Biology subjects

Gallagher, K.

Publications and source records attributed to Gallagher, K..

17 recordsLinked to original sources

bioRxiv2026

Human Osteochondral Granular Extracellular Matrix (gECM) Hydrogels Drive Tissue-Specific Composition and Mechanics

Osteochondral defects remain a major clinical challenge due to the limited regenerative capacity of cartilage and the complexity of the osteochondral interface. Here, we present a human-derived granular extracellular matrix (gECM) hydrogel platform designed for translational osteochondral repair. Using otherwise discarded human donor tissues, we developed cartilage and bone gECM hydrogels under current good manufacturing practice workflows. These materials are shear-thinning, immediately hold their form, and crosslink under physiological conditions to form stable constructs. Proteomic analysis confirmed that cartilage and bone gECM retain distinct tissue-specific biochemical signatures, while mechanical characterization demonstrated tissue-relevant stiffness, with bone gECM hydrogels exhibiting greater stiffness than cartilage gECM hydrogel. Particle packing density primarily governed viscosity, whereas tissue type contributed strongly to bulk stiffness. Together, these findings establish a scalable, human-derived gECM platform that integrates tissue-specific structural and mechanical cues, and advances a clinically translatable strategy for osteochondral repair.

bioengineering↗

bioRxiv2025

Effects of Sodium Glucose Co-Transporter Inhibitors on Work in Human Hypertrophic Cardiomyopathy Living Myocardial Slices

BackgroundDisease modifying therapies for hypertrophic cardiomyopathy (HCM) remain a prevailing unmet need. Human-based experimental platforms capable of controlled manipulation of preload and afterload can distinguish direct myocardial and systemic effects and facilitate development of targeted cardiac therapeutics. Sodium glucose cotransporter inhibitors (SGLTi) may directly affect cardiac contractility, potentially related to increased ketone availability. These effects have not been adequately studied in human HCM myocardium under defined loading conditions. AimsWe sought to establish human living myocardial slices (LMS) as a platform to interrogate load-dependent myocardial mechanics in HCM and to quantify the acute effects of metabolic and pharmacologic interventions--including SGLTi--on myocardial work under physiologic loading conditions. MethodsHuman myocardial tissue was procured from non-failing donor hearts or individuals with HCM undergoing septal myectomy. Freshly prepared human LMS were mechanically tested to generate biomimetic work loops across a range of physiologic preloads and afterloads in either glucose-only fuel or glucose supplemented with ketone. Following baseline measurements, slices were loaded with drug (isoproterenol, mavacamten, sotagliflozin, or empagliflozin) or vehicle (DMSO) and work loop analysis was repeated, allowing each slice to serve as its own control. Mixed effects linear regression models incorporating random effects for heart and slice and fixed effects for clinical characteristics evaluated determinants of myocardial work and drug response across loading conditions. ResultsA total of 120 LMS from 32 individuals (16 non-failing and 16 HCM) were analyzed. At baseline, myocardial work was positively associated with younger age, hypertension, and ejection fraction. Ketone supplementation augmented work and work-strain slope particularly in HCM LMS at high afterloads. We validated our drug testing methodology by demonstrating increased work with known positive inotrope isoproterenol, decreased work with negative inotrope mavacamtem most pronounced in HCM LMS, and a null effect of DMSO. Acute exposure to SGLTi sotagliflozin and empagliflozin directly reduced myocardial work, with increased potency of sotagliflozin at high afterloads. ConclusionsOur LMS platform enables assessment of myocardial mechanics across controlled loading conditions and is an ideal platform to rigorously phenotype human myocardial tissue and interrogate direct effects of pharmacologic intervention. We demonstrate that SGLTi and ketones have distinct and discordant effects on human myocardial contractility.

physiology↗

bioRxiv2025

A-LAVA: Detecting impact of germline variants on metabolic pathways in cancer genomes

The metabolic landscape of cancer has been widely studied, especially in the context of somatic mutations. However, the impact of inherited germline variants upon the metabolic genes interaction still remains unexplored. In this work, we present a computational pipeline named A-LAVA for the detection and analysis of germline variants that affect metabolic pathways in cancer. Our pipeline enables analysis at three different levels: SNP, gene, and pathway-based analysis. The first steps consist of detecting statistically significant SNPs through standardized GWAS pipelines and, subsequently, genes associated with metabolic traits through gene-level analysis. Then, A-LAVA performs gene set analysis (GSA) to further explore the effect of detected associations on metabolic pathways. This analysis is done through a statistical model that newly corrects for the confounding effects arising from overlapping gene sets, in addition to other corrections performed by the current best practices. Our analysis conducted on TCGA data shows that SNP and gene-level results identified key associations and that A-LAVAs GSA approach improved the overall accuracy both on synthetic and real data by correctly correcting for overlapping genes, refining significance thresholds, and reducing false positives, thus leading to more reliable metabolic pathway rankings and a more robust framework for gene set analysis. CCS CONCEPTSO_LIApplied computing [->]Bioinformatics; Metabolomics / metabonomics. C_LI

bioinformatics↗

bioRxiv2025

Rapid Liquid Chromatography-Mass Spectrometry (rLC-MS) for Deep Metabolomics Analysis of Population Scale Studies

Mass spectrometry (MS)-based metabolomics is a key technology for the interrogation of exogenous and endogenous small molecule mediators that influence human health and disease. To date, however, low throughput of MS systems have largely precluded large-scale metabolomics studies of human populations, limiting power to discover physiological roles of metabolites. Here, we introduce a fully automated rapid liquid chromatography-mass spectrometry (rLC-MS) system coupled to an AI-enabled computational pipeline that enables high-throughput, reproducible, non-targeted metabolite measurements across tens of thousands of samples. This system captures thousands of polar, amphipathic and nonpolar (lipid) metabolites in a human plasma sample in 53 seconds of analytical time, enabling analysis of greater than 1,000 samples per day per instrument. To demonstrate the discovery power of the rLC-MS platform, a subset of samples from Sapients DynamiQ biorepository - comprised of 62,039 total plasma samples collected longitudinally from 11,045 individuals - were selected for deep analysis by rLC-MS to capture a rich, dynamic landscape of chemical variation that reflects both physiological processes and environmental influences. 26,042 plasma samples with matched real-world data (RWD) were chosen for the study, representing 6,935 individuals with diverse demographic backgrounds and disease profiles. Unbiased exploratory analysis revealed human metabotypes that correlate with heterogenous disease phenotypes, including key sub-populations of cardiometabolic and other human diseases. Moreover, a metabolic aging clock machine learning model trained on healthy individuals in this dataset accurately predicted accelerated aging in various chronic diseases, with dynamic reversal of metabolic aging following definitive therapy. These data demonstrate that the rLC-MS platform enables prediction of clinically relevant physiological states from plasma metabolomics at scale in human populations.

biochemistry↗

bioRxiv2025

Controlling treatment toxicity in ovarian cancer to prime the patient for tumor extinction therapy

High-grade serous ovarian cancer (HGSOC) remains a major clinical challenge. In particular among those patients with homologous recombination (HR)-proficient tumors (>50%), most eventually succumb to their disease due to high recurrence rates, acquired resistance, and cumulative toxicity. This report summarizes work from the 12th IMO Workshop in which we explored an alternative "extinction therapy" strategy for frontline treatment of HGSOC. Inspired by ecological principles, this multi-strike approach aims to eradicate tumors not through a singular "magic bullet" but through a series of therapies after standard frontline treatment when the tumor is still, and perhaps most, vulnerable. We present a framework leveraging mathematical modeling (MM) to develop personalized multi-strike protocols for HGSOC. Key contributions include: 1) An "IMOme" score using liquid biopsy data to assess patient-specific hematopoietic toxicity risk, guiding the timing and selection of subsequent therapies, 2) MM strategies to design effective lowdose combinations of targeted agents to achieve synthetic lethality while managing toxicity, and 3) A MM framework to analyze the interplay between chemotherapy, gut microbiome toxicity, and immunotherapy, demonstrating how mitigating microbiome damage could enhance immune response. Overall, the computational approaches presented herein aim to support the design of personalized, multi-strike regimens in the frontline setting that proactively target tumor extinction while managing toxicity, ultimately seeking to deliver cures for patients with HGSOC.

cancer biology↗

bioRxiv2025

A Transcriptomic Atlas of Healthy Human Skin Links Regional Identity to Inflammatory Disease.

Human skin is not a uniform organ but a mosaic of anatomically distinct niches, with each site finely tuned to unique environmental demands and immune pressures. Yet, the molecular determinants that define these regional identities and their relationship to site-specific vulnerability to inflammatory disease remain poorly understood. Here, we generate a high-resolution single-cell atlas of human skin, profiling 274,834 cells from 96 healthy samples across 7 anatomically distinct sites (acral, arm, axilla, back, face, leg and scalp). Our analysis reveals striking region-specific transcriptional and cellular networks, uncovering how local immune-stromal crosstalk governs tissue homeostasis and underpins anatomical susceptibility to distinct inflammatory diseases such as such as systemic lupus erythematosus (SLE), atopic dermatitis (AD), and psoriasis. These findings illuminate the tissue-intrinsic foundations of regional immune identity and provide a blueprint/resource for the development of precision therapies tailored to the distinct immunological microenvironments of specific anatomical skin sites.

systems biology↗

bioRxiv2025

Predicting Treatment Outcomes from Adaptive Therapy -- A New Mathematical Biomarker

Standard-of-care cancer therapy regimens are characterized by continuous treatment at the maximum tolerated dose; however, this approach often fails on metastatic cancers due to the emergence of drug resistance. An evolution-based treatment paradigm known as Adaptive Therapy has been proposed to counter this, dynamically adjusting treatment to control, rather than minimize, the tumor burden, thus suppressing the growth of treatment-resistant cell populations and hence delaying patient relapse. Promising clinical results in prostate cancer indicate the potential of adaptive treatment protocols, but demonstrate broad heterogeneity in patient response. This naturally leads to the question: why does this heterogeneity occur, and is a one-size-fits-all protocol best for patients across this spectrum of responses? Using a Lotka-Volterra representation of drug-sensitive and -resistant tumor populations dynamics, we obtain a predictive expression for the expected benefit from Adaptive Therapy and propose two new mathematical biomarkers (the Delta AT Score and the eTTP) that can identify the best responders in a clinical dataset after the first cycle of treatment. Based on prior theoretical analyses, we derive personalized and clinically-feasible optimal treatment strategies, based on individual patients tumor dynamics. These strategies vary significantly between patients, and so we present a framework to generate individual treatment schedules based on a patients response to the first treatment cycle. Finally, we develop metrics to identify which patients have the greatest sensitivity to unplanned schedule changes, such as delayed appointments, allowing clinicians to identify high-risk patients that need to be monitored more closely and potentially more frequently. Overall, the proposed strategies offer personalized treatment schedules that consistently outperform clinical standard-of-care protocols.

cancer biology↗

bioRxiv2025

Perturbed cyclic strain results in a mechanically-induced, premature senescent phenotype in cardiac fibroblasts

The cardiovascular system operates under continuous cyclic mechanical stretch, and changes in mechanics and biochemical responses observed in cardiovascular disorders activate the cardiac fibroblasts (CFs) and increase cellular senescence in the tissue. However, it is unclear if cellular senescence is initiated from biomechanical stimulus alone. Here, we subjected murine CFs to uniaxial stretch and perturbed the mechanical stimulation to examine the impact on induction of a senescent phenotype. Loss of stretch magnitude and increase in frequency, mimicking an injurious hypertrophic or fibrotic response, led to a senescence-like phenotype, including cell cycle, lamin B expression, and DNA damage. Mechanical induction of CF senescence relied on p53/p21, whereas CF senescence induction triggered by reactive oxygen species or involving a mutation in lamin A/C gene occurred through p16. Moreover, mechanical induction of premature senescence was associated with decreases in the nuclear envelope protein emerin. These results demonstrate perturbed mechanical stimulation can initiate a senescent state and altered nuclear integrity may initiate this phenotype. TeaserPerturbed mechanics initiates a premature senescent-like phenotype in cardiac fibroblasts.

biophysics↗

bioRxiv2023

Analysis of ductal carcinoma in situ by self-reported race reveals molecular differences related to outcome.

Ductal carcinoma in situ (DCIS) is a non-obligate precursor to invasive breast cancer (IBC). Studies have indicated differences in DCIS outcome based on race or ethnicity, but molecular differences have not been investigated. We examined the molecular profile of DCIS by self-reported race (SRR) and outcome groups in Black (n=99) and White (n=191) women with DCIS in a large DCIS case-control cohort with longitudinal follow up. Gene expression and pathway analyses indicated that different genes and pathways are involved in ipsilateral breast outcome (DCIS or IBC) after DCIS treatment in White versus Black women. We identified differences in ER and HER2 expression, tumor microenvironment composition, and copy number variations by SRR and outcome groups. Our results suggest that different molecular mechanisms drive subsequent ipsilateral breast events in Black versus White women.

cancer biology↗

bioRxiv2023

Structure of RADX and mechanism for regulation of RAD51 nucleofilaments

Replication fork reversal is a fundamental process required for resolution of encounters with DNA damage. A key step in the stabilization and eventual resolution of reversed forks is formation of RAD51 nucleoprotein filaments on exposed ssDNA. To avoid genome instability, RAD51 filaments are tightly controlled by a variety of positive and negative regulators. RADX is a recently discovered negative regulator that binds tightly to ssDNA, directly interacts with RAD51, and regulates replication fork reversal and stabilization in a context-dependent manner. Here we present a structure-based investigation of RADXs mechanism of action. Mass photometry experiments showed that RADX forms multiple oligomeric states in a concentration dependent manner, with a predominance of trimers in the presence of ssDNA. The structure of RADX, which has no structurally characterized orthologs, was determined ab initio by cryo-electron microscopy (EM) from maps in the 2-3 [A] range. The structure reveals the molecular basis for RADX oligomerization and binding of ssDNA binding. The binding of RADX to RAD51 filaments was imaged by negative stain EM, which showed a RADX oligomer at the end of filaments. Based on these results, we propose a model in which RADX functions by capping and restricting the growing end of RAD51 filaments. SignificanceDespite the central role of RAD51 in DNA replication and repair processes, the mechanisms of action of its many modulators are poorly understood. Here we combine structural and biophysical data to determine how the negative regulator RADX functions. We show that RADX oligomerizes upon binding DNA, and caps RAD51 filaments at the ends to prevent extension. This work advances knowledge of how RAD51 filaments can be modulated to regulate replication fork reversal and maintain genomic stability.

biophysics↗

bioRxiv2023

Learning to Adapt - Deep Reinforcement Learning in Treatment-Resistant Prostate Cancer

Standard-of-care treatment regimes have long been designed to for maximal cell kill, yet these strategies often fail when applied to treatment-resistant tumors, resulting in patient relapse. Adaptive treatment strategies have been developed as an alternative approach, harnessing intra-tumoral competition to suppress the growth of treatment resistant populations, to delay or even prevent tumor progression. Following recent clinical implementations of adaptive therapy, it is of significant interest to optimise adaptive treatment protocols. We propose the application of deep reinforcement learning models to provide generalised solutions within adaptive drug scheduling, and demonstrate this framework can outperform the current adaptive protocols, extending time to progression by up to a quarter. This strategy is robust to varying model parameterisations, and the underlying tumor model. We demonstrate the deep learning framework can produce interpretable, adaptive strategies based on a single tumor burden threshold, replicating and informing a novel, analytically-derived optimal treatment strategy with no knowledge of the underlying mathematical tumor model. This approach is highly relevant beyond the simple, analytically-tractable tumor model considered here, demonstrating the capability of deep learning frameworks to help inform and develop treatment strategies in complex settings. Finally, we propose a pathway to integrate mechanistic modelling with DRL to tailor generalist treatment strategies to individual patients in the clinic, generating personalised treatment schedules that consistently outperform clinical standard-of-care protocols.

cancer biology↗

bioRxiv2022

Two Interaction Surfaces between XPA and RPA Organize the Preincision Complex in Nucleotide Excision Repair

The XPA and RPA proteins fulfill essential roles in the assembly of the preincision complex in the nucleotide excision repair pathway. We have previously characterized the two interaction surfaces between XPA and RPA, with the RPA32 and RPA70AB subunits. Here we show that the mutations in the two individual interaction surfaces reduce NER activity in biochemical and cellular systems, and that combining mutations in two domains leads to an additive inhibition of NER, suggesting that they fulfill distinct roles. Our data suggest that the interaction between XPA and RPA32 is important for the initial association of XPA with NER complexes, while the interaction between XPA and RPA70 is needed for structural organization of the complex to license the dual incision reaction. SAXS analysis of complexes of XPA and RPA bound to ss/dsDNA junction substrates reveals the architecture of XPA and RPA in the preincision complex and shows that the two interaction domains between RPA and XPA are located at opposite sides of the two molecules. We propose a structure for the overall NER preincision complex that shows that the two strands of the NER bubble assume a U-shape with the two ss/dsDNA junctions localized in close proximity, with the interaction between XPA and RPA70 as one of the key organizing elements.

biochemistry↗

bioRxiv2021

The Human Tumor Atlas Network (HTAN) Breast PreCancer Atlas: A multi-omic integrative analysis of ductal carcinoma in situ with clinical outcomes

Ductal carcinoma in situ (DCIS) is the most common precursor of invasive breast cancer (IBC), with variable propensity for progression. We have performed the first multiscale, integrated profiling of DCIS with clinical outcomes by analyzing 677 DCIS samples from 481 patients with 7.1 years median follow-up from the Translational Breast Cancer Research Consortium (TBCRC) 038 study and the Resource of Archival Breast Tissue (RAHBT) cohorts. We identified 812 genes associated with ipsilateral recurrence within 5 years from treatment and developed a classifier that was predictive of DCIS or IBC recurrence in both cohorts. Pathways associated with recurrence include proliferation, immune response, and metabolism. Distinct stromal expression patterns and immune cell compositions were identified. Our multiscale approach employed in situ methods to generate a spatially resolved atlas of breast precancers, where complementary modalities can be directly compared and correlated with conventional pathology findings, disease states, and clinical outcome. HIGHLIGHTS Development of a new classifier for DCIS recurrence or progression Outcome associated pathways identified across multiple data types and compartments Four stroma-specific signatures identified CNAs characterize DCIS subgroups associated with high risk invasive cancers

cancer biology↗

bioRxiv2020

Who acquires infection from whom? Estimating herpesvirus transmission rates between wild rodent host groups

To date, few studies of parasite epidemiology have investigated who acquires infection from whom in wildlife populations. Nonetheless, identifying routes of disease transmission within a population, and determining the key groups of individuals that drive parasite transmission and maintenance, are fundamental to understanding disease dynamics. Gammaherpesviruses are a widespread group of DNA viruses that infect many vertebrate species, and murine gammaherpesviruses (i.e. MuHV-4) are a standard lab model for studying human herpesviruses, for which much about the pathology and immune response elicited to infection is well understood. However, despite this extensive research effort, primarily in the lab, the transmission route of murine gammaherpesviruses within their natural host populations is not well understood. Here, we aimed to understand wood mouse herpesvirus (WMHV) transmission, by fitting a series of population dynamic models to field data on wood mice naturally infected with WMHV and then estimating transmission parameters within and between demographic groups of the host population. Different models accounted for different combinations of host sex (male/female), age (subadult/adult) and transmission functions (density/frequency-dependent). We found that a density-dependent transmission model incorporating explicit sex groups fitted the data better than all other proposed models. Male-to-male transmission was the highest among all demographic groups, suggesting that male behaviour is a key factor driving WMHV transmission. Our models also suggest that transmission between sexes, although important, wasnt symmetrical, with infected males playing a significant role in infecting naive females but not vice versa. Overall this work shows the power of coupling population dynamic models with long-term field data to elucidate otherwise unobservable transmission processes in wild disease systems.

ecology↗

bioRxiv2020

Human and rat skeletal muscle single-nuclei multi-omic integrative analyses nominate causal cell types, regulatory elements, and SNPs for complex traits

BackgroundSkeletal muscle accounts for the largest proportion of human body mass, on average, and is a key tissue in complex diseases, mobility, and quality of life. It is composed of several different cell and muscle fiber types. ResultsHere, we optimize single-nucleus ATAC-seq (snATAC-seq) to map skeletal muscle cell-specific chromatin accessibility landscapes in frozen human and rat samples, and single-nucleus RNA-seq (snRNA-seq) to map cell-specific transcriptomes in human. We capture type I and type II muscle fiber signatures, which are generally missed by existing single-cell RNA-seq methods. We perform cross-modality and cross-species integrative analyses on 30,531 nuclei, representing 11 libraries, profiled in this study, and identify seven distinct cell types ranging in abundance from 63% (type II fibers) to 0.9% (muscle satellite cells) of all nuclei. We introduce a regression-based approach to infer cell types by comparing transcription start site-distal ATAC-seq peaks to reference enhancer maps and show consistency with RNA-based marker gene cell type assignments. We find heterogeneity in enrichment of genetic variants linked to complex phenotypes from the UK Biobank and diabetes genome wide association studies in cell-specific ATAC-seq peaks, with the most striking enrichment patterns in muscle mesenchymal stem cells ([~]3% of nuclei). Finally, we overlay these chromatin accessibility maps on GWAS data to nominate causal cell types, SNPs, and transcription factor motifs for creatinine levels and type 2 diabetes signals. ConclusionsThese chromatin accessibility profiles for human and rat skeletal muscle cell types are a useful resource for investigating specific cell types and nominating causal GWAS SNPs and cell types.

genomics↗

bioRxiv2019

Regulation of heterotopic ossification through local inflammatory monocytes in a mouse model of aberrant wound healing

Heterotopic ossification (HO) is an aberrant regenerative process with ectopic bone induction in response to musculoskeletal trauma, in which mesenchymal stem cells (MSC) differentiate into osteochondrogenic cells instead of myocytes or tenocytes. Despite frequent cases of hospitalized musculoskeletal trauma, the inflammatory responses and cell population dynamics that regulate subsequent wound healing and tissue regeneration are still unclear. Here we examine, using a mouse model of trauma-induced HO, the local microenvironment of the initial post-injury inflammatory response. Single cell transcriptome analyses identify distinct monocyte/macrophage populations at the injury site, with their dynamic changes over time elucidated using trajectory analyses. Mechanistically, transforming growth factor beta-1 (TGF{beta}1)-producing monocytes/macrophages are associated with HO and aberrant chondrogenic progenitor cell differentiation, while CD47-activating peptides that reduce systemic TGF{beta} levels help ameliorate HO. Our data thus implicate CD47 activation as a novel therapeutic approach for modulating monocyte/macrophage phenotypes, MSC differentiation and HO formation during wound healing.

immunology↗

bioRxiv2018

Cell type-specific differences in protein complex stoichiometry and transcriptional regulation affect the timing of stem cell division

Stem cells divide and differentiate to form all the specialized cell types in a multicellular organism. In the Arabidopsis root, stem cells are maintained in an undifferentiated state by a less mitotically active population of cells called the Quiescent Center (QC). Determining how the QC regulates the surrounding stem cell initials, or what makes the QC fundamentally different from the actively dividing initials, is important for understanding how stem cell divisions are maintained. Here, we gained insight into the differences between the QC and the Cortex Endodermis Initials (CEI) by studying the mobile transcription factor SHORTROOT (SHR) and its binding partner SCARECROW (SCR). We constructed an Ordinary Differential Equation (ODE) model of SHR and SCR in the QC and CEI which incorporated the stoichiometry of the SHR-SCR complex as well as upstream transcriptional regulation of SHR and SCR. Our model prediction coupled with experimental validation showed that high levels of the SHR-SCR complex is associated with more CEI division but less QC division. Further, our model prediction allowed us to establish the timing of QC and CEI division and propose that SHR repression of QC division depends on the formation of SHR homodimer. Thus, our results support that SHR-SCR protein complex stoichiometry and regulation of SHR transcription modulate the division timing of two different specialized cell types in the root stem cell niche.

plant biology↗