Biology subjects

Cockell, S. J.

Publications and source records attributed to Cockell, S. J..

8 recordsLinked to original sources

bioRxiv2025

Transcriptomic profiling and machine learning uncover gene signatures of psoriasis endotypes and disease severity

BackgroundDespite increased understanding of psoriasis pathogenesis, molecular classification of clinical phenotypes and disease severity is poorly defined. Knowledge gaps include whether molecular endotypes of psoriasis underlie distinct clinical phenotypes and the positive and negative molecular regulators of disease severity across tissue compartments. MethodsWe performed comprehensive RNA-sequencing of skin and blood (n=718) from prospectively-recruited, deeply-phenotyped discovery and replication cohorts of 146 subjects with moderate-to-severe psoriasis initiating TNF-inhibitor (adalimumab) or IL-12/23-inhibitor (ustekinumab) therapy. ResultsUsing two complementary methods for dimensionality reduction, we defined distinct but interconnected co-expression modules and factors within skin and blood that were significantly associated with disease phenotypes and disease severity, as measured by Psoriasis Area Severity Index (PASI). We identified a 14-gene signature negatively associated with BMI in nonlesional skin and disease severity in lesional skin, respectively. Genotype integration revealed that HLA-DQA1*01 and HLA-DRB1*15 genotypes were positively associated with baseline disease severity. Using Gaussian process regression followed by SHAP (SHapley Additive exPlanations), we defined two core drug independent and disease severity-associated gene modules in lesional skin - one positive, one negative - and a lesional 9-gene signature predictive of disease severity. Disease severity signatures in blood were only seen following adalimumab exposure, suggesting greater systemic impact of adalimumab compared to ustekinumab, in line with its side effect profile. In contrast, a gene signature in blood linked to HLA-C*06:02 status was independent of disease severity or drug. ConclusionsThese findings delineate gene-environmental and genetic effects on the psoriasis transcriptome linked to disease severity. Plain Language SummaryPsoriasis is a common and debilitating skin disease, linked to multiple other inflammatory conditions. A lot is known about the mechanism of psoriasis and its inherited and external influences. Despite this, doctors cannot yet offer personalised treatments as it has been difficult to discover whether biological pathways are associated with disease severity, response to treatment or a persons likelihood of having other linked diseases. To help address this, we collected skin and blood samples and the personal characteristics of a group of people with severe psoriasis across the United Kingdom. Using computer-based methods, we discovered common biological processes underlying different psoriasis types, including genes that connect psoriasis severity with obesity, and another set of genes that help predict disease severity.

systems biology↗

bioRxiv2025

STExplorer: Navigating the Micro-Geography of Spatial Omics Data

Spatial transcriptomics (ST) has the potential to provide unprecedented insights into gene expression across tissue architecture, but existing analytical methods often overlook the full complexity of the spatial dimension. We present STExplorer, an R package that adapts well-established computational geography (CG) methods to explore the micro-geography of spatial omics data. By incorporating techniques like Geographically Weighted Principal Component Analysis (GWPCA), Fuzzy Geographically Weighted Clustering (FGWC), Geographically Weighted Regression (GWR), and analyses of observation Spatial Autocorrelation (SA), STExplorer enables the uncovering of spatially resolved patterns that capture the spatial heterogeneity of biological data. STExplorer provides a complete suite of functions for spatial analyses and visualisations, supporting deeper biological understanding and inference. Built on the Bioconductor ecosystem, the package integrates with SpatialFeatureExperiment objects, ensuring compatibility with existing pipelines. It includes preprocessing capabilities such as data import, quality control, gene count normalisation, and variable gene selection, alongside tools for downstream analysis and detailed visualisations that quantify and map spatial heterogeneity and relationships. We demonstrate the utility of STExplorer through applications to spatial transcriptomics datasets, revealing that spatially varying gene expression and relationships are often masked by standard analyses. By bridging bioinformatics and CG, STExplorer provides a novel and informed approach to spatial transcriptomics analysis, with robust tools to address spatial heterogeneity and its associated underlying biology, thereby advancing our understanding of complex tissue biology without reinventing the wheel.

bioinformatics↗

bioRxiv2023

An anciently diverged family of RNA binding proteins maintain correct splicing of ultra-long exons through cryptic splice site repression.

We previously showed that the germ cell specific nuclear protein RBMXL2 represses cryptic splicing patterns during meiosis and is required for male fertility. RBMXL2 evolved from the X-linked RBMX gene, which is silenced during meiosis due to sex chromosome inactivation. It has been unknown whether RBMXL2 provides a direct replacement for RBMX in meiosis, or whether RBMXL2 evolved to deal with the transcriptionally permissive environment of meiosis. Here we find that RBMX primarily operates as a splicing repressor in somatic cells, and specifically regulates a distinct class of exons that exceed the median human exon size. RBMX protein-RNA interactions are enriched within ultra-long exons, particularly within genes involved in genome stability, and repress the selection of cryptic splice sites that would compromise gene function. These similarities in overall function suggested that RBMXL2 might replace the function of RBMX during meiosis. To test this prediction we carried out inducible expression of RBMXL2 and the more distantly related RBMY protein in somatic cells, finding each could rescue aberrant patterns of RNA processing caused by RBMX depletion. The C-terminal disordered domain of RBMXL2 is sufficient to rescue proper splicing control after RBMX depletion. Our data indicate that RBMX and RBMXL2 have parallel roles in somatic tissues and the germline that must have been conserved for at least 200 million years of mammalian evolution. We propose RBMX family proteins are particularly important for the splicing inclusion of some ultra-long exons with increased intrinsic susceptibility to cryptic splice site selection.

molecular biology↗

bioRxiv2023

Spatial transcriptomics reveals novel genes during the remodelling of the embryonic human arterial valves

Abnormalities of the arterial valves, including bicuspid aortic valve (BAV) are amongst the most common congenital defects and are a significant cause of morbidity as well as predisposition to disease in later life. Despite this, and compounded by their small size and relative inaccessibility, there is still much to understand about how the arterial valves form and remodel during embryogenesis, both at the morphological and genetic level. Here we set out to address this in human embryos, using Spatial Transcriptomics (ST). We show that ST can be used to investigate the transcriptome of the developing arterial valves, circumventing the problems of accurately dissecting out these tiny structures from the developing embryo. We show that the transcriptome of CS16 and CS19 arterial valves overlap considerably, despite being several days apart in terms of human gestation, and that expression data confirm that the great majority of the most differentially expressed genes are valve-specific. Moreover, we show that the transcriptome of the human arterial valves overlaps with that of mouse atrioventricular valves from a range of gestations, validating our dataset but also highlighting novel genes, including four that are not found in the mouse genome and have not previously been linked to valve development. Importantly, our data suggests that valve transcriptomes are under-represented when using commonly used databases to filter for genes important in cardiac development; this means that causative variants in valve-related genes may be excluded during filtering for genomic data analyses for, for example, BAV. Finally, we highlight "novel" pathways that likely play important roles in arterial valve development, showing that mouse knockouts of RBP1 have arterial valve defects. Thus, this study has confirmed the utility of ST for studies of the developing heart valves and broadens our knowledge of the genes and signalling pathways important in human valve development. Non-Technical SummaryCongenital heart defects, particularly those affecting the valves and septa of the heart, are very common. Despite this, few gene variants have been confirmed as disease-causing in human congenital heart (including valve) disease patients. Here we utilise spatial transcriptomics technology, which allows the identification of genes expressed in tissue slices, on embryonic human heart valves and identify a gene dataset that is human arterial valve-specific. We confirm the localisation of key novel genes to the arterial valves and highlight the relevance of the dataset by showing that mice mutant for RBP1, a novel gene identified as being highly differentially expressed in our valve dataset, have previously unidentified arterial valve defects. Using commonly used bioinformatic databases we show that filtering patient genomic data using these terms would likely exclude valve genes and thus may not identify the causative genes. Thus, we confirm that spatial transcriptomics technology can be used to study gene expression in tiny structures such as the developing heart valves and provide a new human embryonic valve dataset that can be used in future genomic studies of patients with congenital valve defects.

developmental biology↗

bioRxiv2022

Type I interferons govern Zika virus resistance of human macrophages and microglia

The role of the human type I interferon (IFN-I) system in restricting Zika virus (ZIKV) is uncertain. Here, genetic and pharmacological ablation of IFN-I signalling enhanced ZIKV replication and cytopathicity in macrophages and microglia, key cells in ZIKV transmission and pathogenesis. Thus, despite the extensive IFN-I countermeasures employed by ZIKV, IFN-I dictates the outcome of infection in macrophages. Therapeutic manipulation of the IFN-I system may bring clinical benefit in ZIKV.

cell biology↗

bioRxiv2021

Integration of probabilistic functional networks without an external Gold Standard

BackgroundProbabilistic functional integrated networks (PFINs) are designed to aid our understanding of cellular biology and can be used to generate testable hypotheses about protein function. PFINs are generally created by scoring the quality of interaction datasets against a Gold Standard dataset, usually chosen from a separate high-quality data source, prior to their integration. Use of an external Gold Standard has several drawbacks, including data redundancy, data loss and the need for identifier mapping, which can complicate the network build and impact on PFIN performance. ResultsWe describe the development of an integration technique, ssNet, that scores and integrates both high-throughput and low-throughout data from a single source database in a consistent manner without the need for an external Gold Standard dataset. Using data from Saccharomyces cerevisiae we show that ssNet is easier and faster, overcoming the challenges of data redundancy, Gold Standard bias and ID mapping, while producing comparable performance. In addition ssNet results in less loss of data and produces a more complete network. ConclusionsThe ssNet method allows PFINs to be built successfully from a single database, while producing comparable network performance to networks scored using an external Gold Standard source.

bioinformatics↗

bioRxiv2021

A de novo paradigm for male infertility

IntroductionDe novo mutations (DNMs) are known to play a prominent role in sporadic disorders with reduced fitness1. We hypothesize that DNMs play an important role in male infertility and explain a significant fraction of the genetic causes of this understudied disorder. To test this hypothesis, we performed trio-based exome-sequencing in a unique cohort of 185 infertile males and their unaffected parents. Following a systematic analysis, 29 of 145 rare protein altering DNMs were classified as possibly causative of the male infertility phenotype. We observed a significant enrichment of Loss-of-Function (LoF) DNMs in LoF-intolerant genes (p-value=1.00x10-5) as well as predicted pathogenic missense DNMs in missense-intolerant genes (p-value=5.01x10-4). One DNM gene identified, RBM5, is an essential regulator of male germ cell pre-mRNA splicing2. In a follow-up study, 5 rare pathogenic missense mutations affecting this gene were observed in a cohort of 2,279 infertile patients, with no such mutations found in a cohort of 5,784 fertile men (p-value=0.009). Our results provide the first evidence for the role of DNMs in severe male infertility and point to many new candidate genes affecting fertility.

genetics↗

bioRxiv2020

RBMX enables productive RNA processing of ultra-long exons important for genome stability

Previously we showed that the germline-specific RNA binding protein RBMXL2 is essential for male meiosis where it represses cryptic splicing patterns (1). Here we find that its ubiquitously expressed paralog RBMX helps underpin human genome stability by preventing non-productive splicing. In particular, RBMX blocks selection of aberrant splice and polyadenylation sites within some ultra-long exons that would interfere with genes needed for normal replication fork activity. Target exons include within the ETAA1 (Ewings Tumour Associated 1) gene, where RBMX collaborates with its interaction partner Tra2{beta} to enable full-length exon inclusion by blocking selection of an aberrant 3 splice site. Our data reveal a novel group of RNA processing targets potently repressed by RBMX, and help explain why RBMX is associated with gene expression networks in cancer, replication and sensitivity to genotoxic drugs.

molecular biology↗