Understanding the genetic factors for Vibrio cholerae virulence

  • Funded by Canadian Institutes of Health Research (CIHR)
  • Total publications:0 publications

Grant number: 473856

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Key facts

  • Disease

    Cholera
  • start year

    2022
  • Known Financial Commitments (USD)

    $98,689.66
  • Funder

    Canadian Institutes of Health Research (CIHR)
  • Principal Investigator

    Lypaczewski Patrick
  • Research Location

    Canada
  • Lead Research Institution

    McGill University
  • Research Priority Alignment

    N/A
  • Research Category

    Pathogen: natural history, transmission and diagnostics

  • Research Subcategory

    Pathogen genomics, mutations and adaptations

  • Special Interest Tags

    N/A

  • Study Type

    Non-Clinical

  • Clinical Trial Details

    N/A

  • Broad Policy Alignment

    Pending

  • Age Group

    Not Applicable

  • Vulnerable Population

    Not applicable

  • Occupations of Interest

    Not applicable

Abstract

Cholera is an infectious disease caused by the Vibrio cholerae bacteria affecting up to 4 million individuals per year, often following wars or natural disasters in developing countries. With climate change however, the risk of outbreaks in North America and in Europe keeps increasing. While the infection has the potential to be lethal, it can also present itself entirely without symptoms. What determines the progression of this infection (lethal/non-lethal, symptomatic/asymptomatic) is not yet fully known. Our project aims at identifying the factors that contribute to cholera virulence to then be able to predict the outcome of an infection based on these factors. To do so, we will collect hundreds of samples of Vibrio cholerae bacteria, the causative agent of cholera disease from infected patients and family members within the same household. We will then use deep sequencing to obtain and analyze the genomes of the different bacteria present to identify variations between lethal/non-lethal or symptomatic/asymptomatic infections. Finally, using the results of our analysis, we will use Artificial Intelligence to build a predictive model based on the genetic variations and the outcome of the disease we observed to be able to predict the outcome of the disease from future samples.