Vinod K. Gupta

Background: Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA. Methods: Ultra-high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients, and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups (‘lower’, DAS28-CRP ≤ 3.2; and ‘higher’, DAS28-CRP > 3.2); and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups (‘low-CRP’, CRP ≤ 3.0 mg/L; ‘high-CRP’, CRP > 3.0 mg/L) were investigated. Results: We identified 33 metabolites differentially abundant between lower and higher disease activity groups ( P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP ( P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE]±SD: 1.51±1.77) compared to a GLM without feature selection (MAE±SD: 2.02±2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted vs. actual DAS28-CRP (with feature selection: Spearman’s ρ = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearman’s ρ = 0.18, 95% CI: [-0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with CRP patient groups while controlling for potential confounders ( P < 0.05). Conclusions: We demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients.

The development of a biologically-interpretable and robust metric that provides clear insight into the general health status (i.e. healthy or non-healthy) of one’s gut microbiome remains an important target in human microbiome research. We introduce the Gut Microbiome Health Index (GMHI), a mathematical formula that determines the degree to which a gut microbiome profile reflects good or adverse health. GMHI was formulated based on microbial species specific to healthy gut ecosystems. These species were identified through a multi-study, integrative analysis on 4,347 human stool metagenomes from 34 published studies across healthy and 12 different disease or abnormal bodyweight conditions. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of general health compared to α-diversity indices commonly considered as markers for gut health. Validation of GMHI on 679 samples from 9 additional studies resulted in remarkable reproducibility in distinguishing healthy and non-healthy groups. Our findings suggest that gut taxonomic signatures can indeed serve as robust predictors of general health, and highlight the importance of how data sharing efforts can provide broadly-applicable novel discoveries.

The relationship between Primary Biliary Cholangitis (PBC), a chronic cholestatic autoimmune liver disease, and the peripheral immune system remains to be fully understood. Herein, we performed the first mass cytometry (CyTOF)-based, immunophenotyping analysis of the peripheral immune system in PBC at single-cell resolution. CyTOF was performed on peripheral blood mononuclear cells (PBMCs) from PBC patients (n=33) and age-/sex-matched healthy controls (n=33) to obtain immune cell abundance and marker expression profiles. Hiearchical clustering methods were applied to identify immune cell types and subsets significantly associated with PBC. Subsets of gamma-delta T cells (CD3 ⁺ TCRgd ⁺ ), CD8 ⁺ T cells (CD3 ⁺ CD8 ⁺ CD161 ⁺ PD1 ⁺ ), and memory B cells (CD3 ^- CD19 ⁺ CD20 ⁺ CD24 ⁺ CD27 ⁺ ) were found to have lower abundance in PBC than in control. In contrast, higher abundance of subsets of monocytes and naïve B cells were observed in PBC compared to control. Furthermore, several naïve B cell (CD3 ^- CD19 ⁺ CD20 ⁺ CD24 ^- CD27 ^- ) subsets were significantly higher in PBC patients with cirrhosis (indicative of late-stage disease) than in those without cirrhosis. Alternatively, subsets of CD8 ⁺ CD161 ⁺ T cells and memory B cells were lower in abundance in cirrhotic relative to non-cirrhotic PBC patients. Future immunophenotyping investigations could lead to better understanding of PBC pathogenesis and progression, and also to the discovery of novel biomarkers and treatment strategies.