Construction & assessment of a unified curated reference database for improving the taxonomic classification of bacteria using 16S rRNA sequence data

Oral bacteriome and oral potentially malignant disorders: A systematic review of the associations

INTRODUCTION

Periodontal bacteria can infiltrate the epithelium, activate signaling pathways, induce inflammation, and block natural killer and cytotoxic cells, all of which contribute to the vicious circle of carcinogenesis. It is unknown whether oral dysbiosis has an impact on the etiology or prognosis of OPMD.

AIMS

Within this paradigm, this work systemically investigated and reported on the composition of oral microbiota in patients with oral potentially malignant disorders (OPMD) versus healthy controls.

METHODS

Observational studies that reported next generation sequencing analysis of oral tissue or salivary samples and found at least three bacterial species were included. Identification, screening, citation analysis, and graphical synthesis were carried out.

RESULTS

For oral lichen planus (OLP), the bacteria with the highest abundance were Fusobacterium, Capnocytophaga, Gemella, Granulicatella, Porphyromonas, and Rothia; for oral leukoplakia (OLK), Prevotella. Streptococci levels in OLK and OLP were lower. The usage of alcohol or smoke had no effect on the outcomes.

CONCLUSIONS

An increase in periodontal pathogenic bacteria could promote the development and exacerbation of lichen. Effective bacteriome-based biomarkers are worthy of further investigation and application, as are bacteriome-based treatments.

Comparison of databases useful for the analysis of vaginal microbiota in Japanese women using next-generation sequencing data (QIIME 2 software)

AIMS

Approximately 10% of children are born prematurely, and bacterial vaginosis during pregnancy is associated with preterm delivery. Highly accurate species-level vaginal microflora analysis helps control bacteria-induced preterm birth. Therefore, we aimed to conduct a bioinformatic analysis of gene sequences using 16S databases and compare their efficacy in comprehensively identifying potentially pathogenic vaginal microbiota in Japanese women.

METHODS AND RESULTS

The 16 s rRNA databases, Silva, Greengenes, and the basic local alignment search tool (BLAST) were compared to determine whether the classification quality could be improved using the V3-V4 region next-generation sequencing (NGS) sequences. It was found that NGS data were aligned using the BLAST database with the QIIME 2 platform, whose classification quality was higher than that of Silva, and the combined Silva and Greengenes databases based on the mutual complementarity of the two databases.

CONCLUSIONS

The reference database selected during the bioinformatic processing influenced the recognized sequence percentage, taxonomic rankings, and accuracy. This study showed that the BLAST database was the best choice for NGS data analysis of Japanese women's vaginal microbiota.

Unlocking the hidden potential of Mexican teosinte seeds: revealing plant growth-promoting bacterial and fungal biocontrol agents

The bacterial component of plant holobiont maintains valuable interactions that contribute to plants' growth, adaptation, stress tolerance, and antagonism to some phytopathogens. Teosinte is the grass plant recognized as the progenitor of modern maize, domesticated by pre-Hispanic civilizations around 9,000 years ago. Three teosinte species are recognized: Zea diploperennis, Zea perennis, and Zea mays. In this work, the bacterial diversity of three species of Mexican teosinte seeds was explored by massive sequencing of 16S rRNA amplicons. Streptomyces, Acinetobacter, Olivibacter, Erwinia, Bacillus, Pseudomonas, Cellvibrio, Achromobacter, Devosia, Lysobacter, Sphingopyxis, Stenotrophomonas, Ochrobactrum, Delftia, Lactobacillus, among others, were the bacterial genera mainly represented. The bacterial alpha diversity in the seeds of Z. diploperennis was the highest, while the alpha diversity in Z. mays subsp. mexicana race was the lowest observed among the species and races. The Mexican teosintes analyzed had a core bacteriome of 38 bacterial genera, including several recognized plant growth promoters or fungal biocontrol agents such as Agrobacterium, Burkholderia, Erwinia, Lactobacillus, Ochrobactrum, Paenibacillus, Pseudomonas, Sphingomonas, Streptomyces, among other. Metabolic inference analysis by PICRUSt2 of bacterial genera showed several pathways related to plant growth promotion (PGP), biological control, and environmental adaptation. The implications of these findings are far-reaching, as they highlight the existence of an exceptional bacterial germplasm reservoir teeming with potential plant growth promotion bacteria (PGPB). This reserve holds the key to cultivating innovative bioinoculants and formidable fungal antagonistic strains, thereby paving the way for a more sustainable and eco-friendly approach to agriculture. Embracing these novel NGS-based techniques and understanding the profound impact of the vertical transference of microorganisms from seeds could revolutionize the future of agriculture and develop a new era of symbiotic harmony between plants and microbes.

Improved species level bacterial characterization from rhizosphere soil of wilt infected Punica granatum

Pomegranate crops are prone to wilt complex disease, which is known to severely hamper the crop yield. There have been limited studies that have explored bacteria-plant-host associations in wilt complex disease affecting pomegranate crops. In the present study, wilt infected rhizosphere soil samples (ISI, ASI) in pomegranate were studied in comparison to a healthy control (HSC). The 16S metagenomics sequencing approach using the MinION platform was employed for screening of bacterial communities and predictive functional pathways. Altered physicochemical properties in the soil samples were recorded showing a comparatively acidic pH in the ISI (6.35) and ASI (6.63) soil samples to the HSC soil (7.66), along with higher electrical conductivity in the ISI (139.5 µS/cm), ASI soil (180 µS/cm), HSC soil sample (123.33 µS/cm). While concentration of micronutrients such as Cl and B were significantly higher in the ISI and ASI soil as compared to the HSC, Cu and Zn were significantly higher in the ASI soil. The effectiveness and accuracy of 16S metagenomics studies in identifying beneficial and pathogenic bacterial communities in multi-pathogen-host systems depend on the completeness and consistency of the available 16S rRNA sequence repositories. Enhancing these repositories could significantly improve the exploratory potential of such studies. Thus, multiple 16S rRNA data repositories (RDP, GTDB, EzBioCloud, SILVA, and GreenGenes) were benchmarked, and the findings indicated that SILVA yields the most reliable matches. Consequently, SILVA was chosen for further analysis at the species level. Relative abundance estimates of bacterial species showed variations of growth promoting bacteria, namely, Staphylococcus epidermidis, Bacillus subtilis, Bacillus megatarium, Pseudomonas aeruginosa, Pseudomonas putida, Pseudomonas stutzeri and Micrococcus luteus. Functional profiling predictions employing PICRUSt2 revealed a number of enriched pathways such as transporter protein families involved in signalling and cellular processes, iron complex transport system substrate binding protein, peptidoglycan biosynthesis II (staphylococci) and TCA cycle VII (acetate-producers). In line with past reports, results suggest that an acidic pH along with the bioavailability of micronutrients such as Fe and Mn could be facilitating the prevalence and virulence of Fusarium oxysporum, a known causative pathogen, against the host and beneficial bacterial communities. This study identifies bacterial communities taking into account the physicochemical and other abiotic soil parameters in wilt-affected pomegranate crops. The insights obtained could be instrumental in developing effective management strategies to enhance crop yield and mitigate the impact of wilt complex disease on pomegranate crops.

16S-ITGDB: An Integrated Database for Improving Species Classification of Prokaryotic 16S Ribosomal RNA Sequences

Analyzing 16S ribosomal RNA (rRNA) sequences allows researchers to elucidate the prokaryotic composition of an environment. In recent years, third-generation sequencing technology has provided opportunities for researchers to perform full-length sequence analysis of bacterial 16S rRNA. RDP, SILVA, and Greengenes are the most widely used 16S rRNA databases. Many 16S rRNA classifiers have used these databases as a reference for taxonomic assignment tasks. However, some of the prokaryotic taxonomies only exist in one of the three databases. Furthermore, Greengenes and SILVA include a considerable number of taxonomies that do not have the resolution to the species level, which has limited the classifiers’ performance. In order to improve the accuracy of taxonomic assignment at the species level for full-length 16S rRNA sequences, we manually curated the three databases and removed the sequences that did not have a species name. We then established a taxonomy-based integrated database by considering both taxonomies and sequences from all three 16S rRNA databases and validated it by a mock community. Results showed that our taxonomy-based integrated database had improved taxonomic resolution to the species level. The integrated database and the related datasets are available at https://github.com/yphsieh/ItgDB.

MetaSquare: an integrated metadatabase of 16S rRNA gene amplicon for microbiome taxonomic classification

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.

Gut Microbiome Composition in Obese and Non-Obese Persons: A Systematic Review and Meta-Analysis

Whether the gut microbiome in obesity is characterized by lower diversity and altered composition at the phylum or genus level may be more accurately investigated using high-throughput sequencing technologies. We conducted a systematic review in PubMed and Embase including 32 cross-sectional studies assessing the gut microbiome composition by high-throughput sequencing in obese and non-obese adults. A significantly lower alpha diversity (Shannon index) in obese versus non-obese adults was observed in nine out of 22 studies, and meta-analysis of seven studies revealed a non-significant mean difference (−0.06, 95% CI −0.24, 0.12, I² = 81%). At the phylum level, significantly more Firmicutes and fewer Bacteroidetes in obese versus non-obese adults were observed in six out of seventeen, and in four out of eighteen studies, respectively. Meta-analyses of six studies revealed significantly higher Firmicutes (5.50, 95% 0.27, 10.73, I² = 81%) and non-significantly lower Bacteroidetes (−4.79, 95% CI −10.77, 1.20, I² = 86%). At the genus level, lower relative proportions of Bifidobacterium and Eggerthella and higher Acidaminococcus, Anaerococcus, Catenibacterium, Dialister, Dorea, Escherichia-Shigella, Eubacterium, Fusobacterium, Megasphera, Prevotella, Roseburia, Streptococcus, and Sutterella were found in obese versus non-obese adults. Although a proportion of studies found lower diversity and differences in gut microbiome composition in obese versus non-obese adults, the observed heterogeneity across studies precludes clear answers.

16S rRNA of Mucosal Colon Microbiome and CCL2 Circulating Levels Are Potential Biomarkers in Colorectal Cancer

Colorectal cancer (CRC) is one of the most common malignancies in the Western world and intestinal dysbiosis might contribute to its pathogenesis. The mucosal colon microbiome and C-C motif chemokine 2 (CCL2) were investigated in 20 healthy controls (HC) and 20 CRC patients using 16S rRNA sequencing and immunoluminescent assay, respectively. A total of 10 HC subjects were classified as overweight/obese (OW/OB_HC) and 10 subjects were normal weight (NW_HC); 15 CRC patients were classified as OW/OB_CRC and 5 patients were NW_CRC. Results: Fusobacterium nucleatum and Escherichia coli were more abundant in OW/OB_HC than in NW_HC microbiomes. Globally, Streptococcus intermedius, Gemella haemolysans, Fusobacterium nucleatum, Bacteroides fragilis and Escherichia coli were significantly increased in CRC patient tumor/lesioned tissue (CRC_LT) and CRC patient unlesioned tissue (CRC_ULT) microbiomes compared to HC microbiomes. CCL2 circulating levels were associated with tumor presence and with the abundance of Fusobacterium nucleatum, Bacteroides fragilis and Gemella haemolysans. Our data suggest that mucosal colon dysbiosis might contribute to CRC pathogenesis by inducing inflammation. Notably, Fusobacterium nucleatum, which was more abundant in the OW/OB_HC than in the NW_HC microbiomes, might represent a putative link between obesity and increased CRC risk.