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

Gut-microbiota in children and adolescents with obesity: inferred functional analysis and machine-learning algorithms to classify microorganisms

The fecal microbiome of 55 obese children and adolescents (BMI-SDS 3.2 ± 0.7) and of 25 normal-weight subjects, matched both for age and sex (BMI-SDS - 0.3 ± 1.1) was analysed. Streptococcus, Acidaminococcus, Sutterella, Prevotella, Sutterella wadsworthensis, Streptococcus thermophilus, and Prevotella copri positively correlated with obesity. The inferred pathways strongly associated with obesity concern the biosynthesis pathways of tyrosine, phenylalanine, tryptophan and methionine pathways. Furthermore, polyamine biosynthesis virulence factors and pro-inflammatory lipopolysaccharide biosynthesis pathway showed higher abundances in obese samples, while the butanediol biosynthesis showed low abundance in obese subjects. Different taxa strongly linked with obesity have been related to an increased risk of multiple diseases involving metabolic pathways related to inflammation (polyamine and lipopolysaccharide biosynthesis). Cholesterol, LDL, and CRP positively correlated with specific clusters of microbial in obese patients. The Firmicutes/Bacteroidetes-ratio was lower in obese samples than in controls and differently from the literature we state that this ratio could not be a biomarker for obesity.

Tomato Plant Microbiota under Conventional and Organic Fertilization Regimes in a Soilless Culture System

Tomato is the main vegetable cultivated under soilless culture systems (SCSs); production of organic tomato under SCSs has increased due to consumer demands for healthier and environmentally friendly vegetables. However, organic tomato production under SCSs has been associated with low crop performance and fruit quality defects. These agricultural deficiencies could be linked to alterations in tomato plant microbiota; nonetheless, this issue has not been sufficiently addressed. Thus, the main goal of the present study was to characterize the rhizosphere and phyllosphere of tomato plants cultivated under conventional and organic SCSs. To accomplish this goal, tomato plants grown in commercial greenhouses under conventional or organic SCSs were tested at 8, 26, and 44 weeks after seedling transplantation. Substrate (n = 24), root (n = 24), and fruit (n = 24) composite samples were subjected to DNA extraction and high-throughput 16S rRNA gene sequencing. The present study revealed that the tomato core microbiota was predominantly constituted by Proteobacteria, Actinobacteria, and Firmicutes. Remarkably, six bacterial families, Bacillaceae, Microbacteriaceae, Nocardioidaceae, Pseudomonadaceae, Rhodobacteraceae, and Sphingomonadaceae, were shared among all substrate, rhizosphere, and fruit samples. Importantly, it was shown that plants under organic SCSs undergo a dysbiosis characterized by significant changes in the relative abundance of Bradyrhizobiaceae, Caulobacteraceae, Chitinophagaceae, Enterobacteriaceae, Erythrobacteraceae, Flavobacteriaceae, Nocardioidaceae, Rhodobacteraceae, and Streptomycetaceae. These results suggest that microbial alterations in substrates, roots, and fruits could be potential factors in contributing to the crop performance and fruit quality deficiencies observed in organic SCSs.

Evaluating the Reproducibility of Amplicon Sequencing Data Derived from Deep-Sea Cold Seep Sediment-Associated Microbiota

Benefiting from the rapid developments and wide applications of high-throughput sequencing, great advancements have been made in investigating microbiota, which are highly diverse and play key roles in both element cycling and the energy flow of ecosystems. There have been inherent limitations of amplicon sequencing that could introduce uncertainty and raise concerns about the accuracy and reproducibility of this technology. However, studies focusing on the reproducibility of amplicon sequencing are limited, especially in characterizing microbial communities in deep-sea sediments. To evaluate reproducibility, 118 deep-sea sediment samples were used for 16S rRNA gene sequencing in technical replicates (repeated measurements of the same sample) that demonstrate the variability of amplicon sequencing. The average occurrence-based overlaps were 35.98% and 27.02% between two and among three technical replicates, respectively, whereas their abundance-based overlaps reached 84.88% and 83.16%, respectively. Although variations of alpha and beta diversity indices were found between/among technical replicates, alpha diversity indices were similar across samples, and the average beta diversity indices were much smaller for technical replicates than among samples. Moreover, clustering methods (i.e., operational taxonomic units [OTUs] and amplicon sequence variants [ASVs]) were shown to have little impact on the alpha and beta diversity patterns of microbial communities. Taken together, although there are variations between/among technical replicates, amplicon sequencing is still a powerful tool with which to reveal diversity patterns of microbiota in deep-sea sediments. IMPORTANCE The reproducibility of amplicon sequencing is vital for whether the diversities of microbial communities could be accurately estimated. Thus, reproducibility influences the drawing of sound ecological conclusions. Nevertheless, few studies have focused on the reproducibility of microbial communities that are characterized by amplicon sequencing, and studies focusing on microbiota in deep-sea sediments have been especially lacking. In this study, we evaluated the reproducibility of amplicon sequencing targeting microbiota in deep-sea sediments of cold seep. Our results revealed that there were variations between/among technical replicates and that amplicon sequencing was still a powerful tool with which to characterize the diversities of microbial communities in deep-sea sediments. This study provides valuable guidelines for the reproducibility evaluation of future work in experimental design and interpretation.

The use of different 16S rRNA gene variable regions in biogeographical studies

16S rRNA gene amplicon sequencing is routinely used in environmental surveys to identify microbial diversity and composition of the samples of interest. The dominant sequencing technology of the past decade (Illumina) is based on the sequencing of 16S rRNA hypervariable regions. Online sequence data repositories, which represent an invaluable resource for investigating microbial distributional patterns across spatial, environmental or temporal scales, contain amplicon datasets from diverse 16S rRNA gene variable regions. However, the utility of these sequence datasets is potentially reduced by the use of different 16S rRNA gene amplified regions. By comparing 10 Antarctic soil samples sequenced for five different 16S rRNA amplicons, we explore whether sequence data derived from diverse 16S rRNA variable regions can be validly used as a resource for biogeographical studies. Patterns of shared and unique taxa differed among samples as a result of variable taxonomic resolutions of the assessed 16S rRNA variable regions. However, our analyses also suggest that the use of multi-primer datasets for biogeographical studies of the domain Bacteria is a valid approach to explore bacterial biogeographical patterns due to the preservation of bacterial taxonomic and diversity patterns across different variable region datasets. We deem composite datasets useful for biogeographical studies.

Microbial Diversity Biased Estimation Caused by Intragenomic Heterogeneity and Interspecific Conservation of 16S rRNA Genes

The 16S rRNA gene has been extensively used as a molecular marker to explore evolutionary relationships and profile microbial composition throughout various environments. Despite its convenience and prevalence, limitations are inevitable. Variable copy numbers, intragenomic heterogeneity, and low taxonomic resolution have caused biases in estimating microbial diversity. Here, analysis of 24,248 complete prokaryotic genomes indicated that the 16S rRNA gene copy number ranged from 1 to 37 in bacteria and 1 to 5 in archaea, and intragenomic heterogeneity was observed in 60% of prokaryotic genomes, most of which were below 1%. The overestimation of microbial diversity caused by intragenomic variation and the underestimation introduced by interspecific conservation were calculated when using full-length or partial 16S rRNA genes. Results showed that, at the 100% threshold, microbial diversity could be overestimated by as much as 156.5% when using the full-length gene. The V4 to V5 region-based analyses introduced the lowest overestimation rate (4.4%) but exhibited slightly lower species resolution than other variable regions under the 97% threshold. For different variable regions, appropriate thresholds rather than the canonical value 97% were proposed for minimizing the risk of splitting a single genome into multiple clusters and lumping together different species into the same cluster. This study has not only updated the 16S rRNA gene copy number and intragenomic variation information for the currently available prokaryotic genomes, but also elucidated the biases in estimating prokaryotic diversity with quantitative data, providing references for choosing amplified regions and clustering thresholds in microbial community surveys. IMPORTANCE Microbial diversity is typically analyzed using marker gene-based methods, of which 16S rRNA gene sequencing is the most widely used approach. However, obtaining an accurate estimation of microbial diversity remains a challenge, due to the intragenomic variation and low taxonomic resolution of 16S rRNA genes. Comprehensive examination of the bias in estimating such prokaryotic diversity using 16S rRNA genes within ever-increasing prokaryotic genomes highlights the importance of the choice of sequencing regions and clustering thresholds based on the specific research objectives.

The Role of Gut Microbiota in the Clinical Outcome of Septic Patients: State of the Art and Future Perspectives

Sepsis is a life-threatening multiple-organ dysfunction caused by a dysregulated host response to infection, with high mortality worldwide; 11 million deaths per year are attributable to sepsis in high-income countries. Several research groups have reported that septic patients display a dysbiotic gut microbiota, often related to high mortality. Based on current knowledge, in this narrative review, we revised original articles, clinical trials, and pilot studies to evaluate the beneficial effect of gut microbiota manipulation in clinical practice, starting from an early diagnosis of sepsis and an in-depth analysis of gut microbiota.

Lacticaseibacillus rhamnosus Strain GG (LGG) Regulate Gut Microbial Metabolites, an In Vitro Study Using Three Mature Human Gut Microbial Cultures in a Simulator of Human Intestinal Microbial Ecosystem (SHIME)

In the present research, we investigated changes in the gut metabolome that occurred in response to the administration of the Laticaseibacillus rhamnosus strain GG (LGG). The probiotics were added to the ascending colon region of mature microbial communities established in a human intestinal microbial ecosystem simulator. Shotgun metagenomic sequencing and metabolome analysis suggested that the changes in microbial community composition corresponded with changes to metabolic output, and we can infer linkages between some metabolites and microorganisms. The in vitro method permits a spatially-resolved view of metabolic transformations under human physiological conditions. By this method, we found that tryptophan and tyrosine were mainly produced in the ascending colon region, while their derivatives were detected in the transverse and descending regions, revealing sequential amino acid metabolic pathways along with the colonic tract. The addition of LGG appeared to promote the production of indole propionic acid, which is positively associated with human health. Furthermore, the microbial community responsible for the production of indole propionic acid may be broader than is currently known.

Selecting 16S rRNA Primers for Microbiome Analysis in a Host-Microbe System: The Case of the Jellyfish Rhopilema nomadica

Amplicon sequencing of the 16S rRNA gene is extensively used to characterize bacterial communities, including those living in association with eukaryotic hosts. Deciding which region of the 16S rRNA gene to analyze and selecting the appropriate PCR primers remains a major decision when initiating any new microbiome study. Based on a detailed literature survey of studies focusing on cnidarian microbiomes, we compared three commonly used primers targeting different hypervariable regions of the 16S rRNA gene, V1V2, V3V4, and V4V5, using the jellyfish Rhopilema nomadica as a model. Although all primers exhibit a similar pattern in bacterial community composition, the performance of the V3V4 primer set was superior to V1V2 and V4V5. The V1V2 primers misclassified bacteria from the Bacilli class and exhibited low classification resolution for Rickettsiales, which represent the second most abundant 16S rRNA gene sequence in all the primers. The V4V5 primer set detected almost the same community composition as the V3V4, but the ability of these primers to also amplify the eukaryotic 18S rRNA gene may hinder bacterial community observations. However, after overcoming the challenges possessed by each one of those primers, we found that all three of them show very similar bacterial community dynamics and compositions. Nevertheless, based on our results, we propose that the V3V4 primer set is potentially the most suitable for studying jellyfish-associated bacterial communities. Our results suggest that, at least for jellyfish samples, it may be feasible to directly compare microbial community estimates from different studies, each using different primers but otherwise similar experimental protocols. More generally, we recommend specifically testing different primers for each new organism or system as a prelude to large-scale 16S rRNA gene amplicon analyses, especially of previously unstudied host-microbe associations.

The Microbial Genetic Diversity and Succession Associated with Processing Waters at Different Broiler Processing Stages in an Abattoir in Australia

The high organic content of abattoir-associated process water provides an alternative for low-cost and non-invasive sample collection. This study investigated the association of microbial diversity from an abattoir processing environment with that of chicken meat. Water samples from scalders, defeathering, evisceration, carcass-washer, chillers, and post-chill carcass rinsate were collected from a large-scale abattoir in Australia. DNA was extracted using the Wizard^® Genomic DNA Purification Kit, and the 16S rRNA v3-v4 gene region was sequenced using Illumina MiSeq. The results revealed that the Firmicutes decreased from scalding to evisceration (72.55%) and increased with chilling (23.47%), with the Proteobacteria and Bacteroidota changing inversely. A diverse bacterial community with 24 phyla and 392 genera was recovered from the post-chill chicken, with Anoxybacillus (71.84%), Megamonas (4.18%), Gallibacterium (2.14%), Unclassified Lachnospiraceae (1.87%), and Lactobacillus (1.80%) being the abundant genera. The alpha diversity increased from scalding to chilling, while the beta diversity revealed a significant separation of clusters at different processing points (p = 0.01). The alpha- and beta-diversity revealed significant contamination during the defeathering, with a redistribution of the bacteria during the chilling. This study concluded that the genetic diversity during the defeathering is strongly associated with the extent of the post-chill contamination, and may be used to indicate the microbial quality of the chicken meat.

Protocol for microbiota analysis of a murine stroke model

Investigations on the microbiota in neurological diseases such as stroke are increasingly common; however, stroke researchers may have limited experience with designing such studies. Here, we describe a protocol to conduct a stroke microbiota study in mice, from experimental stroke surgery and sample collection to data analysis. We provide details on sample processing and sequencing and provide a reproducible data analysis pipeline. In doing so, we hope to enable researchers to conduct robust studies and facilitate identification of stroke-associated microbial signatures. For complete details on the use and execution of this protocol, please refer to Sorbie et al. (2022).1.

Periodontal treatment and microbiome-targeted therapy in management of periodontitis-related nonalcoholic fatty liver disease with oral and gut dysbiosis

A growing body of evidence from multiple areas proposes that periodontal disease, accompanied by oral inflammation and pathological changes in the microbiome, induces gut dysbiosis and is involved in the pathogenesis of nonalcoholic fatty liver disease (NAFLD). A subgroup of NAFLD patients have a severely progressive form, namely nonalcoholic steatohepatitis (NASH), which is characterized by histological findings that include inflammatory cell infiltration and fibrosis. NASH has a high risk of further progression to cirrhosis and hepatocellular carcinoma. The oral microbiota may serve as an endogenous reservoir for gut microbiota, and transport of oral bacteria through the gastro-intestinal tract can set up a gut microbiome dysbiosis. Gut dysbiosis increases the production of potential hepatotoxins, including lipopolysaccharide, ethanol, and other volatile organic compounds such as acetone, phenol and cyclopentane. Moreover, gut dysbiosis increases intestinal permeability by disrupting tight junctions in the intestinal wall, leading to enhanced translocation of these hepatotoxins and enteric bacteria into the liver through the portal circulation. In particular, many animal studies support that oral administration of Porphyromonas gingivalis, a typical periodontopathic bacterium, induces disturbances in glycolipid metabolism and inflammation in the liver with gut dysbiosis. NAFLD, also known as the hepatic phenotype of metabolic syndrome, is strongly associated with metabolic complications, such as obesity and diabetes. Periodontal disease also has a bidirectional relationship with metabolic syndrome, and both diseases may induce oral and gut microbiome dysbiosis with insulin resistance and systemic chronic inflammation cooperatively. In this review, we will describe the link between periodontal disease and NAFLD with a focus on basic, epidemiological, and clinical studies, and discuss potential mechanisms linking the two diseases and possible therapeutic approaches focused on the microbiome. In conclusion, it is presumed that the pathogenesis of NAFLD involves a complex crosstalk between periodontal disease, gut microbiota, and metabolic syndrome. Thus, the conventional periodontal treatment and novel microbiome-targeted therapies that include probiotics, prebiotics and bacteriocins would hold great promise for preventing the onset and progression of NAFLD and subsequent complications in patients with periodontal disease.

Are neuromodulation interventions associated with changes in the gut microbiota? A systematic review

The microbiota-gut-brain axis (MGBA) refers to the bidirectional communication between the brain and the gut microbiota and recent studies have linked the MGBA to health and disease. Research has so far investigated this axis mainly from microbiota to brain but less is known about the other direction. One approach to examine the MGBA from brain to microbiota is through understanding if and how neuromodulation might impact microbiota. Neuromodulation encompasses a wide range of stimulation techniques and is used to treat neurological, psychiatric and metabolic disorders, like Parkinson's Disease, depression and obesity. Here, we performed a systematic review to investigate whether neuromodulation is associated with subsequent changes in the gut microbiota. Searches in PsycINFO and MEDLINE were performed up to March 2022. Included studies needed to be clinical or preclinical studies comparing the effects of deep brain stimulation, electroconvulsive therapy, repetitive transcranial magnetic stimulation, transcranial direct current stimulation or vagal nerve stimulation on the gut microbiota before and after treatment or between active and control groups. Seven studies were identified. Neuromodulation was associated with changes in relative bacterial abundances, but not with (changes in) α-diversity or β-diversity. Summarizing, currently reported findings suggest that neuromodulation interventions are associated with moderate changes in the gut microbiome. However, findings remain inconclusive due to the limited number and varying quality of included studies, as well as the large heterogeneity between studies. More research is required to more conclusively establish whether, and if so, via which mechanism(s) of action neuromodulation interventions might influence the gut microbiota.

The Development of the Bacterial Community of Brown Trout (Salmo trutta) during Ontogeny

Brown trout (Salmo trutta) is an important aquaculture species in Germany, but its production faces challenges due to global warming and a high embryo mortality. Climate factors might influence the fish's bacterial community (BC) and thus increase embryo mortality. Yet, knowledge of the physiological BC during ontogeny in general is scarce. In this project, the BC of brown trout has been investigated in a period from unfertilized egg to 95 days post fertilization (dpf) using 16S rRNA gene amplicon sequencing. Developmental changes differed between early and late ontogeny and major differences in BC occurred especially during early developmental stages. Thus, analysis was conducted separately for 0 to 67 dpf and from 67 to 95 dpf. All analyzed stages were sampled in toto to avoid bias due to different sampling methods in different developmental stages. The most abundant phylum in the BC of all developmental stages was Pseudomonadota, while only two families (Comamonadaceae and Moraxellaceae) occurred in all developmental stages. The early developmental stages until 67 dpf displayed greater shifts in their BC regarding bacterial richness, microbial diversity, and taxonomic composition. Thereafter, in the fry stages, the BC seemed to stabilize and changes were moderate. In future studies, a reduction in the sampling time frames during early development, an increase in sampling numbers, and an attempt for biological reproduction in order to characterize the causes of these variations is recommended.

Revealing the Tick Microbiome: Insights into Midgut and Salivary Gland Microbiota of Female Ixodes ricinus Ticks

The ectoparasite Ixodes ricinus is an important vector for many tick-borne diseases (TBD) in the northern hemisphere, such as Lyme borreliosis, rickettsiosis, human granulocytic anaplasmosis, or tick-borne encephalitis virus. As climate change will lead to rising temperatures in the next years, we expect an increase in tick activity, tick population, and thus in the spread of TBD. Consequently, it has never been more critical to understand relationships within the microbial communities in ticks that might contribute to the tick's fitness and the occurrence of TBD. Therefore, we analyzed the microbiota in different tick tissues such as midgut, salivary glands, and residual tick material, as well as the microbiota in complete Ixodes ricinus ticks using 16S rRNA gene amplicon sequencing. By using a newly developed DNA extraction protocol for tick tissue samples and a self-designed mock community, we were able to detect endosymbionts and pathogens that have been described in the literature previously. Further, this study displayed the usefulness of including a mock community during bioinformatic analysis to identify essential bacteria within the tick.

Bioinformatic Approaches for Describing the Oral Microbiota

Cultivation-independent (molecular) analysis of the oral microbiota can provide a comprehensive picture of microbial community composition, yet there is an at-times bewildering array of approaches that can be employed. This chapter introduces some of the key considerations when undertaking microbiota research and describes two alternative bioinformatic pipelines for conducting such studies. The descriptions are based on analysis of bacterial 16S ribosomal RNA gene sequences, but can be easily adapted for analysis of other microbial taxa such as fungi.

Noninvasive Fecal Cytokine and Microbiota Profiles Predict Commencement of Necrotizing Enterocolitis in a Proof-of-Concept Study

Background and Aims

Necrotizing enterocolitis (NEC) is a life-threatening disease and the most common gastrointestinal emergency in premature infants. Accurate early diagnosis is challenging. Modified Bell's staging is routinely used to guide diagnosis, but early diagnostic signs are nonspecific, potentially leading to unobserved disease progression, which is problematic given the often rapid deterioration observed. We investigated fecal cytokine levels, coupled with gut microbiota profiles, as a noninvasive method to discover specific NEC-associated signatures that can be applied as potential diagnostic markers.

Methods

Premature babies born below 32 weeks of gestation were admitted to the 2-site neonatal intensive care unit (NICU) of Imperial College hospitals (St. Mary's or Queen Charlotte's & Chelsea) between January 2011 and December 2012. During the NICU stay, expert neonatologists grouped individuals by modified Bell's staging (healthy, NEC1, NEC2/3) and fecal samples from diapers were collected consecutively. Microbiota profiles were assessed by 16S rRNA gene amplicon sequencing and cytokine concentrations were measured by V-Plex multiplex assays.

Results

Early evaluation of microbiota profiles revealed only minor differences. However, at later time points, significant changes in microbiota composition were observed for Bacillota (adj. P = .0396), with Enterococcus being the least abundant in Bell stage 2/3 NEC. Evaluation of fecal cytokine levels revealed significantly higher concentrations of IL-1α (P = .045), IL-5 (P = .0074), and IL-10 (P = .032) in Bell stage 1 NEC compared to healthy individuals.

Conclusion

Differences in certain fecal cytokine profiles in patients with NEC indicate their potential use as diagnostic biomarkers to facilitate earlier diagnosis. Additionally, associations between microbial and cytokine profiles contribute to improving knowledge about NEC pathogenesis.

Amplicon Sequencing Pipelines in Metagenomics

Taxonomic profiling among a large number of samples is a fundamental task during amplicon sequencing analysis. The heterogeneity and technical noises in the sample handling, library preparation, and sequencing present a major challenge to how the biological conclusions are drawn from the data analysis, and accordingly, many tools have been developed to address specific issues related to each step of the data analysis. Nowadays, several sophisticated computational pipelines with flexible parameters are made available to provide one-stop comprehensive solutions by integrating various tools, which significantly mitigate the burden imposed by the complexity of the metagenomics data analysis. This chapter discusses the best practices related to the data generation and describes bioinformatics approaches to achieving greater accuracy from data processing. It offers two independent stepwise pipelines using mothur and DADA2 in a parallel way, presents the basic principles in the key steps of the analysis, and enables the comparisons between the two pipelines straightforwardly.

Perspective: Human Milk Composition and Related Data for National Health and Nutrition Monitoring and Related Research

National health and nutrition monitoring is an important federal effort in the United States and Canada, and the basis for many of their nutrition and health policies. Understanding of child exposures through human milk (HM) remains out of reach due to lack of current and representative data on HM's composition and intake volume. This article provides an overview of the current national health and nutrition monitoring activities for HM-fed children, HM composition (HMC) and volume data used for exposure assessment, categories of potential measures in HM, and associated variability factors. In this Perspective, we advocate for a framework for collection and reporting of HMC data for national health and nutrition monitoring and programmatic needs, including a shared vision for a publicly available Human Milk Composition Data Repository (HMCD-R) to include essential metadata associated with HMC. HMCD-R can provide a central, integrated platform for researchers and public health officials for compiling, evaluating, and sharing HMC data. The compiled compositional and metadata in HMCD-R would provide pertinent measures of central tendency and variability and allow use of modeling techniques to approximate compositional profiles for subgroups, providing more accurate exposure assessments for purposes of monitoring and surveillance. HMC and related metadata could facilitate understanding the complexity and variability of HM composition, provide crucial data for assessment of infant and maternal nutritional needs, and inform public health policies, food and nutrition programs, and clinical practice guidelines.

Oral Microbiome in Nonsmoker Patients with Oral Cavity Squamous Cell Carcinoma, Defined by Metagenomic Shotgun Sequencing

Objectives: Smoking is the commonest cause of oral cavity squamous cell carcinoma (OC-SCC), but the etiology of OC-SCC in nonsmokers is unknown. Our primary goal was to use metagenomic shotgun sequencing (MSS) to define the taxonomic composition and functional potential of oral metagenome in nonsmokers with OC-SCC. Methods: We conducted a case-control study with 42 OC-SCC case and 45 control nonsmokers. MSS was performed on DNA extracted from mouthwash samples. Taxonomic analysis and pathway analysis were done using MetaPhlAn2 and HUMAnN2, respectively. Statistical difference was determined using the Mann-Whitney test controlling false discovery rate. Results: There was no significant difference in age, sex, race, or alcohol consumption between OC-SCC and control patients. There was a significant difference in beta diversity between OC-SCC and controls. At the phylum level, Bacteroidetes and Synergistetes were overly represented in OC-SCC while Actinobacteria and Firmicutes were overly represented in controls. At the genus level, Fusobacterium was overly represented in OC-SCC compared with controls, while Corynebacterium, Streptococcus, Actinomyces, Cryptobacterium, and Selenomonas were overly represented in controls. Bacterial pathway analysis identified overrepresentation in OC-SCC of pathways related to metabolism of flavin, biotin, thiamin, heme, sugars, fatty acids, peptidoglycans, and tRNA and overrepresentation of nucleotides and essential amino acids in controls. Conclusions: The oral microbiome in nonsmoker patients with OC-SCC is significantly different from that of nonsmoker control patients in taxonomic compositions and functional potentials. Our study's MSS findings matched with previous 16S-based methods in taxonomic differentiation but varied greatly in functional differentiation of microbiomes in OC-SCC and controls.

Comparison of Metagenomics and Metatranscriptomics Tools: A Guide to Making the Right Choice

The study of microorganisms is a field of great interest due to their environmental (e.g., soil contamination) and biomedical (e.g., parasitic diseases, autism) importance. The advent of revolutionary next-generation sequencing techniques, and their application to the hypervariable regions of the 16S, 18S or 23S ribosomal subunits, have allowed the research of a large variety of organisms more in-depth, including bacteria, archaea, eukaryotes and fungi. Additionally, together with the development of analysis software, the creation of specific databases (e.g., SILVA or RDP) has boosted the enormous growth of these studies. As the cost of sequencing per sample has continuously decreased, new protocols have also emerged, such as shotgun sequencing, which allows the profiling of all taxonomic domains in a sample. The sequencing of hypervariable regions and shotgun sequencing are technologies that enable the taxonomic classification of microorganisms from the DNA present in microbial communities. However, they are not capable of measuring what is actively expressed. Conversely, we advocate that metatranscriptomics is a "new" technology that makes the identification of the mRNAs of a microbial community possible, quantifying gene expression levels and active biological pathways. Furthermore, it can be also used to characterise symbiotic interactions between the host and its microbiome. In this manuscript, we examine the three technologies above, and discuss the implementation of different software and databases, which greatly impact the obtaining of reliable results. Finally, we have developed two easy-to-use pipelines leveraging Nextflow technology. These aim to provide everything required for an average user to perform a metagenomic analysis of marker genes with QIMME2 and a metatranscriptomic study using Kraken2/Bracken.

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.