There are over 250,000 kidney transplant recipients in the United States. Essentially all require ongoing immunosuppression to prevent T cell (Tc) and B cell (Bc) mediated graft rejection. Such medications alter Bc differentiation into PC and memory Bc, impeding Bc and antibody responses to viral pathogens and vaccines, including a risk of severe influenza infection. Understanding Bc differentiation and identifying differences between normal and immunosuppressed patient responses to influenza vaccination using quantitative approaches could significantly improve clinical vaccine composition and methods. This is a significant public health is- sue, as immunosuppressed transplant recipients are recommended to have annual influenza vaccination, even though the response rates may only be 50%. Furthermore, influenza infection carries a substantial morbidity and mortality for this population. Given that current experimental and clinical research methods only permit sampling of peripheral blood immune responses, our proposed approach of (1) novel statistical methods for analyzing vaccine-specific Bc in peripheral blood, combined with (2) in vitro and in vivo branching stochastic process modeling, offers a novel and quantitatively rigorous method of extracting more relevant information from such data. This project is designed to identify peripheral blood Bc subsets associated with vaccine responses healthy volunteers and immunosuppressed kidney transplant recipients. Our primary hypothesis is that circulating Bc populations after vaccination in renal transplant recipients who respond to vaccine and those who do not respond differ in respect to their phenotypes, and their activation, division, differentiation kinetic rates. The overall goal of this project is to test this hypothesis using noel statistical methods and branching process (BP) models of Bc differentiation combined with extensive in vitro and in vivo data collection. The specific goals are: (1) To develop novel statistical methods for the analysis of high dimensional flow cytometery data used to identify Bc populations, (2) To characterize the in vivo and in vitro differentiation of murine Bc after influenza vaccination using these novel statistical methods, (3) to develop branching stochastic process models of in vitro and in vivo murine Bc differentiation, and (4) To identify and study differences in Bc responses to influenza vaccination in healthy and immunosuppressed renal transplant recipients using the novel statistical methods and branching process models. We evaluate how well the statistical methods and the model predict the success or failure of influenza vaccination in healthy volunteers, immunosuppressed vaccine responders and non-responders.

Public Health Relevance

This project proposes to develop a novel statistical methods and mathematical models of immune responses to influenza vaccination in kidney transplant recipients. If successful, it would greatly advance prediction of vaccine responses and vaccine strategies that improve influenza immunity in kidney transplant recipients

National Institute of Health (NIH)
National Institute of Allergy and Infectious Diseases (NIAID)
Research Project (R01)
Project #
Application #
Study Section
Modeling and Analysis of Biological Systems Study Section (MABS)
Program Officer
Gondre-Lewis, Timothy A
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
University of Rochester
Internal Medicine/Medicine
School of Medicine & Dentistry
United States
Zip Code
Li, Dongmei; Xie, Zidian; Zand, Martin et al. (2017) Bon-EV: an improved multiple testing procedure for controlling false discovery rates. BMC Bioinformatics 18:1
Clark, Amelia M; DeDiego, Marta L; Anderson, Christopher S et al. (2017) Antigenicity of the 2015-2016 seasonal H1N1 human influenza virus HA and NA proteins. PLoS One 12:e0188267
Zand, Martin S; Trayhan, Melissa; Farooq, Samir A et al. (2017) Properties of healthcare teaming networks as a function of network construction algorithms. PLoS One 12:e0175876
Yang, Hongmei; Baker, Steven F; González, Mario E et al. (2016) An improved method for estimating antibody titers in microneutralization assay using green fluorescent protein. J Biopharm Stat 26:409-20
Hyrien, Ollivier; Baran, Andrea (2016) Fast Nonparametric Density-Based Clustering of Large Data Sets Using a Stochastic Approximation Mean-Shift Algorithm. J Comput Graph Stat 25:899-916
Sandal, Shaifali; Zand, Martin S (2015) Rational clinical trial design for antibody mediated renal allograft injury. Front Biosci (Landmark Ed) 20:743-62
Wang, Jiong; Hilchey, Shannon P; Hyrien, Ollivier et al. (2015) Multi-Dimensional Measurement of Antibody-Mediated Heterosubtypic Immunity to Influenza. PLoS One 10:e0129858
Wu, Hulin; Miao, Hongyu; Xue, Hongqi et al. (2015) Quantifying Immune Response to Influenza Virus Infection via Multivariate Nonlinear ODE Models with Partially Observed State Variables and Time-Varying Parameters. Stat Biosci 7:147-166
Qiu, Xing; Wu, Shuang; Hilchey, Shannon P et al. (2015) Diversity in Compartmental Dynamics of Gene Regulatory Networks: The Immune Response in Primary Influenza A Infection in Mice. PLoS One 10:e0138110
Hyrien, Ollivier; Yanev, Nikolay M; Jordan, Craig T (2015) A test of homogeneity for age-dependent branching processes with immigration. Electron J Stat 9:898-925

Showing the most recent 10 out of 29 publications