Biology subjects

Cooper-Knock, J.

Publications and source records attributed to Cooper-Knock, J..

At least 19 recordsLinked to original sources

bioRxiv2026

Pathology-defined cell states reveal reproducible transcriptomic signatures across ALS cortical single-nucleus RNA-seq studies

Amyotrophic lateral sclerosis (ALS) is a genetically and biologically heterogeneous neurodegenerative disease in which distinct pathogenic mechanisms operate across patients while overt molecular pathology is confined to only a subset of cells. Such features would act to dilute disease-associated transcriptomic signals and complicate the identification of reproducible molecular signatures across the growing number of ALS single-nucleus RNA sequencing (snRNA-seq) studies. Here, we systematically assessed cross-study reproducibility across four cortical ALS snRNA-seq datasets comprising 140 donors (87 ALS) and tested whether pathology-defined cell states improve detection of conserved molecular signatures. Cell-type annotations were harmonized prior to comparison of cell-type-specific pseudobulk differential expression using gene-level, pathway-level, gene-ranking and alternative polyadenylation analyses. We further examined nuclei exhibiting TDP-43 pathology, identified by expression of the STMN2 cryptic exon. Conventional ALS-versus-control analyses showed limited reproducibility, with minimal overlap of differentially expressed genes or enriched pathways, while fold-change patterns clustered predominantly by study rather than cell type or brain region. Nevertheless, gene-ranking analyses identified reproducible neuronal transcriptional programs, suggesting that biological signal is present but incompletely resolved by current cohort sizes. In contrast, STMN2 cryptic exon-positive nuclei showed substantially greater concordance, revealing robust TDP-43-associated signatures that partially overlapped independent models of TDP-43 dysfunction while also identifying motor cortex-specific changes, including reduced expression of the recently identified ALS risk gene UNC13C. Reproducible ALS-associated alternative polyadenylation changes were not detected, likely reflecting the higher dimensionality and sparsity of polyadenylation site analyses. Together, our findings demonstrate that pathology-defined cell states provide a more reproducible framework for studying ALS transcriptomic alterations than conventional case-control comparisons. We additionally provide an interactive browser to facilitate exploration and comparison of ALS snRNA-seq datasets.

genomics↗

bioRxiv2026

Astrocyte dysfunction distinguishes monozygotic twin C9orf72 expansion carriers discordant for amyotrophic lateral sclerosis.

Hexanucleotide repeat expansions in C9orf72 are the most common genetic cause of amyotrophic lateral sclerosis, yet many carriers remain asymptomatic for decades or never develop disease. This incomplete penetrance suggests that phenoconversion from genetic susceptibility to symptomatic disease onset is governed by epigenetic, environmental and cell-intrinsic modifiers. Astrocytes are key mediators of non-cell-autonomous neurodegeneration in ALS, but whether they undergo disease-associated phenoconversion in C9orf72 expansion carriers remains unclear. We investigated astrocyte-associated mechanisms of phenoconversion using a unique C9orf72 pedigree comprising monozygotic twins discordant for ALS and their asymptomatic father. This enabled analysis across a continuum from non-penetrance to late-stage neurodegeneration while controlling for inherited genetic background. Notably, the unaffected twin exhibited hypermethylation of the expanded C9orf72 allele, identifying an epigenetic correlate of non-penetrance. To define symptomatic and asymptomatic-associated astrocyte states, fibroblasts from this pedigree were directly reprogrammed into astrocytes and subjected to molecular, functional, electrophysiological and translatome analyses. Astrocytes derived from symptomatic ALS individuals were found to exhibit astrocyte-mediated toxicity towards motor neurons, canonical C9orf72 molecular pathology and connexin-related membrane dysfunction. Specifically, RNA foci and dipeptide repeat protein burden peaked in astrocytes obtained early in disease in the symptomatic twin and declined in astrocytes derived from samples obtained at advanced disease stages, whereas motor neuron toxicity increased progressively, demonstrating a dissociation between aggregate burden and functional neurotoxicity. In contrast, connexin-mediated electrophysiological dysfunction emerged with symptomatic disease and closely tracked with maximal toxicity. Translatome profiling revealed early global translational repression and impaired proteostasis in symptomatic astrocytes, whereas unaffected and non-penetrant C9-carriers retained enrichment of protein homeostasis pathways. These findings define distinct astrocyte states associated with asymptomatic, early symptomatic and late-stage disease. Notably, the affected twin reported substantially higher lifetime strenuous physical activity compared to the unaffected twin, consistent with a potential role for sustained exercise-related stress in accelerating disease onset in genetically susceptible individuals. Experimentally modelling increased stress induced aberrant upregulation of connexin-mediated currents in C9orf72 astrocytes, including in asymptomatic carriers, indicating that physiological stress can unmask latent astrocyte-intrinsic vulnerability and precipitate dysfunction in cellular homeostasis. Together, these findings redefine phenoconversion in C9orf72-associated amyotrophic lateral sclerosis to be associated with a significant upregulation of astrocyte toxicity, failure of resilience and demonstrate that the astrocyte disease state can be potentially induced by external stressors.

neuroscience↗

bioRxiv2026

ProMeta: A meta-learning framework for robust disease diagnosis and prediction from plasma proteomics

The plasma proteome offers a dynamic window of human health, capturing the real-time intersections between genetics and physiology. However, the application of deep learning to proteomics is currently hindered by a reliance on large-scale labeled datasets, rendering standard models ineffective for rare or novel diseases where patient samples are inherently scarce. Here, we present ProMeta, a meta-learning framework designed to enable robust disease modeling under extreme data restrictions. By integrating knowledge-guided pathway encoding with bi-level meta-optimization, ProMeta projects unstructured proteomic profiles into biologically interpretable functional tokens. This architecture allows the model to learn a global initialization containing transferable biological priors from biobank-scale data, facilitating rapid adaptation to novel tasks. Through comprehensive benchmark experiments, ProMeta consistently outperformed transfer learning and traditional machine learning baselines in both disease diagnosis and prediction tasks. In the most challenging 4-shot scenarios (utilizing only 2 cases and 2 controls), the model achieved robust generalization with an average AUROC of [~] 0.69, representing a 24.6% relative improvement over the best-performing baseline methods. Mechanistic investigation revealed that ProMeta disentangles cases from controls in the latent space prior to task-specific adaptation, confirming the acquisition of universal biological rules rather than rote memorization. Furthermore, gradient-based interpretation identified disease-specific protein biomarkers and functional pathways consistent with known pathophysiology. Collectively, ProMeta overcomes the data-scarcity bottleneck in precision medicine, providing a scalable, interpretable framework for characterizing the full spectrum of human diseases, particularly for rare conditions lacking extensive clinical cohorts.

bioinformatics↗

bioRxiv2026

STMN2 protein depletion via translation deficits and stress granules and its compensation in ALS

STMN2 is an abundant neurospecific protein dysregulated in neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS). We previously reported that cellular stress can lead to STMN2 loss due to TDP-43 nuclear condensation. Here, using human and murine neuronal cell models, multiple pharmacological tools, single-molecule in situ analysis of mRNA localisation and translation, and longitudinal analysis of neuronal fitness/survival, we establish TDP-43-independent mechanisms of STMN2 depletion linked to stress response. We find that human STMN2 protein is extremely labile under acute high-magnitude stress. Early in stress, STMN2 is suppressed via activated proteasomal degradation, phosphorylation and translational repression by stress granules, independently of TDP-43 loss of function in splicing. We further show that STMN2 protein level is highly sensitive to chronic translation deficits, such as those elicited by prolonged low-grade stress. Finally, we demonstrate that STMN2 mRNA is upregulated in non-TDP ALS such as ALS-FUS, which may compensate for translation/stress granule defects in these disease subtypes. Consistent with the compensation hypothesis, STMN2 mRNA is also upregulated in the relatively spared (cortex), but not severely affected (spinal cord), CNS regions in ALS-TDP. In conclusion, our study implicates two common hallmarks of neurodegeneration, translation impairment and abnormal stress granules, in STMN2 depletion and reports an RNA-level compensation that fails in neurons with TDP-43 pathology. Our study supports the development of stress response targeting therapies in ALS with and without TDP-43 pathology.

cell biology↗

bioRxiv2025

PRSNet-2: End-to-end genotype-to-phenotype prediction via hierarchical graph neural networks

The emergence of large-scale biobanks has opened unprecedented opportunities for the development of data-driven approaches, especially deep learning-based methods, for genotype-to-phenotype (G2P) prediction. However, designing an end-to-end framework capable of directly leveraging extremely high-dimensional genotypic data while simultaneously mitigating overfitting and ensuring robust prediction remains a significant challenge. In this study, we introduce PRSNet-2, an interpretable end-to-end deep learning framework designed to predict complex phenotypes directly from large-scale genotypic data. PRSNet-2 proposes a novel hierarchical graph neural network (GNN) architecture, which first employs a multi-kernel aggregator to map high-dimensional genotypic features to gene-level representations. Next, it models gene-gene interactions through message-passing operations and uses an attention-based readout module to generate interpretable phenotypic predictions. We further introduce significance-guided regularization strategies to boost models generalizability based on prior genetic associations. Extensive empirical evaluations across multiple complex traits and diseases demonstrate that PRSNet-2 consistently outperforms a variety of baseline methods and exhibits superior capabilities in overcoming overfitting, even when modeling around half a million single nucleotide polymorphisms (SNPs). Moreover, PRSNet-2 is shown to be easily extendable to integrate multiple genome-wide association study (GWAS) datasets into a single model, thereby enhancing predictive performance for both single-phenotype and multi-phenotype prediction tasks. The inherent interpretability of PRSNet-2 further facilitates the identification of disease-relevant genes, functional gene modules, and potential therapeutic targets. In summary, PRSNet-2 offers a powerful, versatile tool for both genetic risk stratification and the discovery of biological insights.

genetics↗

bioRxiv2025

Amyotrophic Lateral Sclerosis-associated 3' UTR enhancer embedded within CAV1 risk gene

Enhancer elements that reside within 3' untranslated regions (UTRs) are an understudied phenomenon. Given the independent regulatory functions of enhancers and 3' UTRs - enhancers governing pre-transcriptional control of gene expression and 3' UTRs mediating post-transcriptional regulation of messenger RNA (mRNA) fate - 3' UTR-associated enhancers may integrate these complementary layers to coordinate gene expression across multiple regulatory stages. Non-coding variation, impacting regulatory DNA, underpins the genetic architecture of disease. Indeed, the vast majority of single nucleotide polymorphisms (SNPs) associated with human complex diseases map to the non-coding genome, with causal variants particularly enriched within enhancers. Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disorder associated with non-coding risk variants, many of which are increasingly linked to enhancer disruption. The CAV1 gene, encoding the neuroprotective protein Caveolin-1, is a known ALS risk gene, yet the functional consequences of ALS-associated variation in its regulatory elements remain largely unexplored. Here, we combine genome-wide enhancer profiling with targeted experimental validation to define a previously uncharacterised ALS-associated enhancer embedded within the CAV1 3' UTR, systematically assess its regulatory potential, and evaluate the impact of ALS-associated SNPs on enhancer function. We show that an individual ALS-associated SNP within this 3' UTR-associated enhancer may disrupt function on multiple levels: at the DNA and chromatin level, by altering transcription factor binding with potential effects on recruitment of epigenetic co-regulators; and at the RNA level, by reshaping the structure and stability of a novel enhancer RNA transcribed from this locus. Collectively, our findings highlight this proximal CAV1/CAV2 enhancer as a functionally important regulatory element embedded with the 3' UTR of an ALS risk gene, illustrate how non-coding variants can impact multiple layers of gene regulation, and provide mechanistic insight into how intragenic enhancers contribute to ALS risk. More broadly, this work underscores the importance of 3' UTR-associated enhancers as modulators of risk gene expression and underexplored contributors to human complex disease.

genetics↗

bioRxiv2025

TDP-43 toxic gain of function links ALS/FTLD-TDP and Alzheimer's Disease through splicing

Loss of nuclear TDP-43 splicing activity is a common feature across neurodegenerative diseases including amyotrophic lateral sclerosis (ALS) and frontotemporal lobar degeneration (FTLD), but its relevance to Alzheimers disease (AD) remains unclear. Here, we show that TDP-43 pathology in AD is broadly associated with splicing abnormalities, including aberrant splicing of amyloid precursor protein (APP). TDP-43 drives the formation of elongated APP isoforms, disrupting alternative splicing across ALS, FTLD-TDP and AD, providing a compelling mechanism for a long-standing observation of APP isoform dysregulation. We further establish a mechanistic link between TDP-43, APP splicing, and A{beta} pathology. Surprisingly, the disruption to alternative APP splicing is mediated by a toxic gain of cytoplasmic TDP-43 function, rather than loss of its nuclear role. Using proximity proteomics and base editing in human iPSC-derived neurons, we show that TDP-43 pathology causes cytoplasmic co-sequestration of splicing regulators SCAF11, SRSF5, and TIAL1. Knockdown of these regulators also results in APP mis-splicing and increased A{beta} burden, without affecting other TDP-43 targets such as STMN2 or UNC13A. Together, our findings suggest that TDP-43-mediated splicing dysfunction upstream of APP contributes to the pathogenesis of seemingly disparate neurodegenerative diseases, uniting AD and ALS/FTLD-TDP through a shared molecular mechanism.

neuroscience↗

bioRxiv2025

Human Body Single-Cell Atlas of 3D Genome Organization and DNA Methylation

Higher-order chromatin structure and DNA methylation are critical for gene regulation, but how these vary across the human body remains unclear. We performed multi-omic profiling of 3D genome structure and DNA methylation for 86,689 single nuclei across 16 human tissues, identifying 35 major and 206 cell subtypes. We revealed extensive changes in CG and non-CG methylation across almost all cell types and characterized 3D chromatin structure at an unprecedented cellular resolution. Intriguingly, extensive discrepancies exist between cell types delineated by DNA methylation and genome structure, indicating that the role of distinct epigenomic features in maintaining cell identity may vary by lineage. This study expands our understanding of the diversity of DNA methylation and chromatin structure and offers an extensive reference for exploring gene regulation in human health and disease.

genomics↗

bioRxiv2024

Sex-specific DNA methylation differences in Amyotrophic lateral sclerosis

Sex is an important covariate in all genetic and epigenetic research due to its role in the incidence, progression and outcome of many phenotypic characteristics and human diseases. Amyotrophic lateral sclerosis (ALS) is a motor neuron disease with a sex bias towards higher incidence in males. Here, we report for the first time a blood-based epigenome-wide association study meta-analysis in 9274 individuals after stringent quality control (5529 males and 3975 females). We identified a total of 226 ALS saDMPs (sex-associated DMPs) annotated to a total of 159 unique genes. These ALS saDMPs were depleted at transposable elements yet significantly enriched at enhancers and slightly enriched at 3UTRs. These ALS saDMPs were enriched for transcription factor motifs such as ESR1 and REST. Moreover, we identified an additional 10 genes associated with ALS saDMPs through chromatin loop interactions, suggesting a potential regulatory role for these saDMPs on distant genes. Furthermore, we investigated the relationship between DNA methylation at specific CpG sites and overall survival in ALS using Cox proportional hazards models. We identified two ALS saDMPs, cg14380013 and cg06729676, that showed significant associations with survival. Overall, our study reports a reliable catalogue of sex-associated ALS saDMPs in ALS and elucidates several characteristics of these sites using a large-scale dataset. This resource will benefit future studies aiming to investigate the role of sex in the incidence, progression and risk for ALS.

genomics↗

bioRxiv2024

predicTTE: An accessible and optimal tool for time-to-event prediction in neurological diseases

Time-to-event prediction is a key task for biological discovery, experimental medicine, and clinical care. This is particularly true for neurological diseases where development of reliable biomarkers is often limited by difficulty visualising and sampling relevant cell and molecular pathobiology. To date, much work has relied on Cox regression because of ease-of-use, despite evidence that this model includes incorrect assumptions. We have implemented a set of deep learning and spline models for time-to-event modelling within a fully customizable app and accompanying online portal, both of which can be used for any time-to-event analysis in any disease by a non-expert user. Our online portal includes capacity for end-users including patients, Neurology clinicians, and researchers, to access and perform predictions using a trained model, and to contribute new data for model improvement, all within a data-secure environment. We demonstrate a pipeline for use of our app with three use-cases including imputation of missing data, hyperparameter tuning, model training and independent validation. We show that predictions are optimal for use in downstream applications such as genetic discovery, biomarker interpretation, and personalised choice of medication. We demonstrate the efficiency of an ensemble configuration, including focused training of a deep learning model. We have optimised a pipeline for imputation of missing data in combination with time-to-event prediction models. Overall, we provide a powerful and accessible tool to develop, access and share time-to-event prediction models; all software and tutorials are available at www.predictte.org.

neuroscience↗

bioRxiv2024

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individuals genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimers disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

genetics↗

bioRxiv2024

GRASPS: a simple-to-operate translatome technology reveals omics-hidden disease-associated pathways in TDP-43-related amyotrophic lateral sclerosis

Transcriptomes and translatomes measure genome-wide levels of total and ribosome-associated RNAs. A few hundred translatomes were reported over >250,000 transcriptomes highlighting the challenges of identifying translating RNAs. Here, we used a human isogenic inducible model of TDP-43-linked amyotrophic lateral sclerosis, which exhibits altered expression of thousands of transcripts, as a paradigm for the direct comparison of whole-cell, cytoplasmic and translating RNAs, showing broad uncoupling and poor correlation between disease-altered transcripts. Moreover, based on precipitation of endogenous ribosomes, we developed GRASPS (Genome-wide RNA Analysis of Stalled Protein Synthesis), a simple-to-operate translatome technology. Remarkably, GRASPS identified three times more differentially-expressed transcripts with higher fold changes and statistical significance, providing unprecedented opportunities for data modeling at stringent filtering and discovery of previously omics-missed disease-relevant pathways, which functionally map on dense gene regulatory networks of protein-protein interactions. Based on its simplicity and robustness, GRASPS is widely applicable across disciplines in the biotechnologies and biomedical sciences.

molecular biology↗

bioRxiv2023

hnRNPUL1 ensures efficient Integrator-mediated cleavage of snRNAs and is mutated in amyotrophic lateral sclerosis

Integrator cleaves nascent RNA, triggering RNA polymerase II transcription termination, but how cleavage is regulated is poorly understood. Here we show hnRNPUL1 ensures efficient Integrator-mediated cleavage of nascent RNA downstream of snRNA genes and, in the case of U2 snRNA, binds a terminal stem-loop involved in this process. In the nucleoplasm, hnRNPUL1 binds U4 snRNA and SART3 and enables efficient reformation of the U4:U6 di-snRNP for further rounds of pre-mRNA splicing. Sustained hnRNPUL1 loss leads to reduced levels of snRNAs, defects in histone mRNA 3' end processing and loss of Cajal bodies. hnRNPUL1 binds RNA through multiple domains, including a globular central domain comprising tightly juxtaposed SPRY and dead polynucleotide kinase folds. This latter fold allows binding to 5'-monophosphorylated RNAs in a mutually exclusive manner with ATP binding and functions as an XRN2 antagonist when overexpressed. We identify a cohort of amyotrophic lateral sclerosis patients harbouring disruptive mutations in hnRNPUL1. SMN loss in spinal muscular atrophy and hnRNPUL1 loss both disrupt snRNP biogenesis, leading to motor neuron death, suggesting a common aetiology.

molecular biology↗

bioRxiv2023

Genomewide paired DNA-RNAseq analyses to discover intronic splice mutation hotspots in neurological disorders

Explaining missing heritability in rare disorders requires effective methods to interpret genetic variants. Sequence-to-function models such as SpliceAI support discovery of splice altering variants but filtering their output to identify pathogenic mutations remains challenging. We developed SpliPath to address 2 unmet needs in this process. First, SpliPath links the output of SpliceAI with reference transcriptomics data. This allows users to identify genetic variants that induce unannotated splice isoforms selectively expressed in disease models or patient tissue. Second, SpliPath aggregates variants with similar functional consequences into collapsed splicing quantitative trait loci (csQTLs) for more powerful genetic association analyses. We first used SpliPath to annotate whole genome sequencing (WGS) of 9,467 ALS patients and controls using RNAseq data from an iPSC model of TDP-43 dysfunction. Through this, SpliPath identified 53 variants predicted to enhance cryptic exon (CE) retention events associated with a core ALS pathomechanism. We then applied SpliPath to 294 ALS patients where both WGS and RNAseq were available and discovered missing genetic risk in the known ALS gene KIF5A. This revealed a first of kind intronic mutation hotspot that was validated using minigene reporter assays. Finally, using the same RNAseq data we then predicted 754 candidate csQTL for an independent WGS cohort of 6,625 ALS patients and 2,472 controls. Unbiased genomewide csQTL association testing successfully recovered KIF5A and nominated EPG5 as a potential pathogenic gene. These effects were undetectable using simplistic SpliceAI gene burden tests. Collectively, our study demonstrates the utility of SpliPath for uncovering missing heritability in rare disorders.

bioinformatics↗

bioRxiv2022

GLT8D1 mutations cause amyotrophic lateral sclerosis via disruption of neurotrophin signalling within membrane lipid rafts

Mutations within GLT8D1 contribute to familial amyotrophic lateral sclerosis. Pathogenic mutations impair GLT8D1 glycosyltransferase enzymatic function via a dominant negative mechanism, yet the downstream mechanism leading to neurotoxicity is unclear. Here we show that a p.R92C mutation causes fragmentation of the Golgi network and reduces ganglioside expression within membrane lipid rafts (MLRs), leading to impaired neurotrophin signalling. Expression of p.R92C-GLT8D1 in HEK293 cells and mouse primary neurons reduces expression of GM1 gangliosides within the cell plasma membrane leading to disruption of MLRs. Furthermore, p.R92C-GLT8D1 reduces TrkB-mediated pro-survival signalling in MLRs isolated from primary neurons. Interestingly, up-regulation of wild-type GLT8D1 enhances MLRs and promotes pro-survival signalling through TrkB. This closely mirrors findings for another ALS gene, CAV1, suggesting convergence on a common pathogenic pathway. Other ALS genes have been associated with Golgi dysfunction and may disrupt the same pathway, suggesting a potential new therapeutic approach via upregulation of GLT8D1. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=193 SRC="FIGDIR/small/497990v1_ufig1.gif" ALT="Figure 1"> View larger version (69K): org.highwire.dtl.DTLVardef@1992dc4org.highwire.dtl.DTLVardef@18be04eorg.highwire.dtl.DTLVardef@333216org.highwire.dtl.DTLVardef@11d76c5_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2022

Mitochondrial function determines severity but not risk of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease. Selective vulnerability of energy-intensive motor neurons (MNs) has fostered speculation that mitochondrial function is a determinant of ALS. Previously, the position of mitochondrial function in the pathogenic cascade leading to neurotoxicity has been unclear. We separated upstream genetic determinants of mitochondrial function, including genetic variation within the mitochondrial genome or autosomes; from downstream changeable factors including mitochondrial copy number (mtCN) and MN gene expression. We discovered that functionally validated mitochondrial haplotypes are a determinant of ALS survival but not ALS risk. Loss-of-function genetic variants within, and reduced MN expression of, ACADM and DNA2 lead to shorter ALS survival; both genes impact mitochondrial function. MtCN responds dynamically to the onset of ALS independent of mitochondrial haplotype, and is also significantly correlated with disease severity. We conclude that mitochondrial function impacts ALS progression but not risk; our findings have therapeutic implications.

genetics↗

bioRxiv2022

Low expression of EXOSC2 protects against clinical COVID-19 and impedes SARS-CoV-2 replication

New therapeutic targets are a valuable resource in the struggle to reduce the morbidity and mortality associated with the COVID-19 pandemic, caused by the SARS-CoV-2 virus. Genome-wide association studies (GWAS) have identified risk loci, but some loci are associated with co-morbidities and are not specific to host-virus interactions. Here, we identify and experimentally validate a link between reduced expression of EXOSC2 and reduced SARS-CoV-2 replication. EXOSC2 was one of 332 host proteins examined, all of which interact directly with SARS-CoV-2 proteins; EXOSC2 interacts with Nsp8 which forms part of the viral RNA polymerase. Lung-specific eQTLs were identified from GTEx (v7) for each of the 332 host proteins. Aggregating COVID-19 GWAS statistics for gene-specific eQTLs revealed an association between increased expression of EXOSC2 and higher risk of clinical COVID-19 which survived stringent multiple testing correction. EXOSC2 is a component of the RNA exosome and indeed, LC-MS/MS analysis of protein pulldowns demonstrated an interaction between the SARS-CoV-2 RNA polymerase and the majority of human RNA exosome components. CRISPR/Cas9 introduction of nonsense mutations within EXOSC2 in Calu-3 cells reduced EXOSC2 protein expression, impeded SARS-CoV-2 replication and upregulated oligoadenylate synthase (OAS) genes, which have been linked to a successful immune response against SARS-CoV-2. Reduced EXOSC2 expression did not reduce cellular viability. OAS gene expression changes occurred independent of infection and in the absence of significant upregulation of other interferon-stimulated genes (ISGs). Targeted depletion or functional inhibition of EXOSC2 may be a safe and effective strategy to protect at-risk individuals against clinical COVID-19.

genetics↗

bioRxiv2021

Non-Coding Genetic Analysis Implicates Interleukin 18 Receptor Accessory Protein 3'UTR in Amyotrophic Lateral Sclerosis

The non-coding genome is substantially larger than the protein-coding genome but is largely unexplored by genetic association studies. Here, we performed region-based burden analysis of >25,000 variants in untranslated regions of 6,139 amyotrophic lateral sclerosis (ALS) whole-genomes and 70,403 non-ALS controls. We identified Interleukin-18 Receptor Accessory Protein (IL18RAP) 3'UTR variants significantly enriched in non-ALS genomes, replicated in an independent cohort, and associated with a five-fold reduced risk of developing ALS. Variant IL18RAP 3'UTR reduces mRNA stability and the binding of RNA-binding proteins. Variant IL18RAP 3'UTR further dampens neurotoxicity of human iPSC-derived C9orf72-ALS microglia that depends on NF-{kappa}B signaling. Therefore, the variant IL18RAP 3'UTR provides survival advantage for motor neurons co-cultured with C9-ALS microglia. The study reveals direct genetic evidence and therapeutic targets for neuro-inflammation, and emphasizes the importance of non-coding genetic association studies. One Sentence SummaryNon-coding genetic variants in IL-18 receptor 3UTR decrease ALS risk by modifying IL-18-NF-{kappa}B signaling in microglia.

neuroscience↗