Skip to content
Fowler Lab
Fowler Lab

Predicting antibiotic resistance de novo

  • News
  • Publications
  • Members
  • Research
    • Overview
    • Manifesto
    • Software
    • Reproducibility
  • 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:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • 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: Automated detection of bacterial growth on 96-well plates for high-throughput drug susceptibility testing of M. tuberculosis

26th October 2018

In this Microbiology paper we show how a Python package, called the Automated Mycobacterial Detection Growth…

Share this:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • Share on Mastodon (Opens in new window) Mastodon
Read More
antimicrobial resistance

Diagnosing antibiotic resistance: future trends?

23rd April 20175th August 2018

It is Sunday, I’m in Vienna at the European Congress of Clinical Microbiology and Infectious…

Share this:

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

New preprint: processing 3.9 million SARS-CoV-2 samples to make a consistent phylogenetic tree

7th May 20247th May 2024

Martin Hunt, Zam Iqbal and lots of others have written an epic preprint where they…

Share this:

  • Share on X (Opens in new window) X
  • Share on Bluesky (Opens in new window) Bluesky
  • Email a link to a friend (Opens in new window) Email
  • Share on LinkedIn (Opens in new window) LinkedIn
  • 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
    ©2026 Fowler Lab | WordPress Theme by SuperbThemes