recurrent miscarriage, and non-obstructive azoospermia in humans

The meiosis-specific LINC complex, composed of the KASH5 and SUN1 proteins, tethers the moving chromosomes to the nuclear envelope to facilitate homolog pairing and is essential for gametogenesis. Here, we applied whole-exome sequencing for a consanguineous family with five siblings suffering from reproductive failure, and identified a homozygous frameshift mutation in KASH5 (c.1270_1273del, p.Arg424Thrfs*20). This mutation leads to the absence of KASH5 protein expression in testes and non-obstructive azoospermia (NOA) due to meiotic arrest before the pachytene stage in the affected brother. The four sisters displayed diminished ovarian reserve (DOR), with one sister never being pregnant but still having dominant follicle at 35 years old and three sisters suffering from at least 3 miscarriages occurring within the third month of gestation. The truncated KASH5 mutant protein, when expressed in cultured cells, displays a similar localization encircling the nucleus and a weakened interaction with SUN1, as compared with the full-length KASH5 proteins, which provides a potential explanation for the phenotypes in the affected females. This study reported sexual dimorphism for influence of the KASH5 mutation on human germ cell development, and extends the clinical manifestations associated with KASH5 mutations, providing genetic basis for the molecular diagnosis of NOA, DOR, and recurrent miscarriage.

The rearing environment persistently modulates mouse phenotypes from the molecular to the behavioural level

The phenotype of an organism results from its genotype and the influence of the environment throughout development. Even when using animals of the same genotype, independent studies may test animals of different phenotypes, resulting in poor replicability due to genotype-by-environment interactions. Thus, genetically defined strains of mice may respond differently to experimental treatments depending on their rearing environment. However, the extent of such phenotypic plasticity and its implications for the replicability of research findings have remained unknown. Here, we examined the extent to which common environmental differences between animal facilities modulate the phenotype of genetically homogeneous (inbred) mice. We conducted a comprehensive multicentre study, whereby inbred C57BL/6J mice from a single breeding cohort were allocated to and reared in 5 different animal facilities throughout early life and adolescence, before being transported to a single test laboratory. We found persistent effects of the rearing facility on the composition and heterogeneity of the gut microbial community. These effects were paralleled by persistent differences in body weight and in the behavioural phenotype of the mice. Furthermore, we show that environmental variation among animal facilities is strong enough to influence epigenetic patterns in neurons at the level of chromatin organisation. We detected changes in chromatin organisation in the regulatory regions of genes involved in nucleosome assembly, neuronal differentiation, synaptic plasticity, and regulation of behaviour. Our findings demonstrate that common environmental differences between animal facilities may produce facility-specific phenotypes, from the molecular to the behavioural level. Furthermore, they highlight an important limitation of inferences from single-laboratory studies and thus argue that study designs should take environmental background into account to increase the robustness and replicability of findings.

The phenotype of an organism results not only from its genotype but also the influence of its environment throughout development. This study shows that common environmental differences between animal facilities can induce substantial variation in the phenotype of mice, thereby highlighting an important limitation of inferences from single-laboratory studies in animal research.

Functional compensation of mouse duplicates by their paralogs expressed in the same tissues

Analyses in a number of organisms have shown that duplicated genes are less likely to be essential than singletons. This implies that genes can often compensate for the loss of their paralogs. However, it is unclear why the loss of some duplicates can be compensated by their paralogs, whereas the loss of other duplicates cannot. Surprisingly, initial analyses in mice did not detect differences in the essentiality of duplicates and singletons. Only subsequent analyses, using larger gene knockout datasets and controlling for a number of confounding factors, did detect significant differences. Previous studies have not taken into account the tissues in which duplicates are expressed. We hypothesized that in complex organisms, in order for a gene's loss to be compensated by one or more of its paralogs, such paralogs need to be expressed in at least the same set of tissues as the lost gene. To test our hypothesis, we classified mouse duplicates into two categories based on the expression patterns of their paralogs: "compensable duplicates" (those with paralogs expressed in all the tissues in which the gene is expressed) and "non-compensable duplicates" (those whose paralogs are not expressed in all the tissues where the gene is expressed). In agreement with our hypothesis, the essentiality of non-compensable duplicates is similar to that of singletons, whereas compensable duplicates exhibit a substantially lower essentiality. Our results imply that duplicates can often compensate for the loss of their paralogs, but only if they are expressed in the same tissues. Indeed, the compensation ability is more dependent on expression patterns than on protein sequence similarity. The existence of these two kinds of duplicates with different essentialities, which has been overlooked by prior studies, may have hindered the detection of differences between singletons and duplicates.

KG-Predict: A knowledge graph computational framework for drug repurposing

The emergence of large-scale phenotypic, genetic, and other multi-model biochemical data has offered unprecedented opportunities for drug discovery including drug repurposing. Various knowledge graph-based methods have been developed to integrate and analyze complex and heterogeneous data sources to find new therapeutic applications for existing drugs. However, existing methods have limitations in modeling and capturing context-sensitive inter-relationships among tens of thousands of biomedical entities. In this paper, we developed KG-Predict: a knowledge graph computational framework for drug repurposing. We first integrated multiple types of entities and relations from various genotypic and phenotypic databases to construct a knowledge graph termed GP-KG. GP-KG was composed of 1,246,726 associations between 61,146 entities. KG-Predict then aggregated the heterogeneous topological and semantic information from GP-KG to learn low-dimensional representations of entities and relations, and further utilized these representations to infer new drug-disease interactions. In cross-validation experiments, KG-Predict achieved high performances [AUROC (the area under receiver operating characteristic) = 0.981, AUPR (the area under precision-recall) = 0.409 and MRR (the mean reciprocal rank) = 0.261], outperforming other state-of-art graph embedding methods. We applied KG-Predict in identifying novel repositioned candidate drugs for Alzheimer's disease (AD) and showed that KG-Predict prioritized both FDA-approved and active clinical trial anti-AD drugs among the top (AUROC = 0.868 and AUPR = 0.364).

How much do model organism phenotypes contribute to the computational identification of human disease genes?

Computing phenotypic similarity helps identify new disease genes and diagnose rare diseases. Genotype–phenotype data from orthologous genes in model organisms can compensate for lack of human data and increase genome coverage. In the past decade, cross-species phenotype comparisons have proven valuble, and several ontologies have been developed for this purpose. The relative contribution of different model organisms to computational identification of disease-associated genes is not fully explored. We used phenotype ontologies to semantically relate phenotypes resulting from loss-of-function mutations in model organisms to disease-associated phenotypes in humans. Semantic machine learning methods were used to measure the contribution of different model organisms to the identification of known human gene–disease associations. We found that mouse genotype–phenotype data provided the most important dataset in the identification of human disease genes by semantic similarity and machine learning over phenotype ontologies. Other model organisms' data did not improve identification over that obtained using the mouse alone, and therefore did not contribute significantly to this task. Our work impacts on the development of integrated phenotype ontologies, as well as for the use of model organism phenotypes in human genetic variant interpretation.

This article has an associated First Person interview with the first author of the paper.

Editor's choice: We investigated the use of model organism phenotypes in the computational identification of disease genes, identifying several data biases and concluding that mouse model phenotypes contribute most to computational disease gene identification.

Centralizing neurofibromatosis experimental tool knowledge with the NF Research Tools Database

Experimental tools and resources, such as animal models, cell lines, antibodies, genetic reagents and biobanks, are key ingredients in biomedical research. Investigators face multiple challenges when trying to understand the availability, applicability and accessibility of these tools. A major challenge is keeping up with current information about the numerous tools available for a particular research problem. A variety of disease-agnostic projects such as the Mouse Genome Informatics database and the Resource Identification Initiative curate a number of types of research tools. Here, we describe our efforts to build upon these resources to develop a disease-specific research tool resource for the neurofibromatosis (NF) research community. This resource, the NF Research Tools Database, is an open-access database that enables the exploration and discovery of information about NF type 1-relevant animal models, cell lines, antibodies, genetic reagents and biobanks. Users can search and explore tools, obtain detailed information about each tool as well as read and contribute their observations about the performance, reliability and characteristics of tools in the database. NF researchers will be able to use the NF Research Tools Database to promote, discover, share, reuse and characterize research tools, with the goal of advancing NF research.

Database URL: https://tools.nf.synapse.org/.

SwissBioPics—an interactive library of cell images for the visualization of subcellular location data

SwissBioPics (www.swissbiopics.org) is a freely available resource of interactive, high-resolution cell images designed for the visualization of subcellular location data. SwissBioPics provides images describing cell types from all kingdoms of life—from the specialized muscle, neuronal and epithelial cells of animals, to the rods, cocci, clubs and spirals of prokaryotes. All cell images in SwissBioPics are drawn in Scalable Vector Graphics (SVG), with each subcellular location tagged with a unique identifier from the controlled vocabulary of subcellular locations and organelles of UniProt (https://www.uniprot.org/locations/). Users can search and explore SwissBioPics cell images through our website, which provides a platform for users to learn more about how cells are organized. A web component allows developers to embed SwissBioPics images in their own websites, using the associated JavaScript and a styling template, and to highlight subcellular locations and organelles by simply providing the web component with the appropriate identifier(s) from the UniProt-controlled vocabulary or the ‘Cellular Component’ branch of the Gene Ontology (www.geneontology.org), as well as an organism identifier from the National Center for Biotechnology Information taxonomy (https://www.ncbi.nlm.nih.gov/taxonomy). The UniProt website now uses SwissBioPics to visualize the subcellular locations and organelles where proteins function. SwissBioPics is freely available for anyone to use under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Database URL

www.swissbiopics.org

Functional Similarities of Protein-Coding Genes in Topologically Associating Domains and Spatially-Proximate Genomic Regions

Topologically associating domains (TADs) are the structural and functional units of the genome. However, the functions of protein-coding genes existing in the same or different TADs have not been fully investigated. We compared the functional similarities of protein-coding genes existing in the same TAD and between different TADs, and also in the same gap region (the region between two consecutive TADs) and between different gap regions. We found that the protein-coding genes from the same TAD or gap region are more likely to share similar protein functions, and this trend is more obvious with TADs than the gap regions. We further created two types of gene–gene spatial interaction networks: the first type is based on Hi-C contacts, whereas the second type is based on both Hi-C contacts and the relationship of being in the same TAD. A graph auto-encoder was applied to learn the network topology, reconstruct the two types of networks, and predict the functions of the central genes/nodes based on the functions of the neighboring genes/nodes. It was found that better performance was achieved with the second type of network. Furthermore, we detected long-range spatially-interactive regions based on Hi-C contacts and calculated the functional similarities of the gene pairs from these regions.

Artificial intelligence framework identifies candidate targets for drug repurposing in Alzheimer's disease

BACKGROUND

Genome-wide association studies (GWAS) have identified numerous susceptibility loci for Alzheimer's disease (AD). However, utilizing GWAS and multi-omics data to identify high-confidence AD risk genes (ARGs) and druggable targets that can guide development of new therapeutics for patients suffering from AD has heretofore not been successful.

METHODS

To address this critical problem in the field, we have developed a network-based artificial intelligence framework that is capable of integrating multi-omics data along with human protein-protein interactome networks to accurately infer accurate drug targets impacted by GWAS-identified variants to identify new therapeutics. When applied to AD, this approach integrates GWAS findings, multi-omics data from brain samples of AD patients and AD transgenic animal models, drug-target networks, and the human protein-protein interactome, along with large-scale patient database validation and in vitro mechanistic observations in human microglia cells.

RESULTS

Through this approach, we identified 103 ARGs validated by various levels of pathobiological evidence in AD. Via network-based prediction and population-based validation, we then showed that three drugs (pioglitazone, febuxostat, and atenolol) are significantly associated with decreased risk of AD compared with matched control populations. Pioglitazone usage is significantly associated with decreased risk of AD (hazard ratio (HR) = 0.916, 95% confidence interval [CI] 0.861-0.974, P = 0.005) in a retrospective case-control validation. Pioglitazone is a peroxisome proliferator-activated receptor (PPAR) agonist used to treat type 2 diabetes, and propensity score matching cohort studies confirmed its association with reduced risk of AD in comparison to glipizide (HR = 0.921, 95% CI 0.862-0.984, P = 0.0159), an insulin secretagogue that is also used to treat type 2 diabetes. In vitro experiments showed that pioglitazone downregulated glycogen synthase kinase 3 beta (GSK3β) and cyclin-dependent kinase (CDK5) in human microglia cells, supporting a possible mechanism-of-action for its beneficial effect in AD.

CONCLUSIONS

In summary, we present an integrated, network-based artificial intelligence methodology to rapidly translate GWAS findings and multi-omics data to genotype-informed therapeutic discovery in AD.

Limb development genes underlie variation in human fingerprint patterns

Fingerprints are of long-standing practical and cultural interest, but little is known about the mechanisms that underlie their variation. Using genome-wide scans in Han Chinese cohorts, we identified 18 loci associated with fingerprint type across the digits, including a genetic basis for the long-recognized “pattern-block” correlations among the middle three digits. In particular, we identified a variant near EVI1 that alters regulatory activity and established a role for EVI1 in dermatoglyph patterning in mice. Dynamic EVI1 expression during human development supports its role in shaping the limbs and digits, rather than influencing skin patterning directly. Trans-ethnic meta-analysis identified 43 fingerprint-associated loci, with nearby genes being strongly enriched for general limb development pathways. We also found that fingerprint patterns were genetically correlated with hand proportions. Taken together, these findings support the key role of limb development genes in influencing the outcome of fingerprint patterning.

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GWAS identifies variants associated with fingerprint type across all digits

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Fingerprint-associated genes are strongly enriched for limb development functions

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Evi1 alters dermatoglyphs in mice by modulating limb rather than skin development

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Fingerprint patterns are genetically correlated with hand and finger proportions

The Vulnerability of the Developing Brain: Analysis of Highly Expressed Genes in Infant C57BL/6 Mouse Hippocampus in Relation to Phenotypic Annotation Derived From Mutational Studies

The hippocampus has been shown to have a major role in learning and memory, but also to participate in the regulation of emotions. However, its specific role(s) in memory is still unclear. Hippocampal damage or dysfunction mainly results in memory issues, especially in the declarative memory but, in animal studies, has also shown to lead to hyperactivity and difficulty in inhibiting responses previously taught. The brain structure is affected in neuropathological disorders, such as Alzheimer's, epilepsy, and schizophrenia, and also by depression and stress. The hippocampus structure is far from mature at birth and undergoes substantial development throughout infant and juvenile life. The aim of this study was to survey genes highly expressed throughout the postnatal period in mouse hippocampus and which have also been linked to an abnormal phenotype through mutational studies to achieve a greater understanding about hippocampal functions during postnatal development. Publicly available gene expression data from C57BL/6 mouse hippocampus was analyzed; from a total of 5 time points (at postnatal day 1, 10, 15, 21, and 30), 547 genes highly expressed in all of these time points were selected for analysis. Highly expressed genes are considered to be of potential biological importance and appear to be multifunctional, and hence any dysfunction in such a gene will most likely have a large impact on the development of abilities during the postnatal and juvenile period. Phenotypic annotation data downloaded from Mouse Genomic Informatics database were analyzed for these genes, and the results showed that many of them are important for proper embryo development and infant survival, proper growth, and increase in body size, as well as for voluntary movement functions, motor coordination, and balance. The results also indicated an association with seizures that have primarily been characterized by uncontrolled motor activity and the development of proper grooming abilities. The complete list of genes and their phenotypic annotation data have been compiled in a file for easy access.

Endophenotype-based in silico network medicine discovery combined with insurance record data mining identifies sildenafil as a candidate drug for Alzheimer's disease

We developed an endophenotype disease module-based methodology for Alzheimer's disease (AD) drug repurposing and identified sildenafil as a potential disease risk modifier. Based on retrospective case-control pharmacoepidemiologic analyses of insurance claims data for 7.23 million individuals, we found that sildenafil usage was significantly associated with a 69% reduced risk of AD (hazard ratio = 0.31, 95% confidence interval 0.25-0.39, P<1.0×10^-8). Propensity score stratified analyses confirmed that sildenafil is significantly associated with a decreased risk of AD across all four drug cohorts we tested (diltiazem, glimepiride, losartan and metformin) after adjusting age, sex, race, and disease comorbidities. We also found that sildenafil increases neurite growth and decreases phospho-tau expression in AD patient-induced pluripotent stem cells-derived neuron models, supporting mechanistically its potential beneficial effect in Alzheimer's disease. The association between sildenafil use and decreased incidence of AD does not establish causality or its direction, which requires a randomized clinical trial approach.

The Human Disease Ontology 2022 update

The Human Disease Ontology (DO) (www.disease-ontology.org) database, has significantly expanded the disease content and enhanced our userbase and website since the DO’s 2018 Nucleic Acids Research DATABASE issue paper. Conservatively, based on available resource statistics, terms from the DO have been annotated to over 1.5 million biomedical data elements and citations, a 10× increase in the past 5 years. The DO, funded as a NHGRI Genomic Resource, plays a key role in disease knowledge organization, representation, and standardization, serving as a reference framework for multiscale biomedical data integration and analysis across thousands of clinical, biomedical and computational research projects and genomic resources around the world. This update reports on the addition of 1,793 new disease terms, a 14% increase of textual definitions and the integration of 22 137 new SubClassOf axioms defining disease to disease connections representing the DO’s complex disease classification. The DO’s updated website provides multifaceted etiology searching, enhanced documentation and educational resources.

Gene4HL: An Integrated Genetic Database for Hearing Loss

Hearing loss (HL) is one of the most common disabilities in the world. In industrialized countries, HL occurs in 1–2/1,000 newborns, and approximately 60% of HL is caused by genetic factors. Next generation sequencing (NGS) has been widely used to identify many candidate genes and variants in patients with HL, but the data are scattered in multitudinous studies. It is a challenge for scientists, clinicians, and biologists to easily obtain and analyze HL genes and variant data from these studies. Thus, we developed a one-stop database of HL-related genes and variants, Gene4HL (http://www.genemed.tech/gene4hl/), making it easy to catalog, search, browse and analyze the genetic data. Gene4HL integrates the detailed genetic and clinical data of 326 HL-related genes from 1,608 published studies, along with 62 popular genetic data sources to provide comprehensive knowledge of candidate genes and variants associated with HL. Additionally, Gene4HL supports the users to analyze their own genetic engineering network data, performs comprehensive annotation, and prioritizes candidate genes and variations using custom parameters. Thus, Gene4HL can help users explain the function of HL genes and the clinical significance of variants by correlating the genotypes and phenotypes in humans.

Proteomics and transcriptome reveal the key transcription factors mediating the protection of Panax notoginseng saponins (PNS) against cerebral ischemia/reperfusion injury

BACKGROUND AND PURPOSE

Transcription factors (TFs) play a critical role in the cerebral ischemia/reperfusion injury (IRI). Panax notoginseng saponins (PNS) are extensively used in the treatment of acute cerebral ischemia in China, but the mechanism of their effects, especially at the TF level, remains unclear. In this study, a combination of transcriptomics, proteomics and network pharmacology analysis was used to identify the key TFs involved in the protection of PNS against middle cerebral artery occlusion (MCAO)-induced IRI.

METHODS AND RESULTS

Sprague-Dawley rats which were subjected to 1.5 hours of MCAO-induced occlusionand then followed by reperfusion, were treated with PNS at a concentration of 36 mg/kg or 72 mg/kg daily for 7 days. PNS significantly decreased neurological deficient scores and infarction rate; prevented cerebral tissue damage; and reduced CASP3 activity, levels of TNF, IL1B and CCL2 after IRI. Through a combination of transcriptomics and proteomics, 9 critical TFs were identified, including Excision repair cross-complementing group 2 (ERCC2), Nuclear receptor subfamily 4 group A member 3 (NR4A3) and 7 other TFs. The targets of ERCC2 and NR4A3, such as Ubxn11, Ush2a, Numr2, Oxt, Ubxn11, Scrt2, Ttc34 and Lrrc23, were verified by using real-time PCR analysis. RNA-seq analyses indicated that PNS regulated nerve system development and inflammation, and the majority of the identified TFs were also involved in these processes. By using network pharmacology analysis, 73 chemical components in PNS were predicted to affect ERCC2, NR4A3 and 3 other identified TFs.

CONCLUSION

ERCC2, NR4A3 and 7 other TFs were of importance in the protection of PNS against IRI. This study promoted the understanding of protective mechanism of PNS against cerebral IRI and facilitated the identification of possible targets of PNS.

A Systematic Review of Animal Models of NAFLD Finds High-Fat, High-Fructose Diets Most Closely Resemble Human NAFLD

BACKGROUND AND AIMS

Animal models of human disease are a key component of translational hepatology research, yet there is no consensus on which model is optimal for NAFLD.

APPROACH AND RESULTS

We generated a database of 3,920 rodent models of NAFLD. Study designs were highly heterogeneous, and therefore, few models had been cited more than once. Analysis of genetic models supported the current evidence for the role of adipose dysfunction and suggested a role for innate immunity in the progression of NAFLD. We identified that high-fat, high-fructose diets most closely recapitulate the human phenotype of NAFLD. There was substantial variability in the nomenclature of animal models: a consensus on terminology of specialist diets is needed. More broadly, this analysis demonstrates the variability in preclinical study design, which has wider implications for the reproducibility of in vivo experiments both in the field of hepatology and beyond.

CONCLUSIONS

This systematic analysis provides a framework for phenotypic assessment of NAFLD models and highlights the need for increased standardization and replication.

Whole-exome sequencing of consanguineous families with infertile men and women identifies homologous mutations in SPATA22 and MEIOB

STUDY QUESTION

Can whole-exome sequencing (WES) reveal pathogenic mutations in two consanguineous Pakistani families with infertile patients?

SUMMARY ANSWER

A homozygous spermatogenesis associated 22 (SPATA22) frameshift mutation (c.203del), which disrupts the interaction with meiosis specific with OB-fold (MEIOB), and a MEIOB splicing mutation (c.683-1G>A) that led to loss of MEIOB protein cause familial infertility.

WHAT IS KNOWN ALREADY

MEIOB and SPATA22, direct binding partners and functional collaborators, form a meiosis-specific heterodimer that regulates meiotic recombination. The protein stability and the axial localization of MEIOB and SPATA22 depend on each other. Meiob and Spata22 knockout mice have the same phenotypes: mutant spermatocytes can initiate meiotic recombination but are unable to complete DSB repair, leading to crossover formation failure, meiotic prophase arrest, and sterility.

STUDY DESIGN, SIZE, DURATION

We performed WES for the patients and controls in two consanguineous Pakistani families to screen for mutations. The pathogenicity of the identified mutations was assessed by in vitro assay and mutant mouse model.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Two consanguineous Pakistani families with four patients (three men and one woman) suffering from primary infertility were recruited. SPATA22 and MEIOB mutations were screened from the WES data, followed by functional verification in cultured cells and mice.

MAIN RESULTS AND THE ROLE OF CHANCE

A homozygous SPATA22 frameshift mutation (c.203del) was identified in a patient with non-obstructive azoospermia (NOA) from a consanguineous Pakistani family and a homozygous MEIOB splicing mutation (c.683-1G>A) was identified in two patients with NOA and one infertile woman from another consanguineous Pakistani family. The SPATA22 mutation destroyed the interaction with MEIOB. The MEIOB splicing mutation induced Exon 9 skipping, which causes a 32aa deletion in the oligonucleotide-binding domain without affecting the interaction between MEIOB and SPATA22. Furthermore, analyses of the Meiob mutant mice modelling the patients' mutation revealed that the MEIOB splicing mutation leads to loss of MEIOB proteins, abolished SPATA22 recruitment on chromosome axes, and meiotic arrest due to meiotic recombination failure. Thus, our study suggests that SPATA22 and MEIOB may both be causative genes for human infertility.

LIMITATIONS, REASONS FOR CAUTION

As SPATA22 and MEIOB are interdependent and essential for meiotic recombination, screening for mutations of SPATA22 and MEIOB in both infertile men and women in larger cohorts is important to further reveal the role of the SPATA22 and MEIOB heterodimer in human fertility.

WIDER IMPLICATIONS OF THE FINDINGS

These findings provide direct clinical and functional evidence that mutations in SPATA22 and MEIOB can cause meiotic recombination failure, supporting a role for these mutations in human infertility and their potential use as targets for genetic diagnosis of human infertility.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Key Research and Developmental Program of China (2018YFC1003900, 2018YFC1003700, and 2019YFA0802600), the National Natural Science Foundation of China (31890780, 31630050, 32061143006, 82071709, and 31871514), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB19000000). The authors declare no conflicts of interest.

TRIAL REGISTRATION NUMBER

N/A.

A recurrent ZSWIM7 mutation causes male infertility resulting from decreased meiotic recombination

STUDY QUESTION

Are mutations in the zinc finger SWIM domain-containing protein 7 gene (ZSWIM7) associated with human male infertility?

SUMMARY ANSWER

The homozygous frameshift mutation (c.231_232del) in ZSWIM7 causes decreased meiotic recombination, spermatogenesis arrest, and infertility in men.

WHAT IS KNOWN ALREADY

ZSWIM7 is a SWIM domain-containing Shu2/SWS1 protein family member and a subunit of the Shu complex. Zswim7 knockout mice were infertile due to impaired meiotic recombination. However, so far there is no direct evidence that mutations of ZSWIM7 cause human infertility.

STUDY DESIGN, SIZE, DURATION

Screening for mutations of ZSWIM7 was performed using in-house whole-exome sequencing data from 60 men with non-obstructive azoospermia (NOA). Mice with a corresponding Zswim7 mutation were generated for functional verification.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Sixty Chinese patients, who were from different regions of China, were enrolled. All the patients were diagnosed with NOA owing to spermatocyte maturation arrest based on histopathological analyses and/or immunostaining of spermatocyte chromosome spreads. ZSWIM7 mutations were screened from the whole-exome sequencing data of these patients, followed by functional verification in mice.

MAIN RESULTS AND THE ROLE OF CHANCE

A homozygous frameshift mutation (c.231_232del) in ZSWIM7 was found in two out of the 60 unrelated NOA patients. Both patients displayed small testicular size and spermatocyte maturation arrest in testis histology. Spermatocyte chromosome spreads of one patient revealed meiotic maturation arrest in a pachytene-like stage, with incomplete synapsis and decreased meiotic recombination. Male mice carrying a homozygous mutation similar to that of our patients were generated and also displayed reduced recombination, meiotic arrest and azoospermia, paralleling the spermatogenesis defects in our patients.

LIMITATIONS, REASONS FOR CAUTION

As Zswim7 is also essential for meiosis in female mice, future studies should evaluate the ZSWIM7 mutations more in depth and in larger cohorts of infertile patients, including males and females, to validate the findings.

WIDER IMPLICATIONS OF THE FINDINGS

These findings provide direct clinical and functional evidence that the recurrent ZSWIM7 mutation (c.231_232del) causes decreased meiotic recombination and leads to male infertility, illustrating the genotype-phenotype correlations of meiotic recombination defects in humans.

STUDY FUNDING/COMPETING INTEREST(S)

This work was supported by the National Natural Science Foundation of China (31890780, 31630050, 32061143006, 82071709, and 31871514), the Strategic Priority Research Program of the Chinese Academy of Sciences (XDB19000000), and the National Key Research and Developmental Program of China (2018YFC1003900 and 2019YFA0802600).

TRIAL REGISTRATION NUMBER

Not applicable.

Gene4PD: A Comprehensive Genetic Database of Parkinson's Disease

Parkinson’s disease (PD) is a complex neurodegenerative disorder with a strong genetic component. A growing number of variants and genes have been reported to be associated with PD; however, there is no database that integrate different type of genetic data, and support analyzing of PD-associated genes (PAGs). By systematic review and curation of multiple lines of public studies, we integrate multiple layers of genetic data (rare variants and copy-number variants identified from patients with PD, associated variants identified from genome-wide association studies, differentially expressed genes, and differential DNA methylation genes) and age at onset in PD. We integrated five layers of genetic data (8302 terms) with different levels of evidences from more than 3,000 studies and prioritized 124 PAGs with strong or suggestive evidences. These PAGs were identified to be significantly interacted with each other and formed an interconnected functional network enriched in several functional pathways involved in PD, suggesting these genes may contribute to the pathogenesis of PD. Furthermore, we identified 10 genes were associated with a juvenile-onset (age ≤ 30 years), 11 genes were associated with an early-onset (age of 30–50 years), whereas another 10 genes were associated with a late-onset (age > 50 years). Notably, the AAOs of patients with loss of function variants in five genes were significantly lower than that of patients with deleterious missense variants, while patients with VPS13C (P = 0.01) was opposite. Finally, we developed an online database named Gene4PD (http://genemed.tech/gene4pd) which integrated published genetic data in PD, the PAGs, and 63 popular genomic data sources, as well as an online pipeline for prioritize risk variants in PD. In conclusion, Gene4PD provides researchers and clinicians comprehensive genetic knowledge and analytic platform for PD, and would also improve the understanding of pathogenesis in PD.

GPCards: An integrated database of genotype-phenotype correlations in human genetic diseases

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The patient-level genotype-phenotype correlations in GPCards accelerated the development of medical genetics.

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The integrated 62 genomic sources provide comprehensive information for interpreting pathogenicity.

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Analysis function in GPCards would help users to decipher their data of genotype-phenotype correlations.

Genotype–phenotype correlations are the basis of precision medicine of human genetic diseases. However, it remains a challenge for clinicians and researchers to conveniently access detailed individual-level clinical phenotypic features of patients with various genetic variants. To address this urgent need, we manually searched for genetic studies in PubMed and catalogued 8,309 genetic variants in 1,288 genes from 17,738 patients with detailed clinical phenotypic features from 1,855 publications. Based on genotype–phenotype correlations in this dataset, we developed an user-friendly online database called GPCards (http://genemed.tech/gpcards/), which not only provided the association between genetic diseases and disease genes, but also the prevalence of various clinical phenotypes related to disease genes and the patient-level mapping between these clinical phenotypes and genetic variants. To accelerate the interpretation of genetic variants, we integrated 62 well-known variant-level and gene-level genomic data sources, including functional predictions, allele frequencies in different populations, and disease-related information. Furthermore, GPCards enables automatic analyses of users’ own genetic data, comprehensive annotation, prioritization of candidate functional variants, and identification of genotype–phenotype correlations using custom parameters. In conclusion, GPCards is expected to accelerate the interpretation of genotype–phenotype correlations, subtype classification, and candidate gene prioritisation in human genetic diseases.

Identification of Celecoxib-Targeted Proteins Using Label-Free Thermal Proteome Profiling on Rat Hippocampus

Celecoxib, or Celebrex, a nonsteroidal anti-inflammatory drug, is one of the most common medicines for treating inflammatory diseases. Recently, it has been shown that celecoxib is associated with implications in complex diseases, such as Alzheimer disease and cancer as well as with cardiovascular risk assessment and toxicity, suggesting that celecoxib may affect multiple unknown targets. In this project, we detected targets of celecoxib within the nervous system using a label-free thermal proteome profiling method. First, proteins of the rat hippocampus were treated with multiple drug concentrations and temperatures. Next, we separated the soluble proteins from the denatured and sedimented total protein load by ultracentrifugation. Subsequently, the soluble proteins were analyzed by nano-liquid chromatography tandem mass spectrometry to determine the identity of the celecoxib-targeted proteins based on structural changes by thermal stability variation of targeted proteins toward higher solubility in the higher temperatures. In the analysis of the soluble protein extract at 67°C, 44 proteins were uniquely detected in drug-treated samples out of all 478 identified proteins at this temperature. Ras-associated binding protein 4a, 1 out of these 44 proteins, has previously been reported as one of the celecoxib off targets in the rat central nervous system. Furthermore, we provide more molecular details through biomedical enrichment analysis to explore the potential role of all detected proteins in the biologic systems. We show that the determined proteins play a role in the signaling pathways related to neurodegenerative disease-and cancer pathways. Finally, we fill out molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets. SIGNIFICANCE STATEMENT: This study determined 44 off-target proteins of celecoxib, a nonsteroidal anti-inflammatory and one of the most common medicines for treating inflammatory diseases. It shows that these proteins play a role in the signaling pathways related to neurodegenerative disease and cancer pathways. Finally, the study provides molecular supporting evidence for using celecoxib toward the drug-repurposing approach by exploring drug targets.

GXD's RNA-Seq and Microarray Experiment Search: using curated metadata to reliably find mouse expression studies of interest

The Gene Expression Database (GXD), an extensive community resource of curated expression information for the mouse, has developed an RNA-Seq and Microarray Experiment Search (http://www.informatics.jax.org/gxd/htexp_index). This tool allows users to quickly and reliably find specific experiments in ArrayExpress and the Gene Expression Omnibus (GEO) that study endogenous gene expression in wild-type and mutant mice. Standardized metadata annotations, curated by GXD, allow users to specify the anatomical structure, developmental stage, mutated gene, strain and sex of samples of interest, as well as the study type and key parameters of the experiment. These searches, powered by controlled vocabularies and ontologies, can be combined with free text searching of experiment titles and descriptions. Search result summaries include link-outs to ArrayExpress and GEO, providing easy access to the expression data itself. Links to the PubMed entries for accompanying publications are also included. More information about this tool and GXD can be found at the GXD home page (http://www.informatics.jax.org/expression.shtml).

Database URL:http://www.informatics.jax.org/expression.shtml

Comprehensive mapping of the human cytokine gene regulatory network

Proper cytokine gene expression is essential in development, homeostasis and immune responses. Studies on the transcriptional control of cytokine genes have mostly focused on highly researched transcription factors (TFs) and cytokines, resulting in an incomplete portrait of cytokine gene regulation. Here, we used enhanced yeast one-hybrid (eY1H) assays to derive a comprehensive network comprising 1380 interactions between 265 TFs and 108 cytokine gene promoters. Our eY1H-derived network greatly expands the known repertoire of TF–cytokine gene interactions and the set of TFs known to regulate cytokine genes. We found an enrichment of nuclear receptors and confirmed their role in cytokine regulation in primary macrophages. Additionally, we used the eY1H-derived network as a framework to identify pairs of TFs that can be targeted with commercially-available drugs to synergistically modulate cytokine production. Finally, we integrated the eY1H data with single cell RNA-seq and phenotypic datasets to identify novel TF–cytokine regulatory axes in immune diseases and immune cell lineage development. Overall, the eY1H data provides a rich resource to study cytokine regulation in a variety of physiological and disease contexts.

An Automated Functional Annotation Pipeline That Rapidly Prioritizes Clinically Relevant Genes for Autism Spectrum Disorder

Human genetic studies have implicated more than a hundred genes in Autism Spectrum Disorder (ASD). Understanding how variation in implicated genes influence expression of co-occurring conditions and drug response can inform more effective, personalized approaches for treatment of individuals with ASD. Rapidly translating this information into the clinic requires efficient algorithms to sort through the myriad of genes implicated by rare gene-damaging single nucleotide and copy number variants, and common variation detected in genome-wide association studies (GWAS). To pinpoint genes that are more likely to have clinically relevant variants, we developed a functional annotation pipeline. We defined clinical relevance in this project as any ASD associated gene with evidence indicating a patient may have a complex, co-occurring condition that requires direct intervention (e.g., sleep and gastrointestinal disturbances, attention deficit hyperactivity, anxiety, seizures, depression), or is relevant to drug development and/or approaches to maximizing efficacy and minimizing adverse events (i.e., pharmacogenomics). Starting with a list of all candidate genes implicated in all manifestations of ASD (i.e., idiopathic and syndromic), this pipeline uses databases that represent multiple lines of evidence to identify genes: (1) expressed in the human brain, (2) involved in ASD-relevant biological processes and resulting in analogous phenotypes in mice, (3) whose products are targeted by approved pharmaceutical compounds or possessing pharmacogenetic variation and (4) whose products directly interact with those of genes with variants recommended to be tested for by the American College of Medical Genetics (ACMG). Compared with 1000 gene sets, each with a random selection of human protein coding genes, more genes in the ASD set were annotated for each category evaluated (p ≤ 1.99 × 10⁻²). Of the 956 ASD-implicated genes in the full set, 18 were flagged based on evidence in all categories. Fewer genes from randomly drawn sets were annotated in all categories (x = 8.02, sd = 2.56, p = 7.75 × 10⁻⁴). Notably, none of the prioritized genes are represented among the 59 genes compiled by the ACMG, and 78% had a pathogenic or likely pathogenic variant in ClinVar. Results from this work should rapidly prioritize potentially actionable results from genetic studies and, in turn, inform future work toward clinical decision support for personalized care based on genetic testing.

Genetic parameters and associated genomic regions for global immunocompetence and other health-related traits in pigs

The inclusion of health-related traits, or functionally associated genetic markers, in pig breeding programs could contribute to produce more robust and disease resistant animals. The aim of the present work was to study the genetic determinism and genomic regions associated to global immunocompetence and health in a Duroc pig population. For this purpose, a set of 30 health-related traits covering immune (mainly innate), haematological, and stress parameters were measured in 432 healthy Duroc piglets aged 8 weeks. Moderate to high heritabilities were obtained for most traits and significant genetic correlations among them were observed. A genome wide association study pointed out 31 significantly associated SNPs at whole-genome level, located in six chromosomal regions on pig chromosomes SSC4, SSC6, SSC17 and SSCX, for IgG, γδ T-cells, C-reactive protein, lymphocytes phagocytic capacity, total number of lymphocytes, mean corpuscular volume and mean corpuscular haemoglobin. A total of 16 promising functionally-related candidate genes, including CRP, NFATC2, PRDX1, SLA, ST3GAL1, and VPS4A, have been proposed to explain the variation of immune and haematological traits. Our results enhance the knowledge of the genetic control of traits related with immunity and support the possibility of applying effective selection programs to improve immunocompetence in pigs.

A fully-automated method discovers loss of mouse-lethal and human-monogenic disease genes in 58 mammals

Gene losses provide an insightful route for studying the morphological and physiological adaptations of species, but their discovery is challenging. Existing genome annotation tools focus on annotating intact genes and do not attempt to distinguish nonfunctional genes from genes missing annotation due to sequencing and assembly artifacts. Previous attempts to annotate gene losses have required significant manual curation, which hampers their scalability for the ever-increasing deluge of newly sequenced genomes. Using extreme sequence erosion (amino acid deletions and substitutions) and sister species support as an unambiguous signature of loss, we developed an automated approach for detecting high-confidence gene loss events across a species tree. Our approach relies solely on gene annotation in a single reference genome, raw assemblies for the remaining species to analyze, and the associated phylogenetic tree for all organisms involved. Using human as reference, we discovered over 400 unique human ortholog erosion events across 58 mammals. This includes dozens of clade-specific losses of genes that result in early mouse lethality or are associated with severe human congenital diseases. Our discoveries yield intriguing potential for translational medical genetics and evolutionary biology, and our approach is readily applicable to large-scale genome sequencing efforts across the tree of life.

Genome-wide association study for feed efficiency in collective cage-raised rabbits under full and restricted feeding

Feed efficiency (FE) is one of the most economically and environmentally relevant traits in the animal production sector. The objective of this study was to gain knowledge about the genetic control of FE in rabbits. To this end, GWASs were conducted for individual growth under two feeding regimes (full feeding and restricted) and FE traits collected from cage groups, using 114 604 autosome SNPs segregating in 438 rabbits. Two different models were implemented: (1) an animal model with a linear regression on each SNP allele for growth trait; and (2) a two‐trait animal model, jointly fitting the performance trait and each SNP allele content, for FE traits. This last modeling strategy is a new tool applied to GWAS and allows information to be considered from non‐genotyped individuals whose contribution is relevant in the group average traits. A total of 189 SNPs in 17 chromosomal regions were declared to be significantly associated with any of the five analyzed traits at a chromosome‐wide level. In 12 of these regions, 20 candidate genes were proposed to explain the variation of the analyzed traits, including genes such as FTO, NDUFAF6 and CEBPA previously associated with growth and FE traits in monogastric species. Candidate genes associated with behavioral patterns were also identified. Overall, our results can be considered as the foundation for future functional research to unravel the actual causal mutations regulating growth and FE in rabbits.

Brain-related genes are specifically enriched with long phase 1 introns

Intronic gene regions are mostly considered in the scope of gene expression regulation, such as alternative splicing. However, relations between basic statistical properties of introns are much rarely studied in detail, despite vast available data. Particularly, little is known regarding the relationship between the intron length and the intron phase. Intron phase distribution is significantly different at different intron length thresholds. In this study, we performed GO enrichment analysis of gene sets with a particular intron phase at varying intron length thresholds using a list of 13823 orthologous human-mouse gene pairs. We found a specific group of 153 genes with phase 1 introns longer than 50 kilobases that were specifically expressed in brain, functionally related to synaptic signaling, and strongly associated with schizophrenia and other mental disorders. We propose that the prevalence of long phase 1 introns arises from the presence of the signal peptide sequence and is connected with 1–1 exon shuffling.

Gene4Denovo: an integrated database and analytic platform for de novo mutations in humans

De novo mutations (DNMs) significantly contribute to sporadic diseases, particularly in neuropsychiatric disorders. Whole-exome sequencing (WES) and whole-genome sequencing (WGS) provide effective methods for detecting DNMs and prioritizing candidate genes. However, it remains a challenge for scientists, clinicians, and biologists to conveniently access and analyse data regarding DNMs and candidate genes from scattered publications. To fill the unmet need, we integrated 580 799 DNMs, including 30 060 coding DNMs detected by WES/WGS from 23 951 individuals across 24 phenotypes and prioritized a list of candidate genes with different degrees of statistical evidence, including 346 genes with false discovery rates <0.05. We then developed a database called Gene4Denovo (http://www.genemed.tech/gene4denovo/), which allowed these genetic data to be conveniently catalogued, searched, browsed, and analysed. In addition, Gene4Denovo integrated data from >60 genomic sources to provide comprehensive variant-level and gene-level annotation and information regarding the DNMs and candidate genes. Furthermore, Gene4Denovo provides end-users with limited bioinformatics skills to analyse their own genetic data, perform comprehensive annotation, and prioritize candidate genes using custom parameters. In conclusion, Gene4Denovo conveniently allows for the accelerated interpretation of DNM pathogenicity and the clinical implication of DNMs in humans.

Sestrin2 modulates cardiac inflammatory response through maintaining redox homeostasis during ischemia and reperfusion

Ischemia heart disease is the leading cause of death world-widely and has increased prevalence and exacerbated myocardial infarction with aging. Sestrin2, a stress-inducible protein, declines with aging in the heart and the rescue of Sestrin2 in the aged mouse heart improves the resistance to ischemic insults caused by ischemia and reperfusion. Here, through a combination of transcriptomic, physiological, histological, and biochemical strategies, we found that Sestrin2 deficiency shows an aged-like phenotype in the heart with excessive oxidative stress, provoked immune response, and defected myocardium structure under physiological condition. While challenged with ischemia and reperfusion stress, the transcriptomic alterations in Sestrin2 knockout mouse heart resembled aged wild type mouse heart. It suggests that Sestrin2 is an age-related gene in the heart against ischemia reperfusion stress. Sestrin2 plays a crucial role in modulating inflammatory response through maintaining the intracellular redox homeostasis in the heart under ischemia reperfusion stress condition. Together, the results indicate that Sestrin2 is a potential target for treatment of age-related ischemic heart disease.

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Sestrin2 regulates cardiac redox homeostasis under ischemic stress.

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Alterations in substrate metabolism with aging cause impaired inflammatory response.

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Sestrin2 is a potential target for treatment of ischemic heart disease.

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Sestrin2 modulates cardiac inflammatory response during ischemia and reperfusion.

PATHBIO: an international training program for precision mouse phenotyping

Design and production of genetically engineered mouse strains by individual research laboratories, research teams, large-scale consortia, and the biopharmaceutical industry have magnified the need for qualified personnel to identify, annotate, and validate (phenotype) these potentially new mouse models of human disease. The PATHBIO project has been recently established and funded by the European Union's ERASMUS+ Knowledge Alliance program to address the current shortfall in formally trained personnel. A series of teaching workshops will be given by experts on anatomy, histology, embryology, imaging, and comparative pathology to increase the availability of individuals with formal training to contribute to this important niche of Europe's biomedical research enterprise. These didactic and hands-on workshops are organized into three modules: (1) embryology, anatomy, histology, and the anatomical basis of imaging, (2) image-based phenotyping, and (3) pathology. The workshops are open to all levels of participants from recent graduates to Ph.D., M.D., and veterinary scientists. Participation is available on a competitive basis at no cost for attending. The first series of Workshop Modules was held in 2019 and these will continue for the next 2 years.

Graph-based information diffusion method for prioritizing functionally related genes in protein-protein interaction networks

Shortest path length methods are routinely used to validate whether genes of interest are functionally related to each other based on biological network information. However, the methods are computationally intensive, impeding extensive utilization of network information. In addition, non-weighted shortest path length approach, which is more frequently used, often treat all network connections equally without taking into account of confidence levels of the associations. On the other hand, graph-based information diffusion method, which employs both the presence and confidence weights of network edges, can efficiently explore large networks and has previously detected meaningful biological patterns. Therefore, in this study, we hypothesized that the graph-based information diffusion method could prioritize genes with relevant functions more efficiently and accurately than the shortest path length approaches. We demonstrated that the graph-based information diffusion method substantially differentiated not only genes participating in same biological pathways (p << 0.0001) but also genes associated with specific human drug-induced clinical symptoms (p << 0.0001) from random. Furthermore, the diffusion method prioritized these functionally related genes faster and more accurately than the shortest path length approaches (pathways: p = 2.7e-28, clinical symptoms: p = 0.032). These data show the graph-based information diffusion method can be routinely used for robust prioritization of functionally related genes, facilitating efficient network validation and hypothesis generation, especially for human phenotype-specific genes.

SKmDB: an integrated database of next generation sequencing information in skeletal muscle

MOTIVATION

Skeletal muscles have indispensable functions and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequencing (NGS) data in recent years offers us an unprecedented perspective to understand gene regulatory networks governing skeletal muscle development and regeneration. However, the data from public NGS database are often in raw data format or processed with different procedures, causing obstacles to make full use of them.

RESULTS

We provide SKmDB, an integrated database of NGS information in skeletal muscle. SKmDB not only includes all NGS datasets available in the human and mouse skeletal muscle tissues and cells, but also provide preliminary data analyses including gene/isoform expression levels, gene co-expression subnetworks, as well as assembly of putative lincRNAs, typical and super enhancers and transcription factor hotspots. Users can efficiently search, browse and visualize the information with the well-designed user interface and server side. SKmDB thus will offer wet lab biologists useful information to study gene regulatory mechanisms in the field of skeletal muscle development and regeneration.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://sunlab.cpy.cuhk.edu.hk/SKmDB.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Evolutionary genomics analysis of human nucleus-encoded mitochondrial genes: implications for the roles of energy production and metabolic pathways in the pathogenesis and pathophysiology of demyelinating diseases

The myelin sheath is a plasma membrane extension that lines nerve fibers to protect, support and insulate neurons. The myelination of axons in vertebrates enables fast, saltatory impulse propagation, and this process relies on organelles, especially on mitochondria to supply energy. Approximately 99% of mitochondrial proteins are encoded in the nucleus. Since mitochondria play a central role in the energy production and metabolic pathways, which are essential for myelinogenesis, studying these nucleus-encoded genes (nMGs) may provide new insights into the roles of energy metabolism in demyelinating diseases. In this work, a multiomics-based approach was employed to 1) construct a 1,740 human nMG subset with mitochondrial localization evidence obtained from the Integrated Mitochondrial Protein Index (IMPI) and MitoCarta databases, 2) conduct an evolutionary genomics analysis across mouse, rat, monkey, chimp, and human models, 3) examine dysmyelination phenotype-related genes (nMG subset genes with oligodendrocyte- ​and myelin-related ​phenotypes, OMP-nMGs) in MGI mouse lines and human patients, 4) determine the expression discrepancy of OMP-nMGs in brain tissues of cuprizone-treated mice, multiple sclerosis patients, and normal controls, and 5) conduct literature data mining to explore OMP-nMG-associated disease impacts. By contrasting OMP-nMGs with other genes, OMP-nMGs were found to be more ubiquitously expressed (59.1% vs. 16.1%), disease-associated (67.3% vs. 20.2%), and evolutionarily conserved within the human populations. Our multiomics-based analysis identified 110 OMP-nMGs implicated in energy production and lipid and glycan biosynthesis in the pathogenesis and pathophysiology of demyelinating disorders. Future targeted characterization of OMP-nMGs in abnormal myelination conditions may allow the discovery of novel nMG mediated mechanisms underlying myelinogenesis and related diseases.

'Fat's chances': Loci for phenotypic dispersion in plasma leptin in mouse models of diabetes mellitus

Background

Leptin, a critical mediator of feeding, metabolism and diabetes, is expressed on an incidental basis according to satiety. The genetic regulation of leptin should similarly be episodic.

Methodology

Data from three mouse cohorts hosted by the Jackson Laboratory– 402 (174F, 228M) F₂ Dilute Brown non-Agouti (DBA/2)×DU6i intercrosses, 142 Non Obese Diabetic (NOD/ShiLtJ×(NOD/ShiLtJ×129S1/SvImJ.H2^g7) N₂ backcross females, and 204 male Nonobese Nondiabetic (NON)×New Zealand Obese (NZO/HlLtJ) reciprocal backcrosses–were used to test for loci associated with absolute residuals in plasma leptin and arcsin-transformed percent fat (‘phenotypic dispersion’; PD_pLep and PD_AFP). Individual data from 1,780 mice from 43 inbred strains was also used to estimate genetic variances and covariances for dispersion in each trait.

Principal findings

Several loci for PD_pLep were detected, including possibly syntenic Chr 17 loci, but there was only a single position on Chr 6 for PD_AFP. Coding SNP in genes linked to the consensus Chr 17 PD_pLep locus occurred in immunological and cancer genes, genes linked to diabetes and energy regulation, post-transcriptional processors and vomeronasal variants. There was evidence of intersexual differences in the genetic architecture of PD_pLep. PD_pLep had moderate heritability (hs2=0.29) and PD_AFP low heritability (hs2=0.12); dispersion in these traits was highly genetically correlated r = 0.8).

Conclusions

Greater genetic variance for dispersion in plasma leptin, a physiological trait, may reflect its more ephemeral nature compared to body fat, an accrued progressive character. Genetic effects on incidental phenotypes such as leptin might be effectively characterized with randomization-detection methodologies in addition to classical approaches, helping identify incipient or borderline cases or providing new therapeutic targets.

Consensus coding sequence (CCDS) database: a standardized set of human and mouse protein-coding regions supported by expert curation

The Consensus Coding Sequence (CCDS) project provides a dataset of protein-coding regions that are identically annotated on the human and mouse reference genome assembly in genome annotations produced independently by NCBI and the Ensembl group at EMBL-EBI. This dataset is the product of an international collaboration that includes NCBI, Ensembl, HUGO Gene Nomenclature Committee, Mouse Genome Informatics and University of California, Santa Cruz. Identically annotated coding regions, which are generated using an automated pipeline and pass multiple quality assurance checks, are assigned a stable and tracked identifier (CCDS ID). Additionally, coordinated manual review by expert curators from the CCDS collaboration helps in maintaining the integrity and high quality of the dataset. The CCDS data are available through an interactive web page (https://www.ncbi.nlm.nih.gov/CCDS/CcdsBrowse.cgi) and an FTP site (ftp://ftp.ncbi.nlm.nih.gov/pub/CCDS/). In this paper, we outline the ongoing work, growth and stability of the CCDS dataset and provide updates on new collaboration members and new features added to the CCDS user interface. We also present expert curation scenarios, with specific examples highlighting the importance of an accurate reference genome assembly and the crucial role played by input from the research community.

Global landscape of mouse and human cytokine transcriptional regulation

Cytokines are cell-to-cell signaling proteins that play a central role in immune development, pathogen responses, and diseases. Cytokines are highly regulated at the transcriptional level by combinations of transcription factors (TFs) that recruit cofactors and the transcriptional machinery. Here, we mined through three decades of studies to generate a comprehensive database, CytReg, reporting 843 and 647 interactions between TFs and cytokine genes, in human and mouse respectively. By integrating CytReg with other functional datasets, we determined general principles governing the transcriptional regulation of cytokine genes. In particular, we show a correlation between TF connectivity and immune phenotype and disease, we discuss the balance between tissue-specific and pathogen-activated TFs regulating each cytokine gene, and cooperativity and plasticity in cytokine regulation. We also illustrate the use of our database as a blueprint to predict TF-disease associations and identify potential TF-cytokine regulatory axes in autoimmune diseases. Finally, we discuss research biases in cytokine regulation studies, and use CytReg to predict novel interactions based on co-expression and motif analyses which we further validated experimentally. Overall, this resource provides a framework for the rational design of future cytokine gene regulation studies.

A Mild PUM1 Mutation Is Associated with Adult-Onset Ataxia, whereas Haploinsufficiency Causes Developmental Delay and Seizures

Certain mutations can cause proteins to accumulate in neurons, leading to neurodegeneration. We recently showed, however, that upregulation of a wild-type protein, Ataxin1, caused by haploinsufficiency of its repressor, the RNA-binding protein Pumilio1 (PUM1), also causes neurodegeneration in mice. We therefore searched for human patients with PUM1 mutations. We identified eleven individuals with either PUM1 deletions or de novo missense variants who suffer a developmental syndrome (Pumilio1-associated developmental disability, ataxia, and seizure; PADDAS). We also identified a milder missense mutation in a family with adult-onset ataxia with incomplete penetrance (Pumilio1-related cerebellar ataxia, PRCA). Studies in patient-derived cells revealed that the missense mutations reduced PUM1 protein levels by ∼25% in the adult-onset cases and by ∼50% in the infantile-onset cases; levels of known PUM1 targets increased accordingly. Changes in protein levels thus track with phenotypic severity, and identifying posttranscriptional modulators of protein expression should identify new candidate disease genes.

Prediction of deleterious mutations in coding regions of mammals with transfer learning

The genomes of mammals contain thousands of deleterious mutations. It is important to be able to recognize them with high precision. In conservation biology, the small size of fragmented populations results in accumulation of damaging variants. Preserving animals with less damaged genomes could optimize conservation efforts. In breeding of farm animals, trade-offs between farm performance versus general fitness might be better avoided if deleterious mutations are well classified. In humans, the problem of such a precise classification has been successfully solved, in large part due to large databases of disease-causing mutations. However, this kind of information is very limited for other mammals. Here, we propose to better use information available on human mutations to enable classification of damaging mutations in other mammalian species. Specifically, we apply transfer learning-machine learning methods-improving small dataset for solving a focal problem (recognizing damaging mutations in our companion and farm animals) due to the use of much large datasets available for solving a related problem (recognizing damaging mutations in humans). We validate our tools using mouse and dog annotated datasets and obtain significantly better results in companion to the SIFT classifier. Then, we apply them to predict deleterious mutations in cattle genomewide dataset.