Medical Microbiology and Immunology

MMI University of Toledo 2019-2020 Graduate Research Awardees

Congratulations to Gayatri Subramanian!

Gayatri Subramanian, PhD candidate in the laboratory of Dr. Saurabh Chattopadhyay, Assistant Professor, in the Department of Medical Microbiology and Immunology, was recently awarded the University of Toledo 2019-2020 Graduate Research Award.  This UToledo Graduate Student Association (GSA) Graduate Research Award will provide $2,000 towards Gayatri’s research project that focuses on understanding how herpes simplex virus – 1 (HSV-1), a major human pathogen, hijacks host proteins to its benefit. Current approach to address this question is by using cellular and molecular techniques, such as genetic and pharmacological inhibition of the host protein, which HSV-1 depends on. The target protein was identified in the process of understanding the antiviral mechanisms of a novel interferon stimulated gene (ISG), Tudor domain containing protein 7 (Subramanian et al, PLoS Pathogens, 2018). The GSA award will help investigate the molecular mechanisms of this novel host-virus interaction.

Congratulations to Brenden Tully!

Brenden Tully, MSBS student in Dr. Jason Huntley’s lab in the Department of Medical Microbiology and Immunology, recently was awarded the University of Toledo 2019-2020 Graduate Research Award.  This UToledo Graduate Student Association (GSA) Graduate Research Award will provide $2,000 towards Mr. Tully’s research project to study how the highly-infectious and deadly bacterium, Francisella tularensis – the causative agent of tularemia, infects different U.S. tick species and identify which tick species poses the greatest risk to human health.  Climate change has led to significant expansion of the geographic ranges of different tick species in the U.S.  Ticks are known to harbor and transmit dozens of infectious diseases to humans, including Lyme disease, Rocky Mountain Spotted Fever, and tularemia.  Two ticks, the American dog tick and the Lone Star tick, are believed to be the primary tick vectors for tularemia but actual data is severely lacking.  Mr. Tully has developed a mouse-tick-F. tularensis infection model to naturally infect both American dog ticks and Lone Star ticks ticks with F. tularensis, examine bacterial replication in each tick, and test transmission efficiency of infected ticks to naïve mice.

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MMI researcher collaborates with others from across UToledo on $830,000 R01 grant

Pictured from left, Michael Sloma (BS student in Computer Science & Engineering), Dr. Stanislaw Stepkowski (Professor, Department of Medical Microbiology & Immunology), Dr. Kevin Xu (Assistant Professor, Electrical Engineering & Computer Science Department), Dr. Dulat Bekbolsynov (Post-doctoral Fellow, Department of Medical Microbiology & Immunology), Mohammadreza Nemati (MS student in Computer Science & Engineering), Dr. Tian Chen (Assistant professor, Department of Mathematics & Statistics), and Zhipeng Huang (PhD student in Computer Science & Engineering)

Dr. Stanislaw Stepkowski, professor in the Department of Medical Microbiology and Immunology, and his colleagues from across UToledo, Dr. Kevin Xu in Electrical Engineering and Dr. Tian Chen in Mathematics, have been awarded an $830,000 (direct and indirect cost) 3-year RO1 grant from a joint NSF/NIH program, entitled “Machine learning and network science approaches for prediction of kidney transplant survival.”

Chronic kidney diseases affect about 10% of adults in the United States, often leading to kidney failure that is called end-stage renal disease (ESRD). For patients with ESRD, kidney transplantation is the best treatment compared to dialysis in terms of patient survival, quality of life, and cost. Unfortunately, most patients with ESRD remain on dialysis primarily because of insufficient number of donors. This shortage of kidneys is further complicated by the fact that the average kidney graft survival is only 10 years for grafts from deceased donors and only 12 years from living donors. Patients who lose their kidney transplants are listed again on the waiting list.

The human leukocyte antigens (HLAs) of the organ donor and recipient are known to be a significant contributing factor to graft survival times due to HLA immunogenicity. Seminal work documented that kidney transplant survival is the best without mismatched HLA-A/B/DR antigens and the worst with all six mismatched HLA-A/B/DR antigens (three from father and three from mother). However, preliminary analysis showed that the number of mismatches is as important as the quality of mismatches.

Consequently, their research plan is to use the power of machine learning in predicting the impact of each of the unique mismatches on kidney allograft survival. Some of these unique donor/recipient HLA mismatches are associated with short graft survival times whereas other mismatches are associated with long graft survival times. The intention is to use machine learning capabilities to predict the potential for extended graft survivals for multiple mismatches, based on similarities. Overall approach is to design and integrate new algorithms ─ based on past graft survival data analyses ─ to predict the impact of each unique HLA disparity for every possible donor/recipient combination. Accurate prediction of graft survival times can improve patient transplant outcomes by enabling more efficient allocation of donors and recipients, particularly by reducing the number of re-transplants due to graft failure caused by a poorly matched donor.

 

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Briana Zellner, Ph.D. and Jin Chen, Ph.D.

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Jin Chen, Ph.D.

December 4, 2019
Congratulations!
Jin Chen successfully defended her dissertation titled: "Role of Complement Regulatory Protein Properdin in Hemolytic Anemias Caused by Complement Dysregulation".
Advisor: Viviana Ferreira, D.V.M., Ph.D.

Future Plans: Jin plans to move on to postdoctoral studies at the Cleveland Clinic

2018

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