Dataset Information

Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells.

ABSTRACT: The transcriptional network and protein regulators that govern T helper 17 (Th17) cell differentiation have been studied extensively using advanced genomic approaches. For a better understanding of these biological processes, we have moved a step forward, from gene- to protein-level characterization of Th17 cells. Mass spectrometry-based label-free quantitative (LFQ) proteomics analysis were made of in vitro differentiated murine Th17 and induced regulatory T (iTreg) cells. More than 4,000 proteins, covering almost all subcellular compartments, were detected. Quantitative comparison of the protein expression profiles resulted in the identification of proteins specifically expressed in the Th17 and iTreg cells. Importantly, our combined analysis of proteome and gene expression data revealed protein expression changes that were not associated with changes at the transcriptional level. Our dataset provides a valuable resource, with new insights into the proteomic characteristics of Th17 and iTreg cells, which may prove useful in developing treatment of autoimmune diseases and developing tumor immunotherapy.

SUBMITTER: Mohammad I

PROVIDER: S-EPMC5979006 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells.

Mohammad Imran I Nousiainen Kari K Bhosale Santosh D SD Starskaia Inna I Moulder Robert R Rokka Anne A Cheng Fang F Mohanasundaram Ponnuswamy P Eriksson John E JE Goodlett David R DR Lähdesmäki Harri H Chen Zhi Z

PLoS biology 20180531 5

The transcriptional network and protein regulators that govern T helper 17 (Th17) cell differentiation have been studied extensively using advanced genomic approaches. For a better understanding of these biological processes, we have moved a step forward, from gene- to protein-level characterization of Th17 cells. Mass spectrometry-based label-free quantitative (LFQ) proteomics analysis were made of in vitro differentiated murine Th17 and induced regulatory T (iTreg) cells. More than 4,000 prote ...[more]

PMID: 29851958

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Project description:The reciprocal differentiation of T helper 17 (T(H)17) cells and induced regulatory T (iT(reg)) cells plays a critical role in both the pathogenesis and resolution of diverse human inflammatory diseases. Although initial studies suggested a stable commitment to either the T(H)17 or the iT(reg) lineage, recent results reveal remarkable plasticity and heterogeneity, reflected in the capacity of differentiated effectors cells to be reprogrammed among T(H)17 and iT(reg) lineages and the intriguing phenomenon that a group of naïve precursor CD4(+) T cells can be programmed into phenotypically diverse populations by the same differentiation signal, transforming growth factor beta. To reconcile these observations, we have built a mathematical model of T(H)17/iT(reg) differentiation that exhibits four different stable steady states, governed by pitchfork bifurcations with certain degrees of broken symmetry. According to the model, a group of precursor cells with some small cell-to-cell variability can differentiate into phenotypically distinct subsets of cells, which exhibit distinct levels of the master transcription-factor regulators for the two T cell lineages. A dynamical control system with these properties is flexible enough to be steered down alternative pathways by polarizing signals, such as interleukin-6 and retinoic acid and it may be used by the immune system to generate functionally distinct effector cells in desired fractions in response to a range of differentiation signals. Additionally, the model suggests a quantitative explanation for the phenotype with high expression levels of both master regulators. This phenotype corresponds to a re-stabilized co-expressing state, appearing at a late stage of differentiation, rather than a bipotent precursor state observed under some other circumstances. Our simulations reconcile most published experimental observations and predict novel differentiation states as well as transitions among different phenotypes that have not yet been observed experimentally.

Dataset Information

Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells.

Publications

Quantitative proteomic characterization and comparison of T helper 17 and induced regulatory T cells.

Similar Datasets

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