Biology subjects

Williams, E. C.

Publications and source records attributed to Williams, E. C..

10 recordsLinked to original sources

bioRxiv2025

Spatio-Temporal Multi-Omics Profiling of Mechanisms and Biomarkers in Inhaled Drug-Induced Lung Toxicity

Comprehensive understanding and early detection of drug-induced lung toxicity remain critical challenges in respiratory drug development. In this study, we propose a multi-omics framework that integrates spatial and temporal tissue-specific transcriptomic signatures with proteomics from minimally invasive biofluids to understand mechanisms and identify safety biomarkers associated with lung toxicity. Using this framework, we identified a panel of candidate biomarkers in bronchoalveolar lavage fluid and plasma, including LCN2/NGAL, RETNLA, SP-D, SPP1/osteopontin, and MMP7, that correlate with histopathological features (e.g., inflammation and epithelial remodeling). We confirmed that these molecular biomarkers were consistently dysregulated across a range of inhaled lung toxicants, human disease (IPF), and environmental exposures (smoke, Alternaria), demonstrating broad applicability across different toxic exposures and translatability. Collectively, this study establishes a robust workflow for mechanism-guided biomarker discovery and proposes a panel of candidates for monitoring drug-induced lung injury in humans.

systems biology↗

bioRxiv2025

Spatially resolved integrative analysis of transcriptomic and metabolomic changes in tissue injury studies

Recent developments in spatially resolved -omics have enabled studies linking gene expression and metabolite levels to tissue morphology, offering new insights into biological pathways. By capturing multiple modalities on matched tissue sections, one can better probe how different biological entities interact in a spatially coordinated manner. However, such cross-modality integration presents experimental and computational challenges. To align multimodal datasets into a shared coordinate system and facilitate enhanced integration and analysis, we propose MAGPIE (Multi-modal Alignment of Genes and Peaks for Integrated Exploration), a framework for co-registering spatially resolved transcriptomics, metabolomics, and tissue morphology from the same or consecutive sections. We illustrate the generalisability and scalability of MAGPIE on spatial multi-omics data from multiple tissues, combining Visium with both MALDI and DESI mass spectrometry imaging. MAGPIE was also applied to newly generated multimodal datasets created using specialised experimental sampling strategy to characterise the metabolic and transcriptomic landscape in an in vivo model of drug-induced pulmonary fibrosis, to showcase the linking of small-molecule co-detection with endogenous responses in lung tissue. MAGPIE highlights the refined resolution and increased interpretability of spatial multimodal analyses in studying tissue injury, particularly in pharmacological contexts, and offers a modular, accessible computational workflow for data integration.

bioinformatics↗

bioRxiv2025

Solubilization of Membrane Proteins using designed protein WRAPS

The development of therapies and vaccines targeting integral membrane proteins has been complicated by their extensive hydrophobic surfaces, which can make production and structural characterization difficult. Here we describe a general deep learning-based design approach for solubilizing native membrane proteins while preserving their sequence, fold, and function using genetically encoded de novo protein WRAPs (Water-soluble RFdiffused Amphipathic Proteins) that surround the lipid-interacting hydrophobic surfaces, rendering them stable and water-soluble without the need for detergents. We design WRAPs for both beta-barrel outer membrane and helical multi-pass transmembrane proteins, and show that the solubilized proteins retain the binding and enzymatic functions of the native targets with enhanced stability. Syphilis vaccine development has been hindered by difficulties in characterizing and producing the outer membrane protein antigens; we generated soluble versions of four Treponema pallidum outer membrane beta barrels which are potential syphilis vaccine antigens. A 4.0 [A] cryo-EM map of WRAPed TP0698 is closely consistent with the design model. WRAPs should be broadly useful for facilitating biochemical and structural characterization of integral membrane proteins, enabling therapeutic discovery by screening against purified soluble targets, and generating antigenically intact immunogens for vaccine development.

biochemistry↗

bioRxiv2024

Latent Diffusion For Conditional Generation of Molecules

AO_SCPLOWBSTRACTC_SCPLOWDesigning a small molecule therapeutic is a challenging multi-parameter optimization problem. Key properties, such as potency, selectivity, bioavailability, and safety must be jointly optimized to deliver an effective clinical candidate. We present COATI-LDM, a novel application of latent diffusion models to the conditional generation of property-optimized, drug-like small molecules. Diffusive generation of latent molecular encodings, rather than direct diffusive generation of molecular structures, offers an appealing way to handle the small and mismatched datasets that are common for molecular properties. We benchmark various diffusion guidance schemes and sampling methods against a pre-trained autoregressive transformer and genetic algorithms to evaluate control over potency, expert preference, and various physicochemical properties. We show that conditional diffusion allows control over the properties of generated molecules, with practical and performance advantages over competing methods. We also apply the recently introduced idea of particle guidance to enhance sample diversity. We prospectively survey a panel of medicinal chemists and determine that we can conditionally generate molecules that align with their preferences via a learned preference score. Finally, we present a partial diffusion method for the local optimization of molecular properties starting from a seed molecule. Conditional generation of small molecules using latent diffusion models on molecular encodings provides a highly practical and flexible alternative to prior molecular generation schemes.

bioinformatics↗

bioRxiv2024

Increased Cholesterol Synthesis Drives Neurotoxicity in Patient Stem Cell-Derived Model of Multiple Sclerosis

Senescent neural progenitor cells have been identified in brain lesions of people with progressive multiple sclerosis (PMS). However, their role in disease pathobiology and contribution to the lesion environment remains unclear. By establishing directly induced neural stem/progenitor cell (iNSC) lines from PMS patient fibroblasts, we studied their senescent phenotype in vitro. Senescence was strongly associated with inflammatory signaling, hypermetabolism, and the senescence associated secretory phenotype (SASP). PMS-derived iNSCs displayed increased glucose-dependent fatty acid and cholesterol synthesis, which resulted in the accumulation of cholesteryl ester-enriched lipid droplets. An HMG-CoA reductase-mediated lipogenic state was found to induce secretion of the SASP in PMS iNSC conditioned media via transcriptional regulation by cholesterol-dependent transcription factors. SASP from PMS iNSCs induced neurotoxicity. Chemical targeting of HMG-CoA reductase using the cholesterol-lowering drug simvastatin (SV) prevented SASP release and resulting neurotoxicity. Our findings suggest a disease-associated, cholesterol-related, hypermetabolic phenotype of PMS iNSCs that leads to neurotoxic signaling and is rescuable pharmacologically.

neuroscience↗

bioRxiv2023

Fecal Dysbiosis and Inflammation in Intestinal-Specific Cftr Knockout Mice on Regimens Preventing Intestinal Obstruction

Chronic intestinal inflammation is a poorly understood manifestation of Cystic Fibrosis (CF), which may be refractory to ion channel CFTR modulator therapy. People with CF exhibit intestinal dysbiosis which has potential for stimulating intestinal and systemic inflammation. CFTR is expressed in organ epithelia and in the leukocyte population. Here, we investigate the contribution of intestinal epithelial-specific loss of Cftr (iCftr KO) to dysbiosis and inflammation in mice treated with either of two anti-obstructive dietary regimens necessary to maintain CF mouse models (PEG laxative or a liquid diet, LiqD). Feces collected from iCftr KO mice and their wildtype (WT) sex-matched littermates were used to measure fecal calprotectin and to perform 16S rRNA sequencing to characterize the gut microbiome. Fecal calprotectin was elevated in iCftr KO relative to WT samples of mice consuming either PEG or LiqD. PEG iCftr KO mice did not show a change in -diversity versus WT but demonstrated a significant difference in microbial composition ({beta}-diversity) with increases in phylum Proteobacteria, family Peptostreptococcaceae, four genera of Clostridia including C. innocuum, and mucolytic genus Akkermansia. Fecal microbiome analysis of LiqD iCftr KO mice showed both decreased -diversity and differences in microbial composition with increases in Proteobacteria family Enterobacteriaceae, Firmicutes families Clostridiaceae and Peptostreptococcaceae, and enrichment of Clostridium perfringens, C. innocuum, C. difficile, mucolytic Ruminococcus gnavus, and reduction of Akkermansia. It was concluded that epithelial-specific loss of Cftr is a major driver of CF intestinal dysbiosis and inflammation with significant similarities to previous studies of global Cftr KO mice. New and noteworthyChronic intestinal inflammation is a manifestation of cystic fibrosis (CF), a disease caused by loss of the anion channel CFTR that is expressed in many tissues. This study shows that intestinal epithelial cell-specific loss of CFTR (iCftr KO) in mice is sufficient to induce intestinal dysbiosis and inflammation. Studies were performed on mice consuming either dietary regimen (PEG laxative or liquid diet) routinely used to prevent obstruction in CF mice.

systems biology↗

bioRxiv2021

bulkAnalyseR: An accessible, interactive pipeline for analysing and sharing bulk sequencing results

Bulk sequencing experiments (single- and multi-omics) are essential for exploring wide-ranging biological questions. To facilitate interactive, exploratory tasks, coupled with the sharing of easily accessible information, we present bulkAnalyseR, a package integrating state-of-the-art approaches using an expression matrix as the starting point (pre-processing functions are available as part of the package). Static summary images are replaced with interactive panels illustrating quality-checking, differential expression analysis (with noise detection) and biological interpretation (enrichment analyses, identification expression patterns, followed by inference and comparison of regulatory interactions). bulkAnalyseR can handle different modalities, facilitating robust integration and comparison of cis-, trans- and customised regulatory networks. bulkAnalyseR is available on CRAN and GitHub, with extensive documentation and usage examples (https://github.com/Core-Bioinformatics/bulkAnalyseR, https://cran.r-project.org/web/packages/bulkAnalyseR/) ContactIrina Mohorianu iim22@cam.ac.uk

bioinformatics↗

bioRxiv2021

The sum of two halves may be different from the whole. Effects of splitting sequencing samples across lanes.

The advances in high throughput sequencing (HTS) enabled the characterisation of biological processes at an unprecedented level of detail; the majority of hypotheses in molecular biology rely on analyses of HTS data. However, achieving increased robustness and reproducibility of results remains one of the main challenges. Although variability in results may be introduced at various stages, e.g. alignment, summarisation or detection of differences in expression, one source of variability was systematically omitted: the sequencing design which propagates through analyses and may introduce an additional layer of technical variation. We illustrate qualitative and quantitative differences arising from splitting samples across lanes, on bulk and single-cell sequencing. For bulk mRNAseq data, we focus on differential expression and enrichment analyses; for bulk ChIPseq data, we investigate the effect on peak calling, and peaks properties. At single-cell level, we concentrate on identifying cell subpopulations. We rely on markers used for assigning cell identities; both smartSeq and 10x data are presented. The observed reduction in the number of unique sequenced fragments reduces the level of detail on which the different prediction approaches depend. Further, the sequencing stochasticity adds in a weighting bias corroborated with variable sequencing depths and (yet unexplained) sequencing bias.

bioinformatics↗

bioRxiv2021

noisyR: Enhancing biological signal in sequencing datasets by characterising random technical noise

High-throughput sequencing enables an unprecedented resolution in transcript quantification, at the cost of magnifying the impact of technical noise. The consistent reduction of random background noise to capture functionally meaningful biological signals is still challenging. Intrinsic sequencing variability introducing low-level expression variations can obscure patterns in downstream analyses. We introduce noisyR, a comprehensive noise filter to assess the variation in signal distribution and achieve an optimal information-consistency across replicates and samples; this selection also facilitates meaningful pattern recognition outside the background-noise range. noisyR is applicable to count matrices and sequencing data; it outputs sample-specific signal/noise thresholds and filtered expression matrices. We exemplify the effects of minimising technical noise on several datasets, across various sequencing assays: coding, non-coding RNAs and interactions, at bulk and single-cell level. An immediate consequence of filtering out noise is the convergence of predictions (differential-expression calls, enrichment analyses and inference of gene regulatory networks) across different approaches. TeaserNoise removal from sequencing quantification improves the convergence of downstream tools and robustness of conclusions.

bioinformatics↗

bioRxiv2020

feamiR: Feature selection based on Genetic Algorithms for predicting miRNA-mRNA interactions

microRNAs play a key role in RNA interference, the sequence-driven targeting of mRNAs that regulates their translation to proteins, through translation inhibition or the degradation of the mRNA. Around ~ 30% of animal genes may be tuned by microRNAs. The prediction of miRNA/mRNA interactions is hindered by the short length of the interaction (seed) region (~7- 8nt). We collate several large datasets overviewing validated interactions and propose feamiR, a novel pipeline comprising optimised classification approaches (Decision Trees/Random Forests and an efficient feature selection based on embryonic Genetic Algorithms used in conjunction with Support Vector Machines) aimed at identifying discriminative nucleotide features, on the seed, compensatory and flanking regions, that increase the prediction accuracy for interactions. Common and specific combinations of features illustrate differences between reference organisms, validation techniques or tissue/cell localisation. feamiR revealed new key positions that drive the miRNA/mRNA interactions, leading to novel questions on the mode-of-action of miRNAs.

bioinformatics↗