Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline.
العنوان: | Simultaneous detection of pathogens and antimicrobial resistance genes with the open source, cloud-based, CZ ID pipeline. |
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المؤلفون: | Langelier C; University of California, San Francisco., Lu D; Chan Zuckerberg Initiative., Kalantar K; Chan Zuckerberg Initiative., Chu V; UCSF., Glascock A; Chan Zuckerberg Biohub., Guerrero E; Nova Southeastern University., Bernick N; Chan Zuckerberg Initiative., Butcher X; Chan Zuckerberg Initiative., Ewing K; Chan Zuckerberg Initiative., Fahsbender E; Chan Zuckerberg Initiative., Holmes O; Chan Zuckerberg Initiative., Hoops E; Chan Zuckerberg Initiative., Jones A; Chan Zuckerberg Initiative., Lim R; Chan Zuckerberg Initiative., McCanny S; Chan Zuckerberg Initiative., Reynoso L; Chan Zuckerberg Initiative., Rosario K; Chan Zuckerberg Initiative., Tang J; Chan Zuckerberg Initiative., Valenzuela O; Chan Zuckerberg Initiative., Mourani P; Arkansas Children's., Pickering A; UC Berkeley., Raphenya A; McMaster University., Alcock B; McMaster University., McArthur A; McMaster University. |
المصدر: | Research square [Res Sq] 2024 May 02. Date of Electronic Publication: 2024 May 02. |
نوع المنشور: | Preprint |
اللغة: | English |
بيانات الدورية: | Country of Publication: United States NLM ID: 101768035 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: Res Sq Subsets: PubMed not MEDLINE |
مستخلص: | Antimicrobial resistant (AMR) pathogens represent urgent threats to human health, and their surveillance is of paramount importance. Metagenomic next generation sequencing (mNGS) has revolutionized such efforts, but remains challenging due to the lack of open-access bioinformatics tools capable of simultaneously analyzing both microbial and AMR gene sequences. To address this need, we developed the CZ ID AMR module, an open-access, cloud-based workflow designed to integrate detection of both microbes and AMR genes in mNGS and whole-genome sequencing (WGS) data. It leverages the Comprehensive Antibiotic Resistance Database and associated Resistance Gene Identifier software, and works synergistically with the CZ ID short-read mNGS module to enable broad detection of both microbes and AMR genes. We highlight diverse applications of the AMR module through analysis of both publicly available and newly generated mNGS and WGS data from four clinical cohort studies and an environmental surveillance project. Through genomic investigations of bacterial sepsis and pneumonia cases, hospital outbreaks, and wastewater surveillance data, we gain a deeper understanding of infectious agents and their resistomes, highlighting the value of integrating microbial identification and AMR profiling for both research and public health. We leverage additional functionalities of the CZ ID mNGS platform to couple resistome profiling with the assessment of phylogenetic relationships between nosocomial pathogens, and further demonstrate the potential to capture the longitudinal dynamics of pathogen and AMR genes in hospital acquired bacterial infections. In sum, the new AMR module advances the capabilities of the open-access CZ ID microbial bioinformatics platform by integrating pathogen detection and AMR profiling from mNGS and WGS data. Its development represents a critical step toward democratizing pathogen genomic analysis and supporting collaborative efforts to combat the growing threat of AMR. Competing Interests: Declarations Competing interests The authors declare that they have no competing interests. |
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معلومات مُعتمدة: | R01 HL124103 United States HL NHLBI NIH HHS; R01 HL155418 United States HL NHLBI NIH HHS |
تواريخ الأحداث: | Date Created: 20240515 Latest Revision: 20240522 |
رمز التحديث: | 20240522 |
مُعرف محوري في PubMed: | PMC11092797 |
DOI: | 10.21203/rs.3.rs-4271356/v1 |
PMID: | 38746293 |
قاعدة البيانات: | MEDLINE |
DOI: | 10.21203/rs.3.rs-4271356/v1 |
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