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Microbial Genomics: Targeted Antimicrobial Therapy and Genome Vaccines 

Microbial Genomics: Targeted Antimicrobial Therapy and Genome Vaccines
Chapter:
Microbial Genomics: Targeted Antimicrobial Therapy and Genome Vaccines
Author(s):

Immaculada Margarit

and Rino Rappuoli

DOI:
10.1093/med/9780199896028.003.0011
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date: 30 October 2020

The first evidence that certain infectious diseases have a genetic predisposition initially came from a study showing an increased risk of mortality in children born to parents who also died from an infection. Since then, defects in genes encoding effectors of the immune response have been associated to an increased susceptibility to certain infections, like those caused by S. aureus and M. tuberculosis. High density DNA arrays capturing human genome wide variation can be used to analyze the association of certain single nucleotide polymorphisms (SNP) with susceptibility to different bacterial and viral diseases. Genome wide association studies have also identified a series of markers associated with the quality of individual responses to disease treatment. Large-scale profiling of the full RNA of peripheral blood mononuclear cells (PBMCs) by microarrays or deep RNA sequencing approaches has been applied to the identification of patterns of gene expression characteristic of certain human infections like tuberculosis, dengue, flu, S. aureus, salmonellosis etc. Data derived from these approaches will assist the diagnosis and prognosis of disease, the design of antiviral and antibiotic therapies, and the development of genetic tests to predict adverse reactions to antimicrobial drugs. In the vaccines field, novel tools of systems biology can be applied to the analysis of human immunological response patterns to vaccines in order to uncover molecular signatures of vaccine efficacy and guide the design and evaluation of new vaccines. This “systems vaccinology” strategy has been applied to examine the initial molecular signatures in individuals vaccinated against Yellow Fever or after administration of the trivalent inactivated influenza virus vaccine. Similar approaches have been used to study immune responses to Brucella melitensis and fungal infections. Integration of increasingly complex high throughput data into descriptive and predictive equations for immune responses to vaccines is expected to drive faster and more accurate ways of screening vaccine candidates for their effectiveness. Like in other genomic fields, the successful clinical application of these novel technologies will depend on the development of translational research approaches capable of dealing with the enormous amount and different types of generated information and with the uncertainty that is typical of common clinical scenarios.

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