Monitoring of Antimicrobial Resistance Based on Metagenomics Analyses in Pneumonia Patients

Official title

Monitoring of Antimicrobial Resistance Based on Metagenomics Analyses in Pneumonia Patients: a Genomic Epidemiology Study

Overview

Monitoring of antimicrobial resistance (AMR) based on metagenomics analyses in pneumonia patients is critical for optimizing clinical diagnosis and treatment and improving clinical prognosis. This study is designed to ask the following key questions: 1. What is the microbiome maps of patients with severe pneumonia and mild pneumonia ? 2. How many pathogen resistance genes are carrying in severe pneumonia and mild pneumonia ? 3. What is the genetic diversity of key pathogens detected in severe pneumonia and mild pneumonia during 2019-2025?

Detailed description

This is a historical prospective obsevational study. Patients diagnosed with severe and mild pneumonia are recruited continously from four hospitals (Shanghai General Hospital, Wuxi People's Hospital Affiliated to Nanjing Medical University, Wuhan Union Hospital, and Huanggang Central Hospital) in China during March, 2019 to March, 2025.

Next-generation sequencing: Metagenomics and metatranscriptomic libraries undergo next-generation sequencing using the Illumina Novaseq 6000 platform.

Microbiome analysis: using KneadData (v0.10.0) reference from the human genome (GRCH38 reference database) to filter out the quality control of illumina sequencing data in Virosaurus download virus genome data sets for reference, bowtie2 (v2.3.4.1) (genome coverage \>30%, depth \>1X) was used to locate and analyze the post-host sequence, and then the "samtools idxstats" command was used to calculate the classification and relative abundance of viruses. Meanwhile, in order to obtain the annotation information of bacteria at the species level, PhyloFlash (v3.4) was used to calculate read counts for 16S rRNA genes in the SILVA database, selecting similarity greater than or equal to 98% as a threshold. Using eukaryotic pathogen genome database EUPATHDB46 as reference, bowtie2 and samtools were used for qualitative and quantitative analysis of fungal pathogens.

The criteria for determining the cause of respiratory infection are: (1) existing species known to be associated with human disease (ICD-10), (2) previously unidentified potential novel pathogens (only DNA and RNA viruses whose genera or families have previously been shown to infect mammals), and (3) possible symbiotic bacteria not included.

Analysis of AMR: by comparing the sequence similarity between the sequencing fragments and known drug resistance genes, the detection content can determine whether drug resistance genes exist, and suggest drug resistance caused by modification, inactivation, repression and other drug resistance genes.

Drug resistance genes detection: genes related to drug resistance recorded in CARD (Comprehensive Antibiotic Resistance Database) and ARG-ANNOT database. In this assay, only functional genes with drug resistance activities such as modification, inactivation, and repression, as well as pathway and target changes caused by some point mutations, were reported.

Genetic diversity was computed as the mean pairwise genetic distance within a group. Maximum likelihood phylogenetic trees were constructed using RaxML with a general time-reversible nucleotide substitution model and 1000 bootstraps. The genetic distance between sequences was calculated using MEGAX, with a bootstrap method for variance estimation.

Primary outcome measures

• Microbiome composition [Time frame: during the study period, 2019-2025]

Secondary outcome measures (3)

• Alpha diversity [Time frame: during the study period, 2019-2025]
• Prevalence of bacterial resistance genes [Time frame: during the study period, 2019-2025]
• Beta diversity [Time frame: during the study period, 2019-2025]

Eligibility criteria

Inclusion criteria

• Patients clinically diagnosed as severe pneumonia and mild pneumonia are diagnosed according to the Guidelines for the diagnosis and Treatment of community-acquired pneumonia in Adults (2019 edition) formulated by the American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA), who meet 1 of the following major criteria or ≥3 minor criteria can be diagnosed. The diagnostic criteria for severe and mild pneumonia in children were adopted by the British Thoracic Society (BTS) in 2011.
• Clinical examination was performed, and there was biospecimen (nasopharyngeal swab, oropharyngeal swab, bronchoalveolar lavage fluid, sputum, blood, hydrothorax, lung tissue) remaining in the clinical microbiological examination.

Exclusion criteria

• Patients whose biological samples may be contaminated;
• Patients with alveolar lavage fluid or hydrothorax volume less than 200μl.

Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.

Healthy volunteers: No

Study design

Observational model

Case-control

Study locations

China · 1 center

• Mei Kang — Shanghai

Publications

• GBD 2019 Diseases and Injuries Collaborators. Global burden of 369 diseases and injuries in 204 countries and territories, 1990-2019: a systematic analysis for the Global Burden of Disease Study 2019. Lancet. 2020 Oct 17;396(10258):1204-1222. doi: 10.1016/S0140-6736(20)30925-9. PMID 33069326
• File TM Jr, Ramirez JA. Community-Acquired Pneumonia. N Engl J Med. 2023 Aug 17;389(7):632-641. doi: 10.1056/NEJMcp2303286. No abstract available. PMID 37585629
• Limmathurotsakul D, Dunachie S, Fukuda K, Feasey NA, Okeke IN, Holmes AH, Moore CE, Dolecek C, van Doorn HR, Shetty N, Lopez AD, Peacock SJ; Surveillance and Epidemiology of Drug Resistant Infections Consortium (SEDRIC). Improving the estimation of the global burden of antimicrobial resistant infections. Lancet Infect Dis. 2019 Nov;19(11):e392-e398. doi: 10.1016/S1473-3099(19)30276-2. Epub 2019 Au PMID 31427174
• Park DE, Higdon MM, Prosperi C, Baggett HC, Brooks WA, Feikin DR, Hammitt LL, Howie SRC, Kotloff KL, Levine OS, Madhi SA, Murdoch DR, O'Brien KL, Scott JAG, Thea DM, Antonio M, Awori JO, Baillie VL, Bunthi C, Kwenda G, Mackenzie GA, Moore DP, Morpeth SC, Mwananyanda L, Paveenkittiporn W, Ziaur Rahman M, Rahman M, Rhodes J, Sow SO, Tapia MD, Deloria Knoll M. Upper Respiratory Tract Co-detection of PMID 33883479
• Chiu CY, Miller SA. Clinical metagenomics. Nat Rev Genet. 2019 Jun;20(6):341-355. doi: 10.1038/s41576-019-0113-7. PMID 30918369
• Li N, Cai Q, Miao Q, Song Z, Fang Y, Hu B. High-Throughput Metagenomics for Identification of Pathogens in the Clinical Settings. Small Methods. 2021 Jan 4;5(1):2000792. doi: 10.1002/smtd.202000792. Epub 2020 Dec 13. PMID 33614906
• Zhang YZ, Chen YM, Wang W, Qin XC, Holmes EC. Expanding the RNA Virosphere by Unbiased Metagenomics. Annu Rev Virol. 2019 Sep 29;6(1):119-139. doi: 10.1146/annurev-virology-092818-015851. Epub 2019 May 17. PMID 31100994
• Wu F, Zhao S, Yu B, Chen YM, Wang W, Song ZG, Hu Y, Tao ZW, Tian JH, Pei YY, Yuan ML, Zhang YL, Dai FH, Liu Y, Wang QM, Zheng JJ, Xu L, Holmes EC, Zhang YZ. A new coronavirus associated with human respiratory disease in China. Nature. 2020 Mar;579(7798):265-269. doi: 10.1038/s41586-020-2008-3. Epub 2020 Feb 3. PMID 32015508

Identifiers

NCT: NCT06566898 · 2023234

Primary sources (government registries)

• ClinicalTrials.gov (US NIH)

View this study on ClinicalTrials.gov ↗