Reposts appreciated! What if standard lab mice miss the real microbial game? Honored to be featured on the May cover @cp-trendsmicrobiol.bsky.social: „The microbial companion.“ Our paper “Game of Microbes: A Wildling’s Guide” is out now: dlvr.it/TRl1Zs Thread below with more
@cellpress.bsky.social
🚨New preprint out ‼️
Wondering which tool to use for ARG annotation? … 👀
Well, our work shows how different tools lead to different biological conclusions with large disagreements between them!
Great collaboration with Juan Inda-Díaz and @bigdatabiology.bsky.social as part of SEARCHER
Details👇
I had the opportunity to present today my work at #SETACMaastricht
My research shows how herbicides can affect not only individual organisms but also microbial interactions, with implications for aquatic ecosystem functioning.
Thanks to the organisers and everyone who attended!
So excited to be presenting at @setac.bsky.social Europe 36th Annual Meeting!
I’ll talk about how metolachlor affects a cyanobacterium–chytrid system, & what this reveals about how environmental stressors reshape microbial interactions in aquatic ecosystems
Hope to see u there!
#SETACMaastricht
This is an incredibly relevant preprint. Your favourite pipeline and/or AMR gene database cannot be considered authoritative, but neither can others.
More details to follow later, but this has been in the works for almost 6 years, so it is great to finally see it in preprint form:
"The elusive resistome: a global comparison reveals large discrepancies among detection pipelines"
www.biorxiv.org/content/10.6...
Can we reconstruct ancient pathogen genomes from animal remains? Can paleopathology help prioritize promising specimens? Our new paper demonstrates this by screening more than 300 specimens from across Eurasia over the past 6,000 years. www.nature.com/articles/s41...
Das Meer ist unser Spiegel – auch bei Antibiotikaresistenzen!
Zum Europäischen Tag der Meere lenken wir den Blick unter die Oberfläche.
Die Forschung @schauflerlab.org zeigt: #AMR sind kein isoliertes Problem im Menschen. Sie kommen in Tieren, Boden und Gewässern vor. AMR betrifft das gesamte System
❗Achtung: geänderte Frist ❗
📣 Wir suchen Unterstützung!
Du hast Erfahrung als Laborassistent_in für Biologie und Chemie? Dann komm zu uns ans Helmholtz-Institut für One Health (HIOH) in Greifswald!
Wir suchen eine technische Assistenz (m/w/d). Weitere Infos unter: www.helmholtz-hioh.de/de/jobs/
We often use dimensional reduction to simplify data, but what if geography is the point?
In our @cp-trendsmicrobiol.bsky.social paper, we explore 3D spatial organization, covering latest methods, findings, and the big questions this frontier opens up. (1/3)
www.sciencedirect.com/science/arti...
Stephan P. Rosshart
www.biorxiv.org
Víctor Hugo Jarquín-Díaz
Erika Martínez
Authors investigate ancient DNA from animal remains and identify multiple signatures of ancient zoonotic pathogens. They find ancient pathogen genomics from archaeological animal remains may inform zo...
www.nature.com
Hier finden Sie unsere aktuellen Stellenangebote und gewinnen einen Eindruck über das Arbeiten am HIOH oder das Leben in Greifswald.
Understanding microbial communities requires moving beyond 2D representations toward a holistic view that couples 3D spatial organization with ecologi…
How well do ARG detection pipelines agree when applied to the same data? Spoiler: not very well.
In our new preprint, we ran 10 pipelines on 270M microbial unigenes from GMGCv1. The same data can support conflicting biological conclusions! 🧵
www.biorxiv.org/content/10.6...
How well do ARG detection pipelines agree when applied to the same data? Spoiler: not very well.
In our new preprint, we ran 10 pipelines on 270M microbial unigenes from GMGCv1. The same data can support conflicting biological conclusions! 🧵
www.biorxiv.org/content/10.6...
Identifying antibiotic resistance genes (ARGs) from metagenomic data is critical for studying antimicrobial resistance across microbial communities and pathogens. However, there is no standardized methodology for ARG annotation. Here, we compare ten commonly used ARG detection pipelines by analysing over 270 million prokaryotic genes from the Global Microbial Gene Catalogue across 13 distinct habitats. We observed up to a 45-fold difference in the number of reported ARGs, with a mean Jaccard index of only 16% between pipelines. Pipeline selection profoundly impacted downstream biological interpretations, with drastic changes to estimates of ARG relative abundance and richness, to the characterization of pan- and core-resistomes, and to the class-level composition of the inferred resistome. ARG detection pipelines make different, defensible trade-offs, and no single approach should be treated as authoritative. Therefore, users should justify and communicate choices carefully, as our analyses show that, taken uncritically, the same data can support conflicting biological and ecological interpretations. ### Competing Interest Statement The authors have declared no competing interest. National Health and Medical Research Council of Australia (NHMRC), 2031902 Australian Research Council (ARC), FT230100724 International Development Research Centre (IDRC), 109304-001 Deutsche Forschungsgemeinschaft (DFG), FO1279/6-1 Bundesministerium für Bildung und Forschung (BMBF), F01KI1909A, 01KI2404B Swedish Research Council (VR), 2024-06123, 2019-00299, 2023-01721 Knut and Alice Wallenberg Foundation, KAW 2020.0239 Swedish Foundation for Strategic Research, FFL21-0174
Identifying antibiotic resistance genes (ARGs) from metagenomic data is critical for studying antimicrobial resistance across microbial communities and pathogens. However, there is no standardized methodology for ARG annotation. Here, we compare ten commonly used ARG detection pipelines by analysing over 270 million prokaryotic genes from the Global Microbial Gene Catalogue across 13 distinct habitats. We observed up to a 45-fold difference in the number of reported ARGs, with a mean Jaccard index of only 16% between pipelines. Pipeline selection profoundly impacted downstream biological interpretations, with drastic changes to estimates of ARG relative abundance and richness, to the characterization of pan- and core-resistomes, and to the class-level composition of the inferred resistome. ARG detection pipelines make different, defensible trade-offs, and no single approach should be treated as authoritative. Therefore, users should justify and communicate choices carefully, as our analyses show that, taken uncritically, the same data can support conflicting biological and ecological interpretations. ### Competing Interest Statement The authors have declared no competing interest. National Health and Medical Research Council of Australia (NHMRC), 2031902 Australian Research Council (ARC), FT230100724 International Development Research Centre (IDRC), 109304-001 Deutsche Forschungsgemeinschaft (DFG), FO1279/6-1 Bundesministerium für Bildung und Forschung (BMBF), F01KI1909A, 01KI2404B Swedish Research Council (VR), 2024-06123, 2019-00299, 2023-01721 Knut and Alice Wallenberg Foundation, KAW 2020.0239 Swedish Foundation for Strategic Research, FFL21-0174
