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Abbas, A. R.

Publications and source records attributed to Abbas, A. R..

2 recordsLinked to original sources

Combined antagonism of Oncostatin M (OSM) and Interleukin 6 (IL-6) provides both anti-fibrotic and anti-inflammatory benefit in pulmonary fibrosis

Interstitial lung diseases (ILDs), including idiopathic pulmonary fibrosis (IPF) and systemic sclerosis-associated ILD (SSc-ILD), are irreversible fibrosing diseases with a mean survival time of less than 5 years for IPF. Poorly understood etiology and complex pathogenesis of these diseases have hampered the identification and development of effective therapeutics. Existing treatments can slow progressive fibrosis and lung function decline but do not stop it entirely, resulting in a minimal impact on patient survival. Thus, novel therapeutic interventions are needed. Tocilizumab, an anti-IL-6 receptor antibody, was recently approved by FDA for the treatment of SSc-ILD based on evidence demonstrating a reduction in the rate of lung function decline. In this study, we have characterized an IL-6-driven feed-forward myeloid axis contributing to lung inflammation providing a mechanistic hypothesis for tocilizumab. Concomitantly we have identified an oncostatin M (OSM) orchestrated lung injury response contributing to epithelial and endothelial cell disruption, myofibroblast activation and fibrosis. Despite dysregulated expression of IL11 in IPF, SSc-ILD, and murine models of fibrosis, we found no evidence for a pro-fibrotic role for IL-11 in vitro or in vivo. Instead, in pre-clinical models of IPF we demonstrate that antagonism of OSM alone, or to a greater degree in combination with IL-6, reduced lung fibrosis and inflammation. Translating these observations, we validated gp130:OSMR, rather than gp130:LIFR, as the dominant human receptor complex used by OSM, identifying OSMR as a potential therapeutic target to stall fibrosis.

immunology↗

Generative genomics accurately predicts future experimental results

Realizing AIs promise to accelerate biomedical research requires AI models that are both accurate and sufficiently flexible to capture the diversity of real-life experiments. Here, we describe a generative genomics framework for AI-based experimental prediction that mirrors the process of designing and conducting an experiment in the lab or clinic. We created GEM-1 (Generate Expression Model-1), an AI system that effectively models the enormous range of bulk and single-cell gene expression experiments performed by scientists and benchmarked its performance across multiple biological axes. GEM-1s prediction of future gene expression experiments-RNA-seq data deposited in public archives after our training data cutoff-yielded accuracy comparable to the best-possible performance estimated by comparing the results of matched lab experiments. Overall, our approach illustrates the transformative potential of generative genomics for applications ranging from predicting cellular perturbations in vitro to de novo generation of data from large clinical cohorts.

genomics↗