🧬 Bdには複数の系統がありGPL(global panzootic lineage)が最も毒性が強い。国際取引によるハイブリッド化で新型出現リスク。 #PathogenEvolution

🧬 From Seals to Society: Ancient DNA Is Rewriting TB’s History What if the future of infectious disease prediction lies in ancient genomes? Researchers from Arizona State University highlighted breakthrough findings on the evolutionary history of tuberculosis (TB). At the American Association for the Advancement of Science conference in Phoenix, Anne Stone will present genetic evidence reshaping how we understand TB’s origins in the Americas. 🔍 TB entered the pre-Columbian Americas via multiple zoonotic spillovers from seals. 🌍 After European contact, Eurasian TB strains rapidly replaced earlier strains. 🧬 Ancient DNA enables disease tracking across far longer timescales than modern data alone. Understanding how pathogens emerged and evolved in the past helps anticipate future disease threats and strengthen public health strategies. Ancient DNA isn’t just history — it’s foresight. #AncientDNA #ArizonaStateUniversity #InnoDexis #Paleogenomics #Tuberculosis #InfectiousDisease #Genomics #PublicHealth #GlobalHealth #Epidemiology #DiseaseEmergence #AAAS #ZoonoticDiseases #PathogenEvolution #BiomedicalResearch #ArizonaStateUniversity

The integration of tracking data for migratory birds offers fascinating insights into how pathogenic evolution might coincide with bird migration. Are there specific regions identified as critical hotspots that correlate with known patterns of pathogen spread? Also, it would be interesting to explore how climate change might further influence these patterns. For in-depth biomedical insights into similar topics, check out Sci-Quest, a one-stop platform that can generate comprehensive biomedical reviews: sciqst.com. #MigrationPatterns #PathogenEvolution #Medicine

AlphaFold 3 accurately models natural variants of Helicobacter pylori catalase KatA １．This study rigorously evaluates AlphaFold 3's ability to model natural protein variants using a high-resolution crystal structure of the H. pylori catalase KatA from strain SS1. These variants differ from the canonical 26695 strain at key residues: 234, 237, 255, and 421. ２．The most striking result: AlphaFold 3 accurately predicted both the global architecture and fine structural features of KatASS1, including conservative substitutions and solvent-exposed or interface residues—without access to the variant structure during training. ３．Variant residues modeled by AlphaFold 3 closely matched those resolved in the crystal structure (1.87 Å resolution). For Val234 and Phe255, the predictions reached experimental quality. For the more challenging Glu421 and Tyr237, performance varied by input. ４．Incorrect oligomeric state input (e.g., monomer instead of tetramer) reduced accuracy, particularly at interface-exposed residues like Glu421, which lost native hydrogen bonding seen in the crystal structure. Models with incorrect input can show misleadingly high pLDDT scores. ５．Minor input perturbations like single-residue substitutions or terminal Trp insertions had negligible impact on structure prediction, reinforcing the model's robustness—so long as the correct oligomeric state is used. ６．Among natural KatA variants from 1,931 H. pylori genomes, the four studied positions are common divergence points. Their accurate modeling suggests AlphaFold 3 may be broadly useful in evaluating natural variation in pathogen proteins with experimental backbones. ７．The authors highlight that AlphaFold 3’s accessibility is a double-edged sword—non-expert users may unintentionally degrade prediction quality by providing incorrect inputs. Even confident predictions (e.g., high pLDDT) can be structurally misleading. ８．This case study underscores AlphaFold 3's strong potential for modeling natural variants, especially when paired with known oligomeric context and conservative substitutions—but also highlights the need for caution and biological context in interpretation. ９．The experimental crystal structure of KatASS1, solved here for the first time, provides a new reference for benchmarking modeling of natural variants and has been deposited under PDB ID 9nh3. 📜Paper: biorxiv.org/content/10.1101/… #AlphaFold3 #ProteinStructure #StructuralBiology #HelicobacterPylori #ComputationalBiology #ProteinVariants #PathogenEvolution

AlphaFold 3 accurately models natural variants of Helicobacter pylori catalase KatA １．This study rigorously evaluates AlphaFold 3's ability to model natural protein variants using a high-resolution crystal structure of the H. pylori catalase KatA from strain SS1. These variants differ from the canonical 26695 strain at key residues: 234, 237, 255, and 421. ２．The most striking result: AlphaFold 3 accurately predicted both the global architecture and fine structural features of KatASS1, including conservative substitutions and solvent-exposed or interface residues—without access to the variant structure during training. ３．Variant residues modeled by AlphaFold 3 closely matched those resolved in the crystal structure (1.87 Å resolution). For Val234 and Phe255, the predictions reached experimental quality. For the more challenging Glu421 and Tyr237, performance varied by input. ４．Incorrect oligomeric state input (e.g., monomer instead of tetramer) reduced accuracy, particularly at interface-exposed residues like Glu421, which lost native hydrogen bonding seen in the crystal structure. Models with incorrect input can show misleadingly high pLDDT scores. ５．Minor input perturbations like single-residue substitutions or terminal Trp insertions had negligible impact on structure prediction, reinforcing the model's robustness—so long as the correct oligomeric state is used. ６．Among natural KatA variants from 1,931 H. pylori genomes, the four studied positions are common divergence points. Their accurate modeling suggests AlphaFold 3 may be broadly useful in evaluating natural variation in pathogen proteins with experimental backbones. ７．The authors highlight that AlphaFold 3’s accessibility is a double-edged sword—non-expert users may unintentionally degrade prediction quality by providing incorrect inputs. Even confident predictions (e.g., high pLDDT) can be structurally misleading. ８．This case study underscores AlphaFold 3's strong potential for modeling natural variants, especially when paired with known oligomeric context and conservative substitutions—but also highlights the need for caution and biological context in interpretation. ９．The experimental crystal structure of KatASS1, solved here for the first time, provides a new reference for benchmarking modeling of natural variants and has been deposited under PDB ID 9nh3. 📜Paper: biorxiv.org/content/10.1101/… #AlphaFold3 #ProteinStructure #StructuralBiology #HelicobacterPylori #ComputationalBiology #ProteinVariants #PathogenEvolution

🦠 ETH Zurich is hiring a Postdoctoral Researcher in experimental microbial population biology! Study infectious disease dynamics #Microbiology #InfectiousDiseases #PostdocOpportunity #ETHZurich #ExperimentalBiology #PathogenEvolution jobs.ethz.ch/job/view/10407?…

🚨🚨🚨 Ancient DNA Unlocks Secrets of Relapsing Fever Evolution A groundbreaking study using ancient DNA has revealed how the louse-borne bacteria Borrelia recurrentis, which causes relapsing fever, evolved to become a more effective human pathogen. 🦠 #EvolutionInAction: From Ticks to Lice Around 4,000–6,000 years ago, B. recurrentis split from its tick-borne ancestor. This shift happened as humans transitioned from the Neolithic to the Bronze Age. Adapting to human lice allowed it to spread rapidly in dense, settled populations. 🧬 #GeneticTwists: Bacterial Makeover The pathogen shed genes for tick survival and gained new tools to thrive in lice and dodge the human immune system. By 1,000 years ago, its genome had stabilized—modern strains are nearly identical to ancient ones. 🏛️ #HumansAndDisease: How We Helped It Spread The rise of crowded settlements, animal domestication, and the wool trade created perfect conditions for lice—and for disease. Researchers suggest it may even explain the mysterious "sweating sickness" of medieval Europe. 🔍 #AncientDNA: Clues in Old Teeth Bacterial DNA found in teeth from skeletons 2,300–600 years old offers a window into the evolution of diseases. These insights can help predict how pathogens may adapt to challenges like urbanization and climate change. 💬 #ExpertVoices Scientists stress the co-evolution of humans and pathogens—changes in living conditions, clothing, and hygiene have always influenced the balance. This research reveals the tight link between human history and microbial evolution, providing powerful lessons for how diseases emerge—and how we might stay ahead of them. #PathogenEvolution #AncientGenomes #RelapsingFever #Microbiology #PublicHealth #HumanHistory #EpidemicOrigins #LouseBorneDiseases #Bioarchaeology #DiseaseEcology science.org/doi/10.1126/scie…

#Oligarchy and #BigPharma is RETARDING THE #Evolution of LARGE MAMMAL #ImmuneSystems WHILE ACCELERATING #PathogenEvolution! #LossOfFunction #GainOfProfit #FreeMarket #DrugSupply is IDENTIFYING THOSE SELF MEDICATING due to #SystemFailure! #LibertarianDemocracy #MCQ日本製鉄! :)

Exploring evolution's role in food and medicine. #EvolutionInFood #ScienceVsCreationism #ArtificialSelection #DarwinDiet #PathogenEvolution @renegadescienceteacher

2/ Take a virus’s or bacteria's DNA or RNA. It’s a tiny string of letters. They are aligned against others to spot changes, like how SARS-CoV-2 evolved. That’s phylogenetics. Like mapping a family tree. #PathogenEvolution

No2...Predicting pathogen evolution: using experimental evolution to understand the impact of phage therapy on Clostridioides difficile with @RobFagan tinyurl.com/22vzvb27 #microbialevolution #pathogenevolution #PhDposition

Get to know our newly appointed Assistant Professor of Pathogen Evolution: Barbora Trubenová! Barbora is dedicated to understanding how bacteria, fungi, and parasitic worms develop resistance to drugs. 💊🧬👩🏻‍🔬 usys.ethz.ch/en/news-events/… @BTrubenova #PathogenEvolution

skalo: using SKA split k-mers with coloured de Brujin graphs to genotype indels. #Indels #kmers #MycobacteriumData #PathogenEvolution @biorxivpreprint biorxiv.org/content/10.1101/…

[#phimaward] Meet #ElisabethFournier, expert in #Epidemiology #PathogenEvolution in #rice #agroecosystems #ICPP2023 @INRAE_DPT_SPE

(2/2) We were also encouraged to initiate an experiment where we test the concepts we learn!Excited to see the outcomes! (Pic: Me sitting in a hood after 3 years!) #pathogenevolution #microbiology

Had a fascinating session with Prof. Rahul Roy discussing the research carried out by his group. Combining experimental and modeling approaches, he sheds light on pathogen evolution over decades. Mind-blowing insights! #BiologicalReactions #PathogenEvolution #ICES23

ClubrootOut: Combating the Devastating Effects of Plasmodiophora brassicae on Cruciferous Crops #ClubrootOut #Plasmodiophorabrassicae #CruciferousCrops #CropRotation #Fungicides #PlantBreeding #IntegratedApproach #PathogenEvolution #SustainableAgricultu... vegetables.news/clubrootout-…

#OneHealthMedia 15min @OneHealthTrust podcast episode on Lemurs in Madagascar 'When helping animals means helping people, too' Great work @PlanetMada ! #Conservation #Primates #Cryptosporidiosis #SIV #PathogenEvolution #BioAnthropology #sifaka