Biology subjects

Hicks, M.

Publications and source records attributed to Hicks, M..

9 recordsLinked to original sources

bioRxiv2026

A Workflow for Spatial Transcriptomic Analysis from Intra-operative Human Skeletal Muscle Biopsies

IntroductionSuccessful reinnervation following peripheral nerve injury is highly variable, and the molecular programs underlying human muscle degeneration and recovery remain poorly defined. There is a critical need for high-resolution, spatially resolved gene expression data from human skeletal muscle obtained in clinically relevant settings. This study aimed to establish the feasibility of applying spatial transcriptomics to intra-operatively human muscle biopsies and to generate a framework for identifying gene expression signatures associated with reinnervation outcomes. MethodsTo validate the workflow, we collected biopsies intraoperatively from upper-extremity muscles during standard-of-care orthopaedic surgical procedures 5 months after traumatic brachial plexus injury. The flash-frozen biopsy was processed using the 10x Genomics Visium HD high-resolution platform. Quality metrics confirmed high RNA integrity and robust transcript detection at 8 {micro}m resolution. ResultsGenes involved in neuromuscular junction formation, degeneration, and regeneration were identified at subcellular resolution and showed fiber-type-specific expression patterns. Analyses were performed using complementary approaches in Seurat and Loupe Browser. ConclusionsTogether, these findings demonstrate the feasibility of spatial transcriptomics in human muscle, establish baseline gene-expression signatures, and provide a foundation for future studies aimed at identifying biomarkers associated with successful reinnervation and improved nerve-repair strategies.

physiology↗

bioRxiv2026

Evolution of Reproductive Plasticity in a Seasonal Tropical Environment

Seasonality drives the evolution of reproductive plasticity in butterflies. An extreme form is reproductive diapause, where individuals halt reproduction during unfavourable seasons through cascades translating predictive environmental cues into reproductive phenotypes. How diapause evolves from milder forms of plasticity through changes in those cascades is poorly understood. In the tropics seasonality is common, but it is unclear how phylogenetically widespread reproductive diapause is, and exactly how seasonal tropical climates drive reproductive plasticity. Here, we study reproductive plasticity in Amazon butterflies using a multi-year monthly time series of reproductive phenotypes. Sampling 4 subfamilies across Nymphalids, we observe a phylogenetically widespread occurrence of diapause, suggesting repeated evolutionary changes in reproductive plasticity. Detailed analyses of two Catonephele species reveal a shared temperature response, but dry season diapause only in C. acontius. Thus, evolution of diapause combines cue conservation with species-specific divergence of trait plasticity, suggesting gradual and modular evolution of reproductive plasticity.

evolutionary biology↗

bioRxiv2026

Genome Report: Long read, high-coverage reference genomes of the Nymphalid butterflies Catonephele acontius and C. numilia (Nymphalidae: Biblidinae)

Withdrawal StatementThe authors have withdrawn this manuscript because in February 2026, the authors found that the Catonephele numilia specimen was misidentified and was in fact also Catonephele acontius. Thus, the data in the article describing the Catonephele numilia genome is invalid, as it in fact describes a second Catonephele acontius genome. This data invalidation has resulted in a loss of confidence in the results and conclusions reported in the article as far as they apply to Catonephele numilia. The data describing Catonephele acontius is not affected and remains valid. The authors are, therefore, retracting this article. All authors agree with the retraction. Therefore, the authors do not wish this work to be cited as reference for the project. If you have any questions, please contact the corresponding author.

genomics↗

bioRxiv2024

Multi omics aided small RNA profiling of wheat rhizosphere and their potential targets in contrasting soils for Rhizoctonia solani-AG8 suppression

Next-generation sequencing helps describe microbial communities in rhizosphere environments, but understanding rhizosphere-plant interactions synergistic effects on plant traits and health outcomes remains challenging. This study analyses rhizosphere sRNAs ability to manipulate host gene targets in plants grown in suppressive (SP) and non-suppressive (NSP) soils with an integrated multi omics dataset. The results showed that rhizosphere sRNAs exhibited specific compositional features that may be important for rhizosphere-plant interaction. Small RNAs, less than 30 nt in size, were predominant in both samples, with a 5-prime bias towards cytosine enrichment, suggesting potential association with wheat specific argonauts. Mapping of sRNA reads to microbial metagenomes assembled draft genomes from SP and NSP soils showed sRNA loci were differentially expressed (DE) between the soils with contrasting disease suppressive capacities. In total, 96 and 132 non redundant rhizosphere sRNAs were abundant in SP and NSP rhizosphere communities, respectively. While 55 known bacterial sRNA loci were predicted from both SP and NSP metagenomes, 127 sRNAs originated from these loci were differentially expressed. Global wheat target prediction and functional analysis from DE rhizosphere sRNAs showed both soil type specific and common pathways. Upregulated NSP sRNAs target metabolic pathways, secondary metabolite biosynthesis, MAPK signalling, while SP sRNAs target glycerophospholipid metabolism, pathways such as polycomb repressive complex, starch/sucrose metabolism, and plant-pathogen interactions were targeted by both sets of sRNAs. This is the first study showing evidence for rhizosphere sRNAs and their corresponding plant transcripts in the context of biological disease suppression in agricultural soils. ImportanceSmall RNAs (sRNAs) have gained attention in host-microbe interactions due to their diverse roles in controlling biological processes. Studies have identified numerous sRNAs with novel functions across various organisms. Echoing growing evidence of sRNAs in different plant-microbe interaction, we show an evidence of rhizosphere sRNAs regulating wheat genes in soil disease suppression context. This understanding could significantly enhance our comprehension of gene regulation in biological functions, potentially paving the way for the development of microbiome-based methods to influence host traits. Understanding the microbiome communitys mechanisms in different environments offers opportunities to modify them for agriculture, including modifying farming practices, host genetics/immunity, and synthetic communities for disease suppression.

molecular biology↗

bioRxiv2019

Pharmacological inhibition of acid sensing ion channel 1a protects the heart from ischemia-reperfusion injury

The proton-gated acid-sensing ion channel 1a (ASIC1a) is implicated in the injury response to cerebral ischemia but little is known about its role in cardiac ischemia. We provide genetic evidence that ASIC1a is involved in myocardial ischemia-reperfusion injury (IRI) and show that pharmacological inhibition of ASIC1a yields robust cardioprotection in rodent and human models of cardiac ischemia, resulting in improved post-IRI cardiac viability and function. Consistent with a key role for ASIC1a in cardiac ischemia, we show that polymorphisms in the ASIC1 genetic locus are strongly associated with myocardial infarction. Collectively, our data provide compelling evidence that ASIC1a is a key target for cardioprotective drugs to reduce the burden of disease associated with myocardial ischemia.

physiology↗

bioRxiv2018

Precision Medicine Advancements Using Whole Genome Sequencing, Noninvasive Whole Body Imaging, and Functional Diagnostics

We report the results of a three-year precision medicine study that enrolled 1190 presumed healthy participants at a single research clinic. To enable a better assessment of disease risk and improve diagnosis, a precision health platform that integrates non-invasive functional measurements and clinical tests combined with whole genome sequencing (WGS) was developed. The platform included WGS, comprehensive quantitative non-contrast whole body (WB) and brain magnetic resonance imaging/angiography (MRI/MRA), computed tomography (CT) coronary artery calcium scoring, electrocardiogram, echocardiogram, continuous cardiac monitoring, clinical laboratory tests, and metabolomics. In our cohort, 24.3% had medically significant genetic findings (MSF) which may contribute to increased risk of disease. A total of 206 unique medically significant variants in 111 genes were identified, and forty individuals (3.4%) had more than one MSF. Phenotypic testing revealed: 34.2% of our cohort had a metabolomics profile suggestive of insulin resistance, 29.2% had elevated liver fat identified by MRI, 16.4% had clinically important cardiac structure or cardiac function abnormalities on cardiac MRI or ECHO, 8.8% had a high cardiovascular risk on CT coronary artery calcium scoring (Agatston calcium score > 400, Relative Risk of 7.2), 8.0% had arrhythmia found on continuous rhythm monitoring, 6.5% had cardiac conduction disorders found on EKG, 2% had previously undetected tumors detected by WB MRI, and 2.5% had previously undetected aneurysms detected by non-contrast MRI/MRA. Using family histories, personal histories, and test results, clinical and phenotypic findings were correlated with genomic findings in 130 study participants (63.1%) with high to moderate penetrance variants, suggesting the precision health platform improves the diagnostic process in asymptomatic individuals who were at risk. Cardiovascular and endocrine diseases achieved considerable clinical associations between MSFs and clinical phenotypes (89% and 72%, respectively). These findings demonstrate the value of integrating WGS and noninvasive clinical assessments for a rapid and integrated point-of-care clinical diagnosis of age-related diseases that contribute to premature mortality.

genomics↗

bioRxiv2018

Unsupervised integration of multimodal dataset identifies novel signatures of health and disease

Modern medicine is rapidly moving towards a data-driven paradigm based on comprehensive multimodal health assessments. We collected 1,385 data features from diverse modalities, including metabolome, microbiome, genetics and advanced imaging, from 1,253 individuals and from a longitudinal validation cohort of 1,083 individuals. We utilized an ensemble of unsupervised machine learning techniques to identify multimodal biomarker signatures of health and disease risk. In particular, our method identified a set of cardiometabolic biomarkers that goes beyond standard clinical biomarkers, which were used to cluster individuals into distinct health profiles. Cluster membership was a better predictor for diabetes than established clinical biomarkers such as glucose, insulin resistance, and BMI. The novel biomarkers in the diabetes signature included 1-stearoyl-2-dihomo-linolenoyl-GPC and 1-(1-enyl-palmitoyl)-2-oleoyl-GPC. Another metabolite, cinnamoylglycine, was identified as a potential biomarker for both gut microbiome health and lean mass percentage. We also identified an early disease signature for hypertension, and individuals at-risk for a poor metabolic health outcome. We found novel associations between an uremic toxin, p-cresol sulfate, and the abundance of the microbiome genera Intestinimonas and an unclassified genus in the Erysipelotrichaceae family. Our methodology and results demonstrate the potential of multimodal data integration, from the identification of novel biomarker signatures to a data-driven stratification of individuals into disease subtypes and stages -- an essential step towards personalized, preventative health risk assessment.

bioinformatics↗

bioRxiv2017

Functional characterization of 3D-protein structures informed by human genetic diversity

Sequence variation data of the human proteome can be used to analyze 3-dimensional (3D) protein structures to derive functional insights. We used genetic variant data from nearly 150,000 individuals to analyze 3D positional conservation in 4,390 protein structures using 481,708 missense and 264,257 synonymous variants. Sixty percent of protein structures harbor at least one intolerant 3D site as defined by significant depletion of observed over expected missense variation. We established an Angstrom-scale distribution of annotated pathogenic missense variants and showed that they accumulate in proximity to the most intolerant 3D sites. Structural intolerance data correlated with experimental functional read-outs in vitro. The 3D structural intolerance analysis revealed characteristic features of ligand binding pockets, orthosteric and allosteric sites. The identification of novel functional 3D sites based on human genetic data helps to validate, rank or predict drug target binding sites in vivo.

genomics↗

bioRxiv2016

The human functional genome defined by genetic diversity

Large scale efforts to sequence whole human genomes provide extensive data on the non-coding portion of the genome. We used variation information from 11,257 human genomes to describe the spectrum of sequence conservation in the population. We established the genome-wide variability for each nucleotide in the context of the surrounding sequence in order to identify departure from expectation at the population level (context-dependent conservation). We characterized the population diversity for functional elements in the genome and identified the coordination of conserved sequences of distal and cis enhancers, chromatin marks, promoters, coding and intronic regions. The most context-dependent conserved regions of the genome are associated with unique functional annotations and a genomic organization that spreads up to one megabase. Importantly, these regions are enriched by over 100-fold of non-coding pathogenic variants. This analysis of human genetic diversity thus provides a detailed view of sequence conservation, functional constraint and genomic organization of the human genome. Specifically, it identifies highly conserved non-coding sequences that are not captured by analysis of interspecies conservation and are greatly enriched in disease variants.

genomics↗