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Erwin P Bottinger, MD
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About Me
Language
Position
Research Topics
Apoptosis/Cell Death, Bioinformatics, Cell Biology, Diabetes, Fibrosis, Genomics, Growth Factors and Receptors, Kidney, Knockout Mice, Signal Transduction
About Me
Language
Position
Research Topics
Apoptosis/Cell Death, Bioinformatics, Cell Biology, Diabetes, Fibrosis, Genomics, Growth Factors and Receptors, Kidney, Knockout Mice, Signal Transduction
About Me
Language
Position
Research Topics
Apoptosis/Cell Death, Bioinformatics, Cell Biology, Diabetes, Fibrosis, Genomics, Growth Factors and Receptors, Kidney, Knockout Mice, Signal Transduction
Education
, Nuremberg General Hospital, University of ErIangen-Nuremberg
, Cabrini Medical Center
, Massachusetts General Hospital and Harvard Medical School
, National Cancer Institute, National Institutes of Health
MD, Friedrich-Alexander Universitat School of Medicine, Erlangen-Nurember
Research
Millions of Americans are affected with chronic diabetic and non-diabetic kidney diseases that cause kidney failure (end stage renal disease). When kidneys fail, the average life expectancy is just over two years and survival depends on costly and disabling dialysis or transplantation treatments.
State-of-the-art genomics and bioinformatics approaches are used to discover and characterize new molecular targets and pathways associated with apoptosis, transdifferentiation, and fibrogenesis in specialized renal cells exposed to diabetic and other stresses. TGF-beta signaling pathways and targets are a major theme because TGF-beta is a key mediator of these processes.
Model systems used include renal cell culture and mouse models of diabetic and non-diabetic progressive renal disease. A new genomic medicine program aims at identification and validation of molecular biomarkers that predict progressive kidney disease in humans.
Locations
Publications
Recent Artifacts
- Prescriber Adoption of SLCO1B1 Genotype-Guided Simvastatin Clinical Decision Support in a Clinical Pharmacogenetics Program
- Differential and shared genetic effects on kidney function between diabetic and non-diabetic individuals
- Predicting hypertension onset from longitudinal electronic health records with deep learning
- Quantifying the phenome-wide disease burden of obesity using electronic health records and genomics
- StudyMe: a new mobile app for user-centric N-of-1 trials
- Genetic loci and prioritization of genes for kidney function decline derived from a meta-analysis of 62 longitudinal genome-wide association studies
- Insights From a Large-Scale Whole-Genome Sequencing Study of Systolic Blood Pressure, Diastolic Blood Pressure, and Hypertension
- Evaluation of a machine learning approach utilizing wearable data for prediction of SARS-CoV-2 infection in healthcare workers
- Inducing and Recording Acute Stress Responses on a Large Scale With the Digital Stress Test (DST): Development and Evaluation Study
- StudyU: A Platform for Designing and Conducting Innovative Digital N-of-1 Trials