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The concept of “enterotypes” in microbiome research has attracted substantial interest, particularly focusing on the abundance of Prevotella spp. in the human gut. In this study, the intricate dynamics of Prevotella spp. in the human gut microbiota was investigated, based on the metagenomic method. First, 239 fecal samples from individuals across four regions of China revealed a bimodal distribution, highlighting the abundance and variability in Prevotella spp. within the Chinese population. Second, the longitudinal cohort study included 184 fecal samples from 52 time points collected from seven individuals who demonstrated either the outbreaks or disappearances of Prevotella spp., emphasizing the transient nature of Prevotella abundance levels and suggesting shifts in Prevotella “enterotypes.” Furthermore, a turnover of the dominant Prevotella spp. was observed, indicating the potential presence of diverse subtypes of Prevotella enterotype. Notably, the genomic analysis demonstrated the persistence of specific Prevotella strains within individuals over extended periods, highlighting the enduring presence of Prevotella in the human gut. In conclusion, by integrating the temporal and geographical scales in our research, we gained deeper insights into the dynamics of Prevotella, emphasizing the importance of considering the dynamics at the time and species level in gut microbiota studies and their implications on human health.

A study for human gut microbiota composition in 7 healthy Chinese adult subjects across several years provides a valuable perspective on the dynamics of microbiota composition over time.

ABSTRACT In this study, we aimed to understand the characteristics of the gut microbial composition in a healthy Chinese population and to evaluate if they differed across different regions. In addition, we aimed to understand the changes in the gut microbial composition over time. We collected 239 fecal samples from healthy Chinese adults living in four regions and performed a 1-year time cohort study in a small population in Beijing. The Chinese gut microbiota share 34 core bacterial genera and 39 core bacterial species, which exist in all collected samples. Several disease-related microorganisms (DRMs), virulence factors, and antibiotic resistance genes were found in one or more healthy Chinese samples. Differences in gut microbiota were observed in samples from different regions, locations, individuals, and time points. Compared to other factors, time was associated with a lower degree of change in the gut microbiota. Our findings revealed spatial and temporal changes in the gut microbiota of healthy Chinese individuals. Compared to fecal microbiomes of 152 samples in the publicly released the Human Microbiome Project (HMP) project from the United States, samples in this study have higher variability in the fecal microbiome, with higher richness, Shannon diversity indices, and Pielou evenness indexes, at both the genus and species levels. The microbiota data obtained in this study will provide a detailed basis for further understanding the composition of the gut microbiota in the healthy Chinese population. IMPORTANCE China accounts for approximately 1/5th of the world’s total population. Differences in environment, ethnicity, and living habits could impart unique features to the structure of the gut microbiota of Chinese individuals. In 2016, we started to investigate healthy Chinese people and their gut microbiomes. Phase I results for 16S rRNA amplicons have been released. However, owing to the limitations of 16S rRNA amplicon sequencing, the gut microbiome of a healthy Chinese population could not be examined thoroughly at the species level, and the detailed changes in the gut microbiota over time need to be investigated. To address these knowledge gaps, we started a phase II study and investigated the basis for variations in the gut microbiome composition in a healthy Chinese population at the species level using shotgun metagenomics technology. In the phase II study, we also conducted a time scale analysis of fecal samples from healthy Chinese subjects, as a pioneered study, which quantitatively clarified the changes in the gut microbiota at both the spatial and temporal levels and elucidated the distribution pattern of DRMs in healthy Chinese individuals.

Pathogenic microorganism analysis based on next-generation sequencing technology is an important tool for clinical diagnosis, public health surveillance, and outbreak investigation. However, scientific researchers without the relevant background lack the time, training, or infrastructure to use large data sets or install and use command line tools. Therefore, the bioinformatic team at the Chinese Center for Disease Control and Prevention developed the Microbial Data Analysis Cloud Platform (MDACP) as a safe, professional, and efficient pathogen genetic data analysis platform for rapid microbial data mining, such as for candidate pathogen detection, genome typing, and traceability. MDACP is a web service system based on the Docker platform and can be used for data analysis on various operating systems. The platform focuses on pathogen analysis and continuously develops new analysis processes according to the analysis needs of the users. This platform has a friendly user interface and is easy to operate, allowing users to submit data through data pages or graphical clients, flexibly control parameters according to data conditions, and analyze data in parallel with multiple tasks. Researchers can quickly carry out bioinformatic analyses without coding work, promote follow-up research and information mining of projects, and improve the utilization of big data in the field of disease control. MDACP enables research personnel to conduct data analysis and management and assists clinicians and disease control personnel with mining information, such as pathogen identification, classification, and traceability.

Abstract Background Pathogen detection in clinical samples based on 16S metagenomic sequencing technology in microbiology laboratories is an important strategy for clinical diagnosis, public health surveillance, and investigations of outbreaks. However, the implementation of the technology is limited by its accuracy and the time required for bioinformatics analysis. Therefore, a simple, standardized, and rapid analysis pipeline from the receipt of clinical samples to the generation of a test report is needed to increase the use of metagenomic analyses in clinical settings. Results We developed a comprehensive bioinformatics analysis pipeline for the identification of pathogens in clinical samples based on 16S metagenomic sequencing data, named 16SPIP. This pipeline offers two analysis modes (fast and sensitive mode) for the rapid conversion of clinical 16S metagenomic data to test reports for pathogen detection. The pipeline includes tools for data conversion, quality control, merging of paired-end reads, alignment, and pathogen identification. We validated the feasibility and accuracy of the pipeline using a combination of culture and whole-genome shotgun (WGS) metagenomic analyses. Conclusions 16SPIP may be effective for the analysis of 16S metagenomic sequencing data for real-time, rapid, and unbiased pathogen detection in clinical samples.

Background: The rapid spread of Clostridium difficile NAP1/BI/027 (C. difficile 027) has become one of the leading threats of healthcare-associated infections worldwide. However, C. difficile 027 infections have been rarely reported in Asia, particularly in China. Results: In this study, we identified a rare C. difficile bloodstream infection (BSI) from three isolates of a patient during repeated hospital admission. This finding triggered a retrospective epidemiological study to scan all cases and strains emerged from this ward during the past three years. Using medical personnel interviews, medical record reviews and the genomic epidemiology, two outbreaks in 2012 and 2013–2014 were identified. Through using whole genome sequencing, we succeeded to trace the origin of the BSI strain. Surprisingly, we found the genome sequences were similar to C. difficile 027 strain R20291, indicating the occurrence of a rare C. difficile 027 strain in China. Integrated epidemiological investigation and whole genome sequencing of all strains, we constructed a nosocomial transmission map of these two C. difficile 027 outbreaks and traced the origin of the infection. Conclusions: By genome sequencing, spatio-temporal analysis and field epidemiology investigation, we can estimate their complex transform network and reveal the possible modes of transmission in this ward. Based on their genetic diversity, we can assume that the toilets, bathroom, and janitor’s equipment room may be contaminated area, which may be suggested to improve infection control measures in the following health care. [ABSTRACT FROM AUTHOR]

Background: Early classification of prokaryotes was based solely on phenotypic similarities, but modern prokaryote characterization has been strongly influenced by advances in genetic methods. With the fast development of the sequencing technology, the ever increasing number of genomic sequences per species offers the possibility for developing distance determinations based on whole-genome information. The average nucleotide identity (ANI), calculated from pair-wise comparisons of all sequences shared between two given strains, has been proposed as the new metrics for bacterial species definition and classification. Results: In this study, we developed the web version of ANItools (http://ani.mypathogen. cn/), which helps users directly get ANI values from online sources. A database covering ANI values of any two strains in a genus was also included (2773 strains, 1487 species and 668 genera). Importantly, ANItools web can automatically run genome comparison between the input genomic sequence and data sequences (Genus and Species levels), and generate a graphical report for ANI calculation results. Conclusion: ANItools web is useful for defining the relationship between bacterial strains, further contributing to the classification and identification of bacterial species using genome data. [ABSTRACT FROM AUTHOR]

Escherichia coli (E. coli) is a common microorganism that is widely present in the environment and closely related to human health. The extent of E. coli presence in the human gut has been a subject of ongoing debate. Through whole-genome shotgun metagenomic sequencing, our study revealed that E. coli exists in the human body at a low abundance (average abundance 1.21%), with occasional short-term bursts leading to temporary increases in abundance, with the highest recorded at 50.91%. Further investigations into the factors contributing to these short-term blooms of E. coli showed significant variations in strain types and genomes within fecal samples collected from the same individuals at different time points. Evolutionary tree analysis indicated that samples from different individuals crossed, suggesting a change in the dominant E. coli strains within the human gut. Therefore, it can be inferred that E. coli in the human body are more likely to be transient bacteria rather than permanent residents in the gut. The rapid rate of turnover among months (87.5% within a month) and short-term blooms of E. coli in the human body can establish "latent infections" of nonpathogenic strains in healthy individuals while also posing a potential risk of introducing pathogenic strains, thereby impacting human health. In summary, our study revealed the variation in E. coli abundance and strains within the human gut, influenced by geographic area and temporal factors. These findings contribute to a better understanding of the relationship between E. coli, the gut microbiota, and human health. [ABSTRACT FROM AUTHOR]