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Mouse T cells express a neurotransmitter-receptor signature that is quantitatively modulated in a subset- and activation-dependent manner.

ABSTRACT: Neurotransmitters are known to modulate the course of an immune response by targeting cells in both the innate and adaptive immune systems. Increasing evidence suggests that T cells, by expressing specific neurotransmitter receptors (NR) are directly regulated by them, leading to altered activation and skewed differentiation of the adaptive immune response. Given that gene expression in T cells changes in lineage- and activation-dependent fashion, it is expected that sensitivity to neurotransmitters may also vary along these lines. Here we generate an important resource for further analysis of this tier of immunoregulation, by identifying the distinct profile of NR transcripts that are expressed by peripheral T cells in mice, at different states of activation and differentiation. We find that only about 15% of the total annotated NR genes are transcribed in these T cells and most of them do not change in different subsets of T cells (CD8, CD4 - Naïve vs Memory vs Treg), or even when T cells migrate to different tissues. We suggest that the T cell-expressed NRs, found across all these subsets identifies a core, constitutive NR signature for the T cell lineage. In contrast, a very limited number (<2) of NRs were observed to mark each of the post-activation T cell states, suggesting that very specific neurotransmitter signals are available to modulate T cell responses in vivo in these subsets.

SUBMITTER: Rosenberg KM

PROVIDER: S-EPMC6660347 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Mouse T cells express a neurotransmitter-receptor signature that is quantitatively modulated in a subset- and activation-dependent manner.

Rosenberg Kenneth M KM Singh Nevil J NJ

Brain, behavior, and immunity 20190403

Neurotransmitters are known to modulate the course of an immune response by targeting cells in both the innate and adaptive immune systems. Increasing evidence suggests that T cells, by expressing specific neurotransmitter receptors (NR) are directly regulated by them, leading to altered activation and skewed differentiation of the adaptive immune response. Given that gene expression in T cells changes in lineage- and activation-dependent fashion, it is expected that sensitivity to neurotransmit ...[more]

PMID: 30953766

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Project description:BackgroundThe ability to reliably identify the state (activated, repressed, or latent) of any molecular process in the tumor of a patient from an individual whole-genome gene expression profile obtained from microarray or RNA sequencing (RNA-seq) promises important clinical utility. Unfortunately, all previous bioinformatics tools are only applicable in large and diverse panels of patients, or are limited to a single specific pathway/process (e.g. proliferation).MethodsUsing a panel of 4510 whole-genome gene expression profiles from 10 different studies we built and selected models predicting the activation status of a compendium of 1733 different biological processes. Using a second independent validation dataset of 742 patients we validated the final list of 1773 models to be included in a de novo tool entitled absolute inference of patient signatures (AIPS). We also evaluated the prognostic significance of the 1773 individual models to predict outcome in all and in specific breast cancer subtypes.ResultsWe described the development of the de novo tool entitled AIPS that can identify the activation status of a panel of 1733 different biological processes from an individual breast cancer microarray or RNA-seq profile without recourse to a broad cohort of patients. We demonstrated that AIPS is stable compared to previous tools, as the inferred pathway state is not affected by the composition of a dataset. We also showed that pathway states inferred by AIPS are in agreement with previous tools but use far fewer genes. We determined that several AIPS-defined pathways are prognostic across and within molecularly and clinically define subtypes (two-sided log-rank test false discovery rate (FDR) <5%). Interestingly, 74.5% (1291/1733) of the models are able to distinguish patients with luminal A cancer from those with luminal B cancer (Fisher's exact test FDR <5%).ConclusionAIPS represents the first tool that would allow an individual breast cancer patient to obtain a thorough knowledge of the molecular processes active in their tumor from only one individual gene expression (N-of-1) profile.

Dataset Information

Mouse T cells express a neurotransmitter-receptor signature that is quantitatively modulated in a subset- and activation-dependent manner.

Publications

Mouse T cells express a neurotransmitter-receptor signature that is quantitatively modulated in a subset- and activation-dependent manner.

Similar Datasets

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