Biology subjects

Farrell, G.

Publications and source records attributed to Farrell, G..

6 recordsLinked to original sources

bioRxiv2026

Widespread detection of Polyethylene glycol reveals chronic human exposure through pharmaceutical drugs

Polyethylene glycol (PEG) is a synthetic polymer ubiquitous in pharmaceuticals, personal care products, food additives, and industrial manufacturing. Despite its widespread use and potential importance as an exposure chemical, the prevalence of PEG exposure and its excretion in human populations remain largely uncharacterized. Moreover, PEG detected in human biofluids is frequently assumed to arise from analytical contamination during sample preparation, potentially obscuring its contribution to the human exposome and confounding metabolic phenotyping studies. Here, we show that PEG is as component of the human xenobiotic exposome and is further metabolized into PEG hydoxy acid and diacid metabolites in humans. We further find that PEG exposure is associated with alterations in microbiome composition and short-chain fatty acid metabolism, suggesting its biological impact of its exposure. We identify PEG exposure in approximately 2.3% of publicly available metabolomics data files and provide a reusable 85,484 candidate PEG and PEGylated MS/MS spectral library for future use for the metabolomic community. Together, these findings establish PEG signal in human biofluids can reflect genuine exposure, and that PEG exposure is neither metabolically inert nor biologically silent.

biochemistry↗

bioRxiv2026

Lineage-selective suicide gene system enables post-engraftment editing of cell therapy composition

Human pluripotent stem cell (hPSC)-derived therapies are advancing rapidly toward clinical application, yet heterogeneity of transplanted cell populations remains a major barrier to safety, predictability and scalability. Existing strategies to mitigate this risk either incompletely eliminate proliferative cells or ablate the entire graft, thereby compromising therapeutic benefit. Here we present NeuroGuard, a lineage-selective suicide gene platform that decouples safety from efficacy by preserving functional neurons while enabling inducible elimination of all other cell types after transplantation. NeuroGuard integrates an inducible caspase-9 system with NEUROD1-driven Cre recombination, protecting post-mitotic neurons from apoptosis while rendering non-neuronal and proliferative populations susceptible to ablation. In vitro, activation of the system enriched neuronal content to >90% and increased dopaminergic neuron proportion >3-fold. Following transplantation of ventral midbrain progenitors, timed activation eliminated proliferative and glial populations, resulting in compact, neuron-enriched grafts without loss of dopaminergic neuron number, target innervation or behavioural recovery in Parkinsonian rodents. Single-cell transcriptomics confirmed selective removal of non-neuronal lineages while preserving neuronal identity and maturation programs. This work establishes a generalizable framework for post-engraftment editing of cell therapy composition, providing a versatile strategy to enhance the safety and functional predictability of regenerative therapies.

neuroscience↗

bioRxiv2026

DOME Copilot: Making transparency and reproducibility for artificial intelligence methods simple

Artificial intelligence (AI) methods are transforming life science research and witnessing unprecedented adoption across literature. While AI is driving impactful discoveries, this rapid growth in application has inundated researchers with poorly described models and datasets which lack transparency, impeding reusability and eroding trust. The DOME Recommendations aimed to address this issue and established structured reporting guidelines to standardize AI methodology descriptions. However, the need to manually create these comprehensive disclosures imposed a major bottleneck, requiring substantial effort from authors to comply. To bridge this gap, we present DOME Copilot, an open-source, easily deployable system that automates the generation of transparent AI methodology reports supplementing publications. Evaluated against a benchmark of human-created annotations, DOME Copilot was determined to match or exceed manual annotation quality across the majority of reporting fields while reducing the creation time from hours to minutes. Ultimately, the system provides a novel and scalable solution to restore confidence in complex life science AI method publications.

bioinformatics↗

bioRxiv2025

The novel RNA polymerase I transcription inhibitor PMR-116 exploits a critical therapeutic vulnerability in a broad-spectrum of high MYC malignancies.

Ribosome biogenesis (RiBi) is a key determinant of cell growth and proliferation and is highly elevated in cancer due to the activation by oncogenes such as MYC. First-generation RiBi inhibitor CX-5461, while demonstrating clinical potential for cancer treatment, also induces DNA damage through off-target inhibition of TOP2 and potentially other mechanisms, bringing into question RiBi as a target for cancer therapy. In this study, we test second-generation RiBi inhibitor, PMR-116. PMR-116 exhibits improved drug-like properties compared to first-generation RiBi inhibitors and has robust anti-tumour activity in the absence of global DNA damage signalling in a broad range of pre-clinical models of haematologic and solid cancers, particularly in malignancies where MYC is either the driver of disease or is elevated. Thus, our work demonstrates that RiBi is a genuine target for cancer therapy and highlights the potential to exploit a critical therapeutic vulnerability in high-MYC human cancers with dismal therapeutic outcomes. Statement of significanceDespite the development of new cancer therapies, most advanced malignancies remain incurable. We demonstrate that PMR-116, a second-generation RiBi inhibitor, has robust therapeutic efficacy in preclinical models of cancer, offering great promise to treat a broad spectrum of human solid and haematologic malignancies, especially where MYC is a driver.

cancer biology↗

bioRxiv2022

Integration of mass-spectrometry-based metabolomics and proteomics to characterise different senescence induced molecular sub-phenotypes

Cellular senescence is a key driver of ageing and its related disease. Thus, targeting and eliminating senescent cells is a major focus in biogerontology to predict and ameliorate age-related malady. Many studies have focused on targeting senescence through the identification of its molecular biomarkers. However, these are not specific for senescence and have different expression patterns across various senescence phenotypes. Here we report a combination of molecular studies ({beta}-galactosidase expression, DNA damage and replication immunodetection) with a mass spectrometry analysis integrating intra and extracellular global metabolomics to reveal small molecules differentially expressed across multiple senescence phenotypes (replicative senescence, x-ray, and chemical-induced senescence). Altered key intracellular metabolic changes were identified, depending on the stress stimuli, which were consistent with the presence of pro-inflammatory metabolites in the cellular secretome. Our work shows the advantage of combining molecular and metabolomics studies for the detailed analysis of cellular senescence and that senescence phenotype changes upon induction method.

molecular biology↗

bioRxiv2022

The differential metabolic signature of breast cancer cellular response to olaparib treatment

Metabolic reprogramming and genomic instability are key hallmarks of cancer, the combined analysis of which has gained recent popularity. Given the emerging evidence indicating the role of oncometabolites in DNA damage repair and its routine use in breast cancer treatment, it is timely to fingerprint the impact of olaparib treatment in cellular metabolism. Here, we report the biomolecular response of breast cancer cell lines with DNA damage repair defects to olaparib exposure. Following evaluation of olaparib sensitivity in breast cancer cell lines, we immunoprobed DNA double strand break foci and evaluated changes in cellular metabolism at various olaparib treatment doses using untargeted mass spectrometry-based metabolomics analysis. Following identification of altered features, we performed pathway enrichment analysis to measure key metabolic changes occurring in response to olaparib treatment. We show a cell-line dependent response to olaparib exposure, and an increased susceptibility to DNA damage foci accumulation in triple-negative breast cancer cell lines. Metabolic changes in response to olaparib treatment were cell-line and dose-dependent, where we predominantly observed metabolic reprogramming of glutamine-derived amino acids and lipids metabolism. Our work demonstrates the effectiveness of combining molecular biology and metabolomics studies for the comprehensive characterisation of cell lines with different genetic profiles. Follow-on studies are needed to map the baseline metabolism of breast cancer cells and their unique response to drug treatment. Fused with genomic and transcriptomics data, such readout can be used to identify key oncometabolites and inform the rationale for the design of novel drugs or chemotherapy combinations.

cancer biology↗