Biology subjects

Al-Chalabi, A.

Publications and source records attributed to Al-Chalabi, A..

17 recordsLinked to original sources

bioRxiv2026

Sex-dependent DNA methylation in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) shows well-established sex differences in incidence, age at onset and progression, but their biological basis is unclear. We hypothesised that the DNA methylation changes associated with ALS differ between the sexes, which would point to sex-specific disease processes. To test this, we performed a blood-based epigenome-wide association meta-analysis of ALS cases and controls from Project MinE, an international ALS genomics consortium, and an independent Australian cohort, comprising 8,946 individuals after quality control (5,129 males and 3,817 females; 8,942 with complete covariates in the primary models). We analysed 781,648 autosomal CpGs using complementary sex-stratified case-control models and an ALS-by-sex interaction model that directly tested whether ALS-associated methylation effects differed by sex. Sex-stratified analyses identified no genome-wide significant differences between male and female ALS effects. In contrast, interaction testing identified seven robust sex-dependent ALS-associated differentially methylated positions (P < 9 x 10-8), mapping to KCNQ1, ADPRH, HLA-F, GHRLOS, IRF5, RAD51B and one intergenic region. The strongest signal was at KCNQ1 (P = 3.1 x 10-9). Six of the seven remained genome-wide significant in a four-stratum sensitivity analysis, and none was detected in previous conventional ALS or population sex-associated methylation analyses. Together, these findings suggest a distinct sex-conditional component of the ALS blood methylome, implicating immune, metabolic and ion-channel loci and providing new candidates for investigating biological heterogeneity between males and females with ALS.

genomics↗

bioRxiv2026

Machine learning evaluation of gene expression-based ALS subtypes across brain and blood tissues

The clinical and molecular heterogeneity observed in amyotrophic lateral sclerosis (ALS) presents a challenge for diagnosis, prognosis, and treatment. RNA sequencing of post-mortem brain samples from ALS patients has identified several subtypes with distinct molecular signatures. We sought to evaluate these subtypes across diverse tissues and datasets and assess the feasibility of supervised machine learning models for sample classification. Unsupervised clustering and pathway analysis were performed to confirm the presence of ALS subtypes in motor cortex samples. Three machine learning strategies were then used to create models based on post-mortem motor cortex expression data of 112 people with ALS from the London Neurodegenerative Diseases Brain Bank. These models were subsequently improved through feature selection and evaluated in independent cohorts from motor cortex (n = 257, NYGC ALS Consortium) and blood (n = 96, Macquarie University Neurodegenerative Disease Biobank) samples. Multi-class linear discriminant analysis (LDA) models were then used for subtype classification. Clustering of ALS post-mortem motor cortex samples confirmed the presence of three subtypes: neuroinflammation (ALS-Neu), extracellular matrix organisation and muscle contraction (ALS-OxA), and synaptic and neuropeptide signalling (ALS-SNs). Among all machine learning strategies, random forests produced the most accurate and stable models for binary classification ([~]93% accuracy across the three subtypes). After feature selection, random forest models were able to classify samples from an independent post-mortem motor cortex cohort in their respective subtypes (AUC of [~]0.98 across the three subtypes). When these models were evaluated in blood using LDA, we found consistent clustering patterns, with samples aligning in the same subtype regions of the post-mortem motor cortex samples, with ALS-SNs being the subtype in which samples were classified with the highest confidence (LDA class probability [~]86%). Moreover, classification for this subtype improved when blood samples were collected closer to death. Our findings support the presence of three gene expression-based ALS subtypes in motor cortex samples and the utility of machine learning strategies for subtype classification. We also observed that the subtypes identified in the brain partially match those in the blood, with samples from the late stages of the disease more likely to be correctly predicted into the ALS-SNs cluster. This suggests a longitudinal effect in subtype identification that requires further investigation.

bioinformatics↗

bioRxiv2025

Investigating the Role of Exosome-associated Human Endogenous Retroviruses as Biomarkers in Motor Neuron Disease

Human endogenous retrovirus-K (HERV-K) reactivation is increasingly implicated in amyotrophic lateral sclerosis (ALS), with ongoing clinical trials investigating antiretroviral therapies. However, there is limited understanding of how HERV-K is trafficked in peripheral biofluids, and the role of exosomes, nano-sized extracellular vesicles, in this process remains largely unexplored. Exosomes offer a stable and cell-specific cargo reservoir that may reflect central pathogenic processes and serve as a minimally invasive biomarker source. In this study, we isolated plasma-derived exosomes from ALS patients (n = 21) and healthy controls (n = 16), and quantified exosomal HERV-K gag, env, and pol transcript levels using SYBR Green qPCR with RNase treatment and normalization to both traditional and exosome-enriched reference genes. HERV-K pol expression was significantly elevated in ALS, with fold-changes ranging from 1.59 to 1.85 (P = 0.037-0.051). env and gag also showed increased expression, though with greater variability. Normalization to the exosome-specific gene SOD2 provided the most consistent signal. These findings suggest that exosomal HERV-K transcripts, particularly pol, could serve as accessible biomarkers for patient stratification and treatment monitoring in HERV-K-targeted ALS trials. This work establishes proof-of-concept for using exosomal cargo to track endogenous retroviral activity in neurodegeneration and supports further investigation of liquid biopsy approaches in ALS precision medicine.

neuroscience↗

bioRxiv2025

Systematic comparative benchmarking of computational methods for the detection of transposable elements in long-read sequencing data

BackgroundMobile element insertions, particularly transposable elements (TEs) such as Alu, LINE-1 (L1), SVA, and endogenous retroviruses (ERVs), represent a major source of human genetic variation and have been implicated in evolution, genomic instability, and disease. Although long-read sequencing generally outperforms short-read sequencing for the characterisation of such elements, their accurate detection with long-reads remains challenging, with different computational tools adopting varying approaches and producing divergent call sets. As gold standards currently do not exist for TE detection, benchmarking these methods is essential to understand their strengths, limitations, and biases. Here, we systematically evaluate the performance of available state-of-the-art TE detection tools on both simulated and real human genome data using highly characterised samples from the Genome in a Bottle consortium, population level reference databases and an in-house collection for which matching short-read sequencing data are available. ResultsOur results show significant differences in calling strategies, leading to substantial variation in precision, recall, and the spectrum of TE families detected across tools. Our benchmark also displays the differences between short-read and long-read calls, highlighting the importance of appropriate method selection. ConclusionsThe benchmarking results presented here will aid TE researchers make better informed decisions on which tool to use in their long-read TE analyses. Strengths and limitations of different tools have been highlighted in depth as well as their computational requirements, which will result in less time spent finding the best tool for the job and promote faster TE research.

bioinformatics↗

bioRxiv2025

Landscapes of missense variant impact for human superoxide dismutase 1

Amyotrophic lateral sclerosis (ALS) is a progressive motor neuron disease for which important subtypes are caused by variation in the Superoxide Dismutase 1 gene SOD1. Diagnosis based on SOD1 sequencing can not only be definitive but also indicate specific therapies available for SOD1-associated ALS (SOD1-ALS). Unfortunately, SOD1-ALS diagnosis is limited by the fact that a substantial fraction (currently 26%) of ClinVar SOD1 missense variants are classified as "variants of uncertain significance" (VUS). Although functional assays can provide strong evidence for clinical variant interpretation, SOD1 assay validation is challenging, given the current incomplete and controversial understanding of SOD1-ALS disease mechanism. Using saturation mutagenesis and multiplexed cell-based assays, we measured the functional impact of over two thousand SOD1 amino acid substitutions on both enzymatic function and protein abundance. The resulting missense variant effect maps not only reflect prior biochemical knowledge of SOD1 but also provide sequence-structure-function insights. Importantly, our variant abundance assay can discriminate pathogenic missense variation and provides new evidence for 41% of missense variants that had been previously reported as VUS, offering the potential to identify additional patients who would benefit from therapy approved for SOD1-ALS.

genetics↗

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↗

bioRxiv2023

Mutafy: A webserver to identify high quality mutant protein structures in the Protein Data Bank

Changes in the amino acid sequence of proteins resulting from nonsynonymous variants in the genome, can have significant effects on protein folding, stability, dynamics, and function, which may ultimately lead to diseases. The analysis of large sets of disease associated variants is a common approach for the study of pathogenic mechanisms. In-silico mutagenesis experiments based on wildtype structures of target proteins are a common approach to this aim, however these do not account for the effect of variants on folding and might not accurately reflect conformational changes. A growing number of experimentally solved protein structures harbouring disease-associated mutations, including single amino acid variants, are deposited in the worldwide Protein Data Bank (PDB). Nevertheless, identifying high-quality structures for specific missense variants of interest remains challenging due to the growing number of deposited protein structures in the PDB, and the lack of a dedicated interface and annotation system to search and retrieve mutant protein structures. As a result, mutant protein structures in the PDB are a powerful source of information which is largely underused. To address these shortcomings, we have developed Mutafy, a publicly available webserver to identify high quality mutant protein structures. Given input human genes, the webserver finds structures of the corresponding coded wildtype proteins and their available solved mutants, selects high quality structures, annotates them with information from biomedical databases to favour their interpretation and selection, and allows for the interactive exploration of the results and 3D visualisation. Mutafy is publicly available without requiring user registration at https://mutafy.rosalind.kcl.ac.uk.

bioinformatics↗

bioRxiv2022

Polygenic Prediction of Molecular Traits using Large-Scale Meta-analysis Summary Statistics

IntroductionTranscriptome-wide association study (TWAS) integrates expression quantitative trait loci (eQTL) data with genome-wide association study (GWAS) results to infer differential expression. TWAS uses multi-variant models trained using individual-level genotype-expression datasets, but methodological development is required for TWAS to utilise larger eQTL summary statistics. MethodsTWAS models predicting gene expression were derived using blood-based eQTL summary statistics from eQTLGen, the Young Finns Study (YFS), and MetaBrain. Summary statistic polygenic scoring methods were used to derive TWAS models, evaluating their predictive utility in GTEx v8. We investigated gene inclusion criteria and omnibus tests for aggregating TWAS associations for a given gene. We performed a schizophrenia TWAS using summary statistic-based TWAS models, comparing results to existing resources and methods. ResultsTWAS models derived using eQTL summary statistics performed comparably to models derived using individual-level data. Multi-variant TWAS models significantly improved prediction over single variant models for 8.6% of genes. TWAS models derived using eQTLGen summary statistics significantly improved prediction over models derived using a smaller individual-level dataset. The eQTLGen-based schizophrenia TWAS, using the ACAT omnibus test to aggregate associations for each gene, identified novel significant and colocalised associations compared to summary-based mendelian randomisation (SMR) and SMR-multi. ConclusionsUsing multi-variant TWAS models and larger eQTL summary statistic datasets can improve power to detect differential expression associations. We provide TWAS models based on eQTLGen and MetaBrain summary statistics, and software to easily derive and apply summary statistic-based TWAS models based on eQTL and other molecular QTL datasets released in the future.

genomics↗

bioRxiv2022

DNAscan2: a versatile, scalable, and user-friendly analysis pipeline for next-generation sequencing data

The current widespread adoption of next-generation sequencing (NGS) in all branches of basic and clinical genetics fields means that users with highly variable informatics skills, computing facilities and application purposes need to process, analyse, and interpret NGS data. In this landscape, versatility, scalability, and user-friendliness are key characteristics for an NGS analysis tool. We developed DNAscan2, a highly flexible, end-to-end pipeline for the analysis of NGS data, which (i) can be used for the detection of multiple variant types, including SNVs, small indels, transposable elements, short tandem repeats and other large structural variants; (ii) covers all steps of the analysis, from quality control of raw data to the generation of html reports for the interpretation and prioritisation of results; (iii) is highly adaptable and scalable as it can be deployed and run via either a graphic user interface for non-bioinformaticians, a command line tool for personal computer usage, or as a Snakemake workflow that facilitates parallel multi-sample execution for high-performance computing environments; (iv) is computationally efficient by minimising RAM and CPU time requirements. Availability and ImplementationDNAscan2 is implemented in Python3 and is available to download as a command-line tool and graphical-user interface at https://github.com/KHP-Informatics/DNAscanv2 or a Snakemake workflow at https://github.com/KHP-Informatics/DNAscanv2_snakemake.

bioinformatics↗

bioRxiv2022

The contribution of Neanderthal introgression and natural selection to neurodegenerative diseases

Humans are thought to be more susceptible to neurodegeneration than equivalently-aged primates. It is not known whether this vulnerability is specific to anatomically-modern humans or shared with other hominids. The contribution of introgressed Neanderthal DNA to neurodegenerative disorders remains uncertain. It is also unclear how common variants associated with neurodegenerative disease risk are maintained by natural selection in the population despite their deleterious effects. In this study, we aimed to quantify the genome-wide contribution of Neanderthal introgression and positive selection to the heritability of complex neurodegenerative disorders to address these questions. We used stratified-linkage disequilibrium score regression to investigate the relationship between five SNP-based signatures of natural selection, reflecting different timepoints of evolution, and genome-wide associated variants of the three most prevalent neurodegenerative disorders: Alzheimers disease, amyotrophic lateral sclerosis and Parkinsons disease. We found no evidence for enrichment of positively-selected SNPs in the heritability of Alzheimers disease, amyotrophic lateral sclerosis and Parkinsons disease,, suggesting that common deleterious disease variants are unlikely to be maintained by positive selection. There was no enrichment of Neanderthal introgression in the SNP-heritability of these disorders, suggesting that Neanderthal admixture is unlikely to have contributed to disease risk. These findings provide insight into the origins of neurodegenerative disorders within the evolution of Homo sapiens and addresses a long-standing debate, showing that Neanderthal admixture is unlikely to have contributed to common genetic risk of neurodegeneration in anatomically-modern humans.

neuroscience↗

bioRxiv2022

An assessment of bioinformatics tools for the detection of human endogenous retroviral insertions in short-read genome sequencing data

There is a growing interest in the study of human endogenous retroviruses (HERVs) given the substantial body of evidence that implicates them in many human diseases. Although their genomic characterization presents numerous technical challenges, next-generation sequencing (NGS) has shown potential to detect HERV insertions and their polymorphisms in humans, and a number of computational tools to detect them in short-read NGS data exist. In order to design optimal analysis pipelines, an independent evaluation of the currently available tools is required. We evaluated the performance of a set of such tools using a variety of experimental designs and types of NGS datasets. These included 50 human short read whole-genome sequencing samples, matching long and short read NGS data, and simulated short-read NGS data. Our results highlight the performance variability of the tools across the datasets and suggest that different tools might be suitable for different study designs. Using multiple tools and a consensus approach is advisable if computationally feasible and wet-lab validation via PCR is advisable where biological samples are available.

bioinformatics↗

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↗

bioRxiv2018

Partitioning the genetic architecture of amyotrophic lateral sclerosis

The genetic basis of sporadic amyotrophic lateral sclerosis (ALS) is not well understood. Using large genome-wide association studies and validated tools to quantify genetic overlap, we systematically identified single nucleotide polymorphisms (SNPs) associated with ALS conditional on genetic data from 65 different traits and diseases from >3 million people. We found strong genetic enrichment between ALS and a number of disparate traits including frontotemporal dementia, coronary artery disease, C-reactive protein, celiac disease and memory function. Beyond C9ORF72, we detected novel genetic signal within numerous loci including GIPC1, ELMO1 and COL16A and confirmed previously reported variants, such as ATXN2, KIF5A, UNC13A and MOBP. We found that ALS variants form a small-world co-expression network characterized by highly inter-connected hub genes. This network clustered into smaller sub-networks, each associated with a unique function. Altered gene expression of several sub-networks and hubs was over-represented in neuropathological samples from ALS patients and SOD1 G93A mice. Our collective findings indicate that the genetic architecture of ALS can be partitioned into distinct components where some genes are highly important for developing disease. These findings have implications for stratification and enrichment strategies for ALS clinical trials.

genetics↗

bioRxiv2018

ALSgeneScanner: a pipeline for the analysis and interpretation of DNA NGS data of ALS patients

Amyotrophic lateral sclerosis (ALS, MND) is a neurodegenerative disease of upper and lower motor neurons resulting in death from neuromuscular respiratory failure, typically within two years of first symptoms. Genetic factors are an important cause of ALS, with variants in more than 25 genes having strong evidence, and weaker evidence available for variants in more than 120 genes. With the increasing availability of Next-Generation sequencing data, non-specialists, including health care professionals and patients, are obtaining their genomic information without a corresponding ability to analyse and interpret it. Furthermore, the relevance of novel or existing variants in ALS genes is not always apparent. Here we present ALSgeneScanner, a tool that is easy to install and use, able to provide an automatic, detailed, annotated report, on a list of ALS genes from whole genome sequence data in a few hours and whole exome sequence data in about one hour on a readily available mid-range computer. This will be of value to non-specialists and aid in the interpretation of the relevance of novel and existing variants identified in DNA sequencing data.

bioinformatics↗

bioRxiv2018

DNAscan: a fast, computationally and memory efficient bioinformatics pipeline for the analysis of DNA next-generation-sequencing data

The generation of DNA Next Generation Sequencing (NGS) data is a commonly applied approach for studying the genetic basis of biological processes, including diseases, and underpins the aspirations of precision medicine. However, there are significant challenges when dealing with NGS data. A huge number of bioinformatics tools exist and it is therefore challenging to design an analysis pipeline; NGS analysis is computationally intensive, requiring expensive infrastructure which can be problematic given that many medical and research centres do not have adequate high performance computing facilities and the use of cloud computing facilities is not always possible due to privacy and ownership issues. We have therefore developed a fast and efficient bioinformatics pipeline that allows for the analysis of DNA sequencing data, while requiring little computational effort and memory usage. We achieved this by exploiting state-of-the-art bioinformatics tools. DNAscan can analyse raw, 40x whole genome NGS data in 8 hours, using as little as 8 threads and 16 Gbs of RAM, while guaranteeing a high performance. DNAscan can look for SNVs, small indels, SVs, repeat expansions and viral genetic material (or any other organism). Its results are annotated using a customisable variety of databases including ClinVar, Exac and dbSNP, and a local deployment of the gene.iobio platform is available for an on-the-fly result visualisation.

bioinformatics↗

bioRxiv2017

Project MinE: study design and pilot analyses of a large-scale whole-genome sequencing study in amyotrophic lateral sclerosis

The most recent genome-wide association study in amyotrophic lateral sclerosis (ALS) demonstrates a disproportionate contribution from low-frequency variants to genetic susceptibility of disease. We have therefore begun Project MinE, an international collaboration that seeks to analyse whole-genome sequence data of at least 15,000 ALS patients and 7,500 controls. Here, we report on the design of Project MinE and pilot analyses of newly whole-genome sequenced 1,264 ALS patients and 611 controls drawn from the Netherlands. As has become characteristic of sequencing studies, we find an abundance of rare genetic variation (minor allele frequency < 0.1 %), the vast majority of which is absent in public data sets. Principal component analysis reveals local geographical clustering of these variants within The Netherlands. We use the whole-genome sequence data to explore the implications of poor geographical matching of cases and controls in a sequence-based disease study and to investigate how ancestry-matched, externally sequenced controls can induce false positive associations. Also, we have publicly released genome-wide minor allele counts in cases and controls, as well as results from genic burden tests.

genetics↗

bioRxiv2016

Detection of long repeat expansions from PCR-free whole-genome sequence data

Identifying large repeat expansions such as those that cause amyotrophic lateral sclerosis (ALS) and Fragile X syndrome is challenging for short-read (100-150 bp) whole genome sequencing (WGS) data. A solution to this problem is an important step towards integrating WGS into precision medicine. We have developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3,001 ALS patients who have been tested for the presence of the C9orf72 repeat expansion with repeat-primed PCR (RP-PCR). Taking the RP-PCR calls as the ground truth, our WGS-based method identified pathogenic repeat expansions with 98.1% sensitivity and 99.7% specificity. Further inspection identified that all 11 conflicts were resolved as errors in the original RP-PCR results. Compared against this updated result, ExpansionHunter correctly classified all (212/212) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2,786/2,789) of the wild type samples were correctly classified as wild type by this method with the remaining two identified as possible expansions. We further applied our algorithm to a set of 144 samples where every sample had one of eight different pathogenic repeat expansions including examples associated with fragile X syndrome, Friedreichs ataxia and Huntingtons disease and correctly flagged all of the known repeat expansions. Finally, we tested the accuracy of our method for short repeats by comparing our genotypes with results from 860 samples sized using fragment length analysis and determined that our calls were >95% accurate. ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.

bioinformatics↗