At the beginning of September it was ~10 days.

For more information, see the preprint and the BASS-GWAS GitHub: github.com/dhelekal/BAS...

Thanks to everyone involved in this project; @yhgrad.bsky.social, @sofiablomqvist.bsky.social, Samantha G. Palace; Aditi Mukherjee and @baileybowcutt.bsky.social. /end

After testing fewer than 30 strains, we identified and validated a candidate in parC that conferred cross-resistance in the presence of gyrB D429N. We also found another lineage-specific pathway, highlighting the complexity of this clinically relevant trait. /7

@sofiablomqvist.bsky.social then used BASS-GWAS to probe the genetic basis of previously described and unexplained cross-resistance between Zoliflodacin and Gepotidacin. This experiment required introducing the gyrB D429N variant into different N. gonorrhoeae strains, making it costly. /6

By efficiently integrating information from previous batches with the distribution of genetic variants, we find that BASS-GWAS consistently recovers causal variants in multiple real-world resistance traits with just 10s of isolates tested — a substantial decrease compared to random sampling. /5

To tackle this, we set out to improve the efficiency of bacterial GWAS. BASS-GWAS uses Bayesian Adaptive Experimental Design and sparse whole-genome models to select batches of maximally informative isolates for phenotyping and applies to any measurable trait. /4

Calculations and existing studies show that fixed sampling requires 100s to 1000s of strains to power bacterial GWAS. Experiments at this scale are prohibitive and slow for many traits. High-throughput phenotyping tools do not generalize across traits and organisms. /3

Bacterial GWAS are a powerful tool for discovering the genetics underlying many traits. Past bacterial GWAS mainly used large clinical strain collections; sequencing was the main bottleneck. With large, sequenced strain collections now commonplace, phenotyping is the main limitation. /2

My big hope is that someday we will get community and environmental sampling for ESKAPE pathogens, as I do not see any other way to understand what shapes AMR for those bugs.

Moreover, for many infections, cultures are performed only after treatment failure. The data quality and integrity were one of the big reasons for working on GC as a sort of a 'model' organism. Speaking of GC, I think higher-level azithromycin resistance there falls under the mechanism you propose.

When it comes to resistance data, we often have severely biased datasets. E.G., for ESKAPE pathogens the data usually comes from bloodstream infections, and may be enriched for resistance (or even worse, virulence and resistance). Therefore, we're missing most of the transmission cycle.

On the usage data side, we usually have data aggregated too coarsely, and thus cannot properly assess exposures. Moreover, that data is often very unreliable. The distribution of antibiotic use is also quite 'heavy tailed' pubmed.ncbi.nlm.nih.gov/30560781/.

I very much agree that the mechanism proposed by your model is a major force shaping resistance. I think it's very hard to determine what is shaping resistance (your model, vs directional selection, vs something else) given the exceptionally poor data we usually have.

In most cases, multiple antibiotics are in use, and this is always the case. We therefore usually also have multiple resistant strains/lineages competing, each resistant to different antibiotic classes.

In our work on N. gonorrhoeae, we documented that mutations conferring the same resistance phenotype can have wildly different fitness costs. We saw this both in a phlyodynamic estimate, and an in vitro competition experiment. (From doi.org/10.1038/s415...)

... as well as with different resistance patterns. For example resistance to penicillins can be acquired through a beta-lactamase or mutations in penicillin binding proteins. Resistance to fluoroquinolones can be acquired through different resistance mutations in one of several genes.

Sorry, to clarify i was thinking clonal interference reducing efficiency of selection, and this reduction further amplified by fluctuating pressures. I think clonal interference is quite common in AMR. Often there are multiple strains / lineages competing, both with the same resistance pattern ...

Not to mention, that resistance to different drugs within one class can be hugely variable. E.g. there's a world of difference between carbapenems, third gen. cephalosporins, and second gen. cephalosporins, despite all of these being classed as non-penicillin beta-lactams.

E.g. fluoroquinolone or tetracyline resistant Neisseria Gonorrhoeae in south east asia. With fluctuations in use we would at the very least expect some version of clonal interference in a fluctuating environment on top of mut-sel balance.