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Biology subjects

Uddin, M.

Publications and source records attributed to Uddin, M..

At least 19 recordsLinked to original sources

Altered Steroidogenesis in Response to Impaired Mitochondrial Protein Homeostasis

Mitochondrial proteostasis is critical for organelle function, but how it influences mitochondrial steroidogenesis is not understood. We investigated the impact of impaired mitochondrial proteostasis on mitochondrial protein import and how this impacts cortisol production. We found that mild mitochondrial proteostatic stress increased cortisol synthesis whereas severe stress suppressed cortisol, revealing a biphasic steroidogenic response. Mild stress induced increases in cholesterol import and cortisol production in a manner dependent on STARD1-mediated cholesterol transport, which is the first and rate-limiting step in steroidogenesis. Severe stress reduced cortisol synthesis independently of STARD1 activity, indicating this inhibition occurs downstream in steroidogenesis. These findings reveal mitochondrial protein import as a regulatory node linking mitochondrial proteostasis to steroidogenic output. Furthermore, we observed increased cortisol synthesis in rotenone, A53T alpha-synuclein and PINK1-KO models of PD, suggesting that this response is relevant to PD-associated mitochondrial dysfunction. Together, our findings reveal a biphasic steroidogenic response to impairments in mitochondrial quality control with implications in human disease. Our work recognizes a previously unexplored branch of the mitochondrial stress response that enables cellular and physiological adaptations that can be detrimental if constitutively activated.

cell biology↗

Phenotype Dependent Segregation of a Novel EPS8 Variant for Hearing Loss and an HPDL Variant for Neurodevelopmental Disorders in a Complex Consanguineous Family

Consanguinity increases the risk of autosomal recessive disorders and may result in the co-segregation of multiple pathogenic variants within the same family. Although most affected families are explained by a single genetic diagnosis, multilocus pathogenic variation can produce complex and overlapping clinical phenotypes. We investigated a consanguineous Pakistani family with three affected siblings, including dizygotic twins presenting with neurodevelopmental disorder and hearing loss, using detailed clinical evaluation, long-read whole-genome sequencing, bulk transcriptomics, protein profiling, and segregation analysis to determine the underlying molecular diagnoses. One sibling presented with isolated non-syndromic hearing loss, whereas the dizygotic twins exhibited severe neurodevelopmental impairment characterized by global developmental delay, spastic quadriplegic cerebral palsy, microcephaly, and white matter abnormalities. Long-read whole-genome sequencing identified a homozygous start-loss variant in HPDL (c.3G>C) in both twins, consistent with HPDL-related neurodevelopmental disorder with progressive spasticity and brain white matter abnormalities (NEDSWMA). In addition, a novel homozygous nonsense variant in EPS8 (c.1294C>T) was identified in one twin and the sibling with isolated hearing loss, explaining the auditory phenotype. Long-read transcriptomic analysis demonstrated absence of detectable EPS8 transcripts in both individuals homozygous for the nonsense variant, providing transcript-level evidence consistent with a loss-of-function mechanism. Genome-wide comprehensive proteomic profiling (SomaScan) identified distinct protein abundance profiles across family members, with the most pronounced alterations observed in the twins affected by HPDL-related neurodevelopmental disease, particularly the individual harboring pathogenic variants in both EPS8 and HPDL. This study expands the mutational spectrum of EPS8 and highlights the independent segregation of two autosomal recessive disorders within the complex consanguineous family, resulting in distinct and blended phenotypes.

genetics↗

Isoform-Resolved Genetic Architecture of Epilepsy and SUDEP Reveals Divergent Brain and Heart Channelopathy Signatures

Sudden unexpected death in epilepsy (SUDEP) is the most devastating complication of epilepsy, yet the molecular features distinguishing individuals at risk remain poorly defined. Although epilepsy and SUDEP share substantial genetic overlap, fatal outcomes may arise when shared risk genes are differentially deployed across neuronal and cardiac systems. Here, we identify tissue- and isoform-level regulation as a key determinant of divergence between epilepsy and SUDEP risk. We performed a large-scale integrated analysis of genetic variants reported in epilepsy and SUDEP across 419 sequencing-based studies encompassing 35,659 individuals, and quantified gene-level burden using a Bayesian Poisson-Gamma rate ratio framework. This analysis revealed preferential enrichment of genes related to cardiac electrophysiology and contractile function in SUDEP, whereas epilepsy was dominated by genes involved in neuronal excitability and synaptic signaling. To determine how shared genetic loci are deployed across tissues, we integrated GTEx-based tissue expression profiles with long-read single-cell transcriptomic datasets from human heart and brain to resolve isoform-level expression patterns. These analyses revealed pronounced tissue-specific transcript architectures. Cardiac-associated genes, including HCN4, KCNH2, KCNE1, MYH6, MYO18B, and ATP1A2, showed heart-restricted isoform expression, whereas neuronal genes such as ADGRV1, CACNA1A, GRIN2B, HCN1, HCN2, KCNA1, SCN1A, SCN2A, and SCN8A. Importantly, several shared genes exhibited tissue-partitioned isoform expression, with distinct transcript repertoires in heart and brain, particularly across pathways related to ion transport, signaling, metabolism, and structural organization. Consistent patterns were observed in iPSC-derived cardiomyocytes and neurons, indicating that lineage-dependent deployment of shared genes is preserved in controlled systems. Together, these findings suggest that tissue-specific isoform regulation provides a mechanistic basis linking shared epilepsy genetics to SUDEP susceptibility, whereby the same genetic loci contribute to neuronal dysfunction in epilepsy and to cardiac vulnerability in SUDEP. This positions SUDEP as a neuro-cardiac interface disorder shaped by isoform-level regulatory divergence.

genetics↗

Lung microvascular rarefaction impairs pulmonary gas exchange and exacerbates heart failure with preserved ejection fraction

BackgroundDyspnea and exercise intolerance are the primary clinical symptoms of heart failure. Heart failure patients experience frequent hypoxemic episodes, yet underlying mechanisms and relevance remain poorly understood. In a cohort of heart failure patients and multiple animal models, we identify pulmonary capillary rarefaction driven by excessive autophagy in endothelial cells as a novel mechanism of hypoxemia and cardiac disease progression. MethodsA cohort of heart failure with preserved ejection fraction (HFpEF) patients was analyzed for parameters of left ventricular (LV) dysfunction and pulmonary gas exchange. Morphological and cellular mechanisms of impaired pulmonary oxygenation were assessed in three animal models of heart failure, namely two HFpEF models, SU5416-treated ZSF1 obese rats and high fat diet/L-NAME treated mice, and in rats subjected to aortic banding. Lung microvascular rarefaction was quantified by micro-computed tomography, stereology, flow cytometry and dye efflux. Cellular mechanisms of capillary loss were analyzed by single-cell transcriptomics, electron microscopy and immunofluorescence, and in mice with endothelial-specific deletion of the autophagy gene Atg7 (Atg7EN-KO). ResultsIn 234 HFpEF patients, advancing NYHA class was associated with progressive worsening of arterial oxygen saturation at rest and during exercise and a reduced lung diffusing capacity. Impaired gas diffusion correlated with indices of LV diastolic dysfunction. Impaired oxygenation and reduced exercise capacity were similarly evident in animal models of left heart disease, which showed a distinct loss of pulmonary microvessels and capillaries. Lung microvascular endothelial cells in HFpEF showed characteristics of increased autophagic flux and apoptosis. Relative to their wild type HFpEF controls, Atg7EN-KO mice had less capillary loss, restored normoxemia, improved exercise tolerance, and mitigated LV diastolic dysfunction. Additional studies in HFpEF mice corroborated the functional relevance of impaired gas exchange for the progression of left heart disease by demonstrating that additional hypoxia aggravated, whereas moderate hyperoxia improved LV function. ConclusionOur findings identify pulmonary microvascular rarefaction as a novel pathomechanism in heart failure that i) contributes to dyspnea and exercise intolerance, ii) impairs pulmonary gas exchange and iii) accelerates LV disease progression. Strategies targeting this axis such as moderate oxygen therapy may mitigate cardiopulmonary morbidity in heart failure. Clinical Trial RegistrationRegistered in the DRKS (Deutsches Register fur klinische Studien) as trial# DRKS00032974 at https://drks.de/search/en/trial/DRKS00032974.

physiology↗

ProteoMapper: Alignment-Aware Identification and Quantitative Analysis of Contextual Motif-Domain Patterns in Protein Families

Protein function depends on interactions between structural domains and regulatory motifs. Yet current tools analyze these elements separately, hindering investigation of disease mutations affecting evolutionarily conserved, structurally constrained motifs. We present ProteoMapper, a computational framework integrating HMMER-based domain annotation with user-defined motif detection to quantify motif-domain spatial relationships in protein families. ProteoMapper introduces two discovery metrics: (1) positional conservation scoring, identifying motifs at identical alignment coordinates in [&ge;] N% of sequences (default 60%), indicating purifying selection; (2) Motif-Domain Coverage Score (MDCS), quantifying motif embedding within Pfam domains (MDCS=1: fully embedded; MDCS=0: extra-domain). The platform processes Excel-formatted alignments without programming requirements, delivering color-coded reports with conserved motif positions, domain boundaries, and MDCS values. Parallel execution of sequence batches enables rapid analysis (8 motifs were searched in 150 sequences with complete Pfam scanning in <6 seconds on standard hardware). Validation across three protein families confirmed technical accuracy and biological insight. In PLATZ transcription factors (24 proteins), domain predictions achieved 0.94 mean intersection-over-union versus published annotations, exactly reproducing 22 of 23 reported spans. In Arabidopsis ERD6-like sugar transporters (17 proteins), MDCS analysis revealed canonical PROSITE signatures PS00216 and PS00217 are equally domain-embedded (MDCS=1.0) but evolutionarily divergent. PS00217 shows positional conservation (58.8% of sequences) while PS00216 exhibits dispersal, suggesting subfunctionalization. In tomato actin-depolymerizing factors (11 proteins), domain detection achieved 100% sensitivity with >93% positional concordance. ProteoMapper enables hypothesis-driven investigation of evolutionary constraints, regulatory mechanisms, and variant effect prediction in biomedical and functional proteomics. Source code, documentation, and test results with datasets at https://github.com/sifullah0/ProteoMapper.

bioinformatics↗

PanScan: A Tool for Tertiary Analysis of Human Pangenome Graphs

The genomic representation of populations across the globe is critical to ensuring a comprehensive and equitable human reference. Constructing a pangenome graph reference for different populations is the best approach to addressing local genomic diversities. Although major initiatives across continents are underway to construct pangenome graph references, the field lacks the necessary toolsets for tertiary analysis to characterize telomere-to-telomere (T2T) assemblies and the complexity of haplotypes. PanScan is a bioinformatics software package developed for human pangenome tertiary analysis. It includes multiple modules designed to detect duplicated gene sets from T2T assemblies, identify novel variants and sequences, as well as detect and visualize complex genomic regions through pangenome graph haplotype loops. We have used multiple pangenomes across different populations to assess the tertiary analysis and their accuracy. The tool is designed to streamline tertiary analysis and is compatible with multiple pangenome graph construction algorithms. PanScan is freely available on GitHub (https://github.com/CATG-Github/panscan), where users can provide human pangenome assemblies or VCF files as inputs for automated analyses through command-line operations on Linux systems. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=151 SRC="FIGDIR/small/651685v1_ufig1.gif" ALT="Figure 1"> View larger version (59K): org.highwire.dtl.DTLVardef@16cc212org.highwire.dtl.DTLVardef@13961d0org.highwire.dtl.DTLVardef@44d194org.highwire.dtl.DTLVardef@1b72aa_HPS_FORMAT_FIGEXP M_FIG C_FIG

bioinformatics↗

Clinical progression of clonal hematopoiesis is determined by a combination of mutation timing, fitness, and clonal structure

Clonal hematopoiesis (CH) is characterized by expanding blood cell clones carrying somatic mutations in healthy aged individuals and is associated with various age-related diseases and all-cause mortality. While CH mutations affect diverse genes associated with myeloid malignancies, their mechanisms of expansion and disease associations remain poorly understood. We investigate the relationship between clonal fitness and clinical outcomes by integrating data from three longitudinal aging cohorts (n=713, observations=2,341). We demonstrate pathway-specific fitness advantage and clonal composition influence clonal dynamics. Further, the timing of mutation acquisition is necessary to determine the extent of clonal expansion reached during the host individuals lifetime. We introduce MACS120, a metric combining mutation context, timing, and variant fitness to predict future clonal growth, outperforming traditional variant allele frequency measurements in predicting clinical outcomes. Our unified analytical framework enables standardized clonal dynamics inference across cohorts, advancing our ability to predict and potentially intervene in CH-related pathologies.

genetics↗

Drug Delivery Process Simulation - Quantifying the Conformation Dynamics of Paclitaxel and Cremophor EL

Paclitaxel is a highly successful anti-neoplastic cancer drug. The first clinically successful paclitaxl-delivery method is a mixture with cremophor EL and ethanol, here termed the taxol micelle. Until now, molecular dynamics analysis has not been presented to quantify the structural and conformation properties of these drug molecules when interacting with each other to create this nonstandard micelle. Here we apply systematic molecular simulation and statistical analysis of paclitaxel and cremophor EL conformations based on all atom and coarse-grained approaches. The cremophor EL in the taxol micelle showed a clustering network in a 3D landscape where paclitaxel can be loaded at much higher than standard concentration with no aggregation. Paclitaxel particles within the cremophor EL cavities showed some oscillatory behaviour due to a repeated adsorption/desorption with the surrounding network. Paclitaxel conformations at the lowest energy state can be described when the side-chain phenyl rings are closer relative to the immobile core. Cremophor EL molecules reached the highest energy state when wings were fully spread and at the lowest energy state when wings were fully closed. The spiral shapes were observed to be the dominant species in the cremophor EL population. We then established reliable statistical correlations between molecular conformations and the energy states. Our reliable all atom and coarse-grained modelling approach can also be applied for effective drug design analysis using different drug delivery systems. Table of Contents/Abstract Graphic O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=96 SRC="FIGDIR/small/621777v1_ufig1.gif" ALT="Figure 1"> View larger version (45K): org.highwire.dtl.DTLVardef@26dae7org.highwire.dtl.DTLVardef@ed96a5org.highwire.dtl.DTLVardef@3d552borg.highwire.dtl.DTLVardef@a2b191_HPS_FORMAT_FIGEXP M_FIG C_FIG

biochemistry↗

A draft Arab pangenome reference

Pangenomes represent a significant shift from relying on a single reference sequence to a robust set of assemblies, Arab populations remain significantly underrepresented; hence, we present the first Arab Pangenome Reference (APR) utilizing 53 individuals of diverse Arab ethnicities. We assembled nuclear and mitochondrial pangenomes using 35.27X high-fidelity long reads, 54.22X ultralong reads and 65.46X Hi-C reads yielded contiguous haplotype-phased de novo assemblies of exceptional quality, with an average N50 of 124.28 Mb. We discovered 111.96 million base pairs of novel euchromatic sequences absent from existing human pangenomes, the T2T-CHM13, GRCh38 reference human genomes, and other public datasets. We identified 8.94 million population-specific small variants and 235,195 structural variants within the Arab pangenome. We detected 883 gene duplications including 15.06% associated with recessive diseases and 1,436 bp of novel mitochondrial pangenome sequence. Our study provides a valuable resource for future genomic medicine initiatives in Arab population and other global populations.

genomics↗

Horizon: CNV interpretation through rapid automated ACMG-aligned pathogenicity analysis

PurposeOur study assesses the Horizon model, a novel CNV classification tool developed in line with American College of Medical Genetics (ACMG) guidelines, to enhance the classification of pathogenicity in CNVs. MethodsHorizon utilizes a ranking-based algorithm, incorporating multiple proprietary databases and variant inheritance models as per ACMG standards. The models effectiveness was verified through Area Under the Curve (AUC) analyses on three datasets comprising 696 pathogenic inherited or de novo variants, as classified by clinical geneticists and several established tools. ResultsHorizon achieved an AUC of 0.97 in the discovery cohort, demonstrating high accuracy in CNV interpretation and proficiency in predicting pathogenicity. We observed an AUC of 0.87 in the de novo variant cohort and an overall AUC of 0.94 across all cohorts, surpassing tools like ClassifyCNV and AnnotSV. It showed particular effectiveness in interpreting duplication CNVs and the highest performance for CNVs sized 3-5 Mb. ConclusionThe Horizon model offers robust and accurate CNV interpretation, outperforming existing tools and aligning closely with clinical evaluations. Its comprehensive approach, integrating a range of genomic features and following ACMG guidelines, makes it a crucial tool in the genomic interpretation landscape, facilitating the rapid and accurate diagnosis of genetic disorders.

genomics↗

Flotillin-2 dampens T cell antigen-sensitivity and functionality

T cell receptor (TCR) engagement triggers T cell responses, yet how TCR-mediated activation is regulated at the plasma membrane remains unclear. Here, we report that deleting the membrane scaffolding protein Flotillin-2 (Flot2) increases T cell antigen sensitivity, resulting in enhanced TCR signaling and effector function to weak TCR stimulation. T cell-specific Flot2-deficient mice exhibited reduced tumor growth and enhanced immunity to infection. Flot2-null CD4+ T cells exhibited increased T helper 1 polarization, proliferation, Nur77 induction, and phosphorylation of ZAP70 and LCK upon weak TCR stimulation, indicating a sensitized TCR-triggering threshold. Single cell-RNA sequencing suggested that Flot2-null CD4+ T cells follow a similar route of activation as wild-type CD4+ T cells but exhibit higher occupancy of a discrete activation state under weak TCR stimulation. Given prior reports that TCR clustering influences sensitivity of T cells to stimuli, we evaluated TCR distribution with super-resolution microscopy. Flot2 ablation increased the number of surface TCR nanoclusters on naive CD4+ T cells. Collectively, we posit that Flot2 modulates T cell functionality to weak TCR stimulation, at least in part, by regulating surface TCR clustering. Our findings have implications for improving T cell reactivity in diseases with poor antigenicity, such as cancer and chronic infections.

immunology↗

DUAL: deep unsupervised simultaneous simulation and denoising for cryo-electron tomography

Recent biotechnological developments in cryo-electron tomography allow direct visualization of native sub-cellular structures with unprecedented details and provide essential information on protein functions/dysfunctions. Denoising can enhance the visualization of protein structures and distributions. Automatic annotation via data simulation can ameliorate the time-consuming manual labeling of large-scale datasets. Here, we combine the two major cryo-ET tasks together in DUAL, by a specific cyclic generative adversarial network with novel noise disentanglement. This enables end-to-end unsupervised learning that requires no labeled data for training. The denoising branch outperforms existing works and substantially improves downstream particle picking accuracy on benchmark datasets. The simulation branch provides learning-based cryo-ET simulation for the first time and generates synthetic tomograms indistinguishable from experimental ones. Through comprehensive evaluations, we showcase the effectiveness of DUAL in detecting macromolecular complexes across a wide range of molecular weights in experimental datasets. The versatility of DUAL is expected to empower cryo-ET researchers by improving visual interpretability, enhancing structural detection accuracy, expediting annotation processes, facilitating cross-domain model adaptability, and compensating for missing wedge artifacts. Our work represents a significant advancement in the unsupervised mining of protein structures in cryo-ET, offering a multifaceted tool that facilitates cryo-ET research.

bioinformatics↗

Time-varying mediation analysis for incomplete data with application to DNA methylation study for PTSD

DNA methylation (DNAm) has been shown to mediate causal effects from traumatic experiences to post-traumatic stress disorder (PTSD). However, the scientific question about whether the mediation effect changes over time remains unclear. In this paper, we develop time-varying structural equation models to identify cytosine-phosphate-guanine (CpG) sites where DNAm mediates the effect of trauma exposure on PTSD, and to capture dynamic changes in mediation effects. The proposed methodology is motivated by the Detroit Neighborhood Health Study (DNHS) with high-dimensional and longitudinal DNAm measurements. To handle the non-monotone missing DNAm in the dataset, we propose a novel Longitudinal Multiple Imputation (LMI) method utilizing dependency among repeated measurements, and employ the generalized method of moments to integrate the multiple imputations. Simulations confirm that the proposed method outperforms existing approaches in various longitudinal settings. In DNHS data analysis, our method identifies several CpG sites where DNAm exhibits dynamic mediation effects. Some of the corresponding genes have been shown to be associated with PTSD in the existing literature, and our findings on their time-varying effects could deepen the understanding of the mediation role of DNAm on the causal path from trauma exposure to PTSD risk.

bioinformatics↗

Rapid and Highly Sensitive Detection of Fentanyl and its Analogs by a Novel Chemiluminescence Immunoassay

A highly sensitive immunoassay with sub-picogram limit of detection for fentanyl and a wide range of fentanyl analogs has been developed, using a novel high affinity antibody fused with NanoLuc, a small-size luciferase that can emit strong and stable luminescence. When applied to unaltered clinical human urine samples, the assay has sub-picogram limit of detection for fentanyl, with results fully concordant with gold standard liquid chromatography-mass spectrometry. When applied to environmental samples, the assay can detect as low as 0.25 pg fentanyl per inch2 environment surface. Assay turn-around-time is less than 1 hour, with inexpensive equipment needed and potential for high throughput automation or in-field screening. This novel assay has broad potential applications in clinical, environmental, occupational and forensic scenarios by detecting trace amount of fentanyl and its analogs to keep frontline workers safe.

pharmacology and toxicology↗

Evolutionary constraint genes implicated in autism spectrum disorder across 2,054 nonhuman primate genomes

BackgroundSignificant progress has been made in elucidating the genetic underpinning of Autism Spectrum Disorder (ASD). This childhood-onset chronic disorder of cognition, communication and behaviour ranks among the most severe from a public health perspective, and it is therefore hoped that new discoveries will lead to better therapeutic options. However, there are still significant gaps in our understanding of the link between genomics, neurobiology and clinical phenotype in scientific discovery. New models are therefore needed to address these gaps. Rhesus macaques (Macaca mulatta) have been extensively used for preclinical neurobiological research because of remarkable similarities to humans across biology and behaviour that cannot be captured by other experimental animals. MethodsWe used the macaque Genotype and Phenotype (mGAP) resource (v2.0) consisting of 2,054 macaque genomes to examine patterns of evolutionary constraint in known human neurodevelopmental genes. Residual variation intolerance scores (RVIS) were calculated for all annotated autosomal genes (N = 18,168) and Gene Set Enrichment Analysis (GSEA) was used to examine patterns of constraint across ASD genes and related neurodevelopmental genes. ResultsWe demonstrated that patterns of constraint across autosomal genes are correlated in humans and macaques, and that ASD-implicated genes exhibit significant constraint in macaques (p = 9.4 x 10-27). Among macaques, many key ASD genes were observed to harbour predicted damaging mutations. A small number of key ASD genes that are highly intolerant to mutation in humans, however, showed no evidence of similar intolerance in macaques (CACNA1D, CNTNAP2, MBD5, AUTS2 and NRXN1). Constraint was also observed across genes implicated in intellectual disability (p = 1.1 x 10-46), epilepsy (p = 2.1 x 10-33) and schizophrenia (p = 4.2 x 10-45), and for an overlapping neurodevelopmental gene set (p = 4.0 x 10-10) LimitationsThe lack of behavioural phenotypes among the macaques whose genotypes were studied means that we are unable to further investigate whether genetic variants have similar phenotypic consequences among nonhuman primates. ConclusionThe presence of pathological mutations in ASD genes among macaques, and the evidence of similar constraints in these genes to humans, provide a strong rationale for further investigation of genotype-phenotype relationships in nonhuman primates. This highlights the importance of identifying phenotypic behaviours associated with clinical symptoms, elucidating the neurobiological underpinnings of ASD, and developing primate models for translational research to advance approaches for precision medicine and therapeutic interventions.

genetics↗

Differentially Expressed Heterogeneous Overdispersion Genes Testing for Count Data

The mRNA-seq data analysis is a powerful technology for inferring information from biological systems of interest. Specifically, the sequenced RNA fragments are aligned with genomic reference sequences, and we count the number of sequence fragments corresponding to each gene for each condition. A gene is identified as differentially expressed (DE) if the difference in its count numbers between conditions is statistically significant. Several statistical analysis methods have been developed to detect DE genes based on RNA-seq data. However, the existing methods could suffer decreasing power to identify DE genes arising from overdispersion and limited sample size. We propose a new differential expression analysis procedure: heterogeneous overdispersion genes testing (DEHOGT) based on heterogeneous overdispersion modeling and a post-hoc inference procedure. DEHOGT integrates sample information from all conditions and provides a more flexible and adaptive overdispersion modeling for the RNA-seq read count. DEHOGT adopts a gene-wise estimation scheme to enhance the detection power of differentially expressed genes. DEHOGT is tested on the synthetic RNA-seq read count data and outperforms two popular existing methods, DESeq and EdgeR, in detecting DE genes. We apply the proposed method to a test dataset using RNAseq data from microglial cells. DEHOGT tends to detect more differently expressed genes potentially related to microglial cells under different stress hormones treatments.

genomics↗

Multi-ancestry GWAS of major depression aids locus discovery, fine-mapping, gene prioritisation, and causal inference

Most genome-wide association studies (GWAS) of major depression (MD) have been conducted in samples of European ancestry. Here we report a multi-ancestry GWAS of MD, adding data from 21 studies with 88,316 MD cases and 902,757 controls to previously reported data from individuals of European ancestry. This includes samples of African (36% of effective sample size), East Asian (26%) and South Asian (6%) ancestry and Hispanic/Latinx participants (32%). The multi-ancestry GWAS identified 190 significantly associated loci, 53 of them novel. For previously reported loci from GWAS in European ancestry the power-adjusted transferability ratio was 0.6 in the Hispanic/Latinx group and 0.3 in each of the other groups. Fine-mapping benefited from additional sample diversity: the number of credible sets with [&le;]5 variants increased from 3 to 12. A transcriptome-wide association study identified 354 significantly associated genes, 205 of them novel. Mendelian Randomisation showed a bidirectional relationship with BMI exclusively in samples of European ancestry. This first multi-ancestry GWAS of MD demonstrates the importance of large diverse samples for the identification of target genes and putative mechanisms.

genetics↗

NeuroSCORE: A Genome-wide Omics-Based Model to Identify Candidate Disease Genes of the Central Nervous System

To identify and prioritize candidate disease genes of the central nervous system (CNS) we created the Neurogenetic Systematic Correlation of Omics-Related Evidence (NeuroSCORE). We used five genome-wide metrics highly associated with neurological phenotypes to score 19,598 protein-coding genes. Genes scored one point per metric, resulting in a range of scores from 0-5. Approximately 13,000 genes were then paired with phenotype data from the Online Mendelian Inheritance in Man (OMIM) database. We used logistic regression to determine the odds ratio of each metric and compared genes scoring 1+ to cause a known CNS-related phenotype compared to genes that scored zero. We tested NeuroSCORE using microarray copy number variants (CNVs) in case-control cohorts, mouse model phenotype data, and gene ontology (GO) and pathway analyses. NeuroSCORE identified 8,296 genes scored [&ge;]1, of which 1,580 are "high scoring" genes (scores [&ge;]3). High scoring genes are significantly associated with CNS phenotypes (OR=5.5, p<2x10-16), enriched in case CNVs, and enriched in mouse ortholog genes associated with behavioral and nervous system abnormalities. GO and pathway analyses showed high scoring genes were enriched in chromatin remodeling, mRNA splicing, dendrite development, and neuron projection. OMIM has no phenotype for 1,062 high scoring genes (67%). Top scoring genes include ANKRD17, CCAR1, CLASP1, DOCK9, EIF4G2, G3BP2, GRIA1, MAP4K4, MARK2, PCBP2, RNF145, SF1, SYNCRIP, TNPO2, and ZSWIM8. NeuroSCORE identifies and prioritizes CNS-disease candidate genes, many not yet associated with any phenotype in OMIM. These findings can help direct future research and improve molecular diagnostics for individuals with neurological conditions.

genomics↗