The Rat Genome Database 2015: genomic, phenotypic and environmental variations and disease

Systems Genetics Reveals the Gene Regulatory Mechanisms of Arrb2 in the Development of Autism Spectrum Disorders

BACKGROUND

Autism spectrum disorder (ASD) involves complex interactions between genetic and environmental factors. Recent studies suggest that dysregulation of β-arrestin2 (Arrb2) in the central nervous system is linked to ASD. However, its specific mechanisms remain unknown.

METHODS

This study employs a systems genetics approach to comprehensively investigate Arrb2 in multiple brain tissues, including the amygdala, cerebellum, hippocampus, and prefrontal cortex, using BXD recombinant inbred (RI) strains. In addition, genetic variance analysis, correlation analysis, expression quantitative trait loci (eQTL) mapping, and functional annotation were used to identify the key downstream targets of Arrb2, validated by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and Western blotting (WB).

RESULTS

Arrb2 exhibited expression variations across the four brain regions in BXD mice. eQTL mapping revealed that Arrb2 is cis-regulated, and increased Arrb2 expression levels were significantly correlated with ASD-like symptoms, such as impaired social interactions and abnormal learning and memory. Furthermore, protein-protein interaction (PPI) network analysis, tissue correlation, functional relevance to autism, and differential expression identified eight downstream candidate genes regulated by Arrb2. The experimental results demonstrated that deletion of Arrb2 led to the downregulation of Myh9, Dnmt1, and Brd4 expression, along with protein kinase A (PKA)-induced hyperactivation of Synapsin I. These findings suggest that Arrb2 may contribute to the pathogenesis of autism by modulating the expression of these genes.

CONCLUSIONS

This study highlights the role of Arrb2 in ASD pathogenesis and identifies Myh9, Dnmt1, and Brd4 as key downstream regulators. These findings provide new insights into the molecular mechanisms of ASD and pave the way for novel therapeutic targets.

Camk2n1 deficiency reduces the NaCl cotransporter activity through the CUL3/KLHL3/WNK4 complex in the kidney

Calcium/calmodulin dependent protein kinase II inhibitor 1 (Camk2n1) is closely associated with a peak logarithm of odds score in quantitative trait loci for systolic blood pressure. Increased Camk2n1 mRNA expression has been specifically observed in the kidneys of hypertension mouse models. However, the precise role of Camk2n1 in the kidney remains unclear. We generated Camk2n1-/- mice using the CRISPR/Cas9 system. Compared to controls, Camk2n1-/- mice exhibited consistently lower systolic blood pressure across all measured time points. Deletion of Camk2n1 resulted in decreased apical labeling of phosphorylated and total thiazide-sensitive NaCl cotransporter (NCC) in the distal convoluted tubule. NCC phosphorylation is regulated by activated SPAK/OSR1 kinases, which act downstream of With-No-lysine (K) kinase (WNK). In Camk2n1-/- mice, the elevated abundances of key components of the Cullin 3 (CUL3) RING ubiquitin ligase, including neddylated CUL3 and the adaptor Kelch-like protein 3, promoted proteasomal degradation of WNK4. In renal tissues, Camk2n1 deletion led to increased mRNA and protein levels of ubiquitin-like modifier-activating enzyme 3 (UBA3) and ubiquitin-conjugating enzyme E2 (UBE2M). Conversely, Camk2n1 overexpression in HEK293 cells resulted in decreased levels of UBA3 and UBE2M, along with reduced CUL3 neddylation. Treatment with MLN4924 effectively suppressed CUL3 hyperneddylation and restored WNK4 levels in the kidneys of Camk2n1-/- mice. In summary, Camk2n1 deletion lowers blood pressure, likely by promoting WNK4 degradation through dysregulated CUL3 RING ubiquitin ligase activity, which leads to decreased NCC activity.

Expression of Neuronal Nicotinic Acetylcholine Receptor and Early Oxidative DNA Damage in Aging Rat Brain-The Effects of Memantine

Aging and age-related neurodegenerative disorders are characterized by the dysfunction or loss of brain nicotinic acetylcholine receptors (nAChRs), and these changes may be related to other senescence markers, such as oxidative stress and DNA repair dysfunction. However, the mechanism of nAChR loss in the aging brain and the modification of this process by drugs (e.g., memantine, Mem) are not yet fully understood. To study whether the differences in nAChR expression in the rat brain occur due to aging or oxidative stress and are modulated by Mem, we analyzed nAChR subunits (at RNA and protein levels) and other biomarkers by real-time quantitative polymerase chain reaction (RQ-PCR) and Western blot validation. Twenty-one female Wistar rats were divided into four groups, depending on age, and the oldest group received injections of Mem or water with the use of intragastric catheters. We studied the cerebral grey matter (CGM), subcortical white matter (SCWM), and cerebellum (Ce). Results showed an age-related decrease of α7 nAChR mRNA level in SCWM. The α7 nAChR mRNA loss was accompanied by reduced expression of 8-oxoguanine DNA glycosylase 1 (OGG1) and an increased tumor necrosis factor alpha (TNFα) level. In the water group, we observed a higher level of α7 nAChR protein in the SCWM and Ce. Biomarker levels changed, but to a different extent depending on the brain area. Importantly, the dysfunction in antioxidative status was stopped and even regressed under Mem treatment. After two weeks of treatment, an increase in TP53 protein level and a decrease in 8-oxo-2'deoxyguanosine (8-oxo-2'dG) level were observed. We conclude that Mem administration may be protective against the senescence process by antioxidative mechanisms.

AlzGenPred - CatBoost-based gene classifier for predicting Alzheimer's disease using high-throughput sequencing data

AD is a progressive neurodegenerative disorder characterized by memory loss. Due to the advancement in next-generation sequencing, an enormous amount of AD-associated genomics data is available. However, the information about the involvement of these genes in AD association is still a research topic. Therefore, AlzGenPred is developed to identify the AD-associated genes using machine-learning. A total of 13,504 features derived from eight sequence-encoding schemes were generated and evaluated using 16 machine learning algorithms. Network-based features significantly outperformed sequence-based features, effectively distinguishing AD-associated genes. In contrast, sequence-based features failed to classify accurately. To improve performance, we generated 24 fused features (6020 D) from sequence-based encodings, increasing accuracy by 5-7% using a two-step lightGBM-based recursive feature selection method. However, accuracy remained below 70% even after hyperparameter tuning. Therefore, network-based features were used to generate the CatBoost-based ML method AlzGenPred with 96.55% accuracy and 98.99% AUROC. The developed method is tested on the AlzGene dataset where it showed 96.43% accuracy. Then the model was validated using the transcriptomics dataset. AlzGenPred provides a reliable and user-friendly tool for identifying potential AD biomarkers, accelerating biomarker discovery, and advancing our understanding of AD. It is available at https://www.bioinfoindia.org/alzgenpred/ and https://github.com/shuklarohit815/AlzGenPred .

Regulation of cardiomyocyte t-tubule structure by preload and afterload: Roles in cardiac compensation and decompensation

Mechanical load is a potent regulator of cardiac structure and function. Although high workload during heart failure is associated with disruption of cardiomyocyte t-tubules and Ca2+ homeostasis, it remains unclear whether changes in preload and afterload may promote adaptive t-tubule remodelling. We examined this issue by first investigating isolated effects of stepwise increases in load in cultured rat papillary muscles. Both preload and afterload increases produced a biphasic response, with the highest t-tubule densities observed at moderate loads, whereas excessively low and high loads resulted in low t-tubule levels. To determine the baseline position of the heart on this bell-shaped curve, mice were subjected to mildly elevated preload or afterload (1 week of aortic shunt or banding). Both interventions resulted in compensated cardiac function linked to increased t-tubule density, consistent with ascension up the rising limb of the curve. Similar t-tubule proliferation was observed in human patients with moderately increased preload or afterload (mitral valve regurgitation, aortic stenosis). T-tubule growth was associated with larger Ca2+ transients, linked to upregulation of L-type Ca2+ channels, Na+-Ca2+ exchanger, mechanosensors and regulators of t-tubule structure. By contrast, marked elevation of cardiac load in rodents and patients advanced the heart down the declining limb of the t-tubule-load relationship. This bell-shaped relationship was lost in the absence of electrical stimulation, indicating a key role of systolic stress in controlling t-tubule plasticity. In conclusion, modest augmentation of workload promotes compensatory increases in t-tubule density and Ca2+ cycling, whereas this adaptation is reversed in overloaded hearts during heart failure progression. KEY POINTS: Excised papillary muscle experiments demonstrated a bell-shaped relationship between cardiomyocyte t-tubule density and workload (preload or afterload), which was only present when muscles were electrically stimulated. The in vivo heart at baseline is positioned on the rising phase of this curve because moderate increases in preload (mice with brief aortic shunt surgery, patients with mitral valve regurgitation) resulted in t-tubule growth. Moderate increases in afterload (mice and patients with mild aortic banding/stenosis) similarly increased t-tubule density. T-tubule proliferation was associated with larger Ca2+ transients, with upregulation of the L-type Ca2+ channel, Na+-Ca2+ exchanger, mechanosensors and regulators of t-tubule structure. By contrast, marked elevation of cardiac load in rodents and patients placed the heart on the declining phase of the t-tubule-load relationship, promoting heart failure progression. The dependence of t-tubule structure on preload and afterload thus enables both compensatory and maladaptive remodelling, in rodents and humans.

GenomeMUSter mouse genetic variation service enables multitrait, multipopulation data integration and analysis

Hundreds of inbred mouse strains and intercross populations have been used to characterize the function of genetic variants that contribute to disease. Thousands of disease-relevant traits have been characterized in mice and made publicly available. New strains and populations including consomics, the collaborative cross, expanded BXD, and inbred wild-derived strains add to existing complex disease mouse models, mapping populations, and sensitized backgrounds for engineered mutations. The genome sequences of inbred strains, along with dense genotypes from others, enable integrated analysis of trait-variant associations across populations, but these analyses are hampered by the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense variant resource by harmonizing multiple data sets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extendable to other model organisms. The result is a web- and programmatically accessible data service called GenomeMUSter, comprising single-nucleotide variants covering 657 strains at 106.8 million segregating sites. Interoperation with phenotype databases, analytic tools, and other resources enable a wealth of applications, including multitrait, multipopulation meta-analysis. We show this in cross-species comparisons of type 2 diabetes and substance use disorder meta-analyses, leveraging mouse data to characterize the likely role of human variant effects in disease. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

The genetic and molecular basis of haploinsufficiency in flowering plants

In diploid organisms, haploinsufficiency can be defined as the requirement for more than one fully functional copy of a gene. In contrast to most genes, whose loss-of-function alleles are recessive, loss-of-function alleles of haploinsufficient genes are dominant. However, forward and reverse genetic screens are biased toward obtaining recessive, loss-of-function mutations, and therefore, dominant mutations of all types are underrepresented in mutant collections. Despite this underrepresentation, haploinsufficient loci have intriguing implications for studies of genome evolution, gene dosage, stability of protein complexes, genetic redundancy, and gene expression. Here we review examples of haploinsufficiency in flowering plants and describe the underlying molecular mechanisms and evolutionary forces driving haploinsufficiency. Finally, we discuss the masking of haploinsufficiency by genetic redundancy, a widespread phenomenon among angiosperms.

Gene network based analysis identifies a coexpression module involved in regulating plasma lipids with high-fat diet response

Plasma lipids are modulated by gene variants and many environmental factors, including diet-associated weight gain. However, understanding how these factors jointly interact to influence molecular networks that regulate plasma lipid levels is limited. Here, we took advantage of the BXD recombinant inbred family of mice to query weight gain as an environmental stressor on plasma lipids. Coexpression networks were examined in both nonobese and obese livers, and a network was identified that specifically responded to the obesogenic diet. This obesity-associated module was significantly associated with plasma lipid levels and enriched with genes known to have functions related to inflammation and lipid homeostasis. We identified key drivers of the module, including Cidec, Cidea, Pparg, Cd36, and Apoa4. The Pparg emerged as a potential master regulator of the module as it can directly target 19 of the top 30 hub genes. Importantly, activation of this module is causally linked to lipid metabolism in humans, as illustrated by correlation analysis and inverse-variance weighed Mendelian randomization. Our findings provide novel insights into gene-by-environment interactions for plasma lipid metabolism that may ultimately contribute to new biomarkers, better diagnostics, and improved approaches to prevent or treat dyslipidemia in patients.

GenomeMUSter mouse genetic variation service enables multi-trait, multi-population data integration and analyses

Hundreds of inbred laboratory mouse strains and intercross populations have been used to functionalize genetic variants that contribute to disease. Thousands of disease relevant traits have been characterized in mice and made publicly available. New strains and populations including the Collaborative Cross, expanded BXD and inbred wild-derived strains add to set of complex disease mouse models, genetic mapping resources and sensitized backgrounds against which to evaluate engineered mutations. The genome sequences of many inbred strains, along with dense genotypes from others could allow integrated analysis of trait - variant associations across populations, but these analyses are not feasible due to the sparsity of genotypes available. Moreover, the data are not readily interoperable with other resources. To address these limitations, we created a uniformly dense data resource by harmonizing multiple variant datasets. Missing genotypes were imputed using the Viterbi algorithm with a data-driven technique that incorporates local phylogenetic information, an approach that is extensible to other model organism species. The result is a web- and programmatically-accessible data service called GenomeMUSter ( https://muster.jax.org ), comprising allelic data covering 657 strains at 106.8M segregating sites. Interoperation with phenotype databases, analytic tools and other resources enable a wealth of applications including multi-trait, multi-population meta-analysis. We demonstrate this in a cross-species comparison of the meta-analysis of Type 2 Diabetes and of substance use disorders, resulting in the more specific characterization of the role of human variant effects in light of mouse phenotype data. Other applications include refinement of mapped loci and prioritization of strain backgrounds for disease modeling to further unlock extant mouse diversity for genetic and genomic studies in health and disease.

2022 updates to the Rat Genome Database: a Findable, Accessible, Interoperable, and Reusable (FAIR) resource

The Rat Genome Database (RGD, https://rgd.mcw.edu) has evolved from simply a resource for rat genetic markers, maps, and genes, by adding multiple genomic data types and extensive disease and phenotype annotations and developing tools to effectively mine, analyze, and visualize the available data, to empower investigators in their hypothesis-driven research. Leveraging its robust and flexible infrastructure, RGD has added data for human and eight other model organisms (mouse, 13-lined ground squirrel, chinchilla, naked mole-rat, dog, pig, African green monkey/vervet, and bonobo) besides rat to enhance its translational aspect. This article presents an overview of the database with the most recent additions to RGD's genome, variant, and quantitative phenotype data. We also briefly introduce Virtual Comparative Map (VCMap), an updated tool that explores synteny between species as an improvement to RGD's suite of tools, followed by a discussion regarding the refinements to the existing PhenoMiner tool that assists researchers in finding and comparing quantitative data across rat strains. Collectively, RGD focuses on providing a continuously improving, consistent, and high-quality data resource for researchers while advancing data reproducibility and fulfilling Findable, Accessible, Interoperable, and Reusable (FAIR) data principles.

Molecular and network-level mechanisms explaining individual differences in autism spectrum disorder

The mechanisms underlying phenotypic heterogeneity in autism spectrum disorder (ASD) are not well understood. Using a large neuroimaging dataset, we identified three latent dimensions of functional brain network connectivity that predicted individual differences in ASD behaviors and were stable in cross-validation. Clustering along these three dimensions revealed four reproducible ASD subgroups with distinct functional connectivity alterations in ASD-related networks and clinical symptom profiles that were reproducible in an independent sample. By integrating neuroimaging data with normative gene expression data from two independent transcriptomic atlases, we found that within each subgroup, ASD-related functional connectivity was explained by regional differences in the expression of distinct ASD-related gene sets. These gene sets were differentially associated with distinct molecular signaling pathways involving immune and synapse function, G-protein-coupled receptor signaling, protein synthesis and other processes. Collectively, our findings delineate atypical connectivity patterns underlying different forms of ASD that implicate distinct molecular signaling mechanisms.

Echocardiography phenotyping in murine genetic reference population of BXD strains reveals significant QTLs associated with cardiac function and morphology

The genetic reference population of recombinant inbred BXD mice has been derived from crosses between C57BL/6J and DBA/2J strains. The DBA/2J parent exhibits cardiomyopathy phenotypes, whereas C57BL/6J has normal heart. BXD mice are sequenced for studying genetic interactions in cardiomyopathies. The study aimed to assess cardiomyopathy traits in BXDs and investigate the quantitative genetic architecture of those traits. Echocardiography, blood pressure, and cardiomyocyte size parameters obtained from 44 strains of BXD family (n > 5/sex) at 4-5 mo of age were associated with heart transcriptomes and expression quantitative trait loci (eQTL) mapping was performed. More than twofold variance in ejection fraction (EF%), fractional shortening (FS%), left ventricular volumes (LVVols), internal dimensions (LVIDs), mass (LVM), and posterior wall (LVPW) thickness was found among BXDs. In male BXDs, eQTL mapping identified Ndrg4 on chromosome 8 QTL to be positively correlated with LVVol and LVID and negatively associated with cardiomyocyte diameter. In female BXDs, significant QTLs were found on chromosomes 7 and 3 to be associated with LVPW and EF% and FS%, respectively, and Josd2, Dap3, and Tpm3 were predicted as strong candidate genes. Our study found variable cardiovascular traits among BXD strains and identified multiple associated QTLs, suggesting an influence of genetic background on expression of echocardiographic and cardiomyocyte diameter traits. Increased LVVol and reduced EF% and FS% represented dilated cardiomyopathy, whereas increased LV mass and wall thickness indicated hypertrophic cardiomyopathy traits. The BXD family is ideal for identifying candidate genes, causal and modifier, that influence cardiovascular phenotypes.NEW & NOTEWORTHY This study aimed to establish a cardiac phenotype-genotype correlation in murine genetic reference population of BXD RI strains by phenotyping the echocardiography, blood pressure, and cardiomyocyte diameter traits and associating each collected phenotype with genetic background. Our study identified several QTLs and candidate genes that have significant association with cardiac hypertrophy, ventricular dilation, and function including systolic hyperfunction and dysfunction.

Soil Microbial Community Responses to Different Management Strategies in Almond Crop

A comparative study of organic and conventional farming systems was conducted in almond orchards to determine the effect of management practices on their fungal and bacterial communities. Soils from two orchards under organic (OM) and conventional (CM), and nearby nonmanaged (NM) soil were analyzed and compared. Several biochemical and biological parameters were measured (soil pH, electrical conductivity, total nitrogen, organic material, total phosphorous, total DNA, and fungal and bacterial DNA copies). Massive parallel sequencing of regions from fungal ITS rRNA and bacterial 16 S genes was carried out to characterize their diversity in the soil. We report a larger abundance of bacteria and fungi in soils under OM, with a more balanced fungi:bacteria ratio, compared to bacteria-skewed proportions under CM and NM. The fungal phylum Ascomycota corresponded to around the 75% relative abundance in the soil, whereas for bacteria, the phyla Proteobacteria, Acidobacteriota and Bacteroidota integrated around 50% of their diversity. Alpha diversity was similar across practices, but beta diversity was highly clustered by soil management. Linear discriminant analysis effect size (LEfSE) identified bacterial and fungal taxa associated with each type of soil management. Analyses of fungal functional guilds revealed 3-4 times larger abundance of pathogenic fungi under CM compared to OM and NM treatments. Among them, the genus Cylindrocarpon was more abundant under CM, and Fusarium under OM.

"3D, human renal proximal tubule (RPTEC-TERT1) organoids 'tubuloids' for translatable evaluation of nephrotoxins in high-throughput"

The importance of human cell-based in vitro tools to drug development that are robust, accurate, and predictive cannot be understated. There has been significant effort in recent years to develop such platforms, with increased interest in 3D models that can recapitulate key aspects of biology that 2D models might not be able to deliver. We describe the development of a 3D human cell-based in vitro assay for the investigation of nephrotoxicity, using RPTEC-TERT1 cells. These RPTEC-TERT1 proximal tubule organoids 'tubuloids' demonstrate marked differences in physiologically relevant morphology compared to 2D monolayer cells, increased sensitivity to nephrotoxins observable via secreted protein, and with a higher degree of similarity to native human kidney tissue. Finally, tubuloids incubated with nephrotoxins demonstrate altered Na+/K+-ATPase signal intensity, a potential avenue for a high-throughput, translatable nephrotoxicity assay.

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

Background

The Nile rat (Avicanthis niloticus) is an important animal model because of its robust diurnal rhythm, a cone-rich retina, and a propensity to develop diet-induced diabetes without chemical or genetic modifications. A closer similarity to humans in these aspects, compared to the widely used Mus musculus and Rattus norvegicus models, holds the promise of better translation of research findings to the clinic.

Results

We report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including some that affect genes associated with type 2 diabetes and metabolic dysfunctions. We discuss 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse.

Conclusions

Our findings reflect the exceptional level of genomic resolution present in this assembly, which will greatly expand the potential of the Nile rat as a model organism.

Identification of hub genes and immune cell infiltration characteristics in chronic rhinosinusitis with nasal polyps: Bioinformatics analysis and experimental validation

The aim of our study is to reveal the hub genes related to the pathogenesis of chronic rhinosinusitis with nasal polyps (CRSwNP) and their association with immune cell infiltration through bioinformatics analysis combined with experimental validation. In this study, through differential gene expression analysis, 1,516 upregulated and 1,307 downregulated DEG were obtained from dataset GSE136825 of the GEO database. We identified 14 co-expressed modules using weighted gene co-expression network analysis (WGCNA), among which the most significant positive and negative correlations were MEgreen and MEturquoise modules, containing 1,540 and 3,710 genes respectively. After the intersection of the two modules and DEG, two gene sets—DEG-MEgreen and DEG-MEturquoise—were obtained, containing 395 and 1,168 genes respectively. Through GO term analysis, it was found that immune response and signal transduction are the most important biological processes. We found, based on KEGG pathway enrichment analysis, that osteoclast differentiations, cytokine–cytokine receptor interactions, and neuroactive ligand–receptor interactions are the most important in the two gene sets. Through PPI network analysis, we listed the top-ten genes for the concentrated connectivity of the two gene sets. Next, a few genes were verified by qPCR experiments, and FPR2, ITGAM, C3AR1, FCER1G, CYBB in DEG-MEgreen and GNG4, NMUR2, and GNG7 in DEG-MEturquoise were confirmed to be related to the pathogenesis of CRSwNP. NP immune cell infiltration analysis revealed a significant difference in the proportion of immune cells between the NP group and control group. Finally, correlation analysis between target hub genes and immune cells indicated that FPR2 and GNG7 had a positive or negative correlation with some specific immune cells. In summary, the discoveries of these new hub genes and their association with immune cell infiltration are of great significance for uncovering the specific pathogenesis of CRSwNP and searching for disease biomarkers and potential therapeutic targets.

Comprehensive Assessment of Indian Variations in the Druggable Kinome Landscape Highlights Distinct Insights at the Sequence, Structure and Pharmacogenomic Stratum

India confines more than 17% of the world’s population and has a diverse genetic makeup with several clinically relevant rare mutations belonging to many sub-group which are undervalued in global sequencing datasets like the 1000 Genome data (1KG) containing limited samples for Indian ethnicity. Such databases are critical for the pharmaceutical and drug development industry where diversity plays a crucial role in identifying genetic disposition towards adverse drug reactions. A qualitative and comparative sequence and structural study utilizing variant information present in the recently published, largest curated Indian genome database (IndiGen) and the 1000 Genome data was performed for variants belonging to the kinase coding genes, the second most targeted group of drug targets. The sequence-level analysis identified similarities and differences among different populations based on the nsSNVs and amino acid exchange frequencies whereas a comparative structural analysis of IndiGen variants was performed with pathogenic variants reported in UniProtKB Humsavar data. The influence of these variations on structural features of the protein, such as structural stability, solvent accessibility, hydrophobicity, and the hydrogen-bond network was investigated. In-silico screening of the known drugs to these Indian variation-containing proteins reveals critical differences imparted in the strength of binding due to the variations present in the Indian population. In conclusion, this study constitutes a comprehensive investigation into the understanding of common variations present in the second largest population in the world and investigating its implications in the sequence, structural and pharmacogenomic landscape. The preliminary investigation reported in this paper, supporting the screening and detection of ADRs specific to the Indian population could aid in the development of techniques for pre-clinical and post-market screening of drug-related adverse events in the Indian population.

Genomic changes underlying repeated niche shifts in an adaptive radiation

In adaptive radiations, single lineages rapidly diversify by adapting to many new niches. Little is known yet about the genomic mechanisms involved, that is, the source of genetic variation or genomic architecture facilitating or constraining adaptive radiation. Here, we investigate genomic changes associated with repeated invasion of many different freshwater niches by threespine stickleback in the Haida Gwaii archipelago, Canada, by resequencing single genomes from one marine and 28 freshwater populations. We find 89 likely targets of parallel selection in the genome that are enriched for old standing genetic variation. In contrast to theoretical expectations, their genomic architecture is highly dispersed with little clustering. Candidate genes and genotype-environment correlations match the three major environmental axes predation regime, light environment, and ecosystem size. In a niche space with these three dimensions, we find that the more divergent a new niche from the ancestral marine habitat, the more loci show signatures of parallel selection. Our findings suggest that the genomic architecture of parallel adaptation in adaptive radiation depends on the steepness of ecological gradients and the dimensionality of the niche space.

Invertebrate Model Organisms as a Platform to Investigate Rare Human Neurological Diseases

Patients suffering from rare human diseases often go through a painful journey for finding a definite molecular diagnosis prerequisite of appropriate cures. With a novel variant isolated from a single patient, determination of its pathogenicity to end such "diagnostic odyssey" requires multi-step processes involving experts in diverse areas of interest, including clinicians, bioinformaticians and research scientists. Recent efforts in building large-scale genomic databases and in silico prediction platforms have facilitated identification of potentially pathogenic variants causative of rare human diseases of a Mendelian basis. However, the functional significance of individual variants remains elusive in many cases, thus requiring incorporation of versatile and rapid model organism (MO)-based platforms for functional analyses. In this review, the current scope of rare disease research is briefly discussed. In addition, an overview of invertebrate MOs for their key features relevant to rare neurological diseases is provided, with the characteristics of two representative invertebrate MOs, Drosophila melanogaster and Caenorhabditis elegans, as well as the challenges against them. Finally, recently developed research networks integrating these MOs in collaborative research are portraited with an array of bioinformatical analyses embedded. A comprehensive survey of MO-based research activities provided in this review will help us to design a wellstructured analysis of candidate genes or potentially pathogenic variants for their roles in rare neurological diseases in future.

Machine learning prediction and tau-based screening identifies potential Alzheimer's disease genes relevant to immunity

With increased research funding for Alzheimer's disease (AD) and related disorders across the globe, large amounts of data are being generated. Several studies employed machine learning methods to understand the ever-growing omics data to enhance early diagnosis, map complex disease networks, or uncover potential drug targets. We describe results based on a Target Central Resource Database protein knowledge graph and evidence paths transformed into vectors by metapath matching. We extracted features between specific genes and diseases, then trained and optimized our model using XGBoost, termed MPxgb(AD). To determine our MPxgb(AD) prediction performance, we examined the top twenty predicted genes through an experimental screening pipeline. Our analysis identified potential AD risk genes: FRRS1, CTRAM, SCGB3A1, FAM92B/CIBAR2, and TMEFF2. FRRS1 and FAM92B are considered dark genes, while CTRAM, SCGB3A1, and TMEFF2 are connected to TREM2-TYROBP, IL-1β-TNFα, and MTOR-APP AD-risk nodes, suggesting relevance to the pathogenesis of AD.

Normalizing hyperactivity of the Gunn rat with bilirubin-induced neurological disorders via ketanserin

BACKGROUND

Severe neonatal hyperbilirubinemia has been known to cause the clinical syndrome of kernicterus and a milder one the syndrome of bilirubin-induced neurologic dysfunction (BIND). BIND clinically manifests itself after the neonatal period as developmental delay, cognitive impairment, and related behavioral and psychiatric disorders. The complete picture of BIND is not clear.

METHODS

The Gunn rat is a mutant strain of the Wistar rat with the BIND phenotype, and it demonstrates abnormal behavior. We investigated serotonergic dysfunction in Gunn rats by pharmacological analyses and ex vivo neurochemical analyses.

RESULTS

Ketanserin, the 5-HT2AR antagonist, normalizes hyperlocomotion of Gunn rats. Both serotonin and its metabolites in the frontal cortex of Gunn rats were higher in concentrations than in control Wistar rats. The 5-HT2AR mRNA expression was downregulated without alteration of the protein abundance in the Gunn rat frontal cortex. The TPH2 protein level in the Gunn rat raphe region was significantly higher than that in the Wistar rat.

CONCLUSIONS

It would be of value to be able to postulate that a therapeutic strategy for BIND disorders would be the restoration of brain regions affected by the serotonergic dysfunction to normal operation to prevent before or to normalize after onset of BIND manifestations.

IMPACT

We demonstrated serotonergic dysregulation underlying hyperlocomotion in Gunn rats. This finding suggests that a therapeutic strategy for bilirubin-induced neurologic dysfunction (BIND) would be the restoration of brain regions affected by the serotonergic dysfunction to normal operation to prevent before or to normalize after the onset of the BIND manifestations. Ketanserin normalizes hyperlocomotion of Gunn rats. To our knowledge, this is the first study to demonstrate a hyperlocomotion link to serotonergic dysregulation in Gunn rats.

MOET: a web-based gene set enrichment tool at the Rat Genome Database for multiontology and multispecies analyses

Biological interpretation of a large amount of gene or protein data is complex. Ontology analysis tools are imperative in finding functional similarities through overrepresentation or enrichment of terms associated with the input gene or protein lists. However, most tools are limited by their ability to do ontology-specific and species-limited analyses. Furthermore, some enrichment tools are not updated frequently with recent information from databases, thus giving users inaccurate, outdated or uninformative data. Here, we present MOET or the Multi-Ontology Enrichment Tool (v.1 released in April 2019 and v.2 released in May 2021), an ontology analysis tool leveraging data that the Rat Genome Database (RGD) integrated from in-house expert curation and external databases including the National Center for Biotechnology Information (NCBI), Mouse Genome Informatics (MGI), The Kyoto Encyclopedia of Genes and Genomes (KEGG), The Gene Ontology Resource, UniProt-GOA, and others. Given a gene or protein list, MOET analysis identifies significantly overrepresented ontology terms using a hypergeometric test and provides nominal and Bonferroni corrected P-values and odds ratios for the overrepresented terms. The results are shown as a downloadable list of terms with and without Bonferroni correction, and a graph of the P-values and number of annotated genes for each term in the list. MOET can be accessed freely from https://rgd.mcw.edu/rgdweb/enrichment/start.html.

Association of Genetic Variant at Chromosome 12q23.1 With Neuropathic Pain Susceptibility

IMPORTANCE

Neuropathic pain (NP) has important clinical and socioeconomic consequences for individuals and society. Increasing evidence indicates that genetic factors make a significant contribution to NP, but genome-wide association studies (GWASs) are scant in this field and could help to elucidate susceptibility to NP.

OBJECTIVE

To identify genetic variants associated with NP susceptibility.

DESIGN, SETTING, AND PARTICIPANTS

This genetic association study included a meta-analysis of GWASs of NP using 3 independent cohorts: ie, Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS); Generation Scotland: Scottish Family Health Study (GS:SFHS); and the United Kingdom Biobank (UKBB). Data analysis was conducted from April 2018 to December 2019.

EXPOSURES

Individuals with NP (ie, case participants; those with pain of ≥3 months' duration and a Douleur Neuropathique en 4 Questions score ≥3) and individuals with no pain (ie, control participants) with or without diabetes from GoDARTS and GS:SFHS were identified using validated self-completed questionnaires. In the UKBB, self-reported prescribed medication and hospital records were used as a proxy to identify case participants (patients recorded as receiving specific anti-NP medicines) and control participants.

MAIN OUTCOMES AND MEASURES

GWAS was performed using linear mixed modeling. GWAS summary statistics were combined using fixed-effect meta-analysis. A total of 51 variants previously shown to be associated with NP were tested for replication.

RESULTS

This study included a total of 4512 case participants (2662 [58.9%] women; mean [SD] age, 61.7 [10.8] years) and 428 489 control participants (227 817 [53.2%] women; mean [SD] age, 62.3 [11.5] years) in the meta-analysis of 3 cohorts with European descent. The study found a genome-wide significant locus at chromosome 12q23.1, which mapped to SLC25A3 (rs369920026; odds ratio [OR] for having NP, 1.68; 95% CI, 1.40-2.02; P = 1.30 × 10-8), and a suggestive variant at 13q14.2 near CAB39L (rs7992766; OR, 1.09; 95% CI, 1.05-1.14; P = 1.22 × 10-7). These mitochondrial phosphate carriers and calcium binding genes are expressed in brain and dorsal root ganglia. Colocalization analyses using expression quantitative loci data found that the suggestive variant was associated with expression of CAB39L in the brain cerebellum (P = 1.01 × 10-14). None of the previously reported variants were replicated.

CONCLUSIONS AND RELEVANCE

To our knowledge, this was the largest meta-analyses of GWAS to date. It found novel genetic variants associated with NP susceptibility. These findings provide new insights into the genetic architecture of NP and important information for further studies.

Comparison of the Hepatic Effects of Phenobarbital in Chimeric Mice Containing Either Rat or Human Hepatocytes With Humanized Constitutive Androstane Receptor and Pregnane X Receptor Mice

Using a chimeric mouse humanized liver model, we provided evidence that human hepatocytes are refractory to the mitogenic effects of rodent constitutive androstane receptor (CAR) activators. To evaluate the functional reliability of this model, the present study examined mitogenic responses to phenobarbital (PB) in chimeric mice transplanted with rat hepatocytes, because rats are responsive to CAR activators. Treatment with 1000 ppm PB for 7 days significantly increased replicative DNA synthesis (RDS) in rat hepatocytes of the chimeric mice, demonstrating that the transplanted hepatocyte model is functionally reliable for cell proliferation analysis. Treatment of humanized CAR and pregnane X receptor (PXR) mice (hCAR/hPXR mice) with 1000 ppm PB for 7 days significantly increased hepatocyte RDS together with increases in several mitogenic genes. Global gene expression analysis was performed with liver samples from this and from previous studies focusing on PB-induced Wnt/β-catenin signaling and showed that altered genes in hCAR/hPXR mice clustered most closely with liver tumor samples from a diethylnitrosamine/PB initiation/promotion study than with wild-type mice. However, different gene clusters were observed for chimeric mice with human hepatocytes for Wnt/β-catenin signaling when compared with those of hCAR/hPXR mice, wild-type mice, and liver tumor samples. The results of this study demonstrate clear differences in the effects of PB on hepatocyte RDS and global gene expression between human hepatocytes of chimeric mice and hCAR/hPXR mice, suggesting that the chimeric mouse model is relevant to humans for studies on the hepatic effects of rodent CAR activators whereas the hCAR/hPXR mouse is not.

Protein Interaction Network-based Deep Learning Framework for Identifying Disease-Associated Human Proteins

Infectious diseases in humans appear to be one of the most primary public health issues. Identification of novel disease-associated proteins will furnish an efficient recognition of the novel therapeutic targets. Here, we develop a Graph Convolutional Network (GCN)-based model called PINDeL to identify the disease-associated host proteins by integrating the human Protein Locality Graph and its corresponding topological features. Because of the amalgamation of GCN with the protein interaction network, PINDeL achieves the highest accuracy of 83.45% while AUROC and AUPRC values are 0.90 and 0.88, respectively. With high accuracy, recall, F1-score, specificity, AUROC, and AUPRC, PINDeL outperforms other existing machine-learning and deep-learning techniques for disease gene/protein identification in humans. Application of PINDeL on an independent dataset of 24320 proteins, which are not used for training, validation, or testing purposes, predicts 6448 new disease-protein associations of which we verify 3196 disease-proteins through experimental evidence like disease ontology, Gene Ontology, and KEGG pathway enrichment analyses. Our investigation informs that experimentally-verified 748 proteins are indeed responsible for pathogen-host protein interactions of which 22 disease-proteins share their association with multiple diseases such as cancer, aging, chem-dependency, pharmacogenomics, normal variation, infection, and immune-related diseases. This unique Graph Convolution Network-based prediction model is of utmost use in large-scale disease-protein association prediction and hence, will provide crucial insights on disease pathogenesis and will further aid in developing novel therapeutics.

Characterizing modifier genes of cardiac fibrosis phenotype in hypertrophic cardiomyopathy

BACKGROUND

Clinical phenotypes of hypertrophic cardiomyopathy (HCM) vary greatly even among patients with the same gene mutations. This variability is largely regulated by unidentified modifier loci. The purpose of the study is to identify modifier genes for cardiac fibrosis-a major phenotype of HCM-using the BXD family, a murine cohort.

METHODS

The relative severity of cardiac fibrosis was estimated by quantitation of cardiac collagen volume fraction (CCVF) across 66 members of the BXD family. Quantitative trait locus (QTL) mapping for cardiac fibrosis was done using GeneNetwork. Candidate modifier loci and genes associated with fibrosis were prioritized based on an explicit scoring system. Networks of correlation between fibrosis and cardiac transcriptomes were evaluated to generate causal models of disease susceptibility.

RESULTS

CCVF levels varied greatly within this family. Interval mapping identified a significant CCVF-related QTL on chromosome (Chr) 2 in males, and a significant QTL on Chr 4 Mb in females. The scoring system highlighted two strong candidate genes in the Chr 2 locus-Nek6 and Nr6a1. Both genes are highly expressed in the heart. Cardiac Nek6 mRNA levels are significantly correlated with CCVF. Nipsnap3b and Fktn are lead candidate genes for the Chr 4 locus, and both are also highly expressed in heart. Cardiac Nipsnap3b gene expression correlates well with CCVF.

CONCLUSION

Our study demonstrated that candidate modifier genes of cardiac fibrosis phenotype in HCM are different in males and females. Nek6 and Nr6a1 are strong candidates in males, while Nipsnap3b and Fktn are top candidates in females.

Trans-ethnic meta-analysis identifies new loci associated with longitudinal blood pressure traits

Genome-wide association studies (GWAS) have identified thousands of genetic loci associated with cross-sectional blood pressure (BP) traits; however, GWAS based on longitudinal BP have been underexplored. We performed ethnic-specific and trans-ethnic GWAS meta-analysis using longitudinal and cross-sectional BP data of 33,720 individuals from five cohorts in the US and one in Brazil. In addition to identifying several known loci, we identified thirteen novel loci with nine based on longitudinal and four on cross-sectional BP traits. Most of the novel loci were ethnic- or study-specific, with the majority identified in African Americans (AA). Four of these discoveries showed additional evidence of association in independent datasets, including an intergenic variant (rs4060030, p = 7.3 × 10^–9) with reported regulatory function. We observed a high correlation between the meta-analysis results for baseline and longitudinal average BP (rho = 0.48). BP trajectory results were more correlated with those of average BP (rho = 0.35) than baseline BP(rho = 0.18). Heritability estimates trended higher for longitudinal traits than for cross-sectional traits, providing evidence for different genetic architectures. Furthermore, the longitudinal data identified up to 20% more BP known associations than did cross-sectional data. Our analyses of longitudinal BP data in diverse ethnic groups identified novel BP loci associated with BP trajectory, indicating a need for further longitudinal GWAS on BP and other age-related traits.

Genetic Liability for Internalizing Versus Externalizing Behavior Manifests in the Developing and Adult Hippocampus: Insight From a Meta-analysis of Transcriptional Profiling Studies in a Selectively Bred Rat Model

BACKGROUND

For more than 16 years, we have selectively bred rats for either high or low levels of exploratory activity within a novel environment. These bred high-responder (bHR) and bred low-responder (bLR) rats model temperamental extremes, exhibiting large differences in internalizing and externalizing behaviors relevant to mood and substance use disorders.

METHODS

We characterized persistent differences in gene expression related to bHR/bLR phenotype across development and adulthood in the hippocampus, a region critical for emotional regulation, by meta-analyzing 8 transcriptional profiling datasets (microarray and RNA sequencing) spanning 43 generations of selective breeding (postnatal day 7: n = 22; postnatal day 14: n = 49; postnatal day 21: n = 21; adult: n = 46; all male). We cross-referenced expression differences with exome sequencing within our colony to pinpoint candidates likely to mediate the effect of selective breeding on behavioral phenotype. The results were compared with hippocampal profiling from other bred rat models.

RESULTS

Genetic and transcriptional profiling results converged to implicate multiple candidate genes, including two previously associated with metabolism and mood: Trhr and Ucp2. Results also highlighted bHR/bLR functional differences in the hippocampus, including a network essential for neurodevelopmental programming, proliferation, and differentiation, centering on Bmp4 and Mki67. Finally, we observed differential expression related to microglial activation, which is important for synaptic pruning, including 2 genes within implicated chromosomal regions: C1qa and Mfge8.

CONCLUSIONS

These candidate genes and functional pathways may direct bHR/bLR rats along divergent developmental trajectories and promote a widely different reactivity to the environment.

Sex-dimorphic genetic effects and novel loci for fasting glucose and insulin variability

Differences between sexes contribute to variation in the levels of fasting glucose and insulin. Epidemiological studies established a higher prevalence of impaired fasting glucose in men and impaired glucose tolerance in women, however, the genetic component underlying this phenomenon is not established. We assess sex-dimorphic (73,089/50,404 women and 67,506/47,806 men) and sex-combined (151,188/105,056 individuals) fasting glucose/fasting insulin genetic effects via genome-wide association study meta-analyses in individuals of European descent without diabetes. Here we report sex dimorphism in allelic effects on fasting insulin at IRS1 and ZNF12 loci, the latter showing higher RNA expression in whole blood in women compared to men. We also observe sex-homogeneous effects on fasting glucose at seven novel loci. Fasting insulin in women shows stronger genetic correlations than in men with waist-to-hip ratio and anorexia nervosa. Furthermore, waist-to-hip ratio is causally related to insulin resistance in women, but not in men. These results position dissection of metabolic and glycemic health sex dimorphism as a steppingstone for understanding differences in genetic effects between women and men in related phenotypes.

Genetic Adaptation in New York City Rats

Brown rats (Rattus norvegicus) thrive in urban environments by navigating the anthropocentric environment and taking advantage of human resources and by-products. From the human perspective, rats are a chronic problem that causes billions of dollars in damage to agriculture, health, and infrastructure. Did genetic adaptation play a role in the spread of rats in cities? To approach this question, we collected whole-genome sequences from 29 brown rats from New York City (NYC) and scanned for genetic signatures of adaptation. We tested for 1) high-frequency, extended haplotypes that could indicate selective sweeps and 2) loci of extreme genetic differentiation between the NYC sample and a sample from the presumed ancestral range of brown rats in northeast China. We found candidate selective sweeps near or inside genes associated with metabolism, diet, the nervous system, and locomotory behavior. Patterns of differentiation between NYC and Chinese rats at putative sweep loci suggest that many sweeps began after the split from the ancestral population. Together, our results suggest several hypotheses on adaptation in rats living in proximity to humans.

Transforming the study of organisms: Phenomic data models and knowledge bases

The rapidly decreasing cost of gene sequencing has resulted in a deluge of genomic data from across the tree of life; however, outside a few model organism databases, genomic data are limited in their scientific impact because they are not accompanied by computable phenomic data. The majority of phenomic data are contained in countless small, heterogeneous phenotypic data sets that are very difficult or impossible to integrate at scale because of variable formats, lack of digitization, and linguistic problems. One powerful solution is to represent phenotypic data using data models with precise, computable semantics, but adoption of semantic standards for representing phenotypic data has been slow, especially in biodiversity and ecology. Some phenotypic and trait data are available in a semantic language from knowledge bases, but these are often not interoperable. In this review, we will compare and contrast existing ontology and data models, focusing on nonhuman phenotypes and traits. We discuss barriers to integration of phenotypic data and make recommendations for developing an operationally useful, semantically interoperable phenotypic data ecosystem.

CYP2J2 Molecular Recognition: A New Axis for Therapeutic Design

Cytochrome P450 (CYP) epoxygenases are a special subset of heme-containing CYP enzymes capable of performing the epoxidation of polyunsaturated fatty acids (PUFA) and the metabolism of xenobiotics. This dual functionality positions epoxygenases along a metabolic crossroad. Therefore, structure-function studies are critical for understanding their role in bioactive oxy-lipid synthesis, drug-PUFA interactions, and for designing therapeutics that directly target the epoxygenases. To better exploit CYP epoxygenases as therapeutic targets, there is a need for improved understanding of epoxygenase structure-function. Of the characterized epoxygenases, human CYP2J2 stands out as a potential target because of its role in cardiovascular physiology. In this review, the early research on the discovery and activity of epoxygenases is contextualized to more recent advances in CYP epoxygenase enzymology with respect to PUFA and drug metabolism. Additionally, this review employs CYP2J2 epoxygenase as a model system to highlight both the seminal works and recent advances in epoxygenase enzymology. Herein we cover CYP2J2's interactions with PUFAs and xenobiotics, its tissue-specific physiological roles in diseased states, and its structural features that enable epoxygenase function. Additionally, the enumeration of research on CYP2J2 identifies the future needs for the molecular characterization of CYP2J2 to enable a new axis of therapeutic design.

A comprehensive rat transcriptome built from large scale RNA-seq-based annotation

The rat is an important model organism in biomedical research for studying human disease mechanisms and treatments, but its annotated transcriptome is far from complete. We constructed a Rat Transcriptome Re-annotation named RTR using RNA-seq data from 320 samples in 11 different organs generated by the SEQC consortium. Totally, there are 52 807 genes and 114 152 transcripts in RTR. Transcribed regions and exons in RTR account for ∼42% and ∼6.5% of the genome, respectively. Of all 73 074 newly annotated transcripts in RTR, 34 213 were annotated as high confident coding transcripts and 24 728 as high confident long noncoding transcripts. Different tissues rather than different stages have a significant influence on the expression patterns of transcripts. We also found that 11 715 genes and 15 852 transcripts were expressed in all 11 tissues and that 849 house-keeping genes expressed different isoforms among tissues. This comprehensive transcriptome is freely available at http://www.unimd.org/rtr/. Our new rat transcriptome provides essential reference for genetics and gene expression studies in rat disease and toxicity models.

The lncRNA LOC102549805 (U1) modulates neurotoxicity of HIV-1 Tat protein

HIV-1 Tat is a potent neurotoxic protein that is released by HIV-1 infected cells in the brain and perturbs neuronal homeostasis, causing a broad range of neurological disorders in people living with HIV-1. Furthermore, the effects of Tat have been addressed in numerous studies to investigate the molecular events associated with neuronal cells survival and death. Here, we discovered that exposure of rat primary neurons to Tat resulted in the up-regulation of an uncharacterized long non-coding RNA (lncRNA), LOC102549805 (lncRNA-U1). Our observations showed that increased expression of lncRNA-U1 in neurons disrupts bioenergetic pathways by dysregulating homeostasis of Ca²⁺, mitigating mitochondrial oxygen reduction, and decreasing ATP production, all of which point mitochondrial impairment in neurons via the Tat-mediated lncRNA-U1 induction. These changes were associated with imbalances in autophagy and apoptosis pathways. Additionally, this study showed the ability of Tat to modulate expression of the neuropeptide B/W receptor 1 (NPBWR1) gene via up-regulation of lncRNA-U1. Collectively, our results identified Tat-mediated lncRNA-U1 upregulation resulting in disruption of neuronal homeostasis.

Music-listening regulates human microRNA expression

Music-listening and performance have been shown to affect human gene expression. In order to further elucidate the biological basis of the effects of music on the human body, we studied the effects of music-listening on gene regulation by sequencing microRNAs of the listeners (Music Group) and their controls (Control Group) without music exposure. We identified upregulation of six microRNAs (hsa-miR-132-3p, hsa-miR-361-5p, hsa-miR-421, hsa-miR-23a-3p, hsa-miR-23b-3p, hsa-miR-25-3p) and downregulation of two microRNAs (hsa-miR-378a-3p, hsa-miR-16-2-3p) in Music Group with high musical aptitude. Some upregulated microRNAs were reported to be responsive to neuronal activity (miR-132, miR-23a, miR-23b) and modulators of neuronal plasticity, CNS myelination, and cognitive functions like long-term potentiation and memory. miR-132 plays a critical role in regulating TAU protein levels and is important for preventing tau protein aggregation that causes Alzheimer's disease. miR-132 and DICER, upregulated after music-listening, protect dopaminergic neurons and are important for retaining striatal dopamine levels. Some of the transcriptional regulators (FOS, CREB1, JUN, EGR1, and BDNF) of the upregulated microRNAs were immediate early genes and top candidates associated with musical traits. BDNF and SNCA, co-expressed and upregulated in music-listening and music-performance, are both are activated by GATA2, which is associated with musical aptitude. Several miRNAs were associated with song-learning, singing, and seasonal plasticity networks in songbirds. We did not detect any significant changes in microRNA expressions associated with music education or low musical aptitude. Our data thereby show the importance of inherent musical aptitude for music appreciation and for eliciting the human microRNA response to music-listening.

Striatal Rgs4 regulates feeding and susceptibility to diet-induced obesity

Consumption of high fat, high sugar (western) diets is a major contributor to the current high levels of obesity. Here, we used a multidisciplinary approach to gain insight into the molecular mechanisms underlying susceptibility to diet-induced obesity (DIO). Using positron emission tomography (PET), we identified the dorsal striatum as the brain area most altered in DIO-susceptible rats and molecular studies within this region highlighted regulator of G-protein signaling 4 (Rgs4) within laser-capture micro-dissected striatonigral (SN) and striatopallidal (SP) medium spiny neurons (MSNs) as playing a key role. Rgs4 is a GTPase accelerating enzyme implicated in plasticity mechanisms of SP MSNs, which are known to regulate feeding and disturbances of which are associated with obesity. Compared to DIO-resistant rats, DIO-susceptible rats exhibited increased striatal Rgs4 with mRNA expression levels enriched in SP MSNs. siRNA-mediated knockdown of striatal Rgs4 in DIO-susceptible rats decreased food intake to levels comparable to DIO-resistant animals. Finally, we demonstrated that the human Rgs4 gene locus is associated with increased body weight and obesity susceptibility phenotypes, and that overweight humans exhibit increased striatal Rgs4 protein. Our findings highlight a novel role for involvement of Rgs4 in SP MSNs in feeding and DIO-susceptibility.

A Novel Open Access Web Portal for Integrating Mechanistic and Toxicogenomic Study Results

Applying toxicogenomics to improving the safety profile of drug candidates and crop protection molecules is most useful when it identifies relevant biological and mechanistic information that highlights risks and informs risk mitigation strategies. Pathway-based approaches, such as gene set enrichment analysis, integrate toxicogenomic data with known biological process and pathways. Network methods help define unknown biological processes and offer data reduction advantages. Integrating the 2 approaches would improve interpretation of toxicogenomic information. Barriers to the routine application of these methods in genome-wide transcriptomic studies include a need for “hands-on” computer programming experience, the selection of 1 or more analysis methods (eg pathway analysis methods), the sensitivity of results to algorithm parameters, and challenges in linking differential gene expression to variation in safety outcomes. To facilitate adoption and reproducibility of gene expression analysis in safety studies, we have developed Collaborative Toxicogeomics, an open-access integrated web portal using the Django web framework. The software, developed with the Python programming language, is modular, extensible and implements “best-practice” methods in computational biology. New study results are compared with over 4000 rodent liver experiments from Drug Matrix and open TG-GATEs. A unique feature of the software is the ability to integrate clinical chemistry and histopathology-derived outcomes with results from gene expression studies, leading to relevant mechanistic conclusions. We describe its application by analyzing the effects of several toxicants on liver gene expression and exemplify application to predicting toxicity study outcomes upon chronic treatment from expression changes in acute-duration studies.

HISTome2: a database of histone proteins, modifiers for multiple organisms and epidrugs

BACKGROUND

Epigenetics research is progressing in basic, pre-clinical and clinical studies using various model systems. Hence, updating the knowledge and integration of biological data emerging from in silico, in vitro and in vivo studies for different epigenetic factors is essential. Moreover, new drugs are being discovered which target various epigenetic proteins, tested in pre-clinical studies, clinical trials and approved by the FDA. It brings distinct challenges as well as opportunities to update the existing HIstome database for implementing and applying enormous data for biomedical research.

RESULTS

HISTome2 focuses on the sub-classification of histone proteins as variants and isoforms, post-translational modifications (PTMs) and modifying enzymes for humans (Homo sapiens), rat (Rattus norvegicus) and mouse (Mus musculus) on one interface for integrative analysis. It contains 232, 267 and 350 entries for histone proteins (non-canonical/variants and canonical/isoforms), PTMs and modifying enzymes respectively for human, rat, and mouse. Around 200 EpiDrugs for various classes of epigenetic modifiers, their clinical trial status, and pharmacological relevance have been provided in HISTome2. The additional features like 'Clustal omega' for multiple sequence alignment, link to 'FireBrowse' to visualize TCGA expression data and 'TargetScanHuman' for miRNA targets have been included in the database.

CONCLUSION

The information for multiple organisms and EpiDrugs on a common platform will accelerate the understanding and future development of drugs. Overall, HISTome2 has significantly increased the extent and diversity of its content which will serve as a 'knowledge Infobase' for biologists, pharmacologists, and clinicians. HISTome2: The HISTone Infobase is freely available on http://www.actrec.gov.in/histome2/ .

Underlying Mechanism of Insulin Resistance: A Bioinformatics Analysis Based on Validated Related-Genes from Public Disease Databases

Background

The underlying mechanism of insulin resistance is complex; bioinformatics analysis is used to explore the mechanism based differential expression genes (DEGs) obtained from omics analysis. However, the expression and role of most DEGs involved in bioinformatics analysis are invalidated. This study aimed to disclose the mechanism of insulin resistance via bioinformatics analysis based on validated insulin resistance-related genes (IRRGs) collected from public disease-gene databases.

Material/Methods

IRRGs were collected from 4 disease databases including NCBI-Gene, CTD, RGD, and Phenopedia. GO and KEGG analysis of IRRGs were performed by DAVID. Then, the STRING database was employed to construct a protein–protein interaction (PPI) network of IRRGs. The module analysis and hub genes identification were carried out by MCODE and cytoHubba plugin of Cytoscape based on the primary PPI network, respectively.

Results

A total of 1195 IRRGs were identified. Response to drug, hypoxia, insulin, positive regulation of transcription from RNA polymerase II promoter, cell proliferation, inflammatory response, negative regulation of apoptotic process, glucose homeostasis, cellular response to insulin stimulus, and aging were proposed as the crucial functions related to insulin resistance. Ten insulin resistance-related pathways included the pathways of insulin resistance, pathways in cancer, adipocytokine, prostate cancer, PI3K-Akt, insulin, AMPK, HIF-1, prolactin, and pancreatic cancer signaling pathway were revealed. INS, AKT1, IL-6, TP53, TNF, VEGFA, MAPK3, EGFR, EGF, and SRC were identified as the top 10 hub genes.

Conclusions

The current study presented a landscape view of possible underlying mechanism of insulin resistance by bioinformatics analysis based on validated IRRGs.

Identifying modifier genes for hypertrophic cardiomyopathy

BACKGROUND

Hypertrophic cardiomyopathy (HCM) severity greatly varies among patients even with the same HCM gene mutations. This variation is largely regulated by modifier gene(s), which, however, remain largely unknown. The current study is aimed to identify modifier genes using BXD strains, a large murine genetic reference population (GRP) derived from crosses between C57BL/6 J (B6) and D2 DBA/2 J (D2) mice. D2 mice natualy carrythe genetic basis and phenotypes of HCM.

METHODS

Myocardial hypertrophy, the major phenotype of HCM, was determined by cardiomyocyte size on cardiac sections in 30 BXD strains, and their parental B6 and D2 strains and morphometric analysis was performed. Quantitative Trait Locus (QTL) mapping for cardiomyocyte sizes was conducted with WebQTL in GeneNetwork. Correlation of cardiomyocyte size and cardiac gene expression in BXDs accessed from GeneNetwork were evaluated. QTL candidate genes associated with cardiomyocyte sizes were prioritized based on the score system.

RESULTS

Cardiomyocyte size varied significantly among BXD strains. Interval mapping on cardiomyocyte size data showed a significant QTL on chromosome (Chr) 2 at 66- 73.5 Mb and a suggestive QTL on Chr 5 at 20.9-39.7 Mb. Further score system revealed a high QTL score for Xirp2 in Chr 2. Xirp2 encodes xin actin-binding repeat containing 2, which is highly expressed in cardiac tissue and associate with cardiomyopathy and heart failure. In Chr5 QTL, Nos3, encoding nitric oxide synthase 3, received the highest score, which is significantly correlated with cardiomyocyte size.

CONCLUSION

These results indicate that Xirp2 and Nos3 serve as novel candidate modifier genes for myocardial hypertrophy in HCM. These candidate genes will be validated in our future studies.

Improving Replicability, Reproducibility, And Reliability In Preclinical Research: A Shared Responsibility

Reproducible and reliable scientific investigation depends on the identification and consideration of various intrinsic and extrinsic factors that may affect the model system used. The impact of these factors must be managed during all phases of a study: planning, execution, and reporting. The value of in vivo (animal) research has come under increasing scrutiny over the past decade because of multiple reports documenting poor translatability to human studies. These failures have been attributed to various causes, including poor study design and execution as well as deficiencies in reporting. It is important to recognize that achieving reproducible and reliable preclinical research results is a joint responsibility that requires a partnership between the investigative team and the animal care and use program staff. The myriad of intrinsic factors, such as species, strain/substrain, age, sex, physiologic and health status, and extrinsic factors, including temperature, humidity, lighting, housing system, and diet, need to be recognized and managed during study planning and execution, as they can influence animal physiology and biological response. Of equal importance is the need to document and report these details. The ARRIVE and PREPARE guidelines were developed by concerned scientists, veterinarians, statisticians, journal editors, and funding agencies to assist investigative teams and scientific journals manage and report on intrinsic and extrinsic factors to improve reproducibility and reliability. This issue of the ILAR Journal will focus on the various extrinsic factors that have been recognized to confound animal research.

The Year of the Rat: The Rat Genome Database at 20: a multi-species knowledgebase and analysis platform

Formed in late 1999, the Rat Genome Database (RGD, https://rgd.mcw.edu) will be 20 in 2020, the Year of the Rat. Because the laboratory rat, Rattus norvegicus, has been used as a model for complex human diseases such as cardiovascular disease, diabetes, cancer, neurological disorders and arthritis, among others, for >150 years, RGD has always been disease-focused and committed to providing data and tools for researchers doing comparative genomics and translational studies. At its inception, before the sequencing of the rat genome, RGD started with only a few data types localized on genetic and radiation hybrid (RH) maps and offered only a few tools for querying and consolidating that data. Since that time, RGD has expanded to include a wealth of structured and standardized genetic, genomic, phenotypic, and disease-related data for eight species, and a suite of innovative tools for querying, analyzing and visualizing this data. This article provides an overview of recent substantial additions and improvements to RGD's data and tools that can assist researchers in finding and utilizing the data they need, whether their goal is to develop new precision models of disease or to more fully explore emerging details within a system or across multiple systems.

The DisGeNET knowledge platform for disease genomics: 2019 update

One of the most pressing challenges in genomic medicine is to understand the role played by genetic variation in health and disease. Thanks to the exploration of genomic variants at large scale, hundreds of thousands of disease-associated loci have been uncovered. However, the identification of variants of clinical relevance is a significant challenge that requires comprehensive interrogation of previous knowledge and linkage to new experimental results. To assist in this complex task, we created DisGeNET (http://www.disgenet.org/), a knowledge management platform integrating and standardizing data about disease associated genes and variants from multiple sources, including the scientific literature. DisGeNET covers the full spectrum of human diseases as well as normal and abnormal traits. The current release covers more than 24 000 diseases and traits, 17 000 genes and 117 000 genomic variants. The latest developments of DisGeNET include new sources of data, novel data attributes and prioritization metrics, a redesigned web interface and recently launched APIs. Thanks to the data standardization, the combination of expert curated information with data automatically mined from the scientific literature, and a suite of tools for accessing its publicly available data, DisGeNET is an interoperable resource supporting a variety of applications in genomic medicine and drug R&D.

MitoMiner v4.0: an updated database of mitochondrial localization evidence, phenotypes and diseases

Increasing numbers of diseases are associated with mitochondrial dysfunction. This is unsurprising given mitochondria have major roles in bioenergy generation, signalling, detoxification, apoptosis and biosynthesis. However, fundamental questions of mitochondrial biology remain, including: which nuclear genes encode mitochondrial proteins; how their expression varies with tissue; and which are associated with disease. But experiments to catalogue the mitochondrial proteome are incomplete and sometimes contradictory. This arises because the mitochondrial proteome has tissue- and stage-specific variability, plus differences among experimental techniques and localization evidence types used. This leads to limitations in each technique’s coverage and inevitably conflicting results. To support identification of mitochondrial proteins, we developed MitoMiner (http://mitominer.mrc-mbu.cam.ac.uk/), a database combining evidence of mitochondrial localization with information from public resources. Here we report upgrades to MitoMiner, including its re-engineering to be gene-centric to enable easier sharing of evidence among orthologues and support next generation sequencing, plus new data sources, including expression in different tissues, information on phenotypes and diseases of genetic mutations and a new mitochondrial proteome catalogue. MitoMiner is a powerful platform to investigate mitochondrial localization by providing a unique combination of experimental sub-cellular localization datasets, tissue expression, predictions of mitochondrial targeting sequences, gene annotation and links to phenotype and disease.

Expansion of the Human Phenotype Ontology (HPO) knowledge base and resources

The Human Phenotype Ontology (HPO)—a standardized vocabulary of phenotypic abnormalities associated with 7000+ diseases—is used by thousands of researchers, clinicians, informaticians and electronic health record systems around the world. Its detailed descriptions of clinical abnormalities and computable disease definitions have made HPO the de facto standard for deep phenotyping in the field of rare disease. The HPO’s interoperability with other ontologies has enabled it to be used to improve diagnostic accuracy by incorporating model organism data. It also plays a key role in the popular Exomiser tool, which identifies potential disease-causing variants from whole-exome or whole-genome sequencing data. Since the HPO was first introduced in 2008, its users have become both more numerous and more diverse. To meet these emerging needs, the project has added new content, language translations, mappings and computational tooling, as well as integrations with external community data. The HPO continues to collaborate with clinical adopters to improve specific areas of the ontology and extend standardized disease descriptions. The newly redesigned HPO website (www.human-phenotype-ontology.org) simplifies browsing terms and exploring clinical features, diseases, and human genes.

The RNAcentral Consortium http://orcid.org/0000-0002-6497-2883SweeneyBlake Ahttp://orcid.org/0000-0001-7279-2682PetrovAnton IBurkovBorishttp://orcid.org/0000-0001-8626-2148FinnRobert Dhttp://orcid.org/0000-0002-6982-4660BatemanAlexSzymanskiMaciejKarlowskiWojciech MGorodkinJanSeemannStefan ECannoneJamie JGutellRobin RFeyPetraBasuSiddharthaKaySimonhttp://orcid.org/0000-0001-7954-7057CochraneGuyBillisKostantinosEmmertDavidMarygoldSteven Jhttp://orcid.org/0000-0001-6718-3559HuntleyRachael Phttp://orcid.org/0000-0002-9791-0064LoveringRuth CFrankishAdamChanPatricia Phttp://orcid.org/0000-0003-3253-6021LoweTodd Mhttp://orcid.org/0000-0002-8380-5247BrufordElspethSealRuthhttp://orcid.org/0000-0001-6274-0184VandesompeleJohttp://orcid.org/0000-0002-2685-2637VoldersPieter-JanParaskevopoulouMariaMaLinaZhangZhangGriffiths-JonesSamBujnickiJanusz MBoccalettoPietrohttp://orcid.org/0000-0001-8522-334XBlakeJudith ABultCarol JChenRunshengZhaoYiWoodValerieRutherfordKimhttp://orcid.org/0000-0002-2084-269XRivasElenaColeJameshttp://orcid.org/0000-0001-5356-4174LaulederkindStanley J FShimoyamaMaryGillespieMarc EOrlic-MilacicMarijahttp://orcid.org/0000-0001-9424-9197KalvariIoannahttp://orcid.org/0000-0002-2497-3427NawrockiEricEngelStacia Rhttp://orcid.org/0000-0001-9163-5180CherryJ MichaelTeamSILVABerardiniTanya ZHatzigeorgiouArtemisKaragkouniDimitrahttp://orcid.org/0000-0002-1751-9226HoweKevinDavisPaulDingerMarcelhttp://orcid.org/0000-0002-7294-0865HeShunminYoshihamaMakiKenmochiNaoyaStadlerPeter FWilliamsKelly P. RNAcentral: a hub of information for non-coding RNA sequences

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences, collating information on ncRNA sequences of all types from a broad range of organisms. We have recently added a new genome mapping pipeline that identifies genomic locations for ncRNA sequences in 296 species. We have also added several new types of functional annotations, such as tRNA secondary structures, Gene Ontology annotations, and miRNA-target interactions. A new quality control mechanism based on Rfam family assignments identifies potential contamination, incomplete sequences, and more. The RNAcentral database has become a vital component of many workflows in the RNA community, serving as both the primary source of sequence data for academic and commercial groups, as well as a source of stable accessions for the annotation of genomic and functional features. These examples are facilitated by an improved RNAcentral web interface, which features an updated genome browser, a new sequence feature viewer, and improved text search functionality. RNAcentral is freely available at https://rnacentral.org.

Genenames.org: the HGNC and VGNC resources in 2019

The HUGO Gene Nomenclature Committee (HGNC) based at EMBL's European Bioinformatics Institute (EMBL-EBI) assigns unique symbols and names to human genes. There are over 40 000 approved gene symbols in our current database of which over 19 000 are for protein-coding genes. The Vertebrate Gene Nomenclature Committee (VGNC) was established in 2016 to assign standardized nomenclature in line with human for vertebrate species that lack their own nomenclature committees. The VGNC initially assigned nomenclature for over 15000 protein-coding genes in chimpanzee. We have extended this process to other vertebrate species, naming over 14000 protein-coding genes in cow and dog and over 13 000 in horse to date. Our HGNC website https://www.genenames.org has undergone a major design update, simplifying the homepage to provide easy access to our search tools and making the site more mobile friendly. Our gene families pages are now known as 'gene groups' and have increased in number to over 1200, with nearly half of all named genes currently assigned to at least one gene group. This article provides an overview of our online data and resources, focusing on our work over the last two years.

Gene expression profiles for low-dose exposure to diethyl phthalate in rodents and humans: a translational study with implications for breast carcinogenesis

Phthalates are commonly included as ingredients in personal care products such as cosmetics, shampoos and perfumes. Diethyl phthalate (DEP) has been found to be anti-androgenic and linked with adverse reproductive effects on males, but effects on females are poorly understood. We designed an integrative and translational study to experimentally examine the effects of DEP exposure at a human-equivalent dose on the mammary transcriptome in rats and to subsequently examine the DEP gene signature in breast tissues (both pre-malignant and tumor) from a population study. In Sprague-Dawley rats treated orally with DEP from birth to adulthood, we identified a signature panel of 107 genes predominantly down-regulated by DEP exposure. Univariate analysis of this 107 DEP gene signature in pre-malignant breast tissues revealed that six genes (P4HA1, MPZL3, TMC4, PLEKHA6, CA8, AREG) were inversely associated with monoethyl phthalate (MEP; the urinary metabolite of DEP) concentration (p < 0.05) among postmenopausal women; all six genes loaded on to one of seven factors identified by factor analysis. Transcription factor enrichment analysis revealed that genes in this factor were enriched for androgen receptor binding sites. These six genes were also significantly down-regulated in pre-malignant adjacent tissues compared to the corresponding tumor tissues in pair-wise analyses (p < 0.05). Results from our translational study indicate that low level exposure to diethyl phthalate results in measurable genomic changes in breast tissue with implications in breast carcinogenesis.