KPMP April 2021 Virtual meeting

KPMP April 2021 Virtual meeting

May 10, 2021

The Kidney Precision Medicine Project (KPMP) consortium held its semi-annual consortium virtual meeting April 6th - 8th, 2021. The meeting featured updates on recruitment and enrollment efforts, review of molecular data on participant-derived tissue, and updates digital pathology integration updates. The meeting also featured several virtual breakout sessions, and an introduction of upcoming KPMP atlas tools. A highlight of the meeting was the inclusion of early-stage and mid-career stage investigators. 11 investigators gave lightning talks on how KPMP data could be used to solve their research problems of interest. 8 of the lightning talk presenters are featured below.

Dr. Paul Hoover is a physician-scientist in the Division of Rheumatology at the Brigham and Women's Hospital and Broad Institute where his clinical and research efforts are focused on autoimmune kidney diseases such as lupus nephritis. His research seeks to understand  how immune cells drive kidney injury and repair using data from human patient samples for hypothesis generation, followed by testing causality in murine disease models The topic of his talk was Integrating immune phenotyping data from AMP with KPMP.

Pinaki Sarder, PhD, is currently an associate professor of pathology and anatomical sciences at University at Buffalo, with adjunct appointment in biomedical engineering. Dr. Sarder leads a multi-disciplinary team of researchers focusing on conducting computational image analysis of giga-pixel renal tissue microscopy images, and fusion of the structural data with molecular data to understand better chronic kidney diseases. The topic of his talk was Urinary Proteomics Data Guides AI to Discover new Digital Image Biomarkers for Diabetic Nephropathy Classification.

Dr. Christine Limonte is a Nephrology Clinical Research Fellow at the University of Washington. Her research interests include diabetic kidney disease and precision medicine. The topic of her talk was Deciphering the role of mitochondrial dysfunction in diabetic tubular pathology.

Dana Craw­ford, PhD, is Pro­fes­sor in the Departments of Population and Quantitative Health Sciences (primary) and Genetics and Genome Sciences (secondary) and Associate Director for Population and Diversity Research in the Cleveland Insti­tute for Com­pu­ta­tional Biol­ogy at Case Western Reserve University.  As a genetic epi­demi­ol­o­gist, Dr. Crawford’s broad research inter­ests include apply­ing genetic vari­a­tion data to large-scale epi­demi­o­logic and clin­i­cal cohorts to bet­ter under­stand human genotype-phenotype asso­ci­a­tions with an empha­sis on diverse populations. The topic of her talk was eQTL mapping in KPMP.

Ksenia Sokolova is a Computer Science PhD candidate at Princeton, working in the Olga Troyanskaya lab. Her interests lie in the intersection of machine learning and computational biology. The topic of her talk was Predicting the effect of genetic variants at cell-type resolution from sequence data using deep learning.

Insa Schmidt, MD, MPH is a postdoctoral researcher at the Boston University School of Medicine and Boston Medical Center. Her projects focus on the development of novel biomarkers for kidney disease. The topic of her talk was Plasma Proteomics and KPMP Data.

Fadhl Alakwaa is a computational biologist in the internal medicine department/nephrology division, University of Michigan. Dr Alakwaa main research interest is to improve personalized medicine of patients with kidney disease using developing advanced data integration algorithms and implementing these methods into clinical practice. The topic of his talk was KPMP multi-omics data integration using Multi-Omics Factor Analysis (MOFA) for disease diagnosis and treatment.

Seth Winfree is a post-doctoral researcher at the University of Nebraska Medical Center that works with the KPMP tissue-interrogation site at Indiana University developing and supporting imaging methodology, image-processing and analysis.  His research interests include the use of machine learning to extract novel features for early detection in kidney disease and uncovering novel therapeutic targets in AKI by combining high-throughput omics technologies and mesoscale 3D multiplexed imaging datasets with machine-learning approaches.  The topic of his talk was In situ classification of cells by nuclear morphology-towards identifying early signatures of injury