Primer, Pipelines, Parameters: Issues in 16S rRNA Gene Sequencing

Unique Pakistani gut microbiota highlights population-specific microbiota signatures of type 2 diabetes mellitus

Biogeographic variations in the gut microbiota are pivotal to understanding the global pattern of host-microbiota interactions in prevalent lifestyle-related diseases. Pakistani adults, having an exceptionally high prevalence of type 2 diabetes mellitus (T2D), are one of the most understudied populations in microbiota research to date. The aim of the present study is to examine the gut microbiota across individuals from Pakistan and other populations of non-industrialized and industrialized lifestyles with a focus on T2D. The fecal samples from 94 urban-dwelling Pakistani adults with and without T2D were profiled by bacterial 16S ribosomal RNA gene and fungal internal transcribed spacer (ITS) region amplicon sequencing and eubacterial qPCR, and plasma samples quantified for circulating levels of lipopolysaccharide-binding protein (LBP) and the activation ability of Toll-like receptor (TLR)-signaling. Publicly available datasets generated with comparable molecular methods were retrieved for comparative analysis of the bacterial microbiota. Overall, urbanized Pakistanis' gut microbiota was similar to that of transitional or non-industrialized populations, depleted in Akkermansiaceae and enriched in Prevotellaceae (dominated by the non-Westernized clades of Prevotella copri). The relatively high proportion of Atopobiaceae appeared to be a unique characteristic of the Pakistani gut microbiota. The Pakistanis with T2D had elevated levels of LBP and TLR-signaling in circulation as well as gut microbial signatures atypical of other populations, e.g., increased relative abundance of Libanicoccus/Parolsenella, limiting the inter-population extrapolation of gut microbiota-based classifiers for T2D. Taken together, our findings call for a more global representation of understudied populations to extend the applicability of microbiota-based diagnostics and therapeutics.

Fecal microbiota transplantation ameliorates bone loss in mice with ovariectomy-induced osteoporosis via modulating gut microbiota and metabolic function

Background

Osteoporosis (OP) is a systemic metabolic bone disease characterized by decreased bone mass and destruction of bone microstructure, which tends to result in enhanced bone fragility and related fractures. The postmenopausal osteoporosis (PMOP) has a relatively high proportion, and numerous studies reveal that estrogen-deficiency is related to the imbalance of gut microbiota (GM), impaired intestinal mucosal barrier function and enhanced inflammatory reactivity. However, the underlying mechanisms remain unclear and the existing interventions are also scarce.

Methods

In this study, we established a mouse model induced by ovariectomy (OVX) and conducted fecal microbiota transplantation (FMT) by gavage every day for 8 weeks. Subsequently, the bone mass and microarchitecture of mice were evaluated by the micro computed tomography (Micro-CT). The intestinal permeability, pro-osteoclastogenic cytokines expression, osteogenic and osteoclastic activities were detected by the immunohistological analysis, histological examination, enzyme-linked immunosorbent assay (ELISA) and western blot analysis accordingly. Additionally, the composition and abundance of GM were assessed by 16S rRNA sequencing and the fecal short chain fatty acids (SCFAs) level was measured by metabolomics.

Results

Our results demonstrated that FMT inhibited the excessive osteoclastogenesis and prevented the OVX-induced bone loss. Specifically, compared with the OVX group, FMT enhanced the expressions of tight junction proteins (zonula occludens protein 1 (ZO-1) and Occludin) and suppressed the release of pro-osteoclastogenic cytokines (tumor necrosis factor-α (TNF-α) and interleukin-1β (IL-1β)). Furthermore, FMT also optimized the composition and abundance of GM, and increased the fecal SCFAs level (mainly acetic acid and propionic acid).

Conclusions

Collectively, based on GM-bone axis, FMT prevented the OVX-induced bone loss by correcting the imbalance of GM, improving the SCFAs level, optimizing the intestinal permeability and suppressing the release of pro-osteoclastogenic cytokines, which may be an alternative option to serve as a promising candidate for the prevention and treatment of PMOP in the future.

The translational potential of this article

This study indicates the ingenious involvement of GM-bone axis in PMOP and the role of FMT in reshaping the status of GM and ameliorating the bone loss in OVX-induced mice. FMT might serve as a promising candidate for the prevention and treatment of PMOP in the future.

Beyond Basic Diversity Estimates-Analytical Tools for Mechanistic Interpretations of Amplicon Sequencing Data

Understanding microbial ecology through amplifying short read regions, typically 16S rRNA for prokaryotic species or 18S rRNA for eukaryotic species, remains a popular, economical choice. These methods provide relative abundances of key microbial taxa, which, depending on the experimental design, can be used to infer mechanistic ecological underpinnings. In this review, we discuss recent advancements in in situ analytical tools that have the power to elucidate ecological phenomena, unveil the metabolic potential of microbial communities, identify complex multidimensional interactions between species, and compare stability and complexity under different conditions. Additionally, we highlight methods that incorporate various modalities and additional information, which in combination with abundance data, can help us understand how microbial communities respond to change in a typical ecosystem. Whilst the field of microbial informatics continues to progress substantially, our emphasis is on popular methods that are applicable to a broad range of study designs. The application of these methods can increase our mechanistic understanding of the ongoing dynamics of complex microbial communities.

The combination of sodium alginate and chlorogenic acid enhances the therapeutic effect on ulcerative colitis by the regulation of inflammation and the intestinal flora

Chlorogenic acid (CA) and sodium alginate (SA) each have good therapeutic effects on ulcerative colitis (UC) owing to their antioxidant and anti-inflammatory activity. This study aimed to investigate the effects of CA alone and in combination with SA on inflammatory cells and UC mice. In the Lipopolysaccharide (LPS)-induced RAW 264.7 inflammatory cell model, Nitric oxide (NO) and interleukin-6 (IL-6) levels were significantly lower after treatment with CA plus SA than with CA alone. In the DSS-induced UC mouse model, compared with CA alone, CA plus SA showed a better ability to alleviate weight loss, reduce the disease activity index (DAI), improve the colonic mucosa, reduce the expression of inflammatory factors in the serum and myeloperoxidase (MPO) in colonic tissue, increase superoxide dismutase (SOD) levels, protect the intestinal mucosa and regulate the abundance of Actinobacteriota, Lactobacillus, Bifidobacterium, Bacteroides, Subdoligranulum and Streptococcus. Thus, CA plus SA can improve the therapeutic efficacy of CA in UC by regulating inflammatory factors, oxidative stress, and the intestinal flora and by protecting ulcerative wounds. These findings broaden our understanding of the role of the combination of SA and CA in enhancing the effects of CA on UC and provide strategies for prevention and treatment.

Association between Microbiome-Related Human Genetic Variants and Fasting Plasma Glucose in a High-Cardiovascular-Risk Mediterranean Population

Background and Objectives: The gut microbiota has been increasingly recognized as a relevant factor associated with metabolic diseases. However, directly measuring the microbiota composition is a limiting factor for several studies. Therefore, using genetic variables as proxies for the microbiota composition is an important issue. Landmark microbiome-host genome-wide association studies (mbGWAS) have identified many SNPs associated with gut microbiota. Our aim was to analyze the association between relevant microbiome-related genetic variants (Mi-RSNPs) and fasting glucose and type 2 diabetes in a Mediterranean population, exploring the interaction with Mediterranean diet adherence. Materials and Methods: We performed a cross-sectional study in a high-cardiovascular-risk Mediterranean population (n = 1020), analyzing the association of Mi-RSNPs (from four published mbGWAS) with fasting glucose and type 2 diabetes. A single-variant approach was used for fitting fasting glucose and type 2 diabetes to a multivariable regression model. In addition, a Mendelian randomization analysis with multiple variants was performed as a sub-study. Results: We obtained several associations between Mi-RSNPs and fasting plasma glucose involving gut Gammaproteobacteria_HB, the order Rhizobiales, the genus Rumminococcus torques group, and the genus Tyzzerella as the top ranked. For type 2 diabetes, we also detected significant associations with Mi-RSNPs related to the order Rhizobiales, the family Desulfovibrionaceae, and the genus Romboutsia. In addition, some Mi-RSNPs and adherence to Mediterranean diet interactions were detected. Lastly, the formal Mendelian randomization analysis suggested combined effects. Conclusions: Although the use of Mi-RSNPs as proxies of the microbiome is still in its infancy, and although this is the first study analyzing such associations with fasting plasma glucose and type 2 diabetes in a Mediterranean population, some interesting associations, as well as modulations, with adherence to the Mediterranean diet were detected in these high-cardiovascular-risk subjects, eliciting new hypotheses.

Land-Use Change and Management Intensification Is Associated with Shifts in Composition of Soil Microbial Communities and Their Functional Diversity in Coffee Agroecosystems

Despite the central role of microorganisms in soil fertility, little understanding exists regarding the impact of management practices and soil microbial diversity on soil processes. Strong correlations among soil microbial composition, management practices, and microbially mediated processes have been previously shown. However, limited integration of the different parameters has hindered our understanding of agroecosystem functioning. Multivariate analyses of these systems allow simultaneous evaluation of the parameters and can lead to hypotheses on the microbial groups involved in specific nutrient transformations. In the present study, using a multivariate approach, we investigated the effect of microbial composition (16SrDNA sequencing) and soil properties in carbon mineralization (C_MIN) (BIOLOG™, Hayward, CA, USA) across different management categories on coffee agroecosystems in Mexico. Results showed that (i) changes in soil physicochemical variables were related to management, not to region, (ii) microbial composition was associated with changes in management intensity, (iii) specific bacterial groups were associated with different management categories, and (iv) there was a broader utilization range of carbon sources in non-managed plots. The identification of specific bacterial groups, management practices, and soil parameters, and their correlation with the utilization range of carbon sources, presents the possibility to experimentally test hypotheses on the interplay of all these components and further our understanding of agroecosystem functioning and sustainable management.

The gut microbiome analysis of Anastrepha obliqua reveals inter-kingdom diversity: bacteria, fungi, and archaea

The fruit fly Anastrepha obliqua is an economically important pest. The sterile insect technique to control it involves mass production and release of sterile flies to reduce the reproduction of the wild population. As noted in different Tephritidae, the performance of sterile males may be affected by the assimilation of nutrients under mass-rearing conditions. In the wild, the fly's life cycle suggests the acquisition of different organisms that could modulate its fitness and physiology. For A. obliqua, there is no information regarding microorganisms other than bacteria. This study analyzed bacteria, fungal, and archaea communities in the A. obliqua gut through denaturing gradient gel electrophoresis (DGGE) profiles of 16S (using a different set of primers for bacteria and archaea) and 18S ribosomal DNA markers. We found that wild flies presented higher microbial diversity related to fructose assimilation than laboratory species, suggesting that microorganisms have led to a specialized metabolism to process nutrients associated with an artificial diet. We identified species that have not been previously described in this fruit fly, especially actinobacteria and archaea, by employing different primer sets aimed at the same molecular marker but targeting diverse hypervariable regions of 16S rDNA. The possibility that Archaea affect fly fitness should not be ignored. This report on the intestinal microbial (bacteria, archaea, and fungi) composition of A. obliqua contributes to our understanding of the role of microorganisms in the development and physiology of the flies.

Integrative analysis of gut microbiota and fecal metabolites in metabolic associated fatty liver disease patients

Objective

Metabolic associated fatty liver disease (MAFLD) affects nearly a quarter of the world’s population. Our study aimed to characterize the gut microbiome and overall changes in the fecal and serum metabolomes in MAFLD patients.

Methods

Thirty-two patients diagnosed with MAFLD and 30 healthy individuals (control group, CG) were included in this study, the basic clinical characteristics and laboratory test results including routine biochemistry, etc. were recorded for all, and their serum and fecal samples were collected. A portion of the fecal samples was subjected to 16S rDNA sequencing, and the other portion of the fecal samples and serum samples were subjected to non-targeted metabolomic detection based on liquid chromatography-mass spectrometry (LC–MS). Statistical analysis of clinical data was performed using SPSS software package version 25.0 (SPSS Inc., Chicago, IL, United States). The analysis of 16S rDNA sequencing results was mainly performed by R software (V. 2.15.3), and the metabolomics data analysis was mainly performed by CD 3.1 software. Two-tailed p value < 0.05 was considered statistically significant.

Results

The 16S sequencing data suggested that the species richness and diversity of MAFLD patients were reduced compared with controls. At the phylum level, the relative abundance of Bacteroidota, Pseudomonadota, and Fusobacteriota increased and Bacillota decreased in MAFLD patients. At the genus level, the relative abundances of Prevotella, Bacteroides, Escherichia-Shigella, etc. increased. 2,770 metabolites were detected in stool samples and 1,245 metabolites were detected in serum samples. The proportion of differential lipid metabolites in serum (49%) was higher than that in feces (21%). There were 22 differential metabolites shared in feces and serum. And the association analysis indicated that LPC 18:0 was positively correlated with Christensenellaceae_R-7_group, Oscillospiraceae_UCG-002; neohesperidin was also positively correlated with Peptoniphilus, Phycicoccus, and Stomatobaculum.

Conclusion

Microbial sequencing data suggested decreased species richness and diversity and altered β-diversity in feces. Metabolomic analysis identified overall changes in fecal and serum metabolites dominated by lipid molecules. And the association analysis with gut microbes provided potentially pivotal gut microbiota-metabolite combinations in MAFLD patients, which might provide new clues for further research on the disease mechanism and the development of new diagnostic markers and treatments.

How Metagenomics Has Transformed Our Understanding of Bacteriophages in Microbiome Research

The microbiome is an essential part of most ecosystems. It was originally studied mostly through culturing but relatively few microbes can be cultured, so much of the microbiome was left unexplored. The emergence of metagenomic sequencing techniques changed that and allowed the study of microbiomes from all sorts of habitats. Metagenomic sequencing also allowed for a more thorough exploration of prophages, viruses that integrate into bacterial genomes, and how they benefit their hosts. One issue with using open-access metagenomic data is that sequences added to databases often have little to no metadata to work with, so finding enough sequences can be difficult. Many metagenomes have been manually curated but this is a time-consuming process and relies heavily on the uploader to be accurate and thorough when filling in metadata fields and the curators to be working with the same ontologies. Using algorithms to automatically sort metagenomes based on either the taxonomic profile or the functional profile may be a viable solution to the issues with manually curated metagenomes, but it requires that the algorithm is trained on carefully curated datasets and using the most informative profile possible in order to minimize errors.

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.

Evaluation of Full-Length Versus V4-Region 16S rRNA Sequencing for Phylogenetic Analysis of Mouse Intestinal Microbiota After a Dietary Intervention

The composition of microbial communities is commonly determined by sequence analyses of one of the variable (V) regions in the bacterial 16S rRNA gene. We aimed to assess whether sequencing the full-length versus the V4 region of the 16S rRNA gene affected the results and interpretation of an experiment. To test this, mice were fed a diet without and with the prebiotic inulin and from cecum samples, two primary data sets were generated: (1) a 16S rRNA full-length data set generated by the PacBio platform; (2) a 16S rRNA V4 region data set generated by the Illumina MiSeq platform. A third derived data set was generated by in silico extracting the 16S rRNA V4 region data from the 16S rRNA full-length PacBio data set. Analyses of the primary and derived 16S rRNA V4 region data indicated similar bacterial abundances, and α- and β-diversity. However, comparison of the 16S rRNA full-length data with the primary and derived 16S rRNA V4 region data revealed differences in relative bacterial abundances, and α- and β-diversity. We conclude that the sequence length of 16S rRNA gene and not the sequence analysis platform affected the results and may lead to different interpretations of the effect of an intervention that affects the microbiota.

Optimization and Evaluation of the 30S-S11 rRNA Gene for Taxonomic Profiling of Oral Streptococci

Dental caries is a multifactorial disease driven by interactions between the highly complex microbial biofilm community and host factors like diet, oral hygiene habits, and age. The oral streptococci are one of the most dominant members of the plaque biofilm and are implicated in disease but also in maintaining oral health. Current methods used for studying the supragingival plaque community commonly sequence portions of the16S rRNA gene, which often cannot taxonomically resolve members of the streptococcal community past the genus level due to their sequence similarity. The goal of this study was to design and evaluate a more reliable and cost-effective method to identify oral streptococci at the species level by applying a new locus, the 30S-S11 rRNA gene, for high-throughput amplicon sequencing. The study results demonstrate that the newly developed single-copy 30S-S11 gene locus resolved multiple amplicon sequence variants (ASVs) within numerous species, providing much improved taxonomic resolution over 16S rRNA V4. Moreover, the results reveal that different ASVs within a species were found to change in abundance at different stages of caries progression. These findings suggest that strains of a single species may perform distinct roles along a biochemical spectrum associated with health and disease. The improved identification of oral streptococcal species will provide a better understanding of the different ecological roles of oral streptococci and inform the design of novel oral probiotic formulations for prevention and treatment of dental caries. IMPORTANCE The microbiota associated with the initiation and progression of dental caries has yet to be fully characterized. Although much insight has been gained from 16S rRNA hypervariable region DNA sequencing, this approach has several limitations, including poor taxonomic resolution at the species level. This is particularly relevant for oral streptococci, which are abundant members of oral biofilm communities and major players in health and caries disease. Here, we develop a new method for taxonomic profiling of oral streptococci based on the 30S-S11 rRNA gene, which provides much improved resolution over 16S rRNA V4 (resolving 10 as opposed to 2 species). Importantly, 30S-S11 can resolve multiple amplicon sequence variants (ASVs) within species, providing an unprecedented insight into the ecological progression of caries. For example, our findings reveal multiple incidences of different ASVs within a species with contrasting associations with health or disease, a finding that has high relevance toward the informed design of prebiotic and probiotic therapy.

Management Practices Affecting Lesser Mealworm Larvae (Alphitobius diaperinus) Associated Microbial Community in a Broiler House and After Relocating With the Litter Into Pastureland

Lesser mealworms are often found infesting production houses used to raise broiler chickens. Previous studies have investigated pathogenic microorganisms associated with the larvae, but a more thorough study relating total microbiome changes due to management procedures and flock rotations was needed. Additionally, there is a question of what microbiota are transferred into the environment when the litter, in which larvae reside, is piled in pastureland for use as fertilizer and where interactions with the soil and other fauna can occur. This study chronicled, by the 16S rRNA sequencing, the bacterial community profile of larvae in a broiler grow-out house synchronizing to when birds were added to and removed from the house over 2.5 years. The profile was found to be relatively constant despite 11 flock rotations and management disruptions, specifically litter cleanout procedures and the addition of new birds or bedding. In contrast, once removed from the controlled broiler house environment and placed into open pastureland, the substantial microbial diversity brought with the larvae showed greater fluctuation in structure with environmental conditions, one of which was rainfall. Surprisingly, these larvae survived at least 19 weeks, so the potential for moving larval-associated microbes into the environment needs further assessment to minimize the risk of relocating foodborne pathogens and also to assess those bacteria-generating metabolites that have benefits to plant growth when using the litter as a fertilizer. The characterization of their microbiome is the first step to investigating the influences of their microbes on the manmade and environmental ecosystems.

Comparison Between Full-Length 16S rRNA Metabarcoding and Whole Metagenome Sequencing Suggests the Use of Either Is Suitable for Large-Scale Microbiome Studies

Since the number of studies of the microbial communities related to food and food-associated matrices almost completely reliant on next-generation sequencing techniques is rising, evaluations of these high-throughput methods are critical. Currently, the two most used sequencing methods to profile the microbiota of complex samples, including food and food-related matrices, are the 16S ribosomal RNA (rRNA) metabarcoding and the whole metagenome sequencing (WMS), both of which are powerful tools for the monitoring of foodborne pathogens and the investigation of the microbiome. Herein, the microbial profiles of 20 bulk tank milk filters from different dairy farms were investigated using both the full-length 16S (FL-16S) rRNA metabarcoding, a third-generation sequencing method whose application in food and food-related matrices is yet in its infancy, and the WMS, to evaluate the correlation and the reliability of these two methods to explore the microbiome of food-related matrices. Metabarcoding and metagenomic data were generated on a MinION platform (Oxford Nanopore Technologies) and on a Illumina NovaSeq 6000 platform, respectively. Our findings support the greater resolution of WMS in terms of both increased detection of bacterial taxa and enhanced detection of diversity; in contrast, FL-16S rRNA metabarcoding has proven to be a promising, less expensive, and more practical tool to profile most abundant taxa. The significant correlation of the two technologies both in terms of taxa diversity and richness, together with the similar profiles defined for both highly abundant taxa and core microbiomes, including Acinetobacter, Bacillus, and Escherichia genera, highlights the possible application of both methods for different purposes. This study allowed the first comparison of FL-16S rRNA sequencing and WMS to investigate the microbial composition of a food-related matrix, pointing out the advantageous use of FL-16S rRNA to identify dominant microorganisms and the superior power of WMS for the taxonomic detection of low abundant microorganisms and to perform functional analysis of the microbial communities.

Phytogenic supplement containing menthol, carvacrol and carvone ameliorates gut microbiota and production performance of commercial layers

Consumer push towards open and free-range production systems makes biosecurity on farms challenging, leading to increased disease and animal welfare issues. Phytogenic products are increasingly becoming a viable alternative for the use of antibiotics in livestock production. Here we present a study of the effects of commercial phytogenic supplement containing menthol, carvacrol and carvone on intestinal microbiota of layer hens, microbial functional capacity, and intestinal morphology. A total of 40,000 pullets were randomly assigned to two sides of the experimental shed. Growth performance, mortality, egg production and egg quality parameters were recorded throughout the trial period (18–30 weeks of age). Microbial community was investigated using 16S amplicon sequencing and functional difference using metagenomic sequencing. Phytogen supplemented birds had lower mortality and number of dirty eggs, and their microbial communities showed reduced richness. Although phytogen showed the ability to control the range of poultry pathogens, its action was not restricted to pathogenic taxa, and it involved functional remodelling the intestinal community towards increased cofactor production, heterolactic fermentation and salvage and recycling of metabolites. The phytogen did not alter the antimicrobial resistance profile or the number of antibiotic resistance genes. The study indicates that phytogenic supplementation can mimic the action of antibiotics in altering the gut microbiota and be used as their alternative in industry-scale layer production.

Metagenomics Approaches to Investigate the Neonatal Gut Microbiome

Early infancy is critical for the development of an infant's gut flora. Many factors can influence microbiota development during the pre- and postnatal periods, including maternal factors, antibiotic exposure, mode of delivery, dietary patterns, and feeding type. Therefore, investigating the connection between these variables and host and microbiome interactions in neonatal development would be of great interest. As the "unculturable" era of microbiome research gives way to an intrinsically multidisciplinary field, microbiome research has reaped the advantages of technological advancements in next-generation sequencing, particularly 16S rRNA gene amplicon and shotgun sequencing, which have considerably expanded our knowledge about gut microbiota development during early life. Using omics approaches to explore the neonatal microbiome may help to better understand the link between the microbiome and newborn diseases. Herein, we summarized the metagenomics methods and tools used to advance knowledge on the neonatal microbiome origin and evolution and how the microbiome shapes early and late individuals' lives for health and disease. The way to overcome limitations in neonatal microbiome studies will be discussed.

Ureaplasma and Prevotella colonization with Lactobacillus abundance during pregnancy facilitates term birth

Ureaplasma and Prevotella infections are well-known bacteria associated with preterm birth. However, with the development of metagenome sequencing techniques, it has been found that not all Ureaplasma and Prevotella colonizations cause preterm birth. The purpose of this study was to determine the association between Ureaplasma and Prevotella colonization with the induction of preterm birth even in the presence of Lactobacillus. In this matched case–control study, a total of 203 pregnant Korean women were selected and their cervicovaginal fluid samples were collected during mid-pregnancy. The microbiome profiles of the cervicovaginal fluid were analyzed using 16S rRNA gene amplification. Sequencing data were processed using QIIME1.9.1. Statistical analyses were performed using R software, and microbiome analysis was performed using the MicrobiomeAnalyst and Calypso software. A positive correlation between Ureaplasma and other genera was highly related to preterm birth, but interestingly, there was a negative correlation with Lactobacillus and term birth, with the same pattern observed with Prevotella. Ureaplasma and Prevotella colonization with Lactobacillus abundance during pregnancy facilitates term birth, although Ureaplasma and Prevotella are associated with preterm birth. Balanced colonization between Lactobacillus and Ureaplasma and Prevotella is important to prevent preterm birth.

Identification of shared and disease-specific host gene-microbiome associations across human diseases using multi-omic integration

While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus, are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene-microbiome associations that may influence disease outcomes.

Nanopore-based metagenomic analysis of the impact of nanoparticles on soil microbial communities

The current trend of using nanotechnology products in all spheres of human life, including for crop improvement may have a possible impact on soil microorganisms which influence soil and plant health. Nanopore-based metagenomic study reported here used full-length 16S rRNA gene sequences to assess shifts in community composition of soil microorganisms when treated with silver, titanium dioxide and zinc oxide nanoparticles (S-NP, T-NP, Z-NP, respectively). Firmicutes and Proteobacteria were the two dominant phyla in this soil, and there were no significant differences (p < 0.05) observed in these phyla across treatments. However, in the phylum Firmicutes, the abundance of the order Clostridiales showed a significant decrease (p < 0.05) in the presence of S-NP. Similarly, in the phylum Proteobacteria, a significant decrease in the presence of S-NP was seen for two orders, Vibrionales (p < 0.05) and Rhodobacterales (p < 0.01). Analysis at a further depth revealed that abundance of the genus Clostridium (order Clostridiales) decreased in the presence of both S-NP (p < 0.01) and T-NP (p < 0.05). The abundance of the genus Vibrio (order Vibrionales) was likewise impacted in the presence of all the three NPs — S-NP (p < 0.01), T-NP (p < 0.05) and Z-NP (p < 0.05). Analyses at high taxon ranks such as phyla may not give a good representation of the nature of microbial community shifts, and at times may paint an erroneous picture. The use of full-length 16S rRNA gene sequences here yielded a greater taxonomic depth, and some shifts at the lower ranks were discernible.

Taxonomy Informed Clustering, an Optimized Method for Purer and More Informative Clusters in Diversity Analysis and Microbiome Profiling

Bacterial diversity is often analyzed using 16S rRNA gene amplicon sequencing. Commonly, sequences are clustered based on similarity cutoffs to obtain groups reflecting molecular species, genera, or families. Due to the amount of the generated sequencing data, greedy algorithms are preferred for their time efficiency. Such algorithms rely only on pairwise sequence similarities. Thus, sometimes sequences with diverse phylogenetic background are clustered together. In contrast, taxonomic classifiers use position specific taxonomic information in assigning a probable taxonomy to a given sequence. Here we introduce Taxonomy Informed Clustering (TIC), a novel approach that utilizes classifier-assigned taxonomy to restrict clustering to only those sequences that share the same taxonomic path. Based on this concept, we offer a complete and automated pipeline for processing of 16S rRNA amplicon datasets in diversity analyses. First, raw reads are processed to form denoised amplicons. Next, the denoised amplicons are taxonomically classified. Finally, the TIC algorithm progressively assigning clusters at molecular species, genus and family levels. TIC outperforms greedy clustering algorithms like USEARCH and VSEARCH in terms of clusters’ purity and entropy, when using data from the Living Tree Project as test samples. Furthermore, we applied TIC on a dataset containing all Bifidobacteriaceae-classified sequences from the IMNGS database. Here, TIC identified evidence for 1000s of novel molecular genera and species. These results highlight the straightforward application of the TIC pipeline and superior results compared to former methods in diversity studies. The pipeline is freely available at: https://github.com/Lagkouvardos/TIC.

Microbial Identification Using rRNA Operon Region: Database and Tool for Metataxonomics with Long-Read Sequence

Recent development of long-read sequencing platforms has enabled researchers to explore bacterial community structure through analysis of full-length 16S rRNA gene (∼1,500 bp) or 16S-ITS-23S rRNA operon region (∼4,300 bp), resulting in higher taxonomic resolution than short-read sequencing platforms. Despite the potential of long-read sequencing in metagenomics, resources and protocols for this technology are scarce. Here, we describe MIrROR, the database and analysis tool for metataxonomics using the bacterial 16S-ITS-23S rRNA operon region. We collected 16S-ITS-23S rRNA operon sequences extracted from bacterial genomes from NCBI GenBank and performed curation. A total of 97,781 16S-ITS-23S rRNA operon sequences covering 9,485 species from 43,653 genomes were obtained. For user convenience, we provide an analysis tool based on a mapping strategy that can be used for taxonomic profiling with MIrROR database. To benchmark MIrROR, we compared performance against publicly available databases and tool with mock communities and simulated data sets. Our platform showed promising results in terms of the number of species covered and the accuracy of classification. To encourage active 16S-ITS-23S rRNA operon analysis in the field, BLAST function and taxonomic profiling results with 16S-ITS-23S rRNA operon studies, which have been reported as BioProject on NCBI are provided. MIrROR (http://mirror.egnome.co.kr/) will be a useful platform for researchers who want to perform high-resolution metagenome analysis with a cost-effective sequencer such as MinION from Oxford Nanopore Technologies. IMPORTANCE Metabarcoding is a powerful tool to investigate community diversity in an economic and efficient way by amplifying a specific gene marker region. With the advancement of long-read sequencing technologies, the field of metabarcoding has entered a new phase. The technologies have brought a need for development in several areas, including new markers that long-read can cover, database for the markers, tools that reflect long-read characteristics, and compatibility with downstream analysis tools. By constructing MIrROR, we met the need for a database and tools for the 16S-ITS-23S rRNA operon region, which has recently been shown to have sufficient resolution at the species level. Bacterial community analysis using the 16S-ITS-23S rRNA operon region with MIrROR will provide new insights from various research fields.

Increasing transparency and reproducibility in stroke-microbiota research: A toolbox for microbiota analysis

Homeostasis of gut microbiota is crucial in maintaining human health. Alterations, or "dysbiosis," are increasingly implicated in human diseases, such as cancer, inflammatory bowel diseases, and, more recently, neurological disorders. In ischemic stroke patients, gut microbial profiles are markedly different compared to healthy controls, whereas manipulation of microbiota in animal models of stroke modulates outcome, further implicating microbiota in stroke pathobiology. Despite this, evidence for the involvement of specific microbes or microbial products and microbial signatures have yet to be identified, likely owing to differences in methodology, data analysis, and confounding variables between different studies. Here, we provide a set of guidelines to enable researchers to conduct high-quality, reproducible, and transparent microbiota studies, focusing on 16S rRNA sequencing in the emerging subfield of the stroke-microbiota. In doing so, we aim to facilitate novel and reproducible associations between the microbiota and brain diseases, including stroke, and translation into clinical practice.

Microbiome Analysis via OTU and ASV-Based Pipelines-A Comparative Interpretation of Ecological Data in WWTP Systems

Linking community composition and ecosystem function via the cultivation-independent analysis of marker genes, e.g., the 16S rRNA gene, is a staple of microbial ecology and dependent disciplines. The certainty of results, independent of the bioinformatic handling, is imperative for any advances made within the field. In this work, thermophilic anaerobic co-digestion experimental data, together with primary and waste-activated sludge prokaryotic community data, were analyzed with two pipelines that apply different principles when dealing with technical, sequencing, and PCR biases. One pipeline (VSEARCH) employs clustering methods, generating individual operational taxonomic units (OTUs), while the other (DADA2) is based on sequencing error correction algorithms and generates exact amplicon sequence variants (ASVs). The outcomes of both pipelines were compared within the framework of ecological-driven data analysis. Both pipelines provided comparable results that would generally allow for the same interpretations. Yet, the two approaches also delivered community compositions that differed between 6.75% and 10.81% between pipelines. Inconsistencies were also observed linked to biologically driven variability in the samples, which affected the two pipelines differently. These pipeline-dependent differences in taxonomic assignment could lead to different conclusions and interfere with any downstream analysis made for such mis- or not-identified species, e.g., network analysis or predictions of their respective ecosystem service.

Alterations of the Gut Microbiome in Recurrent Malignant Gliomas Patients Received Bevacizumab and Temozolomide Combination Treatment and Temozolomide Monotherapy

This case-control study explored compositions of gut microbiome in recurrent malignant gliomas patients who had received bevacizumab and Temozolomide combination treatment and Temozolomide monotherapy. We investigated gut microbiota communities in feces of 29 recurrent malignant gliomas patients received combination treatment with bevacizumab and Temozolomide (Group 1) and monotherapy with Temozolomide alone (Group 2). We took advantage of the high-throughput Illumina Miseq sequencing technology by targeting the third and fourth hypervariable (V3-V4) regions of the 16S ribosomal RNA (rRNA) gene. We found that the structures and richness of the fecal microbiota in Group 1 were different from Group 2 with LEfSe analysis. The fecal microbiota in both Group 1 and Group 2 were mainly composed by Firmicutes, Proteobacteria, Bacteroidetes and Actinobacteria. However, Group 1 patients had higher relative abundance of Firmicutes, Bacteroidetes, Actinobacteria and lower relative abundance of Bacteroidetes and Cyanobacteria in their fecal microbiota than that in Group 2 patients. To evaluate bevacizumab involved post-treatment state of the fecal microbiota profile, we used random forest predictive model and ensembled decision trees with an AUC of 0.54. This study confirmed that the gut microbiota was different in recurrent malignant gliomas patients received the combination therapy of bevacizumab and Temozolomide compared with Temozolomide monotherapy. Our discover can help better understand the influence of bevacizumab related treatment on recurrent malignant gliomas patients. Therefore, this finding may also support the potentially therapeutic options for recurrent malignant gliomas patients such as fecal microbiota transplant.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12088-021-00962-2.

16S rRNA gene sequencing analysis of gut microbiome in a mini-pig diabetes model

BACKGROUND

Currently, increasing attention is being paid to the important role of intestinal microbiome in diabetes. However, few studies have evaluated the characteristics of gut microbiome in diabetic miniature pigs, despite it being a good model animal for assessing diabetes.

METHODS

In this study, a mini-pig diabetes model (DM) was established by 9-month high-fat diet (HFD) combined with low-dose streptozotocin, while the animals fed standard chow diet constituted the control group. 16S ribosomal RNA (rRNA) gene sequencing was performed to assess the characteristics of the intestinal microbiome in diabetic mini-pigs.

RESULTS

The results showed that microbial structure in diabetic mini-pigs was altered, reflected by increases in levels of Coprococcus_3 and Clostridium_sensu_stricto_1, which were positively correlated with diabetes, and decreases in levels of the bacteria Rikenellaceae, Clostridiales_vadinBB60_group, and Bacteroidales_RF16_group, which were inversely correlated with blood glucose and insulin resistance. Moreover, PICRUSt-predicted pathways related to the glycolysis and Entner-Doudoroff superpathway, enterobactin biosynthesis, and the l-tryptophan biosynthesis were significantly elevated in the DM group.

CONCLUSION

These results reveal the composition and predictive functions of the intestinal microbiome in the mini-pig diabetes model, further verifying the relationship between HFD, gut microbiome, and diabetes, and providing novel insights into the application of the mini-pig diabetes model in gut microbiome research.

RiboGrove: a database of full-length prokaryotic 16S rRNA genes derived from completely assembled genomes

16S rRNA gene is frequently used for the identification of prokaryotic organisms and for phylogeny inference. Several specialized public databases exist that contain complete and partial sequences of 16S rRNA genes. In this paper, we present RiboGrove: the first publicly available database that comprises only full-length sequences of 16S rRNA genes originating from completely assembled prokaryotic genomes deposited in RefSeq. Despite being strongly biased towards frequently sequenced genomes, RiboGrove is a useful complement to existing 16S rRNA resources and allows for analyses that would not be possible using amplicon-derived gene sequences. For instance, the absence of partial gene sequences in RiboGrove allowed us to make a summary of prokaryotic organisms, which lack core anti-Shine-Dalgarno sequence in their 16S rRNA genes. In this study, we describe the collected sequence data and present the results of exploratory data analysis of 16S rRNA gene sequences.

In-Silico Detection of Oral Prokaryotic Species With Highly Similar 16S rRNA Sequence Segments Using Different Primer Pairs

Although clustering by operational taxonomic units (OTUs) is widely used in the oral microbial literature, no research has specifically evaluated the extent of the limitations of this sequence clustering-based method in the oral microbiome. Consequently, our objectives were to: 1) evaluate in-silico the coverage of a set of previously selected primer pairs to detect oral species having 16S rRNA sequence segments with ≥97% similarity; 2) describe oral species with highly similar sequence segments and determine whether they belong to distinct genera or other higher taxonomic ranks. Thirty-nine primer pairs were employed to obtain the in-silico amplicons from the complete genomes of 186 bacterial and 135 archaeal species. Each fasta file for the same primer pair was inserted as subject and query in BLASTN for obtaining the similarity percentage between amplicons belonging to different oral species. Amplicons with 100% alignment coverage of the query sequences and with an amplicon similarity value ≥97% (ASI97) were selected. For each primer, the species coverage with no ASI97 (SC-NASI97) was calculated. Based on the SC-NASI97 parameter, the best primer pairs were OP_F053-KP_R020 for bacteria (region V1-V3; primer pair position for Escherichia coli J01859.1: 9-356); KP_F018-KP_R002 for archaea (V4; undefined-532); and OP_F114-KP_R031 for both (V3-V5; 340-801). Around 80% of the oral-bacteria and oral-archaea species analyzed had an ASI97 with at least one other species. These very similar species play different roles in the oral microbiota and belong to bacterial genera such as Campylobacter, Rothia, Streptococcus and Tannerella, and archaeal genera such as Halovivax, Methanosarcina and Methanosalsum. Moreover, ~20% and ~30% of these two-by-two similarity relationships were established between species from different bacterial and archaeal genera, respectively. Even taxa from distinct families, orders, and classes could be grouped in the same possible OTU. Consequently, regardless of the primer pair used, sequence clustering with a 97% similarity provides an inaccurate description of oral-bacterial and oral-archaeal species, which can greatly affect microbial diversity parameters. As a result, OTU clustering conditions the credibility of associations between some oral species and certain health and disease conditions. This significantly limits the comparability of the microbial diversity findings reported in oral microbiome literature.

The Influence of Kerosene on Microbiomes of Diverse Soils

One of the most important challenges for soil science is to determine the limits for the sustainable functioning of contaminated ecosystems. The response of soil microbiomes to kerosene pollution is still poorly understood. Here, we model the impact of kerosene leakage on the composition of the topsoil microbiome in pot and field experiments with different loads of added kerosene (loads up to 100 g/kg; retention time up to 360 days). At four time points we measured kerosene concentration and sequenced variable regions of 16S ribosomal RNA in the microbial communities. Mainly alkaline Dystric Arenosols with low content of available phosphorus and soil organic matter had an increased fraction of Actinobacteriota, Firmicutes, Nitrospirota, Planctomycetota, and, to a lesser extent, Acidobacteriota and Verrucomicobacteriota. In contrast, in highly acidic Fibric Histosols, rich in soil organic matter and available phosphorus, the fraction of Acidobacteriota was higher, while the fraction of Actinobacteriota was lower. Albic Luvisols occupied an intermediate position in terms of both physicochemical properties and microbiome composition. The microbiomes of different soils show similar response to equal kerosene loads. In highly contaminated soils, the proportion of anaerobic bacteria-metabolizing hydrocarbons increased, whereas the proportion of aerobic bacteria decreased. During the field experiment, the soil microbiome recovered much faster than in the pot experiments, possibly due to migration of microorganisms from the polluted area. The microbial community of Fibric Histosols recovered in 6 months after kerosene had been loaded, while microbiomes of Dystric Arenosols and Albic Luvisols did not restore even after a year.

Contrasting Community Assembly Mechanisms Underlie Similar Biogeographic Patterns of Surface Microbiota in the Tropical North Pacific Ocean

Marine microbiota are critical components of global biogeochemical cycles. However, the biogeographic patterns and ecological processes that structure them remain poorly understood, especially in the oligotrophic ocean. In this study, we used high-throughput sequencing of 16S and 18S rRNA genes to investigate the distribution patterns of bacterial and microeukaryotic communities and their assembly mechanisms in the surface waters of the tropical North Pacific Ocean. The fact that both the bacterial and the microeukaryotic communities showed similar distribution patterns (i.e., similar distance-decay patterns) and were clustered according to their geographic origin (i.e., the western tropical North Pacific and central tropical North Pacific) suggested that there was a significant biogeographic pattern of microbiota in the North Pacific Ocean. Indices of alpha diversity such as species richness, phylogenetic diversity, and the Shannon diversity index also differed significantly between regions. The correlations were generally similar between spatial and environmental variables and the alpha and beta diversities of bacteria and microeukaryotes across the entire region. The relative importance of ecological processes differed between bacteria and microeukaryotes: ecological drift was the principal mechanism that accounted for the structure of bacterial communities; heterogeneous selection, dispersal limitation, and ecological drift collectively explained much of the turnover of the microeukaryote communities.

IMPORTANCE Bacteria and microeukaryotes are extremely diverse groups in the ocean, where they regulate elemental cycling and energy flow. Studies of marine microbial ecology have benefited greatly from the rapid progress that has been made in genomic sequencing and theoretical microbial ecology. However, the spatial distribution of marine bacteria and microeukaryotes and the nature of the assembly mechanisms that determine their distribution patterns in oligotrophic marine waters are poorly understood. In this study, we used high-throughput sequencing methods to identify the distribution patterns and ecological processes of bacteria and microeukaryotes in an oligotrophic, tropical ocean. Our study showed that contrasting community assembly mechanisms underlaid similar biogeographic patterns of surface bacterial and microeukaryotic communities in the tropical North Pacific Ocean.

Defining and quantifying the core microbiome: Challenges and prospects

The term "core microbiome" has become widely used in microbial ecology over the last decade. Broadly, the core microbiome refers to any set of microbial taxa, or the genomic and functional attributes associated with those taxa, that are characteristic of a host or environment of interest. Most commonly, core microbiomes are measured as the microbial taxa shared among two or more samples from a particular host or environment. Despite the popularity of this term and its growing use, there is little consensus about how a core microbiome should be quantified in practice. Here, we present a brief history of the core microbiome concept and use a representative sample of the literature to review the different metrics commonly used for quantifying the core. Empirical analyses have used a wide range of metrics for quantifying the core microbiome, including arbitrary occurrence and abundance cutoff values, with the focal taxonomic level of the core ranging from phyla to amplicon sequence variants. However, many of these metrics are susceptible to sampling and other biases. Developing a standardized set of metrics for quantifying the core that accounts for such biases is necessary for testing specific hypotheses about the functional and ecological roles of core microbiomes.

ddPCR allows 16S rRNA gene amplicon sequencing of very small DNA amounts from low-biomass samples

BACKGROUND

One limiting factor of short amplicon 16S rRNA gene sequencing approaches is the use of low DNA amounts in the amplicon generation step. Especially for low-biomass samples, insufficient or even commonly undetectable DNA amounts can limit or prohibit further analysis in standard protocols.

RESULTS

Using a newly established protocol, very low DNA input amounts were found sufficient for reliable detection of bacteria using 16S rRNA gene sequencing compared to standard protocols. The improved protocol includes an optimized amplification strategy by using a digital droplet PCR. We demonstrate how PCR products are generated even when using very low concentrated DNA, unable to be detected by using a Qubit. Importantly, the use of different 16S rRNA gene primers had a greater effect on the resulting taxonomical profiles compared to using high or very low initial DNA amounts.

CONCLUSION

Our improved protocol takes advantage of ddPCR and allows faithful amplification of very low amounts of template. With this, samples of low bacterial biomass become comparable to those with high amounts of bacteria, since the first and most biasing steps are the same. Besides, it is imperative to state DNA concentrations and volumes used and to include negative controls indicating possible shifts in taxonomical profiles. Despite this, results produced by using different primer pairs cannot be easily compared.

Host species identity shapes the diversity and structure of insect microbiota

As for most of the life that inhabits our planet, microorganisms play an essential role in insect nutrition, reproduction, defence, and support their host in many other functions. More recently, we assisted to an exponential growth of studies describing the taxonomical composition of bacterial communities across insects' phylogeny. However, there is still an outstanding question that needs to be answered: Which factors contribute most to shape insects' microbiomes? This study tries to find an answer to this question by taking advantage of publicly available sequencing data and reanalysing over 4000 samples of insect-associated bacterial communities under a common framework. Results suggest that insect taxonomy has a wider impact on the structure and diversity of their associated microbial communities than the other factors considered (diet, sex, life stage, sample origin and treatment). However, when specifically testing for signatures of codiversification of insect species and their microbiota, analyses found weak support for this, suggesting that while insect species strongly drive the structure and diversity of insect microbiota, the diversification of those microbial communities did not follow their host's phylogeny. Furthermore, a parallel survey of the literature highlights several methodological limitations that need to be considered in the future research endeavours.

Next- and Third-Generation Sequencing Outperforms Culture-Based Methods in the Diagnosis of Ascitic Fluid Bacterial Infections of ICU Patients

OBJECTIVES

Infections of the ascitic fluid are serious conditions that require rapid diagnosis and treatment. Ascites is often accompanied by other critical pathologies such as gastrointestinal bleeding and bowel perforation, and infection increases the risk of mortality in intensive care patients. Owing to a relatively low success rate of conventional culture methods in identifying the responsible pathogens, new methods may be helpful to guide antimicrobial therapy and to refine empirical regimens. Here, we aim to assess outcomes and to identify responsible pathogens in ascitic fluid infections, in order to improve patients' care and to guide empirical therapy.

METHODS

Between October 2019 and March 2021, we prospectively collected 50 ascitic fluid samples from ICU patients with suspected infection. Beside standard culture-based microbiology methods, excess fluid underwent DNA isolation and was analyzed by next- and third-generation sequencing (NGS) methods.

RESULTS

NGS-based methods had higher sensitivity in detecting additional pathogenic bacteria such as E. faecalis and Klebsiella in 33 out of 50 (66%) ascitic fluid samples compared with culture-based methods (26%). Anaerobic bacteria were especially identified by sequencing-based methods in 28 samples (56%), in comparison with only three samples in culture. Analysis of clinical data showed a correlation between sequencing results and various clinical parameters such as peritonitis and hospitalization outcomes.

CONCLUSIONS

Our results show that, in ascitic fluid infections, NGS-based methods have a higher sensitivity for the identification of clinically relevant pathogens than standard microbiological culture diagnostics, especially in detecting hard-to-culture anaerobic bacteria. Patients with such infections may benefit from the use of NGS methods by the possibility of earlier and better targeted antimicrobial therapy, which has the potential to lower the high morbidity and mortality in critically ill patients with ascitic bacterial infection.

Species-Level Resolution of Female Bladder Microbiota from 16S rRNA Amplicon Sequencing

The human bladder contains bacteria, even in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing. However, the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA amplicon sequencing is commonly used to identify bacteria in urinary specimens, but it is typically restricted to genus-level identification. Our primary aim here was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA amplicon sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a reference database, a 16S rRNA gene variable region, and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible and that the reference database chosen is the most important component, followed by the 16S variable region sequenced. IMPORTANCE Accurate species-level identification from culture-independent techniques is of importance for microbial niches that are less well characterized, such as that of the bladder. 16S rRNA amplicon sequencing, a common culture-independent way to identify bacteria, is often critiqued for lacking species-level resolution. Here, we extensively evaluate classification schemes for species-level bacterial annotation of 16S amplicon data from bladder bacteria. Our results show that the proper choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show that this improvement can be achieved through the more careful application of existing methods and resources. Species-level information may deepen our understanding of associations between bacteria in the bladder and bladder conditions such as lower urinary tract symptoms and urinary tract infections.

Comparison of Periodontal Bacteria of Edo and Modern Periods Using Novel Diagnostic Approach for Periodontitis With Micro-CT

Ancient dental calculus, formed from dental plaque, is a rich source of ancient DNA and can provide information regarding the food and oral microbiology at that time. Genomic analysis of dental calculus from Neanderthals has revealed the difference in bacterial composition of oral microbiome between Neanderthals and modern humans. There are few reports investigating whether the pathogenic bacteria of periodontitis, a polymicrobial disease induced in response to the accumulation of dental plaque, were different between ancient and modern humans. This study aimed to compare the bacterial composition of the oral microbiome in ancient and modern human samples and to investigate whether lifestyle differences depending on the era have altered the bacterial composition of the oral microbiome and the causative bacteria of periodontitis. Additionally, we introduce a novel diagnostic approach for periodontitis in ancient skeletons using micro-computed tomography. Ancient 16S rDNA sequences were obtained from 12 samples at the Unko-in site (18th-19th century) of the Edo era (1603–1867), a characteristic period in Japan when immigrants were not accepted. Furthermore, modern 16S rDNA data from 53 samples were obtained from a database to compare the modern and ancient microbiome. The microbial co-occurrence network was analyzed based on 16S rDNA read abundance. Eubacterium species, Mollicutes species, and Treponema socranskii were the core species in the Edo co-occurrence network. The co-occurrence relationship between Actinomyces oricola and Eggerthella lenta appeared to have played a key role in causing periodontitis in the Edo era. However, Porphyromonas gingivalis, Fusobacterium nucleatum subsp. vincentii, and Prevotella pleuritidis were the core and highly abundant species in the co-occurrence network of modern samples. These results suggest the possibility of differences in the pathogens causing periodontitis during different eras in history.

Full-Length SSU rRNA Gene Sequencing Allows Species-Level Detection of Bacteria, Archaea, and Yeasts Present in Milk

Full-length SSU rRNA gene sequencing allows species-level identification of the microorganisms present in milk samples. Here, we used bulk-tank raw milk samples of two German dairies and detected, using this method, a great diversity of bacteria, archaea, and yeasts within the samples. Moreover, the species-level classification was improved in comparison to short amplicon sequencing. Therefore, we anticipate that this approach might be useful for the detection of possible mastitis-causing species, as well as for the control of spoilage-associated microorganisms. In a proof of concept, we showed that we were able to identify several putative mastitis-causing or mastitis-associated species such as Streptococcusuberis, Streptococcusagalactiae, Streptococcusdysgalactiae, Escherichiacoli and Staphylococcusaureus, as well as several Candida species. Overall, the presented full-length approach for the sequencing of SSU rRNA is easy to conduct, able to be standardized, and allows the screening of microorganisms in labs with Illumina sequencing machines.

Improved Microbial Community Characterization of 16S rRNA via Metagenome Hybridization Capture Enrichment

Environmental microbial diversity is often investigated from a molecular perspective using 16S ribosomal RNA (rRNA) gene amplicons and shotgun metagenomics. While amplicon methods are fast, low-cost, and have curated reference databases, they can suffer from amplification bias and are limited in genomic scope. In contrast, shotgun metagenomic methods sample more genomic regions with fewer sequence acquisition biases, but are much more expensive (even with moderate sequencing depth) and computationally challenging. Here, we develop a set of 16S rRNA sequence capture baits that offer a potential middle ground with the advantages from both approaches for investigating microbial communities. These baits cover the diversity of all 16S rRNA sequences available in the Greengenes (v. 13.5) database, with no sequence having <78% sequence identity to at least one bait for all segments of 16S. The use of our baits provide comparable results to 16S amplicon libraries and shotgun metagenomic libraries when assigning taxonomic units from 16S sequences within the metagenomic reads. We demonstrate that 16S rRNA capture baits can be used on a range of microbial samples (i.e., mock communities and rodent fecal samples) to increase the proportion of 16S rRNA sequences (average > 400-fold) and decrease analysis time to obtain consistent community assessments. Furthermore, our study reveals that bioinformatic methods used to analyze sequencing data may have a greater influence on estimates of community composition than library preparation method used, likely due in part to the extent and curation of the reference databases considered. Thus, enriching existing aliquots of shotgun metagenomic libraries and obtaining modest numbers of reads from them offers an efficient orthogonal method for assessment of bacterial community composition.