Author Of 2 Presentations
LB1264 - Endocrine disrupting chemicals affect T cell phenotypes and show a role for environmental factors in MS pathophysiology. (ID 2162)
Autoimmune diseases, including Multiple Sclerosis (MS), are more common in females, suggesting that immune-endocrine mechanisms are central for polarizing the immune response and maintaining tolerance. The accelerated use of chemicals in consumer products has called attention to a subset of additives used during manufacturing of goods and materials, termed endocrine disrupting chemicals (EDCs). These synthetic compounds are structural mimics of endogenous hormones and may therefore drive immune cells to enhanced or different outcomes. In recent decades, the increased application of EDCs across daily-use products has been coincident with an increase in the female to male MS susceptibility ratio. Involvement of T cells in MS is an active area of research, but the potential role of environmental factors in T cell differentiation has not been fully resolved and could inform on the sex-bias of MS disease.
Our study thus sought to determine whether EDCs adversely modulate T cell phenotypes and to identify which immune populations are most at-risk for deregulation by environmental factors.
Peripheral blood mononuclear cells (PBMCs) were isolated from female healthy controls (HC) and MS patients and in vitro exposure to EDCs, bisphenol A (BPA) and di(2-ethylhexyl) phthalate (DEHP), was maintained throughout T cell conditioned differentiation. Negative selection was carried out on remaining PBMCs to isolate CD4 T cells for additional regulatory T cell (Treg) differentiation. Exposure to EDCs was similarly maintained throughout Treg differentiation. On a set-endpoint, cells were harvested and analyzed by flow cytometry using a Treg or multi-parameter T cell panel. Dimensionality reduction clustering and high parameter discovery using t-distributed stochastic neighbor embedding were used to identify EDC induced changes in T cell phenotypes. Data was collected across two batches (n=8 per group) and validated across one batch (n=3 per group). Final results (n=16) will include batch effect normalization.
EDCs altered the phenotype and activation state of T cells, particularly BPA and low dose DEHP (10 nM) which led to increased central memory T cells (TCM). The TCM population was also more activated across EDC treatments, relative to untreated controls. These observations held true of both CD4 and CD8 T cells. For Treg differentiation, we identified only modest effects for BPA only, on total Tregs (Foxp3+CD25+); EDC treatments had no effect on Treg expression of latency-associated peptide (LAP).
Phenotyping of EDC-regulated T cells helps to elucidate population subtypes that could pose a risk for MS progression and severity. Results also emphasize the role of environmental factors in MS pathophysiology and offer an explanation for the sex-bias of MS disease.
P0063 - Development of a Custom Multivariate Proteomic Serum Based Assay for Association with Radiographic and Clinical Endpoints in MS (ID 833)
Multiple Sclerosis (MS) is a complex and heterogeneous disease. Investigating the biological pathways and cell types involved in MS pathophysiology as represented by protein biomarker expression can help inform the development of tools to monitor disease activity, disease progression, identify early evidence of relapse, and monitor treatment response.
To develop a blood based multiplex proteomic assay that associates with clinical and radiographic endpoints in patients with MS. These endpoints include the presence of gadolinium-enhanced (Gd+) lesions, Annualized Relapse Rate (ARR) and clinically defined relapse status (active versus stable).
Serum samples (n=690 in total) from multiple deeply-phenotyped cohorts (ACP, CLIMB and EPIC) were tested in immunoassays for the measurement of 1196 proteins using Proximity Extension Assays (PEA) from OlinkTM and for 215 proteins using xMAPTM immunoassays from Myriad RBM, Inc. (RBM). Associated radiographic and clinical endpoints at the time of the blood draw were correlated with the protein levels. Twenty-one proteins were selected for inclusion in a custom assay based on their performance in univariate and multivariate statistical models, and replication across independent cohorts. Biological pathway modeling and network analysis were performed to ensure comprehensive representation of MS neurophysiology. Area under the curve (AUC) was selected as the key metric for model performance evaluation.
Multivariate statistical ensembles restricted to the expression levels of the biomarkers selected for the custom assay achieved AUC performance of 0.827 for classification of the presence of Gd+ lesions, 0.802 for classification of clinically defined relapse status, and 0.930 for the classification of patients with Low ARR (≤0.2 relapses) vs High ARR (≥1.0 relapses). A multivariate model utilizing shifts in biomarker expression in longitudinally paired samples achieved the highest observed performance of 0.950 for classification of Gd+ lesion presence. In each case, the multivariate models significantly outperformed (p-value <0.05) the AUC of the highest performing univariate biomarker.
Multivariate models restricted to the 21 selected proteins effectively classified several radiographic and clinical endpoints with stronger performance than any single biomarker. A 21-plex custom assay panel is being developed for further investigation and validation using additional cohorts.