Skip to content
Fowler Lab
Fowler Lab

Predicting antibiotic resistance de novo

  • News
  • Research
    • Overview
    • Manifesto
    • Software
    • Reproducibility
    • Publications
  • Members
  • Teaching
  • Contact
    • PhDs
  • Wiki
Fowler Lab
Fowler Lab

Predicting antibiotic resistance de novo

New publication: detecting minor populations important for predicting fluoroquinolone resistance

Philip Fowler, 5th April 20238th December 2023

When predicting if an infection is resistant or susceptible to a specific antibiotic, it is all too easy to think that the infection is homogeneous and, in fact, many bioinformatic variant callers encourage that point of view. Or, at best, you can subvert the format of, say, a variant call file (VCF) by using the functionality designed to report diploidy for reporting (up to) two mixed populations. (What plant geneticists do I have no idea).

Reality is likely messier, especially in a slow-growing persistent infection like tuberculosis and there have been previous studies suggesting that minor populations that are resistant to an antibiotic can come to dominate and should lead to a prediction of resistant.

In this free-to-read paper, Dr Alice Brankin shows how allowing just two or more reads that support one of the two most common resistance-conferring mutations to levofloxacin and moxifloxacin, leads to a significant improvement in the sensitivity of genetics-based resistance prediction with no significant drop in specificity.

This is important because the fluoroquinolones are present in several different drug regimes used to treat tuberculosis and brings their performance into line with other antibiotics (such as rifampicin and isoniazid) for which we believe we have a similar level of understanding of the mechanisms of resistance.

Share this:

  • Click to share on X (Opens in new window) X
  • Click to share on Bluesky (Opens in new window) Bluesky
  • Click to email a link to a friend (Opens in new window) Email
  • Click to share on LinkedIn (Opens in new window) LinkedIn
  • Click to share on Mastodon (Opens in new window) Mastodon

Related

antimicrobial resistance clinical microbiology tuberculosis

Post navigation

Previous post
Next post

Related Posts

antimicrobial resistance

New publication: how quickly can be calculate the effect of a mutation on an antibiotic?

20th November 202020th November 2020

The idea for this paper arose during talking over coffee at the BioExcel Alchemical Free…

Share this:

  • Click to share on X (Opens in new window) X
  • Click to share on Bluesky (Opens in new window) Bluesky
  • Click to email a link to a friend (Opens in new window) Email
  • Click to share on LinkedIn (Opens in new window) LinkedIn
  • Click to share on Mastodon (Opens in new window) Mastodon
Read More
clinical microbiology

Our World in Data

10th January 202410th January 2024

I didn’t know that Our World in Data was also based at the University of…

Share this:

  • Click to share on X (Opens in new window) X
  • Click to share on Bluesky (Opens in new window) Bluesky
  • Click to email a link to a friend (Opens in new window) Email
  • Click to share on LinkedIn (Opens in new window) LinkedIn
  • Click to share on Mastodon (Opens in new window) Mastodon
Read More

New preprint: automatically building a better bedaquiline catalogue

31st January 202531st January 2025

A catalogue recording whether individual mutations confer resistance or not to specified antibiotics is a…

Share this:

  • Click to share on X (Opens in new window) X
  • Click to share on Bluesky (Opens in new window) Bluesky
  • Click to email a link to a friend (Opens in new window) Email
  • Click to share on LinkedIn (Opens in new window) LinkedIn
  • Click to share on Mastodon (Opens in new window) Mastodon
Read More

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

This site uses Akismet to reduce spam. Learn how your comment data is processed.

Privacy & Cookies: This site uses cookies. By continuing to use this website, you agree to their use.
To find out more, including how to control cookies, see here: Cookie Policy
    ©2025 Fowler Lab | WordPress Theme by SuperbThemes