Biology subjects

Ennis, S.

Publications and source records attributed to Ennis, S..

6 recordsLinked to original sources

bioRxiv2026

Characterisation of Genetically-Derived Patient Clusters Highlights Genetic Heterogeneity of Inflammatory Bowel Disease

Background and aims. Inflammatory Bowel Disease (IBD) is a complex immune-mediated condition with a strong genetic contribution. The genetics of IBD are highly complex, with inflammation, autoimmunity, and mucosal immunity pathways involved in its pathogenicity. GenePy is a gene-level disease burden metric. The integration of clinical and genetic data and the application of machine learning methods can aid patient stratification for personalised care. Methods. Lists of consensus IBD pathway genes were curated. GenePy scores were computed from whole exome sequencing data. A K-Means algorithm was optimised using clustering quality and stability metrics on the GenePy scores of genes in each IBD pathway, and the results replicated in three independent IBD cohorts (SPARC IBD, MSCCR IBD, and Southampton IBD). Chi-square tests assessed the association between cluster assignment and phenotypic features, and were multiple test corrected via Benjamini-Hochberg FDR. Results. A strong association was replicated for NOD signalling genes driving IBD subtype, Crohns disease behaviour, and IBD surgery across all cohorts, as well as granuloma in Southampton IBD. This was mainly driven by NOD2. Similar genetic clustering patterns were observed across all cohorts, with additional phenotypic enrichment in clusters driven by NLRP1 (surgery in MSCCR IBD), and nominally significant enrichment for disease behaviour linked to BIRC2 (B2 in Southampton IBD and B2B3 in SPARC IBD). Conclusions. This analysis identified a common genetic architecture across NOD signalling genes across IBD cohorts, replicated a signal for NOD2 as a monogenic driver of disease with a strong phenotypic enrichment, and identified other putative genetic drivers of IBD, such as the NLRP1 gene.

bioinformatics↗

bioRxiv2026

SMART: A Somatic Mutation Annotation and Reporting Tool for cancer genomics

MotivationTranslational interpretation of somatic variants from targeted oncology panels is hampered by inconsistent transcript prioritisation and by the need for reproducible pipelines that natively integrate OncoKB-derived evidence for research purposes. ResultsWe present SMART (Somatic Mutation Annotation and Reporting Tool), a Dockerised pipeline that embeds OncoKB API-derived annotations, including therapeutic (L1-4), resistance (R1-R3), diagnostic (Dx1-3), prognostic (Px1-3) and FDA levels, directly into a VCF-based workflow. SMART combines this with VEP, CIViC, Cancer Hotspots, ClinVar, SpliceAI, REVEL, LOEUF and gnomAD, applies a unified three-tier transcript prioritisation (whitelist > MANE Select > VEP fallback), and produces three-tiered outputs for computational, bioinformatic and research interpretation. Validation against reference APIs showed full concordance across 804 field-level checks. Availability and ImplementationSource code and Docker image are freely available at https://github.com/WeTGI-colab/SMART under the MIT License. SMART is provided for research use only; use of SMART outputs for patient specific clinical reports, clinical decision-making, or other patient-facing purposes requires appropriate governance and all required third-party licensing, including any OncoKB licence required for patient report generation.

bioinformatics↗

bioRxiv2026

GAE-Δ: A Graph-Learning Framework for Gene Network Rewiring and Clinical Outcome Prediction from Multi-Omics Data

Cancer progression and outcomes are driven in part by changes to molecular networks that result from genetic and/or environmental perturbations. These network changes manifest across multiple interconnected network layers and include accumulation of somatic mutations, altered proteinprotein interactions and dysregulated gene-expression. Here we describe a graph autoencoder-based framework (Graph Autoencoder-Delta (GAE-{Delta})), for characterizing phenotype-specific gene role shifts across multi-omics data. Given samples stratified into two contrasting phenotypic groups and a prior gene interaction network, GAE-{Delta} constructs group-specific gene graphs for each omics modality and trains, for each modality, a single graph autoencoder jointly on both group graphs, so that the two group-conditional embeddings share a common latent space. Contrasting these embeddings defines a multi-omics embedding-shift representation for each gene that reflects how its network role reorganizes across phenotypic contexts. These gene-level shifts are subsequently used for unsupervised gene prioritization, multi-omics late fusion and sample-level classification. Applied to five TCGA cancer types with a survival endpoint, GAE-{Delta} achieves competitive or superior predictive performance compared with classical network-based methods and multi-omics matrix-factorisation methods (MOFA+, iNMF), with statistically significant AUC gains over MOFA+ in three of five cohorts and statistical ties on the remaining two. Beyond predictive performance, the consensus shift genes are significantly enriched for known cancer drivers in three of five cohorts (hypergeometric p < 0.01; 11-17x fold-enrichment), whereas matrix-factorisation baselines reach p < 0.05 in zero of five cohorts (best per-cancer p = 0.06), indicating that GAE-{Delta} captures biological signal that linear factor methods miss. In summary, the GAE-{Delta} approach provides for both improved outcome classification as well as for biological and mechanistic discovery through deep network-based integration of disease-associated multi-omics data.

bioinformatics↗

bioRxiv2022

Single-cell characterisation of the hematopoietic bone marrow interactome in health and disease

1The bone marrow (BM) is a complex microenvironment and the primary site of hematopoiesis, coordinating the production of billions of blood cells every day. Despite the essential role of the hematopoietic niche in maintaining hemostasis and its relevance to hematopoietic diseases, many aspects of this environment remain poorly characterised due to experimental hurdles. Here we present a high-resolution characterisation of the niche in health and acute myeloid leukemia (AML) by establishing a comprehensive single-cell gene expression database of nearly 340,000 BM constituent cells encompassing all disease stages (healthy BM, AML at diagnosis, remission and relapse). We characterised the cell type composition of the BM and found that the proportions of both myeloid and lymphoid lineage cell types are significantly altered in AML. We also determined broadscale dysregulation of gene expression in almost all BM cell types upon establishment of AML, indicating that the entire niche is disrupted by the disease. Given the importance of interactions between hematopoietic cells and their microenvironment in regulating their function and properties, we determined all possible ligand-receptor interactions between hematopoietic stem and progenitor cells (HSPC) and every other BM constituent cell type. This analysis revealed a remarkable expansion of HSPC interactions in AML involving multiple BM constituent cells that can drive dysregulated HSPC-cell adhesion, immunosuppression and enhanced cytokine signalling. In particular, we found that interactions involving TGFB1 become widespread in AML and present evidence that these interactions can drive AML cell quiescence in vitro, thus highlighting TGFB1 signalling as a potential target for increasing drug sensitivity and preventing relapse. Our results shed light on potential mechanisms of enhanced competitiveness of AML HSPCs and an overall skewed microenvironment that fosters AML growth.

cancer biology↗

bioRxiv2018

Diagnostic outcomes of exome gene panel sequencing in patients with unusual syndromic cleft lip/palate phenotypes

Orofacial clefting (OFC) is a common craniofacial birth defect that has a prevalence of 1.2 in 1,000 live births. Syndromic OFC in which patients present with additional developmental deficits are identified to have a strong genetic component. We applied exome gene panel sequencing in a cohort of 14 Colombian patients identified at Operation Smile in Bogota, Colombia, with syndromic orofacial clefting phenotypes and additional features initially suggesting Aarskog-Scott syndrome (AAS). Gene panel sequencing failed to identify any causal variants in the FGD1 gene which underlies this condition, but variants in a number of genes suggest alternative clinical diagnoses across five patients (~ 36%). The novel variants identified here maps to developmentally important genes including SRCAP, OFD1, NIPBL, GRIN2A and KMT2D6. Our result demonstrates the extensive heterogeneity underlying OFC and emphasises the need for systematic phenotyping of patients with rare conditions. Gene panel sequencing has the potential to cost-effectively resolve ambiguous clinical diagnoses, but rigorous attention should be paid to gene coverage as our results suggest highly variable read depths across individual gene exons and this may reduce the quality of variant calls at specific locations.

genomics↗

bioRxiv2018

GenePy - a score for estimating gene pathogenicity in individuals using next-generation sequencing data

NGS is a revolutionising diagnosis and treatment of rare diseases. However, its relatively modest application in common diseases is limited by analytical approaches.\n\nInstead of variant-level approaches, typical for rare disease or large cohort analyses, contemporary investigation of common polygenic disorders requires the development of tools combining mutational burden and biological impact of a personalised set of mutations into single gene scores. GenePy (https://github.com/UoS-HGIG/GenePy) is a gene score for transforming sequencing data capable of estimating whole-gene pathogenicity on a per-patient basis.\n\nGenePy implements known deleteriousness metrics, incorporates allele frequency and individual zygosity information. Individuals harbouring multiple rare highly deleterious mutations accumulate extreme gene scores while the majority of genes usually achieve very low scores. Following correction for gene length, GenePy intuitively prioritises genes within individuals and affords gene/pathway score comparison between groups of individuals. Herein, we generate GenePy scores from whole-exome sequencing data for [~]15,000 genes across a cohort of 508 individuals. We describe score attributes and model behaviour under various biological conditions.\n\nWe demonstrate proof of concept that GenePy sensitively identifies known causal genes by calculating GenePy scores for NOD2 (an established causal Crohns Disease gene), in a modest cohort of patients for comparison against controls. This test of GenePy using a positive control gene demonstrates markedly more significant results (p=1.37 x 10-4) compared to the most commonly applied tool for combining common and rare variation.\n\nIn addition to increasing the biological information content for each variant, the per gene-per individual nature of GenePy transforms the utility of sequencing data. GenePy scores are intuitive when assessing for individual patients or for comparing between groups. Because GenePy intrinsically reflects pathogenicity at the gene level, this specifically facilitates downstream data integration (e.g. into machine learning, network and topological analyses) with transcriptomic and proteomic data that also report at the gene level.\n\nAuthor SummaryRapid technological advances have made DNA sequencing an effective, economic tool for detecting genomic variation. Detecting rare variation at the individual level is proving very successful in identifying the genetic causes of disease when just single mutations are sufficient to manifest disease. However, interpreting genomic data is much less straightforward for common diseases such as asthma, arthritis or heart disease where many genetic changes across multiple genes combine with the environment to bring about disease symptoms.\n\nWe have developed a new scoring system called GenePy that generates whole gene pathogenicity scores for indiviual patients. The score corrects for the length of the gene and is intuitive to use. Unlike many mutation deleteriousness metrics, GenePy also takes into account the population frequency of the variant and the number of copies of any given mutation and combines data for as many variants as are present in a given gene for any one individual.\n\nIn this paper we apply the GenePy scoring system to a cohort of over 500 individuals for whom we have sequencing data across all genomics regions that code for protein. We descibe how GenePy performs and demonstrate superior sensitivity to detect known causal genes in a common autoimmune condition.

genomics↗