Biology subjects

McGrail, D. J.

Publications and source records attributed to McGrail, D. J..

7 recordsLinked to original sources

bioRxiv2026

eSIG-Net: Accurate prediction of single-mutation induced perturbations on protein interactions using a language model

Most proteins exert their functions in complex with other interactors. Single mutations can exhibit a profound impact on perturbing protein interactions, leading to human disease. However, predicting the effect of single mutations on protein interactions remains a major computational challenge. Deep learning, particularly protein language models or transformers, has become an effective tool in bioinformatics for protein structure prediction. However, the functional divergence of mutations makes it difficult to predict their interaction perturbation profiles. To address this fundamental challenge, we present eSIG-Net (edgetic mutation Sequence-based Interaction Grammar Network), a novel sequence-based "Interaction Language Model" for predicting protein interaction alterations caused by single mutations. eSIG-Net combines various protein sequence embeddings, introduces a mutation-encoding module with syntax and evolutionary insights, and employs contrastive learning to evaluate mutation-induced interaction changes. eSIG-Net significantly outperforms current state-of-the-art sequence-based and structure-based prediction methods at predicting mutational impact on protein interactions. We highlight examples where eSIG-Net nominates causal variants with high confidence and elucidates their functional role under relevant biological contexts. Together, eSIG-Net is a first-in-kind "interaction language model" that can accurately predict interaction-specific rewiring by single mutations with only sequence information, and exhibits generalizability across biological contexts.

bioinformatics↗

bioRxiv2025

Succinate dehydrogenase activity supports de novo purine synthesis

The de novo purine synthesis pathway is fundamental for nucleic acid production and cellular energetics, yet the role of mitochondrial metabolism in modulating this process remains underexplored. In many cancers, metabolic reprogramming supports rapid proliferation and survival, but the specific contributions of the tricarboxylic acid (TCA) cycle enzymes to nucleotide biosynthesis are not fully understood. Here, we demonstrate that the TCA cycle enzyme succinate dehydrogenase (SDH) is essential for maintaining optimal de novo purine synthesis in normal and cancer cells. Genetic or pharmacological inhibition of SDH markedly attenuates purine synthesis, leading to a significant reduction in cell proliferation. Mechanistically, SDH inhibition causes an accumulation of succinate, which directly impairs the purine biosynthetic pathway. In response, cancer cells compensate by upregulating the purine salvage pathway, a metabolic adaptation that represents a potential therapeutic vulnerability. Notably, co-inhibition of SDH and the purine salvage pathway induces pronounced antiproliferative and antitumoral effects in preclinical models. These findings not only reveal a signaling role for mitochondrial succinate in regulating nucleotide metabolism but also provide a promising therapeutic strategy for targeting metabolic dependencies in cancer.

biochemistry↗

bioRxiv2022

Beyond BRCA: Discovery of novel causes and consequences of homologous recombination deficiencies

Since the discovery of BRCA1 and BRCA2 mutations as cancer risk factors, we have gained substantial insight into their role in maintaining genomic stability through homologous recombination (HR) DNA repair. However, upon pan-cancer analysis of tumors from The Cancer Genome Atlas (TCGA), we found that mutations in BRCA1/2 and other classical HR genes only identified 10-20% of tumors that display genomic evidence of HR deficiency (HRD), suggesting that the cause of the vast majority of HR defects in tumors is unknown. As HRD both predisposes individuals to cancer development and leads to therapeutic vulnerabilities, it is critical to define the spectrum of genetic events that drive HRD. Here, we employed a network-based approach leveraging the abundance of molecular characterization data from TCGA to identify novel drivers of HRD. We discovered that over half of putative genes driving HRD originated outside of canonical DNA damage response genes, with a particular enrichment for RNA binding protein (RBP)-encoding genes. These novel drivers of HRD were cross-validated using an independent ICGC cohort, and were enriched in GWAS loci associated with cancer risk. Experimental approaches validated over 90% of our predictions in a panel of 50 genes tested by siRNA and 31 additional engineered mutations identified from TCGA patient tumors. Moreover, genetic suppression of identified RBPs or pharmacological inhibition of RBPs induced PARP inhibition. Further mechanistic studies indicate that some RBPs are recruited to sites of DNA damage to facilitate repair, whereas others control the expression of canonical HR genes. Overall, this study greatly expands the repertoire of known drivers of HRD and their contributions to DNA damage repair, which has implications for not only future mechanistic studies, but also for genetic screening and therapy stratification. HIGHLIGHTSO_LIThe majority of HR deficiencies detected cannot be directly attributed to aberrations in canonical HR genes. C_LIO_LIIntegrated network analysis identifies RNA binding proteins (RBPs) as a novel driver of HR deficiency in patient tumors. C_LIO_LIRBP dysfunction can produce HR deficiencies through both dysregulation of canonical HR genes and action at sites of DNA damage. C_LI

systems biology↗

bioRxiv2022

Autophagy is associated with survival and resiliency of fibroblasts in long-term decaying post-mortem parenchymal tissue

Fibroblasts are spindle-shaped mesenchymal cells and an abundantly studied cell type that are easy to culture. Their adaptive response in culture conditions allows for use in many different cell biological experiments, including their utility in generating induced pluripotent stem cells. Despite extensive use of fibroblasts in cell and molecular biology and genetics experiments, fundamental evaluation of their resiliency and survival programs, in comparison with other cell types, is undetermined. Here, we demonstrate that fibroblasts exhibit remarkable survival capacity in post-mortem tissue decaying at room temperature and can be cultured from ear, tail tips, kidney, lung, fetal, and mammary tumor tissue after 12-hours of post-mortem tissue decay. Fibroblasts can be cultured from ear and lung tissue after 24-hours, and from ear after up to 120-hours of post-mortem tissue decay. Gene expression profiling of post-mortem lung tissue fibroblasts compared to fresh tissue cultured fibroblasts suggested a transition to a more quiescent phenotype with activation of nutrient scavenging pathways as evidenced by downregulation of genes associated with DNA replication, ribosomes, cell cycle, and spliceosomes as well as upregulation of genes associated metabolism, autophagy, and lysosomes. Measurement of light chain 3B (LC3B)-I/LC3B-II ratio and lysosomal-associated membrane protein (LAMP)-1 indicate that autophagy is increased in post-mortem fibroblasts, with evidence for potential increase in autolysosomes and senescence program. Our study provides evidence for the ability of normal fibroblasts to overcome extreme stress conditions and offers new insights into cell survival mechanisms and aging, with potential utility in tissue regeneration and repair.

cell biology↗

bioRxiv2022

Drug combination prioritization for cancer treatment using single-cell RNA-seq based transfer learning

Precision oncology seeks to match patients to the optimal pharmacological regimen; yet, due to tumor heterogeneity, this is challenging. Numerous studies have been conducted to produce clinically relevant pharmacological response forecasts by integrating modern machine learning algorithms and several data types. Insufficient patient numbers and lack of knowledge of the molecular targets for each drug under study limit their use. As a proof of concept, we use single-cell RNA-seq based transfer learning to contextualize patients tumor cells in terms of their more similar cell lines with known susceptibility to drug combinations. Our objective is to maximize the translational potential of in-vitro assays for identifying synergistic drug combinations and prioritizing them for clinical use. Consistent findings in a cohort of breast cancer patients corroborated our understanding of the diseases molecular subtypes. To aid in creating personalized treatments and data-driven clinical trials, we identified the most prevalent cell lines and prioritized synergistic combinations based on tumor compositions at various resolution levels.

bioinformatics↗

bioRxiv2020

e-MutPath: Computational modelling reveals the functional landscape of genetic mutations rewiring interactome networks

Understanding the functional impact of cancer somatic mutations represents a critical knowledge gap for implementing precision oncology. It has been increasingly appreciated that the edgotype of a genomic mutation provides a fundamental link between genotype and phenotype. However, specific effects on biological signaling networks for the majority of mutations are largely unknown by experimental approaches. To resolve this challenge, we developed e-MutPath, a network-based computational method to identify candidate edgetic mutations that perturb functional pathways. e-MutPath identifies informative paths that could be used to distinguish disease risk factors from neutral elements and to stratify disease subtypes with clinical relevance. The predicted targets are enriched in cancer vulnerability genes, known drug targets but depleted for proteins associated with side effects, demonstrating the power of network-based strategies to investigate the functional impact and perturbation profiles of genomic mutations. Together, e-MutPath represents a robust computational tool to systematically assign functions to genetic mutations, especially in the context of their specific pathway perturbation effect. The code for e-MutPath is available as a user-friendly R package at the GitHub website (https://github.com/lyshaerbin/eMutPath).

systems biology↗

bioRxiv2020

Comprehensive and Integrated Genomic Characterization of Human Immunome in Cancer

Genetic alterations in immune-related pathways are common hallmarks of cancer. However, to realize the full potential of immunotherapy, a comprehensive understanding of immune networks and how mutations impact network structure and functional output across cancer types is instrumental. Herein we systematically interrogated somatic mutations that could express neoantigens and alter immune responses in cancer patients compared to wild-type controls. To do so, we developed a network-based immunogenomics model (NIPPER) with scoring systems to prioritize critical genes and mutations eliciting differential HLA binding affinity and alternate responses to immunotherapy. These mutations are enriched in essential protein domains and often alter tumor infiltration by immune cells, affecting T cell receptor repertoire and B cell clonal expansion. Furthermore, we devised an interactome network propagation framework integrated with drug associated gene signatures to identify potential immunomodulatory drug candidates. Together, our systems-level analysis results help interpret the heterogeneous immune responses among patients, and serve as a resource for future functional studies and targeted therapeutics. SignificanceCancer cells induce specific immune-related pathway perturbations by mutations, transcriptional dysregulation, and integration of multi-omics data can help identify critical molecular determinants for effective targeted therapeutics.

immunology↗