Gut Microbiota Profile in Patients with Type 1 Diabetes Based on 16S rRNA Gene Sequencing: A Systematic Review.
Zhou He,Zhao Xue,Sun Lin,Liu Yujia,Lv You,Gang Xiaokun,Wang Guixia
Disease markers
The gut microbiota has been presumed to have a role in the pathogenesis of type 1 diabetes (T1D). Significant changes in the microbial composition of T1D patients have been reported in several case-control studies. This study is aimed at systematically reviewing the existing literature, which has investigated the alterations of the intestinal microbiome in T1D patients compared with healthy controls (HCs) using 16S ribosomal RNA-targeted sequencing. The databases of MEDLINE, EMBASE, Web of Science, and the Cochrane Library were searched until April 2019 for case-control studies comparing the composition of the intestinal microbiome in T1D patients and HCs based on 16S rRNA gene sequencing techniques. The Newcastle-Ottawa Scale was used to assess the methodological quality. Ten articles involving 260 patients with T1D and 276 HCs were included in this systematic review. The quality scores of all included studies were 6-8 points. In summary, a decreased microbiota diversity and a significantly distinct pattern of clustering with regard to -diversity were observed in T1D patients when compared with HCs. At the phylum level, T1D was characterised by a reduced ratio of in the structure of the gut community, although no consistent conclusion was reached. At the genus or species level, T1D patients had a reduced abundance of and compared with HCs, whereas and were found to be more enriched in T1D patients. This systematic review identified that there is a close association between the gut microbiota and development of T1D. Moreover, gut dysbiosis might be involved in the pathogenesis of T1D, although the causative role of gut microbiota remains to be established. Further well-controlled prospective studies are needed to better understand the role of the intestinal microbiome in the pathogenesis of T1D, which may help explore novel microbiota-based strategies to prevent and treat T1D.
10.1155/2020/3936247
Changes in the fecal microbiota of breast cancer patients based on 16S rRNA gene sequencing: a systematic review and meta-analysis.
Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico
PURPOSE:Breast cancer (BC) is a devastating disease for women. Microbial influences may be involved in the development and progression of breast cancer. This study aimed to investigate the difference in intestinal flora abundance between breast cancer patients and healthy controls (HC) based on previous 16S ribosomal RNA (rRNA) gene sequencing results, which have been scattered and inconsistent in previous studies. MATERIALS AND METHODS:In agreement with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), we searched for pertinent literature in Pubmed, Embase, Cochrane Library, and Web of Science databases from build until February 1, 2023. Relative abundance, diversity of intestinal microflora by level, microbial composition, community structure, diversity index, and other related data were extracted. We used a fixed or random effects model for data analysis. We also conducted funnel plot analysis, sensitivity analysis, Egger's, and Begg's tests to assess the bias risk. RESULTS:A total of ten studies involving 734 BC patients were enrolled. It was pointed out that there were significant differences in the Chao index between BC and HC in these studies [SMD = - 175.44 (95% CI - 246.50 to - 104.39)]. The relative abundance of Prevotellaceae [SMD = - 0.27 (95% CI - 0.39 to - 0.15)] and Bacteroides [SMD = 0.36 (95% CI 0.23-0.49)] was significantly different. In the included articles, the relative abundance of Prevotellaceae, Ruminococcus, Roseburia inulinivorans, and Faecalibacterium prausnitzii decreased in BC. Accordingly, the relative richness of Erysipelotrichaceae was high in BC. CONCLUSIONS:This observational meta-analysis revealed that the changes in gut microbiota were correlated with BC, and the changes in some primary fecal microbiota might affect the beginning of breast cancer.
10.1007/s12094-023-03373-5
16S rRNA gene high-throughput sequencing data mining of microbial diversity and interactions.
Ju Feng,Zhang Tong
Applied microbiology and biotechnology
The ubiquitous occurrence of microorganisms gives rise to continuous public concerns regarding their pathogenicity and threats to human environment, as well as potential engineering benefits in biotechnology. The development and wide application of environmental biotechnology, for example in bioenergy production, wastewater treatment, bioremediation, and drinking water disinfection, have been bringing us with both environmental and economic benefits. Strikingly, extensive applications of microscopic and molecular techniques since 1990s have allowed engineers to peep into the microbiology in "black box" of engineered microbial communities in biotechnological processes, providing guidelines for process design and optimization. Recently, revolutionary advances in DNA sequencing technologies and rapidly decreasing costs are altering conventional ways of microbiology and ecology research, as it launches an era of next-generation sequencing (NGS). The principal research burdens are now transforming from traditional labor-intensive wet-lab experiments to dealing with analysis of huge and informative NGS data, which is computationally expensive and bioinformatically challenging. This study discusses state-of-the-art bioinformatics and statistical analyses of 16S ribosomal RNA (rRNA) gene high-throughput sequencing (HTS) data from prevalent NGS platforms to promote its applications in exploring microbial diversity of functional and pathogenic microorganisms, as well as their interactions in biotechnological processes.
10.1007/s00253-015-6536-y
Impact of 16S rRNA gene sequence analysis for identification of bacteria on clinical microbiology and infectious diseases.
Clinical microbiology reviews
The traditional identification of bacteria on the basis of phenotypic characteristics is generally not as accurate as identification based on genotypic methods. Comparison of the bacterial 16S rRNA gene sequence has emerged as a preferred genetic technique. 16S rRNA gene sequence analysis can better identify poorly described, rarely isolated, or phenotypically aberrant strains, can be routinely used for identification of mycobacteria, and can lead to the recognition of novel pathogens and noncultured bacteria. Problems remain in that the sequences in some databases are not accurate, there is no consensus quantitative definition of genus or species based on 16S rRNA gene sequence data, the proliferation of species names based on minimal genetic and phenotypic differences raises communication difficulties, and microheterogeneity in 16S rRNA gene sequence within a species is common. Despite its accuracy, 16S rRNA gene sequence analysis lacks widespread use beyond the large and reference laboratories because of technical and cost considerations. Thus, a future challenge is to translate information from 16S rRNA gene sequencing into convenient biochemical testing schemes, making the accuracy of the genotypic identification available to the smaller and routine clinical microbiology laboratories.
10.1128/CMR.17.4.840-862.2004
Next-generation sequencing: insights to advance clinical investigations of the microbiome.
The Journal of clinical investigation
Next-generation sequencing (NGS) technology has advanced our understanding of the human microbiome by allowing for the discovery and characterization of unculturable microbes with prediction of their function. Key NGS methods include 16S rRNA gene sequencing, shotgun metagenomic sequencing, and RNA sequencing. The choice of which NGS methodology to pursue for a given purpose is often unclear for clinicians and researchers. In this Review, we describe the fundamentals of NGS, with a focus on 16S rRNA and shotgun metagenomic sequencing. We also discuss pros and cons of each methodology as well as important concepts in data variability, study design, and clinical metadata collection. We further present examples of how NGS studies of the human microbiome have advanced our understanding of human disease pathophysiology across diverse clinical contexts, including the development of diagnostics and therapeutics. Finally, we share insights as to how NGS might further be integrated into and advance microbiome research and clinical care in the coming years.
10.1172/JCI154944
MetaSquare: an integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification.
Bioinformatics (Oxford, England)
MOTIVATION:Taxonomic classification of 16S ribosomal RNA gene amplicon is an efficient and economic approach in microbiome analysis. 16S rRNA sequence databases like SILVA, RDP, EzBioCloud and HOMD used in downstream bioinformatic pipelines have limitations on either the sequence redundancy or the delay on new sequence recruitment. To improve the 16S rRNA gene-based taxonomic classification, we merged these widely used databases and a collection of novel sequences systemically into an integrated resource. RESULTS:MetaSquare version 1.0 is an integrated 16S rRNA sequence database. It is composed of more than 6 million sequences and improves taxonomic classification resolution on both long-read and short-read methods. AVAILABILITY AND IMPLEMENTATION:Accessible at https://hub.docker.com/r/lsbnb/metasquare_db and https://github.com/lsbnb/MetaSquare. SUPPLEMENTARY INFORMATION:Supplementary data are available at Bioinformatics online.
10.1093/bioinformatics/btac184
Optimization of high-throughput 16S rRNA gene amplicon sequencing: an assessment of PCR pooling, mastermix use and contamination.
Microbial genomics
16S rRNA gene sequencing is widely used to characterize human and environmental microbiomes. Sequencing at scale facilitates better powered studies but is limited by cost and time. We identified two areas in our 16S rRNA gene library preparation protocol where modifications could provide efficiency gains, including (1) pooling of multiple PCR amplifications per sample to reduce PCR drift and (2) manual preparation of mastermix to reduce liquid handling. Using nasal samples from healthy human participants and a serially diluted mock microbial community, we compared alpha and beta diversity, and compositional abundance where the PCR amplification was conducted in triplicate, duplicate or as a single reaction, and where manually prepared or premixed mastermix was used. One hundred and fifty-eight 16S rRNA gene sequencing libraries were prepared, including a replicate experiment. Comparing PCR pooling strategies, we found no significant difference in high-quality read counts and alpha diversity, and beta diversity by Bray-Curtis index clustered by replicate on principal coordinate analysis (PCoA) and non-metric dimensional scaling (NMDS) analysis. Choice of mastermix had no significant impact on high-quality read and alpha diversity, and beta diversity by Bray-Curtis index clustered by replicate in PCoA and NMDS analysis. Importantly, we observed contamination and variability of rare species (<0.01 %) across replicate experiments; the majority of contaminants were accounted for by removal of species present at <0.1 %, or were linked to reagents (including a primer stock). We demonstrate no requirement for pooling of PCR amplifications or manual preparation of PCR mastermix, resulting in a more efficient 16S rRNA gene PCR protocol.
10.1099/mgen.0.001115
On the limits of 16S rRNA gene-based metagenome prediction and functional profiling.
Microbial genomics
Molecular profiling techniques such as metagenomics, metatranscriptomics or metabolomics offer important insights into the functional diversity of the microbiome. In contrast, 16S rRNA gene sequencing, a widespread and cost-effective technique to measure microbial diversity, only allows for indirect estimation of microbial function. To mitigate this, tools such as PICRUSt2, Tax4Fun2, PanFP and MetGEM infer functional profiles from 16S rRNA gene sequencing data using different algorithms. Prior studies have cast doubts on the quality of these predictions, motivating us to systematically evaluate these tools using matched 16S rRNA gene sequencing, metagenomic datasets, and simulated data. Our contribution is threefold: (i) using simulated data, we investigate if technical biases could explain the discordance between inferred and expected results; (ii) considering human cohorts for type two diabetes, colorectal cancer and obesity, we test if health-related differential abundance measures of functional categories are concordant between 16S rRNA gene-inferred and metagenome-derived profiles and; (iii) since 16S rRNA gene copy number is an important confounder in functional profiles inference, we investigate if a customised copy number normalisation with the rrnDB database could improve the results. Our results show that 16S rRNA gene-based functional inference tools generally do not have the necessary sensitivity to delineate health-related functional changes in the microbiome and should thus be used with care. Furthermore, we outline important differences in the individual tools tested and offer recommendations for tool selection.
10.1099/mgen.0.001203