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Deretic, V. P.

Publications and source records attributed to Deretic, V. P..

2 recordsLinked to original sources

Non-autophagy role of Atg5 and NBR1 in unconventional secretion of IL-12 prevents gut dysbiosis and inflammation

Intestinal myeloid cells play a critical role in balancing intestinal homeostasis and inflammation. Here, we report that expression of the autophagy related 5 (Atg5) protein in myeloid cells prevents dysbiosis and excessive intestinal inflammation by limiting IL-12 production. Mice with a selective genetic deletion of Atg5 in myeloid cells (Atg5{Delta}Mye) showed signs of dysbiosis prior to colitis and exhibited severe intestinal inflammation upon colitis induction that was characterized by increased IFN{gamma} production. This increase in IFN{gamma} was due to excess IL-12 secretion from Atg5-deficient myeloid cells. Atg5 functions to limit IL-12 secretion through modulation of late endosome (LE) acidity. Additionally, the autophagy cargo receptor NBR1, which accumulates in Atg5-deficient cells, played a role by delivering IL-12 to LE. Restoration of the intestinal microbiota and alleviation of intestinal inflammation was achieved by genetic deletion of IL-12 in Atg5{Delta}Mye mice. In summary, Atg5 expression in intestinal myeloid cells acts as an anti-inflammatory brake to regulate IL-12 thus preventing dysbiosis and uncontrolled IFN{gamma}-driven intestinal inflammation.

immunology↗

Autophagy dark genes: Can we find them with machine learning?

Identifying novel genes associated with autophagy (ATG) in man remains an important task for gaining complete understanding on this fundamental physiological process. A machine-learning guided approach can highlight potentially \"missing pieces\" linking core autophagy genes with understudied, \"dark\" genes that can help us gain deeper insight into these processes. In this study, we used a set of 103 (out of 288 genes from the Autophagy Database, ATGdb), based on the presence of ATG-associated terms annotated from 3 secondary sources: GO (gene ontology), KEGG pathway and UniProt keywords, respectively. We regarded these as additional confirmation for their importance in ATG. As negative labels, we used the OMIM list of genes associated with monogenic diseases (after excluding the 288 ATG-associated genes). Data associated with these genes from 17 different public sources were compiled and used to derive a Meta Path/XGBoost (MPxgb) machine learning model trained to distinguish ATG and non-ATG genes (10-fold cross-validated, 100-times randomized models, median AUC = 0.994 +/- 0.0084). Sixteen ATG-relevant variables explain 64% of the total model gain, and 23% of the top 251 predicted genes are annotated in ATGdb. Another 15 genes have potential ATG associations, whereas 193 do not. We suggest that some of these 193 genes may represent \"autophagy dark genes\", and argue that machine learning can be used to guide autophagy research in order to gain a more complete functional and pathway annotation of this complex process.

bioinformatics↗