Molecular Medicine Features Work of McGowan Institute Affiliated Faculty Members

The work of McGowan Institute for Regenerative Medicine affiliated faculty members Ruben Zamora, PhD, Research Associate Professor at the University of Pittsburgh in the Department of Surgery, and Yoram Vodovotz, PhD, Professor in the Department of Surgery with secondary appointments in the Department of Computational & Systems Biology, the Department of Bioengineering, the Department of Immunology, the Department of Communication Science and Disorders (of the School of Health and Rehabilitation Science), and the Clinical and Translational Science Institute, and also the Director of the Center for Inflammation and Regenerative Modeling at the McGowan Institute, is featured in the November 2016 issue of the publication, Molecular Medicine.

Drs. Zamora and Vodovotz’s article is entitled “Data-Driven Modeling for Precision Medicine in Pediatric Acute Liver Failure.” The abstract reads:

Absence of early outcome biomarkers for Pediatric Acute Liver Failure (PALF) hinders medical and liver transplant decisions. We sought to define dynamic interactions among circulating inflammatory mediators to gain insights into PALF outcome sub-groups. Serum samples from 101 participants in the PALF study, collected over the first 7 days following enrollment, were assayed for 27 inflammatory mediators. Outcomes (Spontaneous survivors [S, n=61], Non-survivors [NS, n=12], and liver transplant patients [LTx, n=28]) were assessed at 21 days post-enrollment. Dynamic interrelations among mediators were defined using data-driven algorithms. Dynamic Bayesian Network inference identified a common network motif with HMGB1 as a central node in all patient sub-groups. The networks in S and LTx were similar, and differed from NS. Dynamic Network Analysis suggested similar dynamic connectivity in S and LTx, but a more highly-interconnected network in NS that increased with time. A Dynamic Robustness Index calculated to quantify how inflammatory network connectivity changes as a function of correlation stringency differentiated all three patient sub-groups. Our results suggest that increasing inflammatory network connectivity is associated with non-survival in PALF, and may ultimately lead to better patient outcome stratification.