in

Detection of human pathogenic bacteria in rectal DNA samples from Zalophus californianus in the Gulf of California, Mexico

  • Daszak, P., Cunningham, A. A. & Hyatt, A. D. Anthropogenic environmental change and the emergence of infectious diseases in wildlife. Acta Trop. 78, 103–116 (2001).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Jones, K. E. et al. Global trends in emerging infectious diseases. Nature 451, 990–993 (2008).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Wu, T. et al. Economic growth, urbanization, globalization, and the risks of emerging infectious diseases in China: A review. Ambio 46, 18–29 (2017).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Wolfe, N. D., Dunavan, C. P. & Diamond, J. Origins of major human infectious diseases. Nature 447, 279–283 (2007).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Morens, D. M., Folkers, G. K. & Fauci, A. S. Emerging infections: A perpetual challenge. Lancet Infect. Dis. 8, 710–719 (2008).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Cunningham, A. A. A walk on the wild side—emerging wildlife diseases. BMJ 331, 1214–1215 (2005).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Lloyd-Smith, J. O. et al. Epidemic dynamics at the interface, humal.-animal. Science 326, 1362–1368 (2009).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Wu, Z. et al. Comparative analysis of rodent and small mammal viromes to better understand the wildlife origin of emerging infectious diseases. Microbiome 6, 1–14 (2018).

    Article 

    Google Scholar 

  • Sczyrba, A. et al. Critical assessment of metagenome interpretation: A benchmark of metagenomics software. Nat. Methods 14, 1063–1071 (2017).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Álvarez-Romero, J. G., Pressey, R. L., Ban, N. C., Torre-Cosío, J. & Aburto-Oropeza, O. Marine conservation planning in practice: Lessons learned from the gulf of California. Aquat. Conserv. Mar. Freshw. Ecosyst. 23, 483–505 (2013).

    Article 

    Google Scholar 

  • Hazen, E. L. et al. Marine top predators as climate and ecosystem sentinels. Front. Ecol. Environ. 17, 565–574 (2019).

    Article 

    Google Scholar 

  • Sergio, F. et al. Top predators as conservation tools: Ecological rationale, assumptions, and efficacy. Annu. Rev. Ecol. Evol. Syst. 39, 1–19 (2008).

    Article 

    Google Scholar 

  • Deepak, D. et al. Pinniped zoonoses: A review. Int. J. Livest. Res. 9, 1 (2019).

    Article 

    Google Scholar 

  • Hermosilla, C. et al. Gastrointestinal parasites and bacteria in free-living South American sea lions (Otaria flavescens) in Chilean Comau Fjord and new host record of a Diphyllobothrium scoticum-like cestode. Front. Mar. Sci. 5, 1–13 (2018).

    Article 

    Google Scholar 

  • Oxley, A. P. A., Powell, M. & McKay, D. B. Species of the family Helicobacteraceae detected in an Australian sea lion (Neophoca cinerea) with chronic gastritis. J. Clin. Microbiol. 42, 3505–3512 (2004).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Waltzek, T. B., Cortés-Hinojosa, G., Wellehan, J. F. X. & Gray, G. C. Marine mammal zoonoses: A review of disease manifestations. Zoonoses Public Health 59, 521–535 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Dans, S. L., Crespo, E. A. & Coscarella, M. A. Wildlife tourism: Underwater behavioral responses of South American sea lions to swimmers. Appl. Anim. Behav. Sci. 188, 91–96 (2017).

    Article 

    Google Scholar 

  • Creer, S. et al. The ecologist’s field guide to sequence-based identification of biodiversity. Methods Ecol. Evol. 7, 1008–1018 (2016).

    Article 

    Google Scholar 

  • Fuks, G. et al. Combining 16S rRNA gene variable regions enables high-resolution microbial community profiling. Microbime 6, 1–13 (2018).

    Article 

    Google Scholar 

  • Barb, J. J. et al. Development of an analysis pipeline characterizing multiple hypervariable regions of 16S rRNA using mock samples. PLoS ONE 11, e0148047 (2016).

    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 

  • Vargas-Albores, F. et al. Bacterial biota of shrimp intestine is significantly modified by the use of a probiotic mixture: A high throughput sequencing approach. Helgol. Mar. Res. 71, 1–10 (2017).

    Article 

    Google Scholar 

  • Brooks, J. P. et al. The truth about metagenomics: Quantifying and counteracting bias in 16S rRNA studies Ecological and evolutionary microbiology. BMC Microbiol. 15, 1–14 (2015).

    Article 

    Google Scholar 

  • Ramirez-delgado, D. et al. Multi-locus evaluation of gastrointestinal bacterial communities from Zalophus californianus pups in the Gulf of California, México. PeerJ https://doi.org/10.7717/peerj.13235 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chakravorty, S., Helb, D., Burday, M. & Connell, N. A detailed analysis of 16S ribosomal RNA gene segments for the diagnosis of pathogenic bacteria. J. Microbiol. Methods 69, 330–339 (2007).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: Linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinform. 11, 538 (2010).

    Article 

    Google Scholar 

  • Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).

    ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Sperling, J. L. et al. Comparison of bacterial 16S rRNA variable regions for microbiome surveys of ticks. Ticks Tick. Borne. Dis. 8, 453–461 (2017).

    PubMed 
    Article 

    Google Scholar 

  • Gold, Z. et al. Improving metabarcoding taxonomic assignment: A case study of fishes in a large marine ecosystem. Mol. Ecol. Resour. 21, 2546–2564 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Alnajar, S. & Gupta, R. S. Phylogenomics and comparative genomic studies delineate six main clades within the family Enterobacteriaceae and support the reclassification of several polyphyletic members of the family. Infect. Genet. Evol. 54, 108–127 (2017).

    PubMed 
    Article 

    Google Scholar 

  • Jiang, L. et al. Jejubacter calystegiae gen. nov., sp. nov., moderately halophilic, a new member of the family Enterobacteriaceae, isolated from beach morning glory. J. Microbiol. 58, 357–366 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Janda, J. M. & Abbott, S. L. The changing face of the family enterobacteriaceae (Order: Enterobacterales): New members, taxonomic issues, geographic expansion, and new diseases and disease syndromes. Clin. Microbiol. Rev. 34, 1–45 (2021).

    Article 

    Google Scholar 

  • Shi, R. et al. Pathogenicity of Shigella in chickens. PLoS ONE 9, 1–7 (2014).

    Google Scholar 

  • Roy, B., Tousif Ahamed, S. K., Bandyopadhyay, B. & Giri, N. Development of quinolone resistance and prevalence of different virulence genes among Shigella flexneri and Shigella dysenteriae in environmental water samples. Lett. Appl. Microbiol. 71, 86–93 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Clarkson, K. A. et al. Immune response characterization in a human challenge study with a Shigella flexneri 2a bioconjugate vaccine. EBioMedicine 66, 103308 (2021).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Khalil, I. A. et al. Morbidity and mortality due to shigella and enterotoxigenic Escherichia coli diarrhoea: The Global Burden of Disease Study 1990–2016. Lancet Infect. Dis. 18, 1229–1240 (2018).

    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Zhang, L. et al. Detection of Shigella in milk and clinical samples by magnetic immunocaptured-loop-mediated isothermal amplification assay. Front. Microbiol. 9, 1–7 (2018).

    Article 

    Google Scholar 

  • Maurelli, A. T. et al. Shigella infection as observed in the experimentally inoculated domestic pig, Sus scrofa domestica. Microb. Pathog. 25, 189–196 (1998).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Mukarati, N. L. et al. A serological survey of Bacillus anthracis reveals widespread exposure to the pathogen in free-range and captive lions in Zimbabwe. Transbound. Emerg. Dis. 68, 1676–1684 (2021).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Carlson, C. J. et al. The global distribution of Bacillus anthracis and associated anthrax risk to humans, livestock and wildlife. Nat. Microbiol. 4, 1337–1343 (2019).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Norris, M. H. et al. Laboratory strains of Bacillus anthracis lose their ability to rapidly grow and sporulate compared to wildlife outbreak strains. PLoS ONE 15, 1–11 (2020).

    Article 
    CAS 

    Google Scholar 

  • Conesa, A., Garofolo, G., Di Pasquale, A. & Cammà, C. Monitoring AMR in Campylobacter jejuni from Italy in the last 10 years (2011–2021): Microbiological and WGS data risk assessment. EFSA J. 20, 1–12 (2022).

    Article 
    CAS 

    Google Scholar 

  • Buettner, S., Wieland, B., Staerk, K. D. C. & Regula, G. Risk attribution of Campylobacter infection by age group using exposure modelling. Epidemiol. Infect. 138, 1748–1761 (2010).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Diaz-Sanchez, S., Hanning, I., Pendleton, S. & D’Souza, D. Next-generation sequencing: The future of molecular genetics in poultry production and food safety. Poult. Sci. 92, 562–572 (2013).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Dingle, K. E. et al. Multilocus sequence typing system for Campylobacter jejuni. J. Clin. Microbiol. 39, 14–23 (2001).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Yekani, M. et al. To resist and persist: Important factors in the pathogenesis of Bacteroides fragilis. Microb. Pathog. 149, 104506 (2020).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Wexler, H. M. Bacteroides: The good, the bad, and the nitty-gritty. Clin. Microbiol. Rev. 20, 593–621 (2007).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Wareham, D. W., Wilks, M., Ahmed, D., Brazier, J. S. & Millar, M. Anaerobic sepsis due to multidrug-resistant Bacteroides fragilis: Microbiological cure and clinical response with linezolid therapy. Clin. Infect. Dis. 40, 67–68 (2005).

    Article 

    Google Scholar 

  • Yoshino, Y. et al. Clinical features of Bacteroides bacteremia and their association with colorectal carcinoma. Infection 40, 63–67 (2012).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: Improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Katoh, K. & Frith, M. C. Adding unaligned sequences into an existing alignment using MAFFT and LAST. Bioinformatics 28, 3144–3146 (2012).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549 (2018).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).

    Article 
    CAS 

    Google Scholar 

  • Edgar, R. C. Updating the 97% identity threshold for 16S ribosomal RNA OTUs. Bioinformatics 34, 2371–2375 (2018).

    CAS 
    PubMed 
    Article 

    Google Scholar 

  • Committee on Biological Agents (ABAS). TRBA 466 Classification of Prokaryotes (Bacteria and Archaea) into Risk Groups (2010).

  • Benson, D. A. et al. GenBank. Nucleic Acids Res. 41, 36–42 (2013).

    Article 
    CAS 

    Google Scholar 

  • Stamatakis, A. RAxML version 8: A tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).

    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 

  • Heberle, H., Meirelles, V. G., da Silva, F. R., Telles, G. P. & Minghim, R. InteractiVenn: A web-based tool for the analysis of sets through Venn diagrams. BMC Bioinform. 16, 1–7 (2015).

    Article 

    Google Scholar 


  • Source: Ecology - nature.com

    A simple way to significantly increase lifetimes of fuel cells and other devices

    High energy and hungry for the hardest problems