Differences in metabolic flux between non-pathogenic and pathogenic Th17 cells 3357

Abstract Description With the advancement of single cell sequencing technology, researchers have been able to identify and metabolically target a new subset of T-helper 17 (Th17) cells in mice: non-pathogenic (NP Th17) and pathogenic (P Th17) cells. The growing nuances of NP Th17 and P Th17 cells have been influential in understanding autoimmune disorders inflicted with chronic inflammation that is predominantly driven by Th17 cells. However, it is unclear whether these subsets of Th17 cells are present in and can also be metabolically targeted in humans, and in the context of other Th17 inflammatory dominated diseases, like type 2 diabetes (T2D). Previous studies have revealed that CD4+ T cells from T2D patients have decreased fatty acid/beta oxidation. Blocking beta oxidation in healthy T cells induced production of Th17-associated cytokines similar to T2D patients. Our lab has transcriptomically identified the presence and metabolic differences of human NP and P Th17 cells through novel enrichment methods and comparison to in-vitro differentiated Th17 cells. We have been able to validate these metabolic differences through a proteomic, flow cytometry based method called SCENITH that measures metabolic flux and dependencies. SCENITH has revealed metabolic flux differences between in-vitro differentiated NP and P Th17 cells. Identifying the differences in metabolic flux between the two subsets of Th17 cells enables to uncover potential therapeutic targets for treating T2D. Funding Sources NIH DP2AI171121-03S2 Topic Categories Mucosal and Regional Immunology (MUC)

Authors

Institutions

Publication Details

Journal
The Journal of Immunology
Published
2025-11-01
DOI
https://doi.org/10.1093/jimmun/vkaf283.1170
Primary Topic
Diabetes and associated disorders
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Differences in metabolic flux between non-pathogenic and pathogenic Th17 cells 3357

Joshua K. Kim, Dequina Nicholas, Naveena Ujagar
The Journal of Immunology
Diabetes and associated disorders
article

Differences in metabolic flux between non-pathogenic and pathogenic Th17 cells 3357

Joshua K. Kim, Dequina Nicholas, Naveena Ujagar
article en

Abstract

Abstract Description With the advancement of single cell sequencing technology, researchers have been able to identify and metabolically target a new subset of T-helper 17 (Th17) cells in mice: non-pathogenic (NP Th17) and pathogenic (P Th17) cells. The growing nuances of NP Th17 and P Th17 cells have been influential in understanding autoimmune disorders inflicted with chronic inflammation that is predominantly driven by Th17 cells. However, it is unclear whether these subsets of Th17 cells are present in and can also be metabolically targeted in humans, and in the context of other Th17 inflammatory dominated diseases, like type 2 diabetes (T2D). Previous studies have revealed that CD4+ T cells from T2D patients have decreased fatty acid/beta oxidation. Blocking beta oxidation in healthy T cells induced production of Th17-associated cytokines similar to T2D patients. Our lab has transcriptomically identified the presence and metabolic differences of human NP and P Th17 cells through novel enrichment methods and comparison to in-vitro differentiated Th17 cells. We have been able to validate these metabolic differences through a proteomic, flow cytometry based method called SCENITH that measures metabolic flux and dependencies. SCENITH has revealed metabolic flux differences between in-vitro differentiated NP and P Th17 cells. Identifying the differences in metabolic flux between the two subsets of Th17 cells enables to uncover potential therapeutic targets for treating T2D. Funding Sources NIH DP2AI171121-03S2 Topic Categories Mucosal and Regional Immunology (MUC)

The Journal of ImmunologyVol. 214(Supplement_1)
University of California, Irvine (US)
Openalex Percentile: Top 62%
Diabetes and associated disorders
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.