Biology subjects

Peterson, L.

Publications and source records attributed to Peterson, L..

3 recordsLinked to original sources

bioRxiv2024

Microbiota-derived inosine programs protective CD8+ T cell responses against influenza in newborns

The immunological defects causing susceptibility to severe viral respiratory infections due to early-life dysbiosis remain ill-defined. Here, we show that influenza virus susceptibility in dysbiotic infant mice is caused by CD8+ T cell hyporesponsiveness and diminished persistence as tissue-resident memory cells. We describe a previously unknown role for nuclear factor interleukin 3 (NFIL3) in repression of memory differentiation of CD8+ T cells in dysbiotic mice involving epigenetic regulation of T cell factor 1 (TCF 1) expression. Pulmonary CD8+ T cells from dysbiotic human infants share these transcriptional signatures and functional phenotypes. Mechanistically, intestinal inosine was reduced in dysbiotic human infants and newborn mice, and inosine replacement reversed epigenetic dysregulation of Tcf7 and increased memory differentiation and responsiveness of pulmonary CD8+ T cells. Our data unveils new developmental layers controlling immune cell activation and identifies microbial metabolites that may be used therapeutically in the future to protect at-risk newborns.

immunology↗

bioRxiv2020

Integration of Mechanistic Immunological Knowledge into a Machine Learning Pipeline Increases Predictive Power

The dense network of interconnected cellular signaling responses quantifiable in peripheral immune cells provide a wealth of actionable immunological insights. While high-throughput single-cell profiling techniques, including polychromatic flow and mass cytometry, have matured to a point that enables detailed immune profiling of patients in numerous clinical settings, limited cohort size together with the high dimensionality of data increases the possibility of false positive discoveries and model overfitting. We introduce a machine learning platform, the immunological Elastic-Net (iEN), which incorporates immunological knowledge directly into the predictive models. Importantly, the algorithm maintains the exploratory nature of the high-dimensional dataset, allowing for the inclusion of immune features with strong predictive power even if not consistent with prior knowledge. In three independent studies our method demonstrates improved predictive power for clinically-relevant outcomes from mass cytometry data generated from whole blood, as well as a large simulated dataset.

bioinformatics↗

bioRxiv2018

Small molecule docking of DNA repair proteins associated with cancer survival following PCNA metagene adjustment: A potential novel class of repair inhibitors

Natural and synthetic small molecules from the NCI Developmental Therapeutics Program (DTP) were employed in molecular dynamics-based docking with DNA repair proteins whose RNA-Seq based expression was associated with overall cancer survival (OS) after adjustment for the PCNA metagene. The compounds employed were required to elicit a sensitive response (vs. resistance) in more than half of the cell lines tested for each cancer. Methodological approaches included peptide sequence alignments and homology modeling for 3D protein structure determination, ligand preparation, docking, toxicity and ADME prediction. Docking was performed for unique lists of DNA repair proteins which predict OS for AML, cancers of the breast, lung, colon, and ovaries, GBM, melanoma, and renal papillary cancer. Results indicate hundreds of drug-like and lead-like ligands with best-pose binding energies less than -6 kcal/mol. Ligand solubility for the top 20 drug-like hits approached lower bounds, while lipophilicity was acceptable. Most ligands were also blood-brain barrier permeable with high intestinal absorption rates. While the majority of ligands lacked positive prediction for Herg channel blockage and Ames carcinogenicity, there was considerable variation for predicted fathead minnow, honey bee, and Tetrahymena pyriformis toxicity. The computational results suggest the potential for new targets and mechanisms of repair inhibition and can be directly employed for in vitro and in vivo confirmatory laboratory experiments to identify new targets of therapy for cancer survival.

bioinformatics↗