Biology subjects

Shorthouse, D.

Publications and source records attributed to Shorthouse, D..

10 recordsLinked to original sources

bioRxiv2026

Positive selection and structural disruption of KCNA5 by mutation in colorectal cancer

Ion channels regulate cell proliferation, survival and migration, but the functional consequences of many ion channel mutations in cancer are unknown, and electrophysiological characterisation at scale is impractical. Here we use the mutational landscape of colorectal adenocarcinoma as a functional filter to identify potassium channel mutations under selection. A dN/dS screen of 16 potassium channels expressed in the large intestine identified KCNA5 (Kv1.5) as the strongest candidate under positive mutational selection. These mutations cluster in the T1 tetramerisation, S1-S4 voltage-sensor and pore domains, yet avoid the selectivity filter, indicating that selection does not favour loss of conduction. Within the pore region, the observed mutations occupy a biophysical space distinct from known C-type inactivation, activation-gate and toxin-binding mutations. In the voltage sensor, mutations affect gating-charge arginines. In the T1 domain, mutations fall on inter-subunit interfaces conserved across the KCNA family, and saturation mutagenesis energy calculations predict that they destabilise subunit contacts or the folding of the domain. Molecular dynamics simulations of D166N and G128R show loss of coordinated interface dynamics and distortion of the local backbone, respectively. Transcriptomic analysis of KCNA5-mutant tumours shows reduced expression of cell-cycle, KRAS-related and inflammatory pathways, and we observe differences between transcriptional effects induced by T1 and S1-S4 domain mutations. These changes are not reproduced by low KCNA5 expression alone, consistent with an effect specific to the mutant protein. Together, these computational results highlight that KCNA5 is under mutational selection in colorectal adenocarcinoma, and that these mutations are likely functional and induce phenotypic effects on tumors.

cancer biology↗

bioRxiv2025

Validating folding energy estimates as a method for variant interpretation

Interpretation of variants of uncertain significance remains a major problem in genomic analysis. Whilst statistical models can be used to predict pathogenicity, they offer no insights into the biophysical mechanism of variant action, and genomic data available for training is biased towards the subpopulations who have access. Protein misfolding has been found to act as a frequent mechanism for loss of gene or domain activity, where it is typically responsible for [~]2/3 of disease-causing variants and somatic mutations. The accuracy of energy predictions however has consistently been challenged by highly variable correlation coefficients reported from different proteins, and the unknown impact of alternative structures where available. Here we address this directly through a systematic analysis of mega-scale folding experimental results, enabled by a fully automated predictive pipeline based on FoldX. We find that whilst absolute correlation coefficients are mediocre for three highly studied proteins (0.30-0.31), the correlation coefficient alone does not capture the full predictive power of the estimates. Specifically, we find a clear linear relationship between experimental and theoretical result, with a small number of outlier residues responsible for reducing the correlation. We show that the quantitative accuracy of predictions can be improved by aggregating estimates taken from different structures, and that the problematic outlier residues can be both empirically and theoretically identified, allowing us to flag low-confidence values. Our findings not only provide a framework for identifying problematic mutations in advance but also offers new insights into potential improvements of the FoldX protocol for more accurate protein stability predictions. Our insights support the use of FoldX in computational saturation screens to support variant analysis.

biophysics↗

bioRxiv2025

Identification of molecular pathways involved in the anti-liver cancer activity of madecassic acid

Natural products are a time-tested source of medicinal compounds, and there is huge interest in discovery of new drugs from plant sources. However, in many cases the effects are not as well understood, strong, or selective, as would be hoped. An example of this is madecassic acid (MA), which has promising activity against liver cancer, activity which can be improved through chemical modifications, although a molecular understanding of its mechanism of action is unknown. In this report we have used chemical proteomics to identify the proteins with which madecassic interacts in liver cancer cells, and used RNAseq to support those findings, as well as showing more precisely how chemical modifications can focus and amplify MA activity. Our results show that madecassic acid interacts with a number of proteins relating to metabolism, nucleic acid processing, and protein folding, which have been previously identified as linked to liver cancer. This provides a route from phenotypic- to target based-drug discovery and develop new potential treatments for a globally challenging disease.

pharmacology and toxicology↗

bioRxiv2021

Heterogeneity of the Cancer Cell Line Metabolic Landscape

The unravelling of the complexity of cellular metabolism is in its infancy. Cancer-associated genetic alterations may result in changes to cellular metabolism that aid in understanding phenotypic changes, reveal detectable metabolic signatures, or elucidate vulnerabilities to particular drugs. To understand cancer-associated metabolic transformation we performed untargeted metabolite analysis of 173 different cancer cell lines from 11 different tissues under constant conditions for 1099 different species using liquid chromatography-mass spectrometry (LC-MS). We correlate known cancer-associated mutations and gene expression programs with metabolic signatures, generating novel associations of known metabolic pathways with known cancer drivers. We show that metabolic activity correlates with drug sensitivity and use metabolic activity to predict drug response and synergy. Finally, we study the metabolic heterogeneity of cancer mutations across tissues, and find that genes exhibit a range of context specific, and more general metabolic control.

cancer biology↗

bioRxiv2021

SARS-CoV-2 Variants are Selecting for Spike Protein Mutations that Increase Protein Stability

The emergence of SARS-CoV-2 in 2019 has caused severe disruption and a huge number of human deaths across the globe. As the pandemic spreads, a natural result is the emergence of variants with a variety of amino acid mutations. Variants of SARS-CoV-2 with mutations in their spike protein may result in an increased infectivity, increased lethality, or immune escape, and whilst many of these properties can be explained through changes to binding affinity or changes to post-translational modification, many mutations have no known biophysical impact on the structure of protein. The Gibbs free energy of a protein represents a measure of protein stability, with an increased stability resulting in a protein that is more thermodynamically stable, and more robust to changes in external environment. Here we show that mutations in the spike proteins of SARS-CoV-2 are selecting for amino acid changes that result in a more stable protein than expected by chance. We calculate all possible mutations in the SARS-CoV-2 spike protein, and show that many variants are more stable than expected when compared to the background, indicating that protein stability is an important consideration for the understanding of SARS-CoV-2 evolution. Variants exhibit a range of stabilities, and we further suggest that some stabilising mutations may be acting as a "counterbalance" to destabilising mutations that have other properties, such as increasing binding site affinity for the human ACE2 receptor. We suggest that protein folding calculations offer a useful tool for early identification of advantageous mutations.

biophysics↗

bioRxiv2020

Investigating structure function relationships in the NOTCH family through large-scale somatic DNA sequencing studies

The recent development of highly sensitive DNA sequencing techniques has detected large numbers of missense mutations of genes, including NOTCH1 and 2, in ageing normal tissues. Driver mutations persist and propagate in the tissue through a selective advantage over both wild-type cells and alternative mutations. This process of selection can be considered as a large scale, in vivo screen for mutations that increase clone fitness. It follows that the specific missense mutations that are observed in individual genes may offer us insights into the structure-function relationships. Here we show that the positively selected missense mutations in NOTCH1 and NOTCH2 in human oesophageal epithelium cause inactivation predominantly through protein misfolding. Once these mutations are excluded, we further find statistically significant evidence for selection at the ligand binding interface and calcium binding sites. In this, we observe stronger evidence of selection at the ligand interface on EGF12 over EGF11, suggesting that in this tissue EGF12 may play a more important role in ligand interaction. Finally, we show how a mutation hotspot in the NOTCH1 transmembrane helix arises through the intersection of both a high mutation rate and residue conservation. Together these insights offer a route to understanding the mechanism of protein function through in vivo mutant selection.

biophysics↗

bioRxiv2020

KCNQ gene family members act as both tumor suppressors and oncogenes in gastrointestinal cancers

Voltage sensitive potassium channels play an important role in controlling membrane potential and ionic homeostasis in the gut and have been implicated in gastrointestinal (GI) cancers. Through large scale analysis of 1594 patients with GI cancers coupled with in vitro models we find KCNQ family genes are mutated in ~30% of patients, and play therapeutically targetable roles in GI cancer growth. KCNQ1 and KCNQ3 mediate the WNT pathway and MYC to increase proliferation, and its resultant effects on cadherins junctions. This also highlights novel roles for KCNQ3 in non-excitable tissues. We additionally discover that activity of KCNQ3 sensitises cancer cells to existing potassium channel inhibitors, and that inhibition of KCNQ activity reduces proliferation of GI cancer. These findings reveal a novel and exploitable role for potassium channels in the advancement of human cancer, and highlight that supplemental treatments for GI cancers may exist through KCNQ inhibitors. SIGNIFICANCEKCNQ channels modulate the WNT pathway and MYC signalling, and drive growth of gastrointestinal cancers. Available drugs modulate these pathways and offer therapeutic potential in gastrointestinal cancer.

cancer biology↗

bioRxiv2019

The Landscape of Mutations in Fumarate Hydratase

Fumarate Hydratase (FH) is an enzyme of the citric acid (TCA) cycle that is responsible for reversibly catalysing the conversion between fumarate and malate. FH loss and subsequent buildup of the oncometabolite fumarate causes hereditary leiomyomatosis and renal cell carcinoma. We explore the mutational landscape of FH in silico, predict the functional effects of many already detected mutations, and categorise detected but un-characterised mutations in human populations. Using mutational energy predicting tools such as Rosetta and FoldX we accurately predict mutations and mutational hotspots with high disruptive capability. Furthermore, through performing molecular dynamics simulations we show that hinge regions of the protein can be stabilized or destabilized by mutations, with new mechanistic implications of the consequences on the binding affinity of the enzyme for its substrates. Finally, we categorise all potential mutations in FH into functional groups, and predict which known mutations in the human population are loss-of-function, and therefore predispose patients to papillary renal carcinoma - we validate our findings through analysis of metabolomics data of characterized cell lines.

biophysics↗

bioRxiv2018

Tumor pre-conditioning of draining lymph node stroma by lactic acid

Communication between tumors and the stroma of tumor draining lymph nodes (TDLNs) exists before metastasis arises, altering structure and function of the TDLN niche. Transcriptional profiling of fibroblastic reticular cells (FRCs), the dominant stromal population of the LN, revealed reprogramming of these cells in immune related pathways, but also in fibroblast activation and mitochondrial function. However, tumor derived factors driving the changes in FRCs remained to be identified. Taking an unbiased approach, we show that lactate, a metabolite released by cancer cells, elicits upregulation of Pdpn and Thy1 in FRCs of TDLNs, making them akin to activated fibroblasts found at the primary tumor site. Furthermore, we show that tumor-derived lactate alters mitochondrial function of FRCs of TDLNs. Thus, our results demonstrate a novel mechanism by which a tumor-derived metabolite modulates the function of fibroblasts in TDLNs.

cancer biology↗

bioRxiv2018

A Toolbox for Discrete Modelling of Cell Signalling Dynamics

In an age where the volume of data regarding biological systems exceeds our ability to analyse it, many researchers are looking towards systems biology and computational modelling to help unravel the complexities of gene and protein regulatory networks. In particular, the use of discrete modelling allows generation of signalling networks in the absence of full quantitative descriptions of systems, which are necessary for ordinary differential equation (ODE) models. In order to make such techniques more accessible to mainstream researchers, tools such as the BioModelAnalyzer (BMA) have been developed to provide a user-friendly graphical interface for discrete modelling of biological systems. Here we use the BMA to build a library of discrete target functions of known canonical molecular interactions, translated from ordinary differential equations (ODEs). We then show that these BMA target functions can be used to reconstruct complex networks, which can correctly predict many known genetic perturbations. This new library supports the accessibility ethos behind the creation of BMA, providing a toolbox for the construction of complex cell signalling models without the need for extensive experience in computer programming or mathematical modelling, and allows for construction and simulation of complex biological systems with only small amounts of quantitative data.\n\nAUTHOR SUMMARYOrdinary differential equation (ODE) based models are a popular approach for modelling biological networks. A limitation of ODE models is that they require complete networks and detailed kinetic parameterisation. An alternative is the use of discrete, executable models, in which nodes are assigned discrete value ranges, and the relationship between them defined with simple mathematical operations. One tool for constructing such models is the BioModelAnalyzer (BMA), an open source and publicly available (https://www.biomodelanalyzer.org) software, aimed to be fully usable by researchers without extensive computational or mathematical experience. A fundamental question for executable models is whether the high level of abstraction substantially reduces expressivity relative to continuous approaches. Here, we present a canonical library of biological signalling motifs, initially defined by Tyson et al (2003), translated for the first time into the BMA. We show that; 1) these motifs are easily and fully translatable from continuous to discrete models, 2) Combining these motifs in a computationally naive way generates a fully functional and predictive model of the yeast cell cycle.

systems biology↗