Biology subjects

Barrett, I.

Publications and source records attributed to Barrett, I..

3 recordsLinked to original sources

bioRxiv2024

Representation Learning of Human Disease Mechanisms for a Foundation Model in Rare and Common Diseases

A fundamental challenge in translational medicine is the computational modeling of complex human diseases to accelerate therapeutic development. Representation learning provides a powerful framework to address this, yet creating models that capture deep biological mechanisms remains a critical need. To this end, we propose a novel strategy that partitions the disease landscape into rare and non-rare categories, enabling systematic knowledge repurposing both within and between these groups. Here, we introduce Dis2Vec (Disease to Vector), a representation learning framework designed to operationalize this concept. Dis2Vec generates biologically grounded disease embeddings by learning from human genetic and phenotypic data, forming the foundation for Disease-Disease Association Learning (DDAL) and unsupervised disease clustering. We evaluate Dis2Vec representations in two downstream applications. First, we assess DDAL performance on a transfer learning benchmark designed to predict therapeutic transferability, using real-world drug repurposing investment decisions made in clinical trials. Second, unsupervised clustering analyses reveal shared biological mechanisms across diseases. By modeling the disease landscape in this way, Dis2Vec enhances translational research efficiency across both rare and non-rare diseases, advancing the development of foundational models for therapeutic science. Furthermore, Dis2Vec establishes a biologically grounded disease-representation and benchmarking layer that paves the way for trustworthy agentic biomedical AI systems in rare-disease indication expansion.

systems biology↗

bioRxiv2022

Small gene networks can delineate immune cell states and characterize immunotherapy response in melanoma

BackgroundSingle-cell sequencing studies have elucidated some of the underlying mechanisms responsible for immune checkpoint inhibitor (ICI) response, but are difficult to implement as a general strategy or in a clinical diagnostic setting. In contrast, bulk RNAseq is now routine for both research and clinical applications. Therefore, our analysis extracts small transcription factor-directed co-expression networks (regulons) from single-cell RNA-seq data and uses them to deconvolute immune functional states from bulk RNA-seq data to characterize patient responses. MethodsRegulons were inferred in pre-treatment CD45+ cells from metastatic melanoma samples (n=19) treated with first-line ICI therapy (discovery dataset). A logistic regression-based classifier identified immune cell states associated with response, which were characterized according to differentially active, cell-state specific regulons. The complexity of these regulons was reduced and scored in bulk RNAseq melanoma samples from four independent studies (n=209, validation dataset). Patients were clustered according to their regulon scores, and the associations between cluster assignment, response, and survival were determined. Intercellular communication analysis of cell states was performed, and the resulting effector genes were analyzed by trajectory inference. ResultsRegulons preserved the information of gene expression data and accurately delineated immune cell phenotypes, despite reducing dimensionality by > 100-fold. Four cell states, termed exhausted T cells, monocyte lineage cells, memory T cells, and B cells, were associated with therapeutic responses in the discovery dataset. The cell states were characterized by seven differentially active and specific regulons that showed low specificity in non-immune cells. Four clusters with significantly different response outcomes (P <0.001) were identified in the bulk RNAseq validation cohort. An intercellular link between exhausted T cells and monocyte lineage cells was established, whereby their cell numbers were correlated, and exhausted T cells predicted prognosis as a function of monocyte lineage cell number. Analysis of ligand - receptor expression suggested that monocyte lineage cells drive exhausted T cells into terminal exhaustion through programs that regulate antigen presentation, chronic inflammation, and negative co-stimulation. ConclusionsRegulon-based characterization of cell states provides robust and functionally informative markers that can deconvolve bulk RNA-seq data to identify ICI responders.

immunology↗

bioRxiv2022

Cell morphological profiling enables high-throughput screening for PROteolysis TArgeting Chimera (PROTAC) phenotypic signature

PROTACs (PROteolysis TArgeting Chimeras) use the ubiquitin-proteasome system to degrade a protein of interest for therapeutic benefit. Advances in targeted protein degradation technology have been remarkable with several molecules moving into clinical studies. However, robust routes to assess and better understand the safety risks of PROTACs need to be identified, which is an essential step towards delivering efficacious and safe compounds to patients. In this work, we used Cell Painting, an unbiased high content imaging method, to identify phenotypic signatures of PROTACs. Chemical clustering and model prediction allowed the identification of a mitotoxicity signature that could not be expected by screening the individual PROTAC components. The data highlighted the benefit of unbiased phenotypic methods for identifying toxic signatures and the potential to impact drug design. HighlightsO_LIMorphological profiling detects various PROTACs phenotypic signatures C_LIO_LIPhenotypic signatures can be attributed to diverse biological responses C_LIO_LIChemical clustering from phenotypic signatures separates on drug selection C_LIO_LITrained in-silico machine learning models to predict PROTACs mitochondrial toxicity C_LI

pharmacology and toxicology↗