MSGID: 1:317/3 6270b04c   
   PID: hpt/lnx 1.9.0-cur 2019-01-08   
   TID: hpt/lnx 1.9.0-cur 2019-01-08   
    Mapping study yields novel insights into DNA-protein connection, paving   
   way for researchers to target new treatments    
      
     Date:   
         May 2, 2022   
     Source:   
         Johns Hopkins University Bloomberg School of Public Health   
     Summary:   
         DNA-to-protein mapping could help researchers understand some   
         health disparities.   
      
      
      
   FULL STORY   
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   A new genetic mapping study led by researchers at the Johns Hopkins   
   Bloomberg School of Public Health traces links between DNA variations and   
   thousands of blood proteins in two large and distinct populations. The   
   results should help researchers better understand the molecular causes   
   of diseases and identify proteins that could be targeted to treat these   
   diseases.   
      
      
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   The study included more than 9,000 Americans of European or African   
   ancestry, and generated maps of DNA-to-protein links for both groups. The   
   study is thought to be the first of its kind to include two large and   
   ancestrally distinct population cohorts. Proteins play a critical role in   
   cellular function, and changes in protein mechanisms -- often regulated   
   by DNA variations -- can lead to disease. DNA-to-protein mapping could   
   help explain differences in the rates of some diseases in the two groups   
   and help researchers understand some health disparities.   
      
   The study appears May 2 in Nature Genetics.   
      
   Researchers have been mapping the molecular roots of human diseases for   
   decades through so-called genetic mapping studies. The best known is the   
   genome-wide association study (GWAS). A GWAS typically links variations   
   in DNA to disease risk by analyzing the DNA of subjects -- often tens   
   or hundreds of thousands of individuals at a time -- along with their   
   history of a given disease. This uncovers statistical associations   
   linking the disease to specific DNA variations.   
      
   Missing from the GWAS picture: Most of the disease-linked DNA   
   variants identified by GWAS analysis do not lie within protein-coding   
   genes. Researchers therefore assumed that many -- even most --   
   disease-linked DNA variants affect proteins indirectly, by regulating one   
   or more steps in the gene-to-protein production process, thereby altering   
   protein levels. Linking diseases directly to proteins, researchers can   
   better understand the roots of disease -- and also identify protein   
   targets for disease prevention and treatments.   
      
   "This relatively new kind of mapping study provides a wealth of   
   information that will allow researchers to test for potential links of   
   proteins on various types of health outcomes--risk of cancers, heart   
   disease, severe COVID -- and help to develop or repurpose therapeutic   
   drugs," says study senior author Nilanjan Chatterjee, PhD, Bloomberg   
   Distinguished Professor in the Department of Biostatistics at the   
   Bloomberg School.   
      
      
      
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   To demonstrate the DNA-protein mapping's application, the researchers   
   used it to identify an existing rheumatoid arthritis drug as a plausible   
   new treatment for the common joint-pain disorder known as gout.   
      
   The study was a collaboration between Chatterjee's team and the research   
   group of Josef Coresh, MD, George W. Comstock Professor in the Bloomberg   
   School's Department of Epidemiology and one of the paper's co-authors,   
   and colleagues at several institutions.   
      
   The analysis covered 7,213 Americans of European ancestry and 1,871   
   African Americans in the long-running Atherosclerosis Risk in Communities   
   (ARIC) study, headed by Coresh; and 467 African Americans from the African   
   American Study of Kidney Disease and Hypertension (AASK). In both of these   
   studies, the research teams had sequenced the genomes of the participants   
   and recorded bloodstream levels of thousands of distinct proteins.   
      
   For their mapping study, Chatterjee's team analyzed the ARIC and AASK   
   genomic data to identify more than two thousand common DNA variations   
   that lie close to the genes encoding many of these proteins and correlate   
   with the proteins' bloodstream levels.   
      
   "The value of knowing about these DNA variants that predict certain   
   protein levels is that we can then examine much larger GWAS datasets to   
   see if those same DNA variants are linked to disease risks," Chatterjee   
   says.   
      
      
      
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   Using a European-American dataset, they found that it predicted several   
   proteins whose levels would influence the risk of gout or bloodstream   
   levels of the gout-related chemical urate. These proteins included the   
   interleukin 1 receptor antagonist (IL1RN) protein, which appears to lower   
   gout risk -- a finding that suggests the existing rheumatoid arthritis   
   drug anakinra, which mimics IL1RN, as a plausible new therapy for gout.   
      
   Having data from both white and Black Americans allowed the researchers   
   to map protein-linked DNA variants more finely than if they had been   
   restricted to one or the other. The African-ancestry models generated   
   in the study will allow future analyses of how different populations'   
   genetic backgrounds might contribute to differences in disease rates.   
      
   "We know that prostate cancer risk, for example, is higher in African   
   American men, so in principle, one could combine prostate cancer GWAS   
   data on African Americans with our protein data to identify proteins   
   that contribute to elevated prostate cancer risk in that population,"   
   Chatterjee says.   
      
   The team has made its datasets and protein prediction models publicly   
   available online so researchers can use the resource. Chatterjee's team   
   and collaborators anticipate doing further studies in the ARIC and AASK   
   cohorts, as well as in other diverse cohorts, to gather information   
   on proteins and other factors that influence the DNA-to-disease chain   
   of causality.   
      
   "Plasma proteome analyses in individuals of European and African ancestry   
   identify cis-pQTLs and models for proteome-wide association studies"   
   was co- authored by first authors Jingning Zhang and Diptavo Dutta,   
   and by Anna Ko"ttgen, Adrienne Tin, Pascal Schlosser, Morgan Grams,   
   Benjamin Harvey, CKDGen Consortium, Bing Yu, Eric Boerwinkle, Josef   
   Coresh, and Nilanjan Chatterjee.   
      
   The analysis of this project was supported by a RO1 grant from the   
   National Human Genome Research Institute at the National Institutes   
   of Health (1 R01 HG010480-01). Additional NIH grants supporting   
   this research include R01 HL134320, R01 AR073178, R01 DK124399, and   
   HL148218. The Atherosclerosis Risk in Communities study has been   
   funded in whole or in part by the National Heart, Lung, and Blood   
   Institute; National Institutes of Health; Department of Health and   
   Human Services (HHSN268201700001I, HHSN268201700002I, HHSN268201700003I,   
   HHSN268201700005I, HHSN268201700004I).   
      
      
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   Story Source: Materials provided by   
   Johns_Hopkins_University_Bloomberg_School_of_Public Health. Note:   
   Content may be edited for style and length.   
      
      
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   Journal Reference:   
      1. Jingning Zhang, Diptavo Dutta, Anna Ko"ttgen, Adrienne Tin, Pascal   
         Schlosser, Morgan E. Grams, Benjamin Harvey, Bing Yu, Eric   
         Boerwinkle, Josef Coresh, Nilanjan Chatterjee. Plasma proteome   
         analyses in individuals of European and African ancestry identify   
         cis-pQTLs and models for proteome-wide association studies. Nature   
         Genetics, 2022; DOI: 10.1038/s41588-022-01051-w   
   ==========================================================================   
      
   Link to news story:   
   https://www.sciencedaily.com/releases/2022/05/220502125402.htm   
      
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