Title |
Deep Immune Profiling of an Arginine-Enriched Nutritional Intervention in Patients Undergoing Surgery
|
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Published in |
The Journal of Immunology, September 2017
|
DOI | 10.4049/jimmunol.1700421 |
Pubmed ID | |
Authors |
Nima Aghaeepour, Cindy Kin, Edward A. Ganio, Kent P. Jensen, Dyani K. Gaudilliere, Martha Tingle, Amy Tsai, Hope L. Lancero, Benjamin Choisy, Leslie S. McNeil, Robin Okada, Andrew A. Shelton, Garry P. Nolan, Martin S. Angst, Brice L. Gaudilliere |
Abstract |
Application of high-content immune profiling technologies has enormous potential to advance medicine. Whether these technologies reveal pertinent biology when implemented in interventional clinical trials is an important question. The beneficial effects of preoperative arginine-enriched dietary supplements (AES) are highly context specific, as they reduce infection rates in elective surgery, but possibly increase morbidity in critically ill patients. This study combined single-cell mass cytometry with the multiplex analysis of relevant plasma cytokines to comprehensively profile the immune-modifying effects of this much-debated intervention in patients undergoing surgery. An elastic net algorithm applied to the high-dimensional mass cytometry dataset identified a cross-validated model consisting of 20 interrelated immune features that separated patients assigned to AES from controls. The model revealed wide-ranging effects of AES on innate and adaptive immune compartments. Notably, AES increased STAT1 and STAT3 signaling responses in lymphoid cell subsets after surgery, consistent with enhanced adaptive mechanisms that may protect against postsurgical infection. Unexpectedly, AES also increased ERK and P38 MAPK signaling responses in monocytic myeloid-derived suppressor cells, which was paired with their pronounced expansion. These results provide novel mechanistic arguments as to why AES may exert context-specific beneficial or adverse effects in patients with critical illness. This study lays out an analytical framework to distill high-dimensional datasets gathered in an interventional clinical trial into a fairly simple model that converges with known biology and provides insight into novel and clinically relevant cellular mechanisms. |
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