Amir Mitchell

Our recent paper in npj Antimicrobials and Resistance is a great example of scientific serendipity: after staring at thousands of bacterial growth curves over many studies, we started wondering whether the curve shapes themselves carry mechanistic information 1/9 🦠🧪 www.nature.com/articles/s44...

Btw, note that “inactivation” can mean more than enzymatic degradation and includes any process that reduces effective drug activity over time (chemical modification, sequestration, etc) 8/9

Our recent paper in npj Antimicrobials and Resistance is a great example of scientific serendipity: after staring at thousands of bacterial growth curves over many studies, we started wondering whether the curve shapes themselves carry mechanistic information 1/9 🦠🧪 www.nature.com/articles/s44...

That pushed us to ask whether cellular defenses might impact curve profiles. We cloned different resistance cassettes and measured how they altered the potency-matched growth curves. This strongly hinted that active drug inactivation underlies a long-lag inhibition profile 6/9

Our recent paper in npj Antimicrobials and Resistance is a great example of scientific serendipity: after staring at thousands of bacterial growth curves over many studies, we started wondering whether the curve shapes themselves carry mechanistic information 1/9 🦠🧪 www.nature.com/articles/s44...

Clustering drug by their impact on lag/rate/yield clearly revealed that they vary hugely in how they inhibited growth. In extreme cases, a drug solely affected only a single parameter 4/9

Our recent paper in npj Antimicrobials and Resistance is a great example of scientific serendipity: after staring at thousands of bacterial growth curves over many studies, we started wondering whether the curve shapes themselves carry mechanistic information 1/9 🦠🧪 www.nature.com/articles/s44...

So we assembled a new carefully curated dataset with growth curves across almost forty drugs, measured across multiple sub-inhibitory concentrations. For each curve, we quantified intuitive its key features: lag, growth rate, and yield 2/9

We just published in @molsystbiol.org with the Mugler lab (UPitt) on bacterial population dynamics during tumor colonization (mouse model). Our study was guided by a Luria–Delbrück-style idea: infer mechanism from statistics (1/7) 🧪🦠 doi.org/10.1038/s443...

Beyond providing (to our knowledge) the first dynamical model for tumor colonization, our study matters given the fierce debate on the tumor microbiome. These statistical “fingerprints” may help distinguish genuine colonizers from technical artifacts/contamination (possibly even by microscopy) (6/7)

We just published in @molsystbiol.org with the Mugler lab (UPitt) on bacterial population dynamics during tumor colonization (mouse model). Our study was guided by a Luria–Delbrück-style idea: infer mechanism from statistics (1/7) 🧪🦠 doi.org/10.1038/s443...

When we injected bacteria directly into the tumor (circumventing the bottleneck), we detected thousands of colonizing lineages, yet their sizes were still highly uneven (ruling out early tumor arrivers dominate) (4/7)

We just published in @molsystbiol.org with the Mugler lab (UPitt) on bacterial population dynamics during tumor colonization (mouse model). Our study was guided by a Luria–Delbrück-style idea: infer mechanism from statistics (1/7) 🧪🦠 doi.org/10.1038/s443...

Main takeaways: Post systemic infection, there's a tight colonization bottleneck (per-cell colonization probability ~0.005%). Yet, once colonization happens, growth is remarkably fast (~50 min generation time) and bacterial load in tumors approaches saturation within a day (2/7)

Our second paper on the bacterial toxin colibactin is now out on mBio, this time we critically evaluated the claim that colibactin toxicity necessitates cell-cell contact 🧪🦠🧵(1/9) journals.asm.org/doi/10.1128/...

I love this work since it a wonderful demonstration of (1) Serendipity in research – it started unexpectedly when we saw an active reporter even in non-contacting colonies and (2) it shows the power of simple experiments and quantitative microscopy (8/9)

Our second paper on the bacterial toxin colibactin is now out on mBio, this time we critically evaluated the claim that colibactin toxicity necessitates cell-cell contact 🧪🦠🧵(1/9) journals.asm.org/doi/10.1128/...

Next, we monitored colibactin damage in separated colonies. This setup allowed us not only to validate contact independence, but also to accurately quantify how DNA-damage decays across distance (6/9)

Our second paper on the bacterial toxin colibactin is now out on mBio, this time we critically evaluated the claim that colibactin toxicity necessitates cell-cell contact 🧪🦠🧵(1/9) journals.asm.org/doi/10.1128/...

We cloned a YFP DNA-damage reporter in E. coli and tested how far colibactin induced damage “travels” across a lawn of cells (we tagged secreters and responders with constitutive mCherry and CFP to tell them apart) (4/9)

Our second paper on the bacterial toxin colibactin is now out on mBio, this time we critically evaluated the claim that colibactin toxicity necessitates cell-cell contact 🧪🦠🧵(1/9) journals.asm.org/doi/10.1128/...

Colibactin is deeply studied in human cells due to its involvement in colorectal cancers, yet far less is known in the context of microbial communities. Our first study with @daganlab.bsky.social revealed self-inflicted DNA-damage in colibactin producers (2/9) genome.cshlp.org/content/earl...