Title |
Disrupted human–pathogen co-evolution: a model for disease
|
---|---|
Published in |
Frontiers in Genetics, August 2014
|
DOI | 10.3389/fgene.2014.00290 |
Pubmed ID | |
Authors |
Nuri Kodaman, Rafal S. Sobota, Robertino Mera, Barbara G. Schneider, Scott M. Williams |
Abstract |
A major goal in infectious disease research is to identify the human and pathogenic genetic variants that explain differences in microbial pathogenesis. However, neither pathogenic strain nor human genetic variation in isolation has proven adequate to explain the heterogeneity of disease pathology. We suggest that disrupted co-evolution between a pathogen and its human host can explain variation in disease outcomes, and that genome-by-genome interactions should therefore be incorporated into genetic models of disease caused by infectious agents. Genetic epidemiological studies that fail to take both the pathogen and host into account can lead to false and misleading conclusions about disease etiology. We discuss our model in the context of three pathogens, Helicobacter pylori, Mycobacterium tuberculosis and human papillomavirus, and generalize the conditions under which it may be applicable. |
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Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 5 | 22% |
Unknown | 18 | 78% |
Demographic breakdown
Type | Count | As % |
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Scientists | 15 | 65% |
Members of the public | 6 | 26% |
Practitioners (doctors, other healthcare professionals) | 2 | 9% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Canada | 2 | 1% |
Germany | 1 | <1% |
Italy | 1 | <1% |
Colombia | 1 | <1% |
Brazil | 1 | <1% |
Russia | 1 | <1% |
Mexico | 1 | <1% |
Unknown | 123 | 92% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Ph. D. Student | 30 | 22% |
Researcher | 18 | 13% |
Student > Master | 17 | 13% |
Student > Bachelor | 16 | 12% |
Student > Doctoral Student | 11 | 8% |
Other | 20 | 15% |
Unknown | 22 | 16% |
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Biochemistry, Genetics and Molecular Biology | 25 | 19% |
Medicine and Dentistry | 13 | 10% |
Immunology and Microbiology | 10 | 7% |
Social Sciences | 6 | 4% |
Other | 9 | 7% |
Unknown | 27 | 20% |