Perspectives on Systems Modeling of Human Peripheral Blood Mononuclear Cells

Abstract

Human peripheral blood mononuclear cells (PBMCs) are the key drivers of the immune responses. These cells undergo activation, proliferation and differentiation into various subsets. During these processes they initiate metabolic reprogramming, which is coordinated by specific gene and protein activities. PBMCs as a model system have been widely used to study metabolic and autoimmune diseases. Herein we review various omics and systems-based approaches such as transcriptomics, epigenomics, proteomics, and metabolomics as applied to PBMCs, particularly T helper subsets, that unveiled disease markers and the underlying mechanisms. We also discuss and emphasize several aspects of T cell metabolic modeling in healthy and disease states using genome-scale metabolic models.

Keywords: PBMCs; genome-scale metabolic models; immune system; metabolomics; multi-omics; pathways; peripheral blood mononuclear cells; systems biology.

Figure 1

(A) General illustration of T cell activation and differentiation. (B) Several omics based approaches applied to samples obtained from disease and healthy individuals (controls). (C) Stratification of individuals based on metabolic phenotype. (D) Identification and validation of biomarkers. (E) Down-stream analysis of omics datasets for identification and enrichments of differential pathways.

Figure 2

(A) It shows disease and healthy individuals (controls) from which PBMCs samples are obtained for omics analysis. (B) Differential omics expression and analysis for contextualization. (C) Reconstruction and contextualization of condition specific genome-scale metabolic models. (D) Reaction components (R) of Genome-Scale metabolic models: S, substrates; E, enzymes; P, products. (E) Stoichiometric matrix (S) of M_n metabolites and R_n reactions, directionality of each metabolites consumed (−1) or produced (+1) or not involved in the reaction (0). (F) Flux-Balance Analysis (FBA) for model simulation, optimization and estimation of flux (v) phenotype at the steady state. (G–I) The panel shows functionalities of genome-scale metabolic models such as regulations of metabolic pathway, metabolic marker identification and identification of differential pathways.