Biology subjects

Hutchinson, A.

Publications and source records attributed to Hutchinson, A..

17 recordsLinked to original sources

bioRxiv2026

Hepatocyte growth factor activator inhibitor-2 rapidly inactivates airway-expressed human Type II Transmembrane Serine Proteases

Human Type II Transmembrane Serine Proteases (TTSPs) are essential entry factors for various influenza A and coronaviruses, and drive cancer metastasis when they are overexpressed by tumor cells. However, the natural inhibition mechanisms that regulate these proteases are not well understood. One natural transmembrane protease inhibitor, hepatocyte growth factor activator inhibitor-2 (HAI-2), has been shown to block TMPRSS2 activity and can prevent SARS-CoV-2 infection and reduce TMPRSS2-driven prostate cancer metastasis when overexpressed. In this study, we present biochemical and biophysical evidence showing that HAI-2 effectively inactivates TMPRSS2 and other TTSPs only after they have undergone zymogen activation. Through mutagenesis and ligand binding assays, we demonstrate that Kunitz Domain 1 (KD1) and KD2 can form stable ternary complexes with TMPRSS2 and other TTSPs, but do not employ the typical Laskowski inhibitor mechanism found for other macromolecular serine protease inhibitors. We also show that HAI-2 proteins do not inhibit the coagulation protease thrombin and that multivalent human IgG-tagged HAI-2 proteins are highly potent TMPRSS2 inhibitors. Our findings provide a mechanistic understanding of how TTSP activity is regulated in human airway cells and offer a foundation for developing engineered soluble HAI-2 proteins as anti-TTSP antivirals and anti-cancer therapeutics.

biochemistry↗

bioRxiv2025

Structure-based discovery of inhibitors of Mac1 domain of nonstructural protein-3 of SARS-CoV-2 by machine learning-augmented screening of chemical space

Significant efforts have been recently dedicated to the discovery of small molecule inhibitors against the Macrodomain 1 (Mac1) of nonstructural protein 3 (NSP3) as potential antivirals for SARS-CoV-2. Thus, Mac1 has also been selected as the target for the Critical Assessment of Hit-finding Experiments (CACHE) challenge #3. As contestants in that challenge, we developed a computational strategy that ranked on the top among all 23 participants in the competition and resulted in the discovery of a novel chemical series of non-charged Mac1 inhibitors. Those have been identified through the combination of machine learning-accelerated virtual screening of Enamine REAL Diversity Subset of approximately 25 million compounds and consequent hit expansion into the entire Enamine REAL Space library. In particular, the initially identified hit compound CACHE3-HI_1706_56 (KD = 20 {micro}M) was explored by probing 17 close analogues from a library of 44 billion molecules from the Enamine REAL. All those analogues effectively displaced the Mac1-binding ADP-ribose peptide, and 12 were confirmed to engage with Mac1 by the Surface Plasmon Resonance experiments, revealing a new chemical series of compounds for hit-to-lead optimization. The structure of the CACHE3-HI_1706_56-Mac1 complex was further determined at high resolution with crystallography, confirming initial computational predictions. Our results illustrate the effectiveness of ML-accelerated docking to rapidly identify novel chemical series and provide a strong foundation for the development of SARS-CoV-2 NSP3 Mac1 inhibitors.

biophysics↗

bioRxiv2025

A prognostic signature for lung adenocarcinoma in people who have never smoked

Knowledge of tumor cell dynamics can inform prognosis and treatment yet is largely lacking for lung adenocarcinoma in people who have never smoked (NS-LUAD). With RNA-seq data from 684 NS-LUAD and validation in an independent dataset, we identified three subtypes with distinct phenotypic traits and cell compositions. Additional genomic and histological data further characterized the subtypes. Steady, marked by low proliferation, high alveolar cell fraction, moderate-to-well differentiation, and fewer driver genes alterations, is linked to prolonged survival and low immune evasion. Proliferative shows high proliferation markers, TP53 mutations, and gene fusions. Chaotic, with high epithelial-to-mesenchymal transition markers, has the worst prognosis even within stage I tumors. Lacking known molecular or histological characteristics, this aggressive subtype is solely identified by transcriptomic data. A 60-gene signature recapitulates the overall classification and strongly predicts survival even within subgroups based on tumor stage or known genomic features, emphasizing its potential for improving NS-LUAD prognostication in clinical settings. SignificanceThe transcriptome of 684 lung adenocarcinomas in people who have never smoked (NS-LUAD) identifies three subtypes with different cellular dynamics, and genomic and morphologic features. A 60-gene signature accurately stratifies subjects for mortality risk, even in stage I, offering a clinically applicable tool for treatment decision making in NS-LUAD patients.

genomics↗

bioRxiv2024

APOBEC shapes tumor evolution and age at onset of lung cancer in smokers

APOBEC enzymes are part of the innate immunity and are responsible for restricting viruses and retroelements by deaminating cytosine residues1,2. Most solid tumors harbor different levels of somatic mutations attributed to the off-target activities of APOBEC3A (A3A) and/or APOBEC3B (A3B)3-6. However, how APOBEC3A/B enzymes shape the tumor evolution in the presence of exogenous mutagenic processes is largely unknown. Here, by combining deep whole-genome sequencing with multi-omics profiling of 309 lung cancers from smokers with detailed tobacco smoking information, we identify two subtypes defined by low (LAS) and high (HAS) APOBEC mutagenesis. LAS are enriched for A3B-like mutagenesis and KRAS mutations, whereas HAS for A3A-like mutagenesis and TP53 mutations. Unlike APOBEC3A, APOBEC3B expression is strongly associated with an upregulation of the base excision repair pathway. Hypermutation by unrepaired A3A and tobacco smoking mutagenesis combined with TP53-induced genomic instability can trigger senescence7, apoptosis8, and cell regeneration9, as indicated by high expression of pulmonary healing signaling pathway, stemness markers and distal cell-of-origin in HAS. The expected association of tobacco smoking variables (e.g., time to first cigarette) with genomic/epigenomic changes are not observed in HAS, a plausible consequence of frequent cell senescence or apoptosis. HAS have more neoantigens, slower clonal expansion, and older age at onset compared to LAS, particularly in heavy smokers, consistent with high proportions of newly generated, unmutated cells and frequent immuno-editing. These findings show how heterogeneity in mutational burden across co-occurring mutational processes and cell types contributes to tumor development, with important clinical implications.

genomics↗

bioRxiv2024

A resource to enable chemical biology and drug discovery of WDR Proteins

Protein class-focused drug discovery has a long and successful history in pharmaceutical research, yet most members of druggable protein families remain unliganded, often for practical reasons. Here we combined experiment and computation to enable discovery of ligands for WD40 repeat (WDR) proteins, one of the largest human protein families. This resource includes expression clones, purification protocols, and a comprehensive assessment of the druggability for hundreds of WDR proteins. We solved 21 high resolution crystal structures, and have made available a suite of biophysical, biochemical, and cellular assays to facilitate the discovery and characterization of small molecule ligands. To this end, we use the resource in a hit-finding pilot involving DNA-encoded library (DEL) selection followed by machine learning (ML). This led to the discovery of first-in-class, drug-like ligands for 9 of 20 targets. This result demonstrates the broad ligandability of WDRs. This extensive resource of reagents and knowledge will enable further discovery of chemical tools and potential therapeutics for this important class of proteins.

biochemistry↗

bioRxiv2023

Discovery of a first-in-class small molecule ligand for WDR91 using DNA-encoded chemical library selection followed by machine learning

WD40 repeat-containing protein 91 regulates endosomal phosphatidylinositol 3-phosphate levels at the critical stage of endosome maturation and plays vital roles in endosome fusion, recycling, and transport by mediating protein-protein interactions. Due to its various roles in endocytic pathways, WDR91 has recently been identified as a potential host factor responsible for viral infection. We employed DNA-Encoded Chemical Library (DEL) selection against the WDR domain of WDR91, followed by machine learning to generate a model that was then used to predict ligands from the synthetically accessible Enamine REAL database. Screening of predicted compounds enabled us to identify the hit compound 1, which binds selectively to WDR91 with a KD of 6 {+/-} 2 M by surface plasmon resonance. The co-crystal structure confirmed the binding of 1 to the WDR91 side pocket, in proximity to cysteine 487. Machine learning-assisted structure activity relationship-by-catalog validated the chemotype of 1 and led to the discovery of covalent analogs 18 and 19. Intact mass LC-MS and differential scanning fluorimetry confirmed the formation of a covalent adduct, and thermal stabilization, respectively. The discovery of 1, 18, 19, accompanying SAR, and co-crystal structures will provide valuable insights for designing more potent and selective compounds against WDR91, thus accelerating the development of novel chemical tools to evaluate the therapeutic potential of WDR91 in disease.

molecular biology↗

bioRxiv2023

Mapping the functional impact of non-coding regulatory elements in primary T cells through single-cell CRISPR screens

Drug targets with human genetic evidence are expected to increase clinical success by at least two-fold. Yet, translating disease-associated genetic variants into functional knowledge remains a fundamental challenge of early drug discovery. A key issue is that, currently, the vast majority of complex disease associations cannot be cleanly mapped to a gene. Immune disease-associated variants are enriched within regulatory elements, such as distal enhancers, found in T cell-specific open chromatin regions. To identify the genes and thus the molecular programs modulated by these regulatory elements, we developed a CRISPRi-based single-cell functional screening approach in primary human CD4+ T cells. Our pipeline enables the interrogation of transcriptomic changes induced by the perturbation of regulatory elements at scale. We first optimised a highly efficient CRISPRi protocol in primary human CD4+ T cells via CROPseq vectors. Subsequently, we performed a proof-of-concept screen targeting 45 non-coding regulatory elements and 35 transcription start sites and profiled approximately 250,000 CD4+ T cell single-cell transcriptomes. We developed a bespoke analytical pipeline for element-to-gene (E2G) mapping and demonstrate that our method can identify both previously annotated and novel E2G links. Lastly, we integrated genetic association data for immune-related traits and demonstrate how our platform can aid in the identification of effector genes for GWAS loci.

genomics↗

bioRxiv2022

Expanding the Huntingtons disease research toolbox; validated huntingtin subdomain constructs for biochemical and structural investigation of the huntingtin protein

Huntingtons disease is characterised by CAG expansion in the huntingtin gene above a critical threshold of ~35 repeats, resulting in polyglutamine expansion of the huntingtin protein (HTT). The biological role of wildtype HTT and the associated mechanisms of disease pathology caused by expanded HTT remain incompletely understood, in part, due to challenges characterising interactions between HTT and putative binding partners. Here we describe a biochemical toolkit of rationally designed, high-quality recombinant HTT subdomains; one spanning the N-terminal HEAT and bridge domains (NTD) and the second spanning the C-terminal HEAT domain (CTD). Using biophysical methods and cryo-electron microscopy, we show these smaller subdomains are natively folded and can associate to reconstitute a functional full-length HTT structure capable of forming a near native-like complex with 40 kDa HTT-associated protein (HAP40). We report biotin-tagged variants of these subdomains, as well as full-length HTT, that permit immobilisation of each protein for quantitative biophysical assays without impacting protein quality. We demonstrate the CTD alone can form a stable complex when co-expressed with HAP40, which can be structurally resolved. The CTD-HAP40 complex binds the NTD, with a dissociation constant of approximately 10 nM as measured by bio-layer interferometry. We validate the interaction between the CTD and HAP40 using a luciferase two-hybrid assay and use subdomain constructs to demonstrate their respective stabilization of HAP40 in cells. These open-source biochemical tools will enable the wider HD community to study fundamental HTT biology, discover new macromolecular or small-molecule binding partners and map interaction sites across this very large protein.

biochemistry↗

bioRxiv2022

Time to first cigarette and its impact on lung tumorigenesis

Time to first cigarette (TTFC) in the morning has been identified as the best indicator of nicotine dependence and is associated with lung cancer risk beyond other measures of tobacco smoking. Using deep whole genome sequencing of 218 lung cancers from smokers, we show that TTFC is the strongest marker of tobacco smoking mutagenicity, with impact on lung tumor mutational burden, mutational signatures, intratumor heterogeneity, KRAS mutations and overall survival. These results pave the way for using TTFC as an easily measurable marker of lung tumorigenesis, with plausible therapeutic and prognostic implications.

genomics↗

bioRxiv2021

fcfdr: an R package to leverage continuous and binary functional genomic data in GWAS

SummaryGWAS discovery is limited in power to detect associations that exceed the stringent genome-wide significance threshold, but this limitation can be alleviated by leveraging relevant auxiliary data. Frameworks utilising the conditional false discovery rate (cFDR) can be used to leverage continuous auxiliary data (including GWAS and functional genomic data) with GWAS test statistics and have been shown to increase power for GWAS discovery whilst controlling the FDR. Here, we describe an extension to the cFDR framework for binary auxiliary data (such as whether SNPs reside in regions of the genome with specific activity states) and introduce an all-encompassing R package to implement the cFDR approach, fcfdr, demonstrating its utility in an application to type 1 diabetes. Availability and implementationThe fcfdr R package is freely available at: https://github.com/annahutch/fcfdr. Scripts and data to reproduce the analysis in this paper are freely available at: https://annahutch.github.io/fcfdr/articles/t1d_app.html

genetics↗

bioRxiv2021

Structure, activity and inhibition of human TMPRSS2, a protease implicated in SARS-CoV-2 activation

Transmembrane protease, serine 2 (TMPRSS2) has been identified as key host cell factor for viral entry and pathogenesis of SARS-coronavirus-2 (SARS-CoV-2). Specifically, TMPRSS2 proteolytically processes the SARS-CoV-2 Spike (S) Protein, enabling virus-host membrane fusion and infection of the lungs. We present here an efficient recombinant production strategy for enzymatically active TMPRSS2 ectodomain enabling enzymatic characterization, and the 1.95 [A] X-ray crystal structure. To stabilize the enzyme for co-crystallization, we pre-treated TMPRSS2 with the synthetic protease inhibitor nafamosat to form a stable but slowly reversible (15 hour half-life) phenylguanidino acyl-enzyme complex. Our study provides a structural basis for the potent but non-specific inhibition by nafamostat and identifies distinguishing features of the TMPRSS2 substrate binding pocket that will guide future generations of inhibitors to improve selectivity. TMPRSS2 cleaved recombinant SARS-CoV-2 S protein ectodomain at the canonical S1/S2 cleavage site and at least two additional minor sites previously uncharacterized. We established enzymatic activity and inhibition assays that enabled ranking of clinical protease inhibitors with half-maximal inhibitory concentrations ranging from 1.7 nM to 120 M and determination of inhibitor mechanisms of action. These results provide a body of data and reagents to support future drug development efforts to selectively inhibit TMPRSS2 and other type 2 transmembrane serine proteases involved in viral glycoprotein processing, in order to combat current and future viral threats. SUMMARY PARAGRAPHViruses hijack the biochemical activity of host proteins for viral invasion and replication. Transmembrane protease, serine-2 (TMPRSS2) is a surface-expressed protease implicated in the activation of influenza A, influenza B, and coronaviruses, including SARS-CoV-2, to drive efficient infection of the lungs1-5. TMPRSS2 is an attractive target for antiviral therapies, as inhibiting its proteolytic activity blocks efficient viral entry5,6. However, a structural and biochemical understanding of the protease has remained elusive and no selective inhibitors are available. We engineered on-demand activatable TMPRSS2 ectodomain and determined the 1.95 [A] X-ray crystal structure of the stabilized acyl-enzyme after treatment with nafamostat, a protease inhibitor under investigation as a COVID-19 therapeutic. The structure reveals unique features of the TMPRSS2 substrate recognition pocket and domain architecture, and explains the potent, but nonselective inhibition by nafamostat. TMPRSS2 efficiently cleaved the SARS-CoV-2 S protein at the canonical S1/S2 site as well as two minor sites previously uncharacterized. We further established a robust enzymatic assay system and characterized inhibition by two additional clinical protease inhibitors under study for COVID-19, camostat and bromhexine. Our results provide a body of data and reagents to enable ongoing drug development efforts to selectively inhibit TMPRSS2 and other TTSPs involved in viral glycoprotein processing, in order to combat current and future viral threats.

biochemistry↗

bioRxiv2021

HAP40 orchestrates huntingtin structure for differential interaction with polyglutamine expanded exon 1

Huntingtons disease results from expansion of a glutamine-coding CAG tract in the huntingtin (HTT) gene, producing an aberrantly functioning form of HTT. Both wildtype and disease-state HTT form a hetero-dimer with HAP40 of unknown functional relevance. We demonstrate in vivo that HTT and HAP40 cellular abundance are coupled. Integrating data from a 2.6 [A] cryo-electron microscopy structure, cross-linking mass spectrometry, small-angle X-ray scattering, and modeling, we provide a near-atomic-level view of HTT, its molecular interaction surfaces and compacted domain architecture, orchestrated by HAP40. Native mass-spectrometry reveals a remarkably stable hetero-dimer, potentially explaining the cellular inter-dependence of HTT and HAP40. The polyglutamine tract containing N-terminal exon 1 region of HTT is dynamic, but shows greater conformational variety in the mutant than wildtype exon 1. By providing novel insight into the structural consequences of HTT polyglutamine expansion, our data provide a foundation for future functional and drug discovery studies targeting Huntingtons disease.

biochemistry↗

bioRxiv2020

Leveraging auxiliary data from arbitrary distributions to boost GWAS discovery with Flexible cFDR

Genome-wide association studies (GWAS) have identified thousands of genetic variants that are associated with complex traits. However, a stringent significance threshold is required to identify robust genetic associations. Leveraging relevant auxiliary covariates has the potential to boost statistical power to exceed the significance threshold. Particularly, abundant pleiotropy and the non-random distribution of SNPs across various functional categories suggests that leveraging GWAS test statistics from related traits and/or functional genomic data may boost GWAS discovery. While type 1 error rate control has become standard in GWAS, control of the false discovery rate can be a more powerful approach. The conditional false discovery rate (cFDR) extends the standard FDR framework by conditioning on auxiliary data to call significant associations, but current implementations are restricted to auxiliary data satisfying specific parametric distributions, typically GWAS p-values for related traits. We relax these distributional assumptions, enabling an extension of the cFDR framework that supports auxiliary covariates from arbitrary continuous distributions ("Flexible cFDR"). Our method can be applied iteratively, thereby supporting multi-dimensional covariate data. Through simulations we show that Flexible cFDR increases sensitivity whilst controlling FDR after one or several iterations. We further demonstrate its practical potential through application to an asthma GWAS, leveraging various functional genomic data to find additional genetic associations for asthma, which we validate in the larger, independent, UK Biobank data resource. Author summaryGenome-wide association studies (GWAS) detect regions of the human genome that are associated with various traits, including complex diseases, but the power to detect these genomic regions is currently limited by sample size. The conditional false discovery rate (cFDR) provides a tool to leverage one GWAS study to improve power in another. The motivation is that if two traits have some genetic correlation, then our interpretation of a low but not significant p-value for the trait of interest will differ depending on whether that SNP shows strong or absent evidence of association with the related trait. Here, we describe an extension to the cFDR framework, called "Flexible cFDR", that controls the FDR and supports auxiliary data from arbitrary distributions, surpassing current implementations of cFDR which are restricted to leveraging GWAS p-values from related traits. In practice, our method can be used to iteratively leverage various types of functional genomic data with GWAS data to increase power for GWAS discovery. We describe the use of Flexible cFDR to supplement data from a GWAS of asthma with auxiliary data from functional genomic experiments. We identify associations novel to the original GWAS and validate these discoveries with reference to a larger, more highly-powered GWAS of asthma.

genetics↗

bioRxiv2020

Resolving mechanisms of immune-mediated disease in primary CD4 T cells

Deriving mechanisms of immune-mediated disease from GWAS data remains a formidable challenge, with attempts to identify causal variants being frequently hampered by linkage disequilibrium. To determine whether causal variants could be identified via their functional effects, we adapted a massively-parallel reporter assay for use in primary CD4 T-cells, key effectors of many immune-mediated diseases. Using the results to guide further study, we provide a generalisable framework for resolving disease mechanisms from non-coding associations - illustrated by a locus linked to 6 immune-mediated diseases, where the lead functional variant causally disrupts a super-enhancer within an NF-{kappa}B-driven regulatory circuit, triggering unrestrained T-cell activation.

genetics↗

bioRxiv2019

Correcting the coverage of credible sets in Bayesian genetic fine-mapping

Genome Wide Association Studies (GWAS) have successfully identified thousands of loci associated with human diseases. Bayesian genetic fine-mapping studies aim to identify the specific causal variants within GWAS loci responsible for each association, reporting credible sets of plausible causal variants, which are interpreted as containing the causal variant with some "coverage probability". Here, we use simulations to demonstrate that the coverage probabilities are over-conservative in most fine-mapping situations. We show that this is because fine-mapping data sets are not randomly selected from amongst all causal variants, but from amongst causal variants with larger effect sizes. We present a method to re-estimate the coverage of credible sets using rapid simulations based on the observed, or estimated, SNP correlation structure, we call this the "corrected coverage estimate". This is extended to find "corrected credible sets", which are the smallest set of variants such that their corrected coverage estimate meets the target coverage. We use our method to improve the resolution of a fine-mapping study of type 1 diabetes. We found that in 27 out of 39 associated genomic regions our method could reduce the number of potentially causal variants to consider for follow-up, and found that none of the 95% or 99% credible sets required the inclusion of more variants - a pattern matched in simulations of well powered GWAS. Crucially, our correction method requires only GWAS summary statistics and remains accurate when SNP correlations are estimated from a large reference panel. Using our method to improve the resolution of fine-mapping studies will enable more efficient expenditure of resources in the follow-up process of annotating the variants in the credible set to determine the implicated genes and pathways in human diseases. Author summaryPinpointing specific genetic variants within the genome that are causal for human diseases is difficult due to complex correlation patterns existing between variants. Consequently, researchers typically prioritise a set of plausible causal variants for functional validation - these sets of putative causal variants are called "credible sets". We find that the probabilistic interpretation that these credible sets do indeed contain the true causal variant is variable, in that the reported probabilities often underestimate the true coverage of the causal variant in the credible set. We have developed a method to provide researchers with a "corrected coverage estimate" that the true causal variant appears in the credible set, and this has been extended to find "corrected credible sets", allowing for more efficient allocation of resources in the expensive follow-up laboratory experiments. We used our method to reduce the number of genetic variants to consider as causal candidates for follow-up in 27 genomic regions that are associated with type 1 diabetes.

genetics↗

bioRxiv2019

Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Disease heterogeneity of immune gene expression patterns of luminal breast cancer (BC) has not been well studied. We performed immune gene expression profiling of tumor and adjacent normal tissue in 92 Asian luminal BC patients and identified three distinct immune subtypes. Tumors in one subtype exhibited signs of T-cell activation, lower ESR1/ESR2 expression ratio and higher expression of immune checkpoint genes, nonsynonymous mutation burden, APOBEC-signature mutations, and increasing body mass index compared to other luminal tumors. Tumors in a second subtype were characterized by increased expression of interferon-stimulated genes and enrichment for TP53 somatic mutations. The presence of three immune subtypes within luminal BC was replicated in cases drawn from The Cancer Genome Atlas and a Korean breast cancer study. Our findings suggest that immune gene expression and associated genomic features could be useful to further stratify luminal BC beyond the current luminal A/B classification.

genomics↗

bioRxiv2018

Design and characterisation of mutant and wild-type huntingtin proteins produced from a toolkit of scalable eukaryotic expression systems

The gene mutated in Huntingtons disease (HD) patients encodes the 348 kDa huntingtin (HTT) protein. The pathogenic HD CAG-expansion mutation causes a polyglutamine (polyQ) tract at the N-terminus of the HTT protein to expand above a critical threshold of ~35 glutamine residues. The effect of HD mutations on HTT is not well understood, in part due to difficulties in carrying out biochemical, biophysical and structural studies of this large protein. To facilitate such studies, we have generated expression constructs for the scalable production of HTT in multiple eukaryotic expression systems. Our set of HTT expression clones comprises both N and C-terminally FLAG-tagged HTT constructs with polyQ lengths representative of the general population, HD patients, juvenile HD patients as well as the more extreme polyQ expansions used in some HD tissue and animal models. These reagents yield milligram quantities of pure recombinant HTT protein, including many of the previously mapped posttranslational modifications. We have characterised both apo and HTT-HAP40 complex samples produced using this HD resource, demonstrating that this toolkit can be used to generate physiologically meaningful complexes of HTT. We demonstrate how these resources can produce sufficient material for protein-intensive experiments such as small angle X-ray scattering (SAXS), providing biochemical insight into HTT protein structure. The work outlined in this manuscript and the tools generated, lay a foundation for further biochemical and structural work on the HTT protein and its functional interactions with other biomolecules.

biochemistry↗