Biology subjects

Hardiman, O.

Publications and source records attributed to Hardiman, O..

15 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↗

bioRxiv2025

Spinal electrophysiology reveals frequency-specific spatial patterns of neural activity and corticospinal coherence during pincer-grip

BackgroundNeural dynamics within sensory-motor networks involved in motor control exhibit well-established frequency-dependent patterns of cortical activity. In contrast, corresponding neural patterns of spinal cord activity remain poorly understood. As an active functional part of motor control, characterising spinal cord activity is essential for understanding sensorimotor function. High-density electrospinography presents a novel technique to assess spinal neural dynamics by non-invasively recording task-relevant electrical activity. ObjectiveTo non-invasively investigate spatio-spectral patterns of task-related spinal activity and its functional connectivity with cortical regions during isometric pincer-grip contraction. MethodologyHere, we simultaneously recorded 128-channel electroencephalography (EEG), 64-channel electrospinography (ESG), and two bipolar electromyography (EMG) signals during an isometric pincer-grip task. Frequency-specific spatial patterns of spinal activity and cortico-spinal connectivity were evaluated by calculating task-related ESG power and cortico-spinal coherence between ESG and EEG signals during the isometric hold. ResultsDistinct frequency-specific spatial patterns of spinal activity were observed at lower cervical levels during sustained hold, with significant activation over the ipsilateral anterolateral region. In the beta band, task-related spinal activity was significantly ipsilateralised, and exhibited significant cortico-spinal connectivity between contralateral motor cortex and ipsi-anterolateral spinal region. ConclusionsThis first-of-its-kind application of HD non-invasive spinal electrophysiology revealed that the spinal cord exhibits distinct frequency-specific spatial activation and cortical connectivity patterns during pincer-grip sustained hold. Specifically, the ipsi-anterolateral spinal region demonstrated high task-relevance, potentially indicating anterior horn activity and its connectivity with motor cortex. Furthermore, beta-band activation was observed as a key signature during sustained hold, further underpinning its relevance during motor control.

neuroscience↗

bioRxiv2025

Very low amplitude muscle activity increases probability of motor evoked potentials in healthy individuals and in ALS

IntroductionIn many clinical and research settings, transcranial magnetic stimulation (TMS) intensities are standardised based on resting motor threshold (RMT). It is well-established that contraction of the target muscle increases motor evoked potential (MEP) amplitude and correspondingly decreases RMT. As such, when estimating RMT it is crucial to ensure the target muscle is relaxed. Typically trials in which baseline electromyographic (EMG) amplitude exceeds a specified threshold are rejected. The influence of motor activity below typical rejection thresholds on MEP amplitudes has yet to be established. MethodsWe retrospectively analysed TMS-EMG data collected during RMT measurement in 45 healthy controls (1761 datapoints) and 35 people with amyotrophic lateral sclerosis (ALS, 1238 datapoints). Trials where root mean squared (RMS) baseline EMG amplitude exceeded 10{micro}V were rejected. Generalised linear mixed-effects models were used to assess effects of muscle activity below this rejection threshold on probability of evoking an MEP with peak-to-peak amplitude [≥]50{micro}V. ResultsGreater sub-rejection-threshold activity significantly increases MEP probability in controls (p<0.0004) and people with ALS (p=0.0010). Models predicted a 31-38% increase in MEP probability when baseline RMS-EMG amplitude increased from 1{micro}V to 9{micro}V. Sub-rejection- threshold baseline activity was significantly greater in ALS than controls (p=0.0055). DiscussionWe have shown for the first time that higher EMG amplitudes below a typical rejection threshold markedly increase probability of evoking an MEP with peak-to-peak amplitude [≥]50{micro}V. Researchers should take measures to account for effects of sub-rejection-threshold activity on RMT, particularly in populations where baseline activity may be elevated, such as in ALS.

neuroscience↗

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

SC10X/U: A High-density Electrode System for Non-Invasive Recording of Neural Activity of the Cervical Spinal Cord

ObjectiveTo design and develop a high-density (HD) electrode system that describes the position of surface electrodes for recording electrophysiological signals from the human cervical spinal cord. The system is intended to standardize experimental recordings and facilitate the subsequent analysis of evoked and spontaneous spinal cord neural activity, using high-density electrospinography (HD-ESG). MethodThe proposed system (SC10-X/U) describes the locations of up to 76 channels with a unique nomenclature, where the division of the spinal cord (SC) electrode space was inspired by the EEG 10-10 system. As proof of concept, spinal evoked potentials in response to median nerve stimulation at the wrist were recorded from 10 participants and characterized based on a 64-channel derivation from the SC10-X/U system. ResultsFollowing the design criteria, the SC10-X/U defines 76 electrode positions and its configuration. HD-ESG system was utilized to successfully record evoked spinal responses and significant N13 and P9 potential were observed in response to the stimulation. The spinal N13 potential had a latency of 13.2 +-1.1ms (mean +-SD) after stimulation. A topographic map of the N13 electro-spinal activity using the 64-channel recording system revealed an epicentre at C5-C7 dorsal-vertebral locations (ML4 - ML6 electrodes). ConclusionThe proposed SC10-X/U system will facilitate standardized recording and analysis of high-density ESG signals from the human cervical spinal cord. The system defines electrode locations to promote standardization across different individuals, studies, and clinical and research centres. The HD-ESG evoked potentials recorded using the proposed system were comparable to those observed in previous non-HD studies. The presented topographic maps conform to known neurophysiological and neuroanatomical findings. This served to validate the design and development of the electrode system and patch for future studies.

neuroscience↗

bioRxiv2024

A genetic perspective on the recent demographic history of Ireland and Britain

BackgroundWhile subtle yet discrete clusters of genetic identity across Ireland and Britain have been identified, their demographic history is unclear. MethodsUsing genotype data from 6,574 individuals with associated regional Irish or British ancestry, we identified Irish-like and British-like genetic communities using network community detection. We segregated Identity-by-Descent (IBD) and Runs-of-Homozygosity (ROH) segments by length and approximated their corresponding time periods. Through this, we inferred the regional Irish and British demographic histories in these time periods by (1) estimating genetic relatedness between communities, (2) estimating changes in effective population sizes, (3) inferring recent migration rates across Ireland and Britain, and (4) estimating changing affinities to regional European populations. For a subset of the Irish communities, we determined the enrichment/depletion of surnames within the genetic communities. ResultsThrough patterns of IBD-sharing and ROH, we find evidence of recent population bottlenecks in the Orcadian, Manx and Welsh communities. While the structure in Ireland is subtler, the communities share relatively more IBD segments that are shorter in length, and the genetic differences between the Irish communities are more subtle on average, when compared to the British communities. Regional effective population size trajectories indicate a similar demographic history throughout the island of Ireland. Further, we observe a stable migration corridor between north-east Ireland and south-west Scotland while there is a recent migration barrier between South-Eastern Ireland and Western Ireland. We observed an enrichment of Anglo-Norman and English surnames in the Wexford community while within the West Ulster-Argyll community, we saw an enrichment of Gallowglass and Scottish surnames. ConclusionsUsing well-annotated Irish and British reference genotypes, we observed temporal changes in genetic affinities within and between genetic communities in Ireland and Britain. In addition, using effective population size estimates and levels of haplotype-sharing, we detected varying degrees of genetic isolation in some Irish and British genetic communities across time. Using these new insights into the regional demographic history of Ireland and Britain across different time periods, we hope to understand the driving forces of rare allele frequencies and disease risk association within these populations.

genetics↗

bioRxiv2022

Molecular dynamics analysis of Superoxide Dismutase 1 mutations suggests decoupling between mechanisms underlying ALS onset and progression

Mutations in the superoxide dismutase 1 (SOD1) gene are the second most common known cause of ALS. SOD1 variants express high phenotypic variability and over 200 have been reported in people with ALS. Investigating how different SOD1 variants affect the protein dynamics might help in understanding their pathogenic mechanism and explaining their heterogeneous clinical presentation. It was previously proposed that variants can be broadly classified in two groups, wild-type like (WTL) and metal binding region (MBR) variants, based on their structural location and biophysical properties. MBR variants are associated with a loss of SOD1 enzymatic activity. In this study we used molecular dynamics and large clinical datasets to characterise the differences in the structural and dynamic behaviour of WTL and MBR variants with respect to the wild-type SOD1, and how such differences influence the ALS clinical phenotype. Our study identified marked structural differences, some of which are observed in both variant groups, while others are group specific. Moreover, applying graph theory to a network representation of the proteins, we identified differences in the intramolecular contacts of the two classes of variants. Finally, collecting clinical data of approximately 500 SOD1 ALS patients carrying variants from both classes, we showed that the survival time of patients carrying an MBR variant is generally longer (~6 years median difference, p < 0.001) with respect to patients with a WTL variant. In conclusion, our study highlights key differences in the dynamic behaviour of the WTL and MBR SOD1 variants, and wild-type SOD1 at an atomic and molecular level. We identified interesting structural features that could be further investigated to explain the associated phenotypic variability. Our results support the hypothesis of a decoupling between mechanisms of onset and progression of SOD1 ALS, and an involvement of loss-of-function of SOD1 with the disease progression.

neuroscience↗

bioRxiv2022

Mining impactful discoveries from the biomedical literature

MotivationLiterature-Based Discovery (LBD) aims to help researchers to identify relations between concepts which are worthy of further investigation by text-mining the biomedical literature. While the LBD literature is rich and the field is considered mature, standard practice in the evaluation of LBD methods is methodologically poor and has not progressed on par with the domain. The lack of properly designed and decent-sized benchmark dataset hinders the progress of the field and its development into applications usable by biomedical experts. ResultsThis work presents a method for mining past discoveries from the biomedical literature. It leverages the impact made by a discovery, using descriptive statistics to detect surges in the prevalence of a relation across time. This method allows the collection of a large amount of time-stamped discoveries which can be used for LBD evaluation or other applications. The validity of the method is tested against a baseline representing the state of the art "time sliced" method. AvailabilityThe source data used in this article are publicly available. The implementation and the resulting data are published under open-source license: https://github.com/erwanm/medline-discoveries (code) https://zenodo.org/record/5888572 (datasets). An online exploration tool is also provided at https://brainmend.adaptcentre.ie/. Contacterwan.moreau@adaptcentre.ie

bioinformatics↗

bioRxiv2021

Literature-Based Discovery beyond the ABC paradigm: a contrastive approach

Literature-Based Discovery (LBD) aims to help researchers to identify relations between concepts which are worthy of further investigation by text-mining the biomedical literature. The vast majority of the LBD research follows the ABC model: a relation (A,C) is a candidate for discovery if there is some intermediate concept B which is related to both A and C. The ABC model has been successful in applications where the search space is strongly constrained, but there is limited evidence about its usefulness when applied in a broader context. Through a case study of 8 recent discoveries related to neurodegenerative diseases (NDs), we show the limitations of the ABC model in an open-ended context. The study emphasizes the impact of the choice of source data and extraction method on the resulting knowledge base: different "views" of the biomedical literature offer different levels of accuracy and coverage. We propose a novel contrastive approach which leverages these differences between "views" in order to target relations between concepts of interest. We explore various parameters and demonstrate the relevance of our approach through quantitative evaluation on the 8 target discoveries. The source data used in this article are publicly available. The different parts of the software used to process the data are published under open-source license and provided with detailed instructions. The main code for this paper is available at https://github.com/erwanm/lbd-contrast (required dependencies are detailed in the documentation). A prototype of the system is also provided as an online exploration tool at brainmend.adaptcentre.ie.

bioinformatics↗

bioRxiv2019

Non-Parametric Rank Statistics for Spectral Power and Coherence

Despite advances in multivariate spectral analysis of neural signals, the statistical inference of measures such as spectral power and coherence in practical and real-life scenarios remains a challenge. The non-normal distribution of the neural signals and presence of artefactual components make it difficult to use the parametric methods for robust estimation of measures or to infer the presence of specific spectral components above the chance level. Furthermore, the bias of the coherence measures and their complex statistical distributions are impediments in robust statistical comparisons between 2 different levels of coherence. Non-parametric methods based on the median of auto-/cross-spectra have shown promise for robust estimation of spectral power and coherence estimates. However, the statistical inference based on these non-parametric estimates remain to be formulated and tested. In this report a set of methods based on non-parametric rank statistics for 1-sample and 2-sample testing of spectral power and coherence is provided. The proposed methods were demonstrated and tested using simulated neural signals in different conditions. The results show that non-parametric methods provide robustness against artefactual components. Moreover, they provide new possibilities for robust 1-sample and 2-sample testing of the complex coherency function, including both the magnitude and phase, where existing methods fall short of functionality. The utility of the methods were further demonstrated by examples on experimental neural data. The proposed approach provides a new framework for non-parametric spectral analysis of digital signals. These methods are especially suited to neuroscience and neural engineering applications, given the attractive properties such as minimal assumption on distributions, statistical robustness, and the diverse testing scenarios afforded.

bioinformatics↗

bioRxiv2018

Stratification of amyotrophic lateral sclerosis patients: a crowdsourcing approach

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease with substantial heterogeneity in clinical presentation with an urgent need for better stratification tools for clinical development and care. In this study we used a crowdsourcing approach to address the problem of ALS patient stratification. The DREAM Prize4Life ALS Stratification Challenge was a crowdsourcing initiative using data from >10,000 patients from completed ALS clinical trials and 1479 patients from community-based patient registers. Challenge participants used machine learning and clustering techniques to predict ALS progression and survival. By developing new approaches, the best performing teams were able to predict disease outcomes better than currently available methods. At the same time, the integration of clustering components across methods led to the emergence of distinct consensus clusters, separating patients into four consistent groups, each with its unique predictors for classification. This analysis reveals for the first time the potential of a crowdsourcing approach to uncover covert patient sub-populations, and to accelerate disease understanding and therapeutic development.

bioinformatics↗

bioRxiv2017

Insular Celtic population structure and genomic footprints of migration

Previous studies of the genetic landscape of Ireland have suggested homogeneity, with population substructure undetectable using single-marker methods. Here we have harnessed the haplotype-based method fineSTRUCTURE in an Irish genome-wide SNP dataset, identifying 23 discrete genetic clusters which segregate with geographical provenance. Cluster diversity is pronounced in the west of Ireland but reduced in the east where older structure has been eroded by historical migrations. Accordingly, when populations from the neighbouring island of Britain are included, a west-east cline of Celtic-British ancestry is revealed along with a particularly striking correlation between haplotypes and geography across both islands. A strong relationship is revealed between subsets of Northern Irish and Scottish populations, where discordant genetic and geographic affinities reflect major migrations in recent centuries. Additionally, Irish genetic proximity of all Scottish samples likely reflects older strata of communication across the narrowest inter-island crossing. Using GLOBETROTTER we detected Irish admixture signals from Britain and Europe and estimated dates for events consistent with the historical migrations of the Norse-Vikings, the Anglo-Normans and the British Plantations. The influence of the former is greater than previously estimated from Y chromosome haplotypes. In all, we paint a new picture of the genetic landscape of Ireland, revealing structure which should be considered in the design of studies examining rare genetic variation and its association with traits.\n\nAuthor summaryA recent genetic study of the UK (People of the British Isles; PoBI) expanded our understanding of population history of the islands, using newly-developed, powerful techniques that harness the rich information embedded in chunks of genetic code called haplotypes. These methods revealed subtle regional diversity across the UK, and, using genetic data alone, timed key migration events into southeast England and Orkney. We have extended these methods to Ireland, identifying regional differences in genetics across the island that adhere to geography at a resolution not previously reported. Our study reveals relative western diversity and eastern homogeneity in Ireland owing to a history of settlement concentrated on the east coast and longstanding Celtic diversity in the west. We show that Irish Celtic diversity enriches the findings of PoBI; haplotypes mirror geography across Britain and Ireland, with relic Celtic populations contributing greatly to haplotypic diversity. Finally, we used genetic information to date migrations into Ireland from Europe and Britain consistent with historical records of Viking and Norman invasions, demonstrating the signatures of these migrations the on modern Irish genome. Our findings demonstrate that genetic structure exists in even small isolated populations, which has important implications for population-based genetic association studies.

genetics↗

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↗

bioRxiv2017

EyeBallGUI: A Tool For Visual Inspection And Binary Marking of Multi-Channel Bio-Signals

A wide range of studies in human neuroscience rely on the analysis of electrophysiological bio-signals such as electroencephalogram (EEG) where customized data analysis may require supervised artefact rejection, binary marking through visual inspection, selection of noise and artefact samples for pre-processing algorithms, and selection of clinically-relevant signal segments in neurological conditions. Nevertheless, the existing preprocessing tools do not provide the needed flexibility to handle such tasks efficiently. We therefore developed a free open-source Graphical User Interface (GUI), EyeBallGUI, that allows visualization and flexible, manual marking (binary classification) of multi-channel bio-signal data. EyeBallGUI, developed for MATLAB(R), allows the user to interactively and accurately inspect and mark multi-channel digitized data with no restriction on marking periods of data in subsets of channels (a restriction in place in existing tools). The new tool facilitates precise, manual marking of bio-signals by allowing any desired segment of data to be marked in any subset of channels. It is therefore of utility in circumstances where such flexibility is essential. The developed GUI is an auxiliary analysis tool that shall facilitate neural signal (pre-)processing applications where it is desirable to perform accurate supervised artefact rejection, flexible data marking for increased data retention yield, extraction of specific signal segments by expert users from sample data, or labeling of data for clinical and scientific research purposes.

neuroscience↗

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↗