Biology subjects

Williams, E. D.

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

7 recordsLinked to original sources

bioRxiv2025

Integration of Multiomic and Multi-phenotypic Data Identifies Biological Pathways Associated with Physical Fitness

Unraveling the complex associations between human phenotypes and molecular pathways can pave the way to improved health and performance, but faces a fundamental challenge: the measurable genes, proteins, and metabolites vastly outnumber the participants in even the largest studies, yielding spurious correlations. To address this imbalance, we have developed a bioinformatic framework and computational approach ("PhenoMol") to discover biological drivers of phenotypic characteristics that integrates all available phenotypic data predictive of outcomes and reduces multi-omic data dimensionality by generating "expression circuits" via graph theory constrained by prior biological knowledge of molecular interactions. We applied PhenoMol to analyze causal patterns and predict elite physical performance in a healthy cohort with deep physiological, physical, behavioral, cognitive, and molecular characterization. PhenoMol outperforms regression models based on equivalent analytic methodologies that do not employ network biology for dimensionality reduction. The PhenoMol software is provided for future studies.

bioinformatics↗

bioRxiv2025

Explainable machine learning identifies features and thresholds predictive of immunotherapy response

BackgroundImmunotherapy has improved patient survival for multiple cancer types, including melanoma. While a variety of molecular features have been linked to response to immune checkpoint inhibitors (ICI) treatment, clinically established biomarkers, such as tumour mutation burden (TMB) and PD-L1 expression, have shown limitations in accurately categorising responders versus non-responders. Due to the complex nature of ICI response, which includes cancer intrinsic and extrinsic features within the tumour microenvironment (TME), using a single biomarker to predict response is insufficient, necessitating the need to identify accurate clinical and multi-omic molecular predictors. MethodsWe integrate clinical, DNA and RNA sequencing data from four datasets, comprising 138 melanoma patients treated with ICI, to develop machine learning models for predicting ICI response. The performance of each model was evaluated using an independent dataset of patients with cutaneous melanoma (n=53). Interactions between trained models and features were rationalised using the explainability method SHAP. ResultsThe most optimal model was the multi-omic random forest model, with AUC-ROC of 0.78 when predicting response in the independent test dataset. Using SHAP, we predicted thresholds for mutational signatures, neoantigen load, immune cell-type abundance and immune receptor LAG3 expression. The relationship between these influential features and their SHAP scores revealed numerical thresholds constituting good and poor patient response. ConclusionsThis approach highlights patient response to ICI is influenced by both cancer intrinsic and extrinsic features, as well as identifies candidate biomarkers that could inform the use of ICI and potentially assist in the selection of combination therapy in melanoma treatment.

bioinformatics↗

bioRxiv2024

Female mice lacking GluA3 show early onset of hearing loss, synaptopathy, and afferent swellings in ambient sound levels

AMPA-type glutamate receptors (AMPAR) mediate excitatory cochlear transmission. However, the unique roles of AMPAR subunits are unresolved. Lack of subunit GluA3 (Gria3KO) in male mice reduced cochlear output by 8-weeks of age. Since Gria3 is X-linked and considering sex differences in hearing vulnerability, we hypothesized accelerated presbycusis in Gria3KO females. Here, auditory brainstem responses (ABR) were similar in 3-week-old female Gria3WTand Gria3KO mice. However, when raised in ambient sound, ABR thresholds were elevated and wave-1 amplitudes were diminished at 5-weeks and older in Gria3KO. In contrast, these metrics were similar between genotypes when raised in quiet. Paired synapses were similar in number, but lone ribbons and ribbonless synapses were increased in female Gria3KOmice in ambient sound compared to Gria3WT or to either genotype raised in quiet. Synaptic GluA4:GluA2 ratios increased relative to Gria3WT, particularly in ambient sound, suggesting an activity-dependent increase in calcium-permeable AMPARs in Gria3KO. Swollen afferent terminals were observed by 5-weeks only in Gria3KO females reared in ambient sound. We propose that lack of GluA3 induces sex-dependent vulnerability to AMPAR-mediated excitotoxicity.

neuroscience↗

bioRxiv2022

Computationally efficient framework for diagnosing, understanding, and predicting biphasic population growth

Throughout the life sciences, biological populations undergo multiple phases of growth, often referred to as biphasic growth for the commonly-encountered situation involving two phases. Biphasic population growth occurs over a massive range of spatial and temporal scales, ranging from microscopic growth of tumours over several days, to decades-long re-growth of corals in coral reefs that can extend for hundreds of kilometres. Different mathematical models and statistical methods are used to diagnose, understand, and predict biphasic growth. Common approaches can lead to inaccurate predictions of future growth that may result in inappropriate management and intervention strategies being implemented. Here we develop a very general computationally efficient framework, based on profile likelihood analysis, for diagnosing, understanding, and predicting biphasic population growth. The two key components of the framework are: (i) an efficient method to form approximate confidence intervals for the change point of the growth dynamics and model parameters; and, (ii) parameter-wise profile predictions that systematically reveal the influence of individual model parameters on predictions. To illustrate our framework we explore real-world case studies across the life sciences.

systems biology↗

bioRxiv2021

A suite of activity-based probes to dissect the KLK activome in drug-resistant prostate cancer

Kallikrein-related peptidases (KLKs) are a family of secreted serine proteases, which form a network - the KLK activome - with an important role in proteolysis and signaling. In prostate cancer (PCa), increased KLK activity promotes tumor growth and metastasis through multiple biochemical pathways, and specific quantification and tracking of changes in the KLK activome could contribute to validation of KLKs as potential drug targets. Herein we report a technology platform based on novel activity-based probes (ABPs) and inhibitors with unprecedented potency and selectivity enabling simultaneous orthogonal analysis of KLK2, KLK3 and KLK14 activity in hormone-responsive PCa cell lines and tumor homogenates. Using selective inhibitors and multiplexed fluorescent activity-based protein profiling (ABPP) we dissect the KLK activome in PCa cells and show that increased KLK14 activity leads to a migratory phenotype. Furthermore, using biotinylated ABPs we show that active KLK molecules are secreted into the bone microenvironment by PCa cells following stimulation by osteoblasts suggesting KLK-mediated signaling mechanisms could contribute to PCa metastasis to bone. Together our findings show that ABPP is a powerful approach to dissect dysregulation of the KLK activome as a promising and previously underappreciated therapeutic target in advanced PCa.

biochemistry↗

bioRxiv2020

Metabolic plasticity in cancer activates apocryphal pathways for lipid desaturation

Fatty acid (FA) modifications, such as enzymatic desaturation and elongation, have long been thought to involve sequential and highly specific enzyme-substrate interactions, which result in canonical products that are well-defined in their chain lengths, degree of unsaturation and double bond positions.1 These products act as a supply of building blocks for the synthesis of complex lipids supporting a symphony of lipid signals and membrane macrostructure. Recently, it was brought to light that differences in substrate availability due to enzyme inhibition can activate alternative pathways in a range of cancers, potentially altering the total species repertoire of FA metabolism.2,3 We have used isomer-resolved lipidomics to analyse human prostate tumours and cancer cell lines and reveal, for the first-time, the full extent of metabolic plasticity in cancer. Assigning the double bond position(s) in simple and complex lipids allows mapping of fatty acid desaturation and elongation via hitherto apocryphal metabolic pathways that generate FAs with unusual sites of unsaturation. Downstream utilisation of these FAs is demonstrated by their incorporation into complex structural lipids. The unsaturation profiles of different phospholipids reveal substantive structural variation between classes that will, necessarily, modulate lipid-centred biological processes in cancer cells including membrane fluidity3-5 and signal transduction.6-8

biochemistry↗

bioRxiv2019

The long non-coding RNA GHSROS reprograms prostate cancer cell lines toward a more aggressive phenotype

It is now appreciated that long non-coding RNAs (lncRNAs) are important players in the orchestration of cancer progression. In this study we characterized GHSROS, a human lncRNA gene on the opposite DNA strand (antisense) to the ghrelin receptor gene, in prostate cancer. The lncRNA was upregulated by prostate tumors from different clinical datasets. Consistently, transcriptome data revealed that GHSROS alters the expression of cancer-associated genes. Functional analyses in vitro showed that GHSROS mediates tumor growth, migration, and survival and resistance to the cytotoxic drug docetaxel. Increased cellular proliferation of GHSROS-overexpressing PC3, DU145, and LNCaP prostate cancer cell lines in vitro was recapitulated in a subcutaneous xenograft model. Conversely, in vitro antisense oligonucleotide inhibition of the lncRNA reciprocally regulated cell growth and migration, and gene expression. Notably, GHSROS modulates the expression of PPP2R2C, the loss of which may drive androgen receptor pathway-independent prostate tumor progression in a subset of prostate cancers. Collectively, our findings suggest that GHSROS can reprogram prostate cancer cells toward a more aggressive phenotype and that this lncRNA may represent a potential therapeutic target.

cancer biology↗