An integrated respiratory microbial gene catalogue to better understand the microbial aetiology of Mycoplasma pneumoniae pneumonia

Abstract

Background The imbalanced respiratory microbiota observed in pneumonia causes high morbidity and mortality in childhood. Respiratory metagenomic analysis demands a comprehensive microbial gene catalogue, which will significantly advance our understanding of host–microorganism interactions. Results We collected 334 respiratory microbial samples from 171 healthy children and 76 children with pneumonia. The respiratory microbial gene catalogue we established comprised 2.25 million non-redundant microbial genes, covering 90.52% of prevalent genes. The major oropharyngeal microbial species found in healthy children were Prevotella and Streptococcus. In children with Mycoplasma pneumoniae pneumonia (MPP), oropharyngeal microbial diversity and associated gene numbers decreased compared with those of healthy children. The concurrence network of oropharyngeal microorganisms in patients predominantly featured Staphylococcus spp. and M. pneumoniae. Functional orthologues, which are associated with the metabolism of various lipids, membrane transport, and signal transduction, accumulated in the oropharyngeal microbiome of children with pneumonia. Several antibiotic resistance genes and virulence factor genes were identified in the genomes of M. pneumoniae and 13 other microorganisms reconstructed via metagenomic data. Although the common macrolide/β-lactam resistance genes were not identified in the assembled M. pneumoniae genome, a single-nucleotide polymorphism (A2063G) related to macrolide resistance was identified in a 23S ribosomal RNA gene. Conclusions The results of this study will facilitate exploration of unknown microbial components and host–microorganism interactions in studies of the respiratory microbiome. They will also yield further insights into the microbial aetiology of MPP.

A critical assessment of gene catalogs for metagenomic analysis

“…Following these initial studies, gene catalogs have become ubiquitous in the analysis of metagenomic datasets, and have been created for the gut microbiota of multiple animals [e.g. mouse ( Xiao et al ., 2015 ), rat ( Pan et al ., 2018 ), pig ( Xiao et al ., 2016a , b ), dog ( Coelho et al , 2018 ), cow ( Li et al ., 2020 ), macaque ( Li et al ., 2018 ), chicken ( Huang et al ., 2018 ), lion, leopard and tiger ( Mittal et al ., 2020 )], ocean bacteria ( Sunagawa et al , 2015 ), soil bacteria ( Lou et al ., 2019 ) and the human vagina ( Ma et al ., 2020 ) and respiratory tract ( Dai et al ., 2019 ). Gene catalogs are commonly used to: (i) reduce redundancy in the data, thereby improving estimates of diversity ( Yooseph et al , 2007 ); (ii) act as a common frame of reference across samples and studies; (iii) serve as a basis for metagenomic-wide association studies ( Wang and Jia, 2016 ); and (iv) guide the binning of metagenomic contigs into organism-specific groups ( Nielsen et al ., 2014 ; Plaza Oñate et al., 2018 ).…”

An analysis of 20 clinical cases of refractory mycoplasma pneumonia in children

“…Mycoplasma pneumoniae (MP) is one of the important pathogens of community-acquired pneumonia (CAP) in children. Mycoplasma pneumoniae pneumonia (MPP) accounts for 10% to 40% (1)(2)(3)(4)(5)(6) of CAP in hospitalized children, which is a clinical concern of all pediatricians. Among them, refractory mycoplasma pneumonia (RMPP) cases have gradually increased in recent years (7).…”

Body site-specific and disease-specific virulome in the human microbiome

“…Specifically, adhesinrelated genes in Mycoplasma pneumoniae were more abundant in children with pneumonia ( Figure S17). There were significant differences in respiratory microbial virulomes between healthy children and children with pneumonia, probably due to the differences in oropharyngeal microbial diversity 24 .…”

“…The SRA datasets were converted to fastq using the fastq-dump module in the NCBI SRA Toolkit. We collected 2,712 samples from 13 types of diseases, including colorectal carcinoma (CRC) [36][37][38][39] , atherosclerotic cardiovascular disease (ACVD) 40 , inflammatory bowel disease (IBD) 3,41 , obesity 42 , hypertension 43 , Parkinson's disease (PD) 44 , nonsmall cell lung cancer (NSCLC) 45 , hepatocellular carcinoma (HCC) 46 , gastric cancer (GC) 47 , cirrhosis 48 , melanoma 49,50 , renal cell carcinoma (RCC) 45 and children with Mycoplasma pneumoniae pneumonia (MPP) 24,51 . In total, we analyzed more than 4,000 metagenomic samples.…”