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Sam Brown

@brownlab.bsky.social
327 followers 158 following 7 posts

Evolutionary microbiologist. Bacterial social life, virulence, drug resistance.

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Sam Brown @brownlab.bsky.social · 14/09/2026
Great to learn about the amazing phage defence lit (thanks @ellinoralseth.bsky.social!), def want this to be a positive contrib– many more Qs to ask from evol perspective. Hopefully a useful framework to think about other puzzling phenotypes in microbiology authors.elsevier.com/a/1nllx,L%7E...
A workflow for evaluating defense mechanisms in an evolutionary context: Starting from an initial discovery of a defense mechanism [typically bioinformatic (2) or experimental (3)], we emphasize the importance of explicitly identifying alternative defense and nondefense evolutionary hypotheses (1) and using these hypotheses to drive additional experimental and bioinformatic tests. Arrows highlight iterative integration (4): bioinformatic discoveries (2) can drive experimental predictions (3) by inspiring new testable hypotheses (1). Similarly, experimental data (3) can drive bioinformatic predictions (2), a process mediated by new functional hypotheses (1). Mechanisms can cycle through these steps multiple times as new data accumulate, both within and across multiple studies (4).
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Paul Hoskisson 🧫 🦠🐸 @paulhoskisson.bsky.social · 11/09/2026
Really important from @ellinoralseth.bsky.social & @brownlab.bsky.social - a framework for defense as an evolved adaptation, in a world where ‘defense’ & ‘immune’ are often used to mean anti-infection The bacterial immune system: identifying evolved defense adaptations www.cell.com/trends/micro...
cell.com
The bacterial immune system: identifying evolved defense adaptations
Recent years have witnessed a rapid expansion in bacterial defense mechanisms. Alongside established defenses against molecular parasites, hundreds of novel mechanisms are being described annually, co...
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Ellinor Alseth @ellinoralseth.bsky.social · 11/09/2026
It's finally published! What a journey @brownlab.bsky.social and I have been on with this one (ask me about it over a pint 🍻). Super excited to be able to say that it's finally out in Trends (author gift link to follow)! 🦠🧫 #phagesky
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Iris Irby @irisirby.bsky.social · 21/08/2026
We are excited to share our new preprint, “Canonical pathoadaptive cystic fibrosis genes in Pseudomonas aeruginosa are not CF-specific”! 🦠 Great project with @emehlferber.bsky.social and @brownlab.bsky.social www.biorxiv.org/content/10.6... Thread below 🧵
biorxiv.org
Canonical pathoadaptive cystic fibrosis genes in Pseudomonas aeruginosa are not CF-specific
Research on Pseudomonas aeruginosa adaptation in cystic fibrosis (CF) has historically relied on comparing chronic isolates to laboratory reference strains, or evolving reference strains in environmen...
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Eli Mehlferber @emehlferber.bsky.social · 21/08/2026
Hey everyone! Happy to announce the completion of a longstanding project in partnership with @irisirby.bsky.social and @brownlab.bsky.social that I have been really excited about. www.biorxiv.org/content/10.6...
biorxiv.org
Canonical pathoadaptive cystic fibrosis genes in Pseudomonas aeruginosa are not CF-specific
Research on Pseudomonas aeruginosa adaptation in cystic fibrosis (CF) has historically relied on comparing chronic isolates to laboratory reference strains, or evolving reference strains in environmen...
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Sam Brown @brownlab.bsky.social · 11/06/2026
Thanks @markowenmartin.bsky.social, this was great fun to do, I really enjoyed our chat. Keep up the great work!
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Mark O. Martin @markowenmartin.bsky.social · 04/06/2026
It’s a day late, but worth the wait: on this episode of #MattersMicrobial, Dr. Sam Brown chats with the #QualityQuorum about generalists, specialists, and sociomicrobiology. Please spread the #GoodMicrobialWorld. @univpugetsound @ASMicrobiology @microbe.tv youtu.be/K_gEeazaCQM?...
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Sam Brown @brownlab.bsky.social · 21/05/2026
Congrats @emehlferber.bsky.social and team! This is one of my fave visualizations: summarizing how PA “sees” some environments as more or less similar based on shared genomic signatures. Chronic infections cluster tightly, but are still distinguishable by a smaller set of features.
Force-directed network where each node is an environment category and edges indicate stronger similarity in model-derived genomic signatures (shared directionally consistent features). Chronic lung environments cluster closely; acute infection categories form a looser cluster; non-host environments are more separated.
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Eli Mehlferber @emehlferber.bsky.social · 20/05/2026
Hey all! Happy to finally join everyone here on Bluesky, and also to share the culmination of my longest-running postdoc project, now on bioRxiv: “Apparent generalism in Pseudomonas aeruginosa is underpinned by convergent cryptic specialization.” www.biorxiv.org/content/10.6...
biorxiv.org
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David Sünderhauf @davvi36.bsky.social · 13/05/2026
I also often think "hang on, is this defence system actually there for phage defence?" Great work both!
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Sam Brown @brownlab.bsky.social · 13/05/2026
This was a really fun collaboration and I learnt a ton, including how much more we have to learn about defense mechanisms (or are they attack mechanisms? or alt-hypothesis-here mechanisms?). Such a great arena for interdisc. work. Thanks @ellinoralseth.bsky.social for getting me on board!
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Sheyda Azimi @sazimi.bsky.social · 16/12/2025
Recent work from the lab showing how presence of O-antigen deficient Pseudomonas variants change aggregate assembly and microbiogeography of infection journals.asm.org/doi/10.1128/...
journals.asm.org
Whole-tissue imaging reveals intrastrain diversity shapes the spatial organization of Pseudomonas aeruginosa in a murine infection model | mSphere
Intrastrain genetic and phenotypic diversity within Pseudomonas aeruginosa populations is common in chronic pulmonary infections. While this intrastrain heterogeneity is a hallmark of chronic infection, its consequences for the spatial organization of P. aeruginosa within the airways remain unclear. Here, we demonstrate that the loss of O-specific antigen in a subpopulation of P. aeruginosa significantly alters the spatial architecture of P. aeruginosa, without changing the total population size or composition. Using a combination of tissue clearing and hybridization chain reaction RNA-FISH in a murine lung infection model, we mapped the localization of genetically distinct P. aeruginosa variants in mixed populations in vivo. These findings reveal that genetic diversification within a strain can reshape the infection landscape at the micron scale, highlighting the overlooked role of intrastrain dynamics in shaping the microbiogeography of infections and influencing host-pathogen interactions.
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Center for Microbial Dynamics and Infection @cmdi.bsky.social · 16/12/2025
A bacterial predator, Halobacteriovorax, acts as a living "probiotic" that halts Vibrio-induced disease progression in endangered Caribbean corals. This shows microbial predators are promising new tools for coral disease therapy! #CoralReefs #MicrobialEcology #ISMEJ academic.oup.com/ismej/advanc...
academic.oup.com
Halobacteriovorax halts disease progression in endangered Caribbean corals
Abstract. Predation is a top-down regulator of ecosystem integrity and a key driver of community structure and evolution in plants and animals. Despite our
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Ellinor Alseth @ellinoralseth.bsky.social · 19/11/2025
Very happy to see this piece out in @plosbiology.org, on the bacterial immune systems and microbial communities. It was a great team effort with Rafael Custodio, @brockhurstlab.bsky.social , @brownlab.bsky.social, and Edze Westra! 🦠🧫 #phagesky #mevosky journals.plos.org/plosbiology/...
journals.plos.org
Bacterial immune systems as causes and consequences of microbiome structure
Bacterial immune systems have evolved in response to diverse molecular "parasites", yet their ecological roles remain poorly understood. This Essay explores how interactions between mobile genetic ele...
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PLOS Biology @plosbiology.org · 20/11/2025
The ecological roles of bacterial #immune systems are poorly understood. Rafael Custodio @ellinoralseth.bsky.social @brockhurstlab.bsky.social @brownlab.bsky.social & Edze Westra explore how mobile genetic elements and bacterial defenses shape #microbiome structure and function 🧪
plos.io
Bacterial immune systems as causes and consequences of microbiome structure
Bacterial immune systems have evolved in response to diverse molecular "parasites", yet their ecological roles remain poorly understood. This Essay explores how interactions between mobile genetic…
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Sam Brown @brownlab.bsky.social · 17/09/2025
Thanks Andrew, @jrchandler.bsky.social and @ajaidandekar.bsky.social for great commentary! I totally agree, so much more to do. One exciting prospect is using regulatory wiring knowledge to improve our abilities to predict bug behaviors 'in the wild'. Our fig 7B is encouraging start, IMO.
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PLOS Biology @plosbiology.org · 08/09/2025
Bacterial #QuorumSensing is often assumed to follow a strict hierarchy. @brownlab.bsky.social &co find that #Pseudomonas aeruginosa instead uses a reciprocal, cooperative system that enhances responsiveness to population density & environmental changes @plosbiology.org 🧪 plos.io/3I7DhRG
The las and rhl QS systems have a reciprocal, synergistic, and unequal relationship. Top: Single-signal models demonstrate that the summed effects of single signals (3 oxo C12 HSL alone, red; C4 HSL alone, orange) cannot account for the maximal expression of lasI or rhlI. The upper green, flat surfaces in the plots indicate the maximum mean expression level measured across all combinations of signal concentrations while the lower semi-transparent surfaces mark the sum of single signal effects. The plotted points represent observed expression levels when C4 HSL is withheld (red) and when 3 oxo C12 HSL is withheld (yellow). Lines indicate the model predictions. Bottom: Multi-signal non-linear models capture the synergistic effects of both signals and match observed expression levels. Model estimates are shown as grid lines. Horizontal bars show the mean value of expression observed at each combination of signal concentrations. Lines extend from these mean values to the relevant grid point for clarity. Right: Data show that relationship of the las and rhl systems is reciprocal, and the multi-signal model quantifies the strength of those interactions. In particular, it reveals the contribution of both signals to the maximum fold-change in expression of both synthases. The charts in this panel summarize the contribution of 3 oxo C12 HSL (red), C4 HSL (yellow), and the synergistic combination of both (orange).
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David Sünderhauf @davvi36.bsky.social · 12/05/2025
I *finally* get to share our work on plasmid competition– super proud to now have this on bioRxiv: CRISPR-Cas is beneficial in plasmid competition, but limited by competitor toxin-antitoxin activity when horizontally transferred www.biorxiv.org/content/10.1... 1/6 👇
pKJK5 vs RP4 cartoon sketch on plasmid competition
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Sam Brown @brownlab.bsky.social · 17/01/2025
Hello world! I'm starting out on bluesky with a job posting - we're looking for lab research tech who shares our love of bugs. More info here brownlab.biology.gatech.edu/opportunities/
brownlab.biology.gatech.edu
Opportunities
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