Interpreting 16S metagenomic data without clustering to achieve sub-OTU resolution

ASV vs OTUs clustering: Effects on alpha, beta, and gamma diversities in microbiome metabarcoding studies

In microbial community sequencing, involving bacterial ribosomal 16S rDNA or fungal ITS, the targeted genes are the basis for taxonomical assignment. The traditional bioinformatical procedure has for decades made use of a clustering protocol by which sequences are pooled into packages of shared percent identity, typically at 97%, to yield Operational Technical Units (OTUs). Progress in the data processing methods has however led to the possibility of minimizing technical sequencers errors, which were the main reason for the OTU choice, and to analyze instead the exact Amplicon Sequence Variants (ASV) which is a choice yielding much less agglomerated reads. We have tested the two procedures on the same 16S metabarcoded bacterial amplicons dataset encompassing a series of samples from 17 adjacent habitats, taken across a 700 meter-long transect of different ecological conditions unfolding in a gradient spanning from cropland, through meadows, forest and all successional transitions up to the seashore, within the same coastal area. This design allowed to scan a high biodiversity basin and to measure alpha, beta and gamma diversity of the area, to verify the effect of the bioinformatics on the same data as concerns the values of ten different ecological indexes and other parameters. Two levels of progressive OTUs clustering, (99% and 97%) were compared with the ASV data. The results showed that the OTUs clustering proportionally led to a marked underestimation of the ecological indicators values for species diversity and to a distorted behaviour of the dominance and evenness indexes with respect to the direct use of the ASV data. Multivariate ordination analyses resulted also sensitive in terms of tree topology and coherence. Overall, data support the view that reference-based OTU clustering carries several misleading disadvantageous biases, including the risk of missing novel taxa which are yet unreferenced in databases. Since its alternatives as de novo clustering have on the other hand drawbacks due to heavier computational demand and results comparability, especially for environmental studies which contain several yet uncharacterized species, the direct ASV based analysis, at least for prokaryotes, appears to warrant significand advantages in comparison to OTU clustering at every level of percent identity cutoff.

Clinical Investigation of Bacteriome in Primary Endodontic Infections With Apical Periodontitis Using High-Throughput Sequencing Analysis

INTRODUCTION

This study characterized the bacteriome in primary endodontic infection (PEI) with apical periodontitis (AP), identified core and rare bacteriome species and community diversity metrics, and analyzed the relationship between the bacteriome composition, diversity and features, and patient variables.

METHODS

Twenty-seven patients with PEI and AP were sampled. The DNA was extracted and quantified using quantitative polymerase chain reaction. Raw V3-V4 amplicon sequencing data were processed with the DADA2 pipeline to generate amplicon sequence variants, and taxonomic assignment of the amplicon sequence variants up to the species level was done against the Human Oral Microbiome Database. Core bacteriome and differential abundance analyses were performed using ANCOM. Alpha diversity was determined using Chao1, Shannon, and Simpson indexes. LeFse analysis was used to identify abundant taxa. Sparse Estimation of Correlations among Microbiomes analysis estimated linear and nonlinear relationships among bacteria.

RESULTS

Of 27, 24 root canal samples were analyzed, and 3 root canal sampling were filtered out with a low read count. The bacterial phyla with top mean relative abundance were Bacteroidetes, Firmicutes, Synergistetes, Fusobacteria, and Actinobacteria. A total of 113 genera and 215 species were identified. The samples were gathered into 3 clusters. LefSe analysis identified differences in abundant taxa between distinct age, gender, symptomatology, and lesion size groups. Sparse Estimation of Correlations among Microbiomes distance analysis indicated Slackia exigua as the node with the highest degree.

CONCLUSIONS

The bacteriome in PEI with AP among the patients in this study was complex and displayed high microbial heterogeneity. Moreover, age, gender, symptomatology, and lesion size were associated with differences in bacteriome features in PEI with AP.

Impact of root canal disinfection on the bacteriome present in primary endodontic infection: A next generation sequencing study

AIM

To investigate the bacteriome present in teeth with primary endodontic infection (PEI) and apical periodontitis (AP) and to determine quantitatively and qualitatively the impact of chemomechanical preparation (CMP) using 2.5% sodium hypochlorite NAOCl on the bacteriome found in PEI with AP using the Illumina MiSeq platform.

METHODOLOGY

Thirty-six paired samples from 18 patients were successfully sequenced and analysed. Samples were collected at two sampling times: before (s1) and after (s2) CMP using 2.5% NaOCl. The DNA was extracted from s1 and s2 samples and quantified using quantitative PCR (qPCR). All 36 samples were sequenced using the Illumina MiSeq platform. Raw V3-V4 amplicon sequencing data were processed with the DADA2 pipeline to generate amplicon sequence variants (ASVs). Alpha diversity metrics representing abundance (Chao1) and diversity and evenness (Shannon, Simpson) were computed. The paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2. The PERMANOVA method (with 999 permutations) was applied to compare community composition between sample types (s1 versus s2) and between patient IDs. ALDEx2 (ANOVA-like differential expression tool for high-throughput sequencing data) to investigate differentially abundant taxa between s1 and s2. A paired-sample Wilcoxon's test was used to compare alpha diversity metrics and qPCR counts between s1 and s2.

RESULTS

The qPCR counts were significantly higher in s1 compared to s2 (p = .0007). The Chao1 index indicated no difference in alpha diversity (p < .7019); whereas Shannon (p = .0056) and Simpson (p = .02685) indexes showed higher values in s2. The PERMANOVA test using Adonis2 showed a significant effect of sample time on community composition (R2 = .0630, p = .012). Patient ID also showed a significant effect on community composition (R2 = .6961, p = .001). At the genus level, Dialister, Mogibacterium, Prevotella, and Olsenella were differentially enriched at s1, while Actinomyces, Stenotrophomonas_unclassified, Enterococcus_unclassified, and Actinomyces_unclassified were differentially enriched in s2.

CONCLUSION

The bacteriome present in teeth with PEI with AP is complex and diverse. CMP using 2.5% NaOCl showed a high quantitatively and qualitatively disinfectant impact on the bacteriome present in PEI with AP.

The oral-gut microbiome axis in health and disease

The human body hosts trillions of microorganisms throughout many diverse habitats with different physico-chemical characteristics. Among them, the oral cavity and the gut harbour some of the most dense and diverse microbial communities. Although these two sites are physiologically distinct, they are directly connected and can influence each other in several ways. For example, oral microorganisms can reach and colonize the gastrointestinal tract, particularly in the context of gut dysbiosis. However, the mechanisms of colonization and the role that the oral microbiome plays in causing or exacerbating diseases in other organs have not yet been fully elucidated. Here, we describe recent advances in our understanding of how the oral and intestinal microbiota interplay in relation to their impact on human health and disease.

Population-level prokaryotic community structures associated with ferromanganese nodules in the Clarion-Clipperton Zone (Pacific Ocean) revealed by 16S rRNA gene amplicon sequencing

Although deep-sea ferromanganese nodules are a potential resource for exploitation, their formation mechanisms remain unclear. Several nodule-associated prokaryotic species have been identified by amplicon sequencing of 16S rRNA genes and are assumed to contribute to nodule formation. However, the recent development of amplicon sequence variant (ASV)-level monitoring revealed that closely related prokaryotic populations within an operational taxonomic unit often exhibit distinct ecological properties. Thus, conventional species-level monitoring might have overlooked nodule-specific populations when distinct populations of the same species were present in surrounding environments. Herein, we examined the prokaryotic community diversity of nodules and surrounding environments at the Clarion-Clipperton Zone in Japanese licensed areas by 16S rRNA gene amplicon sequencing with ASV-level resolution for three cruises from 2017 to 2019. Prokaryotic community composition and diversity were distinct by habitat type: nodule, nodule-surface mud, sediment, bottom water and water column. Most ASVs (~80%) were habitat-specific. We identified 178 nodule-associated ASVs and 41 ASVs associated with nodule-surface mud via linear discriminant effect size analysis. Moreover, several ASVs, such as members of SAR324 and Woeseia, were highly specific to nodules. These nodule-specific ASVs are promising targets for future investigation of the nodule formation process.

Mock microbial community meta-analysis using different trimming of amplicon read lengths

Trimming of sequencing reads is a pre-processing step that aims to discard sequence segments such as primers, adapters and low quality nucleotides that will interfere with clustering and classification steps. We evaluated the impact of trimming length of paired-end 16S and 18S rRNA amplicon reads on the ability to reconstruct the taxonomic composition and relative abundances of communities with a known composition in both even and uneven proportions. We found that maximizing read retention maximizes recall but reduces precision by increasing false positives. The presence of expected taxa was accurately predicted across broad trim length ranges but recovering original relative proportions remains a difficult challenge. We show that parameters that maximize taxonomic recovery do not simultaneously maximize relative abundance accuracy. Trim length represents one of several experimental parameters that have non-uniform impact across microbial clades, making it a difficult parameter to optimize. This study offers insights, guidelines, and helps researchers assess the significance of their decisions when trimming raw reads in a microbiome analysis based on overlapping or non-overlapping paired-end amplicons.

Microbiome depiction through user-adapted bioinformatic pipelines and parameters

Introduction. The role of the microbiome in health and disease continues to be increasingly recognized. However, there is significant variability in the bioinformatic protocols for analysing genomic data. This, in part, has impeded the potential incorporation of microbiomics into the clinical setting and has challenged interstudy reproducibility. In microbial compositional analysis, there is a growing recognition for the need to move away from a one-size-fits-all approach to data processing.Gap Statement. Few evidence-based recommendations exist for setting parameters of programs that infer microbiota community profiles despite these parameters significantly impacting the accuracy of taxonomic inference.Aim. To compare three commonly used programs (DADA2, QIIME2, and mothur) and optimize them into four user-adapted pipelines for processing paired-end amplicon reads. We aim to increase the accuracy of compositional inference and help standardize microbiomic protocol.Methods. Two key parameters were isolated across four pipelines: filtering sequence reads based on a whole-number error threshold (maxEE) and truncating read ends based on a quality score threshold (QTrim). Closeness of sample inference was then evaluated using a mock community of known composition.Results. We observed that raw genomic data lost were proportionate to how stringently parameters were set. Exactly how much data were lost varied by pipeline. Accuracy of sample inference correlated with increased sequence read retention. Falsely detected taxa and unaccounted for microbial constituents were unique to pipeline and parameter. Implementation of optimized parameter values led to better approximation of the known mock community.Conclusions. Microbial compositions generated based on the 16S rRNA marker gene should be interpreted with caution. To improve microbial community profiling, bioinformatic protocols must be user-adapted. Analysis should be performed with consideration for the select target amplicon, pipelines and parameters used, and taxa of interest.

The respiratory microbiota and its impact on health and disease in dogs and cats: A One Health perspective

Healthy lungs were long thought of as sterile, with presence of bacteria identified by culture representing contamination. Recent advances in metagenomics have refuted this belief by detecting rich, diverse, and complex microbial communities in the healthy lower airways of many species, albeit at low concentrations. Although research has only begun to investigate causality and potential mechanisms, alterations in these microbial communities (known as dysbiosis) have been described in association with inflammatory, infectious, and neoplastic respiratory diseases in humans. Similar studies in dogs and cats are scarce. The microbial communities in the respiratory tract are linked to distant microbial communities such as in the gut (ie, the gut-lung axis), allowing interplay of microbes and microbial products in health and disease. This review summarizes considerations for studying local microbial communities, key features of the respiratory microbiota and its role in the gut-lung axis, current understanding of the healthy respiratory microbiota, and examples of dysbiosis in selected respiratory diseases of dogs and cats.

Bacterial bioindicators enable biological status classification along the continental Danube river

Despite the importance of bacteria in aquatic ecosystems and their predictable diversity patterns across space and time, biomonitoring tools for status assessment relying on these organisms are widely lacking. This is partly due to insufficient data and models to identify reliable microbial predictors. Here, we show metabarcoding in combination with multivariate statistics and machine learning allows to identify bacterial bioindicators for existing biological status classification systems. Bacterial beta-diversity dynamics follow environmental gradients and the observed associations highlight potential bioindicators for ecological outcomes. Spatio-temporal links spanning the microbial communities along the river allow accurate prediction of downstream biological status from upstream information. Network analysis on amplicon sequence veariants identify as good indicators genera Fluviicola, Acinetobacter, Flavobacterium, and Rhodoluna, and reveal informational redundancy among taxa, which coincides with taxonomic relatedness. The redundancy among bacterial bioindicators reveals mutually exclusive taxa, which allow accurate biological status modeling using as few as 2-3 amplicon sequence variants. As such our models show that using a few bacterial amplicon sequence variants from globally distributed genera allows for biological status assessment along river systems.

Enhanced biological phosphorus removal in low-temperature sewage with iron-carbon SBR system

This study proposed an AO-SBR (Anaerobic Aerobic Sequencing Batch Reactor) combined with iron-carbon micro-electrolysis (ICME) particles system for sewage treatment at low temperature and explored the dephosphorisation mechanism and microbial community structure. The experimental results illustrated that ICME particles contributed to phosphorus removal, metabolic mechanism of poly-phosphorus accumulating organism (PAO) and microbial community structure in the AO-SBR system. The optimal treatment effect was achieved under the conditions of pH 7, DO 3.0 mg/L and particle dosage of 2.6 g Fe-C/g MLSS, and the removal rates of COD, TP, NH4+-N and TN reached 80.56%, 91.46%, 69.42% and 57.57%. The proportion of phosphorus accumulating organisms (PAOs) increased from 4.54% in the SBR system to 10.89% in the ICME-SBR system at 10°C. Additionally, the metabolic rate of PAOs was promoted, and the activities of DHA and ETS both reached the maximum value of 13.34 and 102.88 μg·mg-1VSS·h-1. These results suggest that the ICME particles could improve the performance of activated sludge under low-temperature conditions. This technology provides a new way for upgrading the performance of sewage treatment in the cold area.

Purified Plant-Derived Phenolic Acids Inhibit Salmonella Typhimurium without Alteration of Microbiota in a Simulated Chicken Cecum Condition

Salmonella enterica serovar Typhimurium (ST) remains a predominant zoonotic pathogen because of its colonization in poultry, survivability in the environment, and increasing antibiotic-resistance pattern. Plant-derived phenolics, gallic acid (GA), protocatechuic acid (PA), and vanillic acids (VA) have demonstrated antimicrobial activity in vitro; therefore, this study collected chicken cecal fluid and supplemented it with these phenolics to evaluate their potential for eliminating ST and mod-ulating the microbiota of complex environments. ST was quantified through plating, while micro-biome analysis was performed through pair-end 16S-rRNA gene sequencing. CFU/mL of ST in cecal fluid with GA was significantly reduced by 3.28 and 2.78 log at 24 h and 48 h, while PA only had a slight numerical decrease. VA significantly reduced ST by 4.81 and 5.20 log at 24 h and 48 h. Changes in relative abundance of major phyla were observed at 24 h for samples with GA and VA as Firmicute levels increased 8.30% and 20.90%, while Proteobacteria decreased 12.86% and 18.48%, respectively. Significant changes in major genre were observed in Acinetobacter (3.41% for GA) and Escherichia (13.53% for VA), while Bifidobacterium increased (3.44% for GA) and Lactobacillus remained unchanged. Results suggest that phenolic compounds exert different effects on certain pathogens, while supporting some commensal bacteria.

Amplicon-based next-generation sequencing for comparative analysis of root canal microbiome of teeth with primary and persistent/secondary endodontic infections

OBJECTIVES

To compare the root canal microbiome profiles of primary and persistent/secondary infections using high-throughput sequencing with the help of a reliable bioinformatics algorithm.

MATERIALS AND METHODS

Root canal samples of 10 teeth in the primary endodontic infection (PEI) group and 10 teeth in the persistent/secondary endodontic infection (SEI) group were included resulting in a total of 20 samples. After DNA extraction from the samples, sequencing was performed on the Illumina MiSeq platform. Pair-end Illumina reads were imported to QIIME 2; amplicon sequence variants (ASVs) generated by DADA2 were mapped to GreenGenes database. Weighted UniFrac distances were calculated and principal coordinates analysis (PCoA) was used to compare beta diversity patterns. The multiple response permutation procedure (MRPP), the analysis of similarities (ANOSIM), and permutational multivariate analysis of variance (adonis) were conducted for testing group differences. Linear discriminant analysis effect size (LEfSe) analysis was utilized to identify differentially abundant taxa between the groups. The linear discriminant analysis (LDA) score threshold was set to 4.0.

RESULTS

Within the Gram-negative facultative anaerobic Gammaproteobacteria class outgroup, two orders (Pasteurellales, Vibrionales) and two families (Pasteurellaceae, Vibrionaceae) were significantly more abundant in the PEI group, whereas Gram-positive bacteria, Actinomycetales order, and Gram-positive anaerobic taxa, one genus (Olsenella) and one species (Olsenella uli), were identified as significantly more abundant in the SEI group.

CONCLUSIONS

A few taxa were differentially abundant within either the PEI or SEI group.

CLINICAL RELEVANCE

Reliable bioinformatic tools are needed to define microbial profiles of endodontic infections. Based on a limited number of samples, no distinct variation was determined between the bacterial diversity of initial and recurrent endodontic infections.

Multiple phase transitions shape biodiversity of a migrating population

In a wide variety of natural systems, closely related microbial strains coexist stably, resulting in high levels of fine-scale biodiversity. However, the mechanisms that stabilize this coexistence are not fully understood. Spatial heterogeneity is one common stabilizing mechanism, but the rate at which organisms disperse throughout the heterogeneous environment may strongly impact the stabilizing effect that heterogeneity can provide. An intriguing example is the gut microbiome, where active mechanisms affect the movement of microbes and potentially maintain diversity. We investigate how biodiversity is affected by migration rate using a simple evolutionary model with heterogeneous selection pressure. We find that the biodiversity-migration rate relationship is shaped by multiple phase transitions, including a reentrant phase transition to coexistence. At each transition, an ecotype goes extinct and dynamics exhibit critical slowing down (CSD). CSD is encoded in the statistics of fluctuations due to demographic noise-this may provide an experimental means for detecting and altering impending extinction.

Prevalence of Viral Frequency-Dependent Infection in Coastal Marine Prokaryotes Revealed Using Monthly Time Series Virome Analysis

Viruses infecting marine prokaryotes have a large impact on the diversity and dynamics of their hosts. Model systems suggest that viral infection is frequency dependent and constrained by the virus-host encounter rate. However, it is unclear whether frequency-dependent infection is pervasive among the abundant prokaryotic populations with different temporal dynamics. To address this question, we performed a comparison of prokaryotic and viral communities using 16S rRNA amplicon and virome sequencing based on samples collected monthly for 2 years at a Japanese coastal site, Osaka Bay. Concurrent seasonal shifts observed in prokaryotic and viral community dynamics indicated that the abundance of viruses correlated with that of their predicted host phyla (or classes). Cooccurrence network analysis between abundant prokaryotes and viruses revealed 6,423 cooccurring pairs, suggesting a tight coupling of host and viral abundances and their "one-to-many" correspondence. Although stable dominant species, such as SAR11, showed few cooccurring viruses, a fast succession of their viruses suggests that viruses infecting these populations changed continuously. Our results suggest that frequency-dependent viral infection prevails in coastal marine prokaryotes regardless of host taxa and temporal dynamics. IMPORTANCE There is little room for doubt that viral infection is prevalent among abundant marine prokaryotes regardless of their taxa or growth strategy. However, comprehensive evaluations of viral infections in natural prokaryotic communities are still technically difficult. In this study, we examined viral infection in abundant prokaryotes by monitoring the monthly dynamics of prokaryotic and viral communities at a eutrophic coastal site, Osaka Bay. We compared the community dynamics of viruses with those of their putative hosts based on genome-based in silico host prediction. We observed frequent cooccurrence among the predicted virus-host pairs, suggesting that viral infection is prevalent in abundant prokaryotes regardless of their taxa or temporal dynamics. This likely indicates that frequent lysis of the abundant prokaryotes via viral infection has a considerable contribution to the biogeochemical cycling and maintenance of prokaryotic community diversity.

Coupled C, H, N, S and Fe biogeochemical cycles operating in the continental deep subsurface of the Iberian Pyrite Belt

Microbial activity is a major contributor to the biogeochemical cycles that make up the life support system of planet Earth. A 613 m deep geomicrobiological perforation and a systematic multi-analytical characterization revealed an unexpected diversity associated with the rock matrix microbiome that operates in the subsurface of the Iberian Pyrite Belt (IPB). Members of 1 class and 16 genera were deemed the most representative microorganisms of the IPB deep subsurface and selected for a deeper analysis. The use of fluorescence in situ hybridization allowed not only the identification of microorganisms but also the detection of novel activities in the subsurface such as anaerobic ammonium oxidation (ANAMMOX) and anaerobic methane oxidation, the co-occurrence of microorganisms able to maintain complementary metabolic activities and the existence of biofilms. The use of enrichment cultures sensed the presence of five different complementary metabolic activities along the length of the borehole and isolated 29 bacterial species. Genomic analysis of nine isolates identified the genes involved in the complete operation of the light-independent coupled C, H, N, S and Fe biogeochemical cycles. This study revealed the importance of nitrate reduction microorganisms in the oxidation of iron in the anoxic conditions existing in the subsurface of the IPB.

Characterization of Microbial Communities and Naturally Occurring Radionuclides in Soilless Growth Media Amended with Different Concentrations of Biochar

Biochar, derived from the pyrolysis of plant materials has the potential to enhance plant growth in soilless media. Howevetar, little is known about the impact of biochar amendments to soilless growth media, microbial community composition, and fate of chemical constituents in the media. In this study, different concentrations of biochar were added to soilless media and microbial composition, and chemical constituents were analyzed using metagenomics and gamma spectroscopy techniques, respectively. Across treatments, carboxyl-C, phenolic-C, and aromatic-C were the main carbon sources that influenced microbial community composition. Flavobacterium (39.7%), was the predominantly bacteria genus, followed by Acidibacter (12.2%), Terrimonas (10.1%), Cytophaga (7.5%), Ferruginibacter (6.0%), Lacunisphaera (5.9%), Cellvibrio (5.8%), Opitutus (4.8%), Mucilaginibacter (4.0%) and Bryobacter (4.0%). Negative relationships were found between Cytophaga and 226Ra (r = −0.84, p = 0.0047), 40K (r = −0.82, p = 0.0069) and 137Cs (r = −0.93, p = 0.0002). Similarly, Mucilaginibacter was negatively correlated with 226Ra (r = −0.83, p = 0.0054) and 137Cs (r = −0.87, p = 0.0021). Overall, the data suggest that high % biochar amended samples have high radioactivity concentration levels. Some microorganisms have less presence in high radioactivity concentration levels.

Association between Sleeping Patterns and Mealtime with Gut Microbiome: A Pilot Study

BACKGROUND

Disruptions in sleep related to mealtime may contribute to gut microbial imbalances, and put individuals at higher risk for metabolic diseases. The aim of this pilot study was to investigate the relationships between late-night eating habits and sleep quality and duration, with gut microbiota (GM) profiles.

METHODS

In this cross-sectional study, 36 men referred to a clinic were enrolled. In addition to demographic information, each participant completed questionnaires regarding medical history, physical activity, late-night eating habits, sleep quality and sleep duration. The scores from these questionnaires were used to categorize study participants into the following groups: sleep quality (good or poor), late-night eating (yes or no) and sleep duration (<7 or ≥7 hours). Five grams of stool was also obtained from each participant for GM profiling analysis by sequencing.

RESULTS

The mean age of the study population was 42.1 ± 1.6 years. Firmicutes and Actinobacteria were the two dominant phyla present in all participant samples. Differences in the relative abundance of GM at each taxonomic rank between study groups were insignificant. Only Erysipelotrichales at the order level were found to be significantly different between individuals who had late-night eating habits and those who did not (P & q < 0.05). No other parameter demonstrated a significant difference in GM profiles of participants.

CONCLUSION

In this pilot study, we found Erysipelotrichales to be more abundant in individuals with late-night eating habits. Studies with higher sample sizes are warranted to better delineate the possible effects of time of eating on microbial composition.

Analysis of Fecal, Salivary, and Tissue Microbiome in Barrett's Esophagus, Dysplasia, and Esophageal Adenocarcinoma

Background and Aims

Esophageal adenocarcinoma (EAC) incidence has risen dramatically in the Western countries over the past decades. The underlying reasons are incompletely understood, and shifts in the esophageal microbiome have been postulated to increase predisposition to disease development. Multiple factors including medications, lifestyle, and diet could influence microbiome composition and disease progression. The aim of this study was (1) to identify a feasible method to characterize the tissue-associated microbiome, and (2) to investigate differences in the microbiome of saliva, esophageal tissue, and fecal samples by disease state and validate with 2 external cohorts.

Methods

Forty-eight patients (15 Barrett's esophagus [BE], 4 dysplasia, 15 EAC, and 14 healthy) were enrolled in this cross-sectional study (Munich cohort). Demographics, epidemiologic and clinical data, medications, smoking, and alcohol consumption were assessed. 16S rRNA Gene sequencing was performed on saliva, tissue biopsy and fecal samples. PAXgene fixation was used as a novel methodology. Microbial community alpha- and beta-diversity, as well as microbial composition at phylum and genus level, were characterized for this cohort and compared with 2 external cohorts: New York cohort and Cooperative Health Research in the Augsburg Region cohort.

Results

We first established PAXgene fixation is a feasible method for microbiome analysis and utilized it to identify a distinct microbial shift in tissue biopsies from patients with EAC, whereas overall microbial diversity in salivary and fecal samples did not differ significantly between disease states. Our findings were similar in a reanalysis to those from a US cohort that used a standardized fresh frozen biopsy collection protocol (New York cohort, N = 75 biopsies). Nevertheless, we could not distinguish German Munich cohort patients from a German population-based cohort (Cooperative Health Research in the Augsburg Region cohort, N = 2140 individuals) when fecal bacterial profiles were compared between both cohorts. In addition, we used data integration of diagnosis and risk factors of patients and found associations with microbiome alterations.

Conclusion

Sample collection and microbiome analysis are indeed feasible and can be implemented into clinical routine by an easy-to-use biopsy protocol. The presence of BE and EAC together with epidemiologic factors can be associated with alterations of the salivary, tissue, and fecal microbial community in an easy-to-use data integration concept. Given a possible role of the microbiome in BE and EAC, it will be important in future studies to take tissue-specific microbial communities and individual taxa into account in larger prospective studies.

Unsupervised Bayesian Ising Approximation for decoding neural activity and other biological dictionaries

The problem of deciphering how low-level patterns (action potentials in the brain, amino acids in a protein, etc.) drive high-level biological features (sensorimotor behavior, enzymatic function) represents the central challenge of quantitative biology. The lack of general methods for doing so from the size of datasets that can be collected experimentally severely limits our understanding of the biological world. For example, in neuroscience, some sensory and motor codes have been shown to consist of precisely timed multi-spike patterns. However, the combinatorial complexity of such pattern codes have precluded development of methods for their comprehensive analysis. Thus, just as it is hard to predict a protein's function based on its sequence, we still do not understand how to accurately predict an organism's behavior based on neural activity. Here, we introduce the unsupervised Bayesian Ising Approximation (uBIA) for solving this class of problems. We demonstrate its utility in an application to neural data, detecting precisely timed spike patterns that code for specific motor behaviors in a songbird vocal system. In data recorded during singing from neurons in a vocal control region, our method detects such codewords with an arbitrary number of spikes, does so from small data sets, and accounts for dependencies in occurrences of codewords. Detecting such comprehensive motor control dictionaries can improve our understanding of skilled motor control and the neural bases of sensorimotor learning in animals. To further illustrate the utility of uBIA, we used it to identify the distinct sets of activity patterns that encode vocal motor exploration versus typical song production. Crucially, our method can be used not only for analysis of neural systems, but also for understanding the structure of correlations in other biological and nonbiological datasets.

The early life microbiota mediates maternal effects on offspring growth in a nonhuman primate

Maternal parity can impact offspring growth, but the mechanisms driving this effect are unclear. Here, we test the hypothesis that vertically transmitted microbiota may be one potential mechanism. We analyzed 118 fecal and milk samples from mother-offspring vervet monkey dyads across the first 6 months of life. Despite poorer milk production, offspring born to low parity females grew larger than their counterparts. These offspring exhibited reduced alpha diversity in the first days of life, stronger seeding of maternal milk microbiota, Bacteroides fragilis dominance, and a greater abundance of glycan utilization pathways. Moreover, the attainment of greater body mass by 6 months of age was mediated by reduced early life alpha diversity and B. fragilis dominance. This work demonstrates that the establishment of a specialized, milk-oriented gut microbiota promotes infant growth and suggests an evolutionarily conserved developmental role of B. fragilis in primates.

Interactions between strains govern the eco-evolutionary dynamics of microbial communities

Genomic data has revealed that genotypic variants of the same species, that is, strains, coexist and are abundant in natural microbial communities. However, it is not clear if strains are ecologically equivalent, and at what characteristic genetic distance they might exhibit distinct interactions and dynamics. Here, we address this problem by tracking 10 taxonomically diverse microbial communities from the pitcher plant Sarracenia purpurea in the laboratory for more than 300 generations. Using metagenomic sequencing, we reconstruct their dynamics over time and across scales, from distant phyla to closely related genotypes. We find that most strains are not ecologically equivalent and exhibit distinct dynamical patterns, often being significantly more correlated with strains from another species than their own. Although even a single mutation can affect laboratory strains, on average, natural strains typically decouple in their dynamics beyond a genetic distance of 100 base pairs. Using mathematical consumer-resource models, we show that these taxonomic patterns emerge naturally from ecological interactions between community members, but only if the interactions are coarse-grained at the level of strains, not species. Finally, by analyzing genomic differences between strains, we identify major functional hubs such as transporters, regulators, and carbohydrate-catabolizing enzymes, which might be the basis for strain-specific interactions. Our work suggests that fine-scale genetic differences in natural communities could be created and stabilized via the rapid diversification of ecological interactions between strains.

Gut microbiota profile in patients with nonalcoholic fatty liver disease and presumed nonalcoholic steatohepatitis

Background:

The main composition of intestinal microbiota in nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH) patients has not yet been elucidated. In this, case-control study, we identified differences of intestinal microbiota in male patients with NAFLD, presumed NASH, and healthy controls.

Materials and Methods:

We compared gut microbial composition of 25 patients with NAFLD, 13 patients with presumed NASH, and 12 healthy controls. Demographic information as well as clinical, nutritional, and physical activity data was gathered. Stool and blood samples were collected to perform the laboratory analysis. The taxonomic composition of gut microbiota was assessed using V4 regions of microbial small subunit ribosomal Ribonucleic acid genes sequencing of stool samples.

Results:

Firmicutes, Actinobacteria, and Bacteroidetes were the most frequently phyla in all groups. Our results revealed that Veillonella was the only genus with significantly different amounts in presumed NASH patients compared with patients with NAFLD (P = 2.76 × 10⁻⁶, q = 2.07 × 10⁻⁴, logFC = 5.52).

Conclusion:

This pilot study was the first study to compare gut microbial composition in patients with NAFLD and presumed NASH in the Middle East. Given the potential effects of gut microbiota on the management and prevention of NAFLD, larger, prospective studies are recommended to confirm this study's findings.

Biomonitoring of Fungal and Oomycete Plant Pathogens by Using Metabarcoding

Fungal and oomycete plant pathogens are responsible for the devastation of various ecosystems such as forest and crop species worldwide. In an effort to protect such natural resources for food, lumber, etc., early detection of non-indigenous phytopathogenic fungi in new areas is a key approach in managing threats at their source of introduction. A workflow was developed using high-throughput sequencing (HTS), more specifically metabarcoding, a method for rapid and higher throughput species screening near high-risk areas, and over larger geographical spaces. Biomonitoring of fungal and oomycete entities of plant pathogens (e.g., airborne spores) regained from environmental samples and their processing by metabarcoding is thoroughly described here. The amplicon-based approach goes from DNA and sequencing library preparation using custom-designed polymerase chain reaction (PCR) fusion primers that target the internal transcribed spacer 1 (ITS1) from fungi and oomycetes and extends to multiplex HTS with the Ion Torrent platform. In addition, a brief and simplified overview of the bioinformatics analysis pipeline and other available tools required to process amplicon sequences is also included. The raw data obtained and processed enable users to select a bioinformatics pipeline in order to directly perform biodiversity, presence/absence, geographical distribution, and abundance analyses through the tools suggested, which allows for accelerated identification of phytopathogens of interest.

Knockout of Anopheles stephensi immune gene LRIM1 by CRISPR-Cas9 reveals its unexpected role in reproduction and vector competence

PfSPZ Vaccine against malaria is composed of Plasmodium falciparum (Pf) sporozoites (SPZ) manufactured using aseptically reared Anopheles stephensi mosquitoes. Immune response genes of Anopheles mosquitoes such as Leucin-Rich protein (LRIM1), inhibit Plasmodium SPZ development (sporogony) in mosquitoes by supporting melanization and phagocytosis of ookinetes. With the aim of increasing PfSPZ infection intensities, we generated an A. stephensi LRIM1 knockout line, Δaslrim1, by embryonic genome editing using CRISPR-Cas9. Δaslrim1 mosquitoes had a significantly increased midgut bacterial load and an altered microbiome composition, including elimination of commensal acetic acid bacteria. The alterations in the microbiome caused increased mosquito mortality and unexpectedly, significantly reduced sporogony. The survival rate of Δaslrim1 mosquitoes and their ability to support PfSPZ development, were partially restored by antibiotic treatment of the mosquitoes, and fully restored to baseline when Δaslrim1 mosquitoes were produced aseptically. Deletion of LRIM1 also affected reproductive capacity: oviposition, fecundity and male fertility were significantly compromised. Attenuation in fecundity was not associated with the altered microbiome. This work demonstrates that LRIM1's regulation of the microbiome has a major impact on vector competence and longevity of A. stephensi. Additionally, LRIM1 deletion identified an unexpected role for this gene in fecundity and reduction of sperm transfer by males.

High-resolution microbiome analysis enabled by linking of 16S rRNA gene sequences with adjacent genomic contexts

Sequence-based characterization of bacterial communities has long been a hostage of limitations of both 16S rRNA gene and whole metagenome sequencing. Neither approach is universally applicable, and the main efforts to resolve constraints have been devoted to improvement of computational prediction tools. Here, we present semi-targeted 16S rRNA sequencing (st16S-seq), a method designed for sequencing V1-V2 regions of the 16S rRNA gene along with the genomic locus upstream of the gene. By in silico analysis of 13 570 bacterial genome assemblies, we show that genome-linked 16S rRNA sequencing is superior to individual hypervariable regions or full-length gene sequences in terms of classification accuracy and identification of gene copy numbers. Using mock communities and soil samples we experimentally validate st16S-seq and benchmark it against the established microbial classification techniques. We show that st16S-seq delivers accurate estimation of 16S rRNA gene copy numbers, enables taxonomic resolution at the species level and closely approximates community structures obtainable by whole metagenome sequencing.

On the use of sequence-quality information in OTU clustering

Background

High-throughput sequencing has become an essential technology in life science research. Despite continuous improvements in technology, the produced sequences are still not entirely accurate. Consequently, the sequences are usually equipped with error probabilities. The quality information is already employed to find better solutions to a number of bioinformatics problems (e.g. read mapping). Data processing pipelines benefit in particular (especially when incorporating the quality information early), since enhanced outcomes of one step can improve all subsequent ones. Preprocessing steps, thus, quite regularly consider the sequence quality to fix errors or discard low-quality data. Other steps, however, like clustering sequences into operational taxonomic units (OTUs), a common task in the analysis of microbial communities, are typically performed without making use of the available quality information.

Results

In this paper, we present quality-aware clustering methods inspired by quality-weighted alignments and model-based denoising, and explore their applicability to OTU clustering. We implemented the quality-aware methods in a revised version of our de novo clustering tool GeFaST and evaluated their clustering quality and performance on mock-community data sets. Quality-weighted alignments were able to improve the clustering quality of GeFaST by up to 10%. The examination of the model-supported methods provided a more diverse picture, hinting at a narrower applicability, but they were able to attain similar improvements. Considering the quality information enlarged both runtime and memory consumption, even though the increase of the former depended heavily on the applied method and clustering threshold.

Conclusions

The quality-aware methods expand the iterative, de novo clustering approach by new clustering and cluster refinement methods. Our results indicate that OTU clustering constitutes yet another analysis step benefiting from the integration of quality information. Beyond the shown potential, the quality-aware methods offer a range of opportunities for fine-tuning and further extensions.

Strain-Level Profiling of Oral Microbiota with Targeted Sequencing

Targeted sequencing of one or more regions of the bacterial 16S rRNA gene fragment has emerged as a gold standard for investigating taxonomic diversity in complex microbial communities, such as those found in the oral cavity. While this approach is useful for identifying bacteria up to genus level, its ability to distinguish between many closely related oral species, or explore strain-level variations within each species, is very limited. Here we present an approach based on targeted sequencing the 16S-23S Intergenic Spacer Region (ISR) in the bacterial ribosomal operon for taxonomic characterization of microbial communities at a subspecies or strain level. This approach retains the advantages of 16S-based methods, such as easy library preparation, high throughput, short amplicon sizes, and low cost of sequencing, while providing subspecies-level resolution as a result of naturally higher genetic diversity present in the ISR compared to the 16S hypervariable regions. These advantages make it an excellent tool for high-resolution oral microbiota characterization.

AmpliCI: a high-resolution model-based approach for denoising Illumina amplicon data

Motivation

Next-generation amplicon sequencing is a powerful tool for investigating microbial communities. A main challenge is to distinguish true biological variants from errors caused by amplification and sequencing. In traditional analyses, such errors are eliminated by clustering reads within a sequence similarity threshold, usually 97%, and constructing operational taxonomic units, but the arbitrary threshold leads to low resolution and high false-positive rates. Recently developed ‘denoising’ methods have proven able to resolve single-nucleotide amplicon variants, but they still miss low-frequency sequences, especially those near more frequent sequences, because they ignore the sequencing quality information.

Results

We introduce AmpliCI, a reference-free, model-based method for rapidly resolving the number, abundance and identity of error-free sequences in massive Illumina amplicon datasets. AmpliCI considers the quality information and allows the data, not an arbitrary threshold or an external database, to drive conclusions. AmpliCI estimates a finite mixture model, using a greedy strategy to gradually select error-free sequences and approximately maximize the likelihood. AmpliCI has better performance than three popular denoising methods, with acceptable computation time and memory usage.

Availability and implementation

Source code is available at https://github.com/DormanLab/AmpliCI.

Supplementary information

Supplementary material are available at Bioinformatics online.

Key Technologies for Progressing Discovery of Microbiome-Based Medicines

A growing number of experimental and computational approaches are illuminating the “microbial dark matter” and uncovering the integral role of commensal microbes in human health. Through this work, it is now clear that the human microbiome presents great potential as a therapeutic target for a plethora of diseases, including inflammatory bowel disease, diabetes and obesity. The development of more efficacious and targeted treatments relies on identification of causal links between the microbiome and disease; with future progress dependent on effective links between state-of-the-art sequencing approaches, computational analyses and experimental assays. We argue determining causation is essential, which can be attained by generating hypotheses using multi-omic functional analyses and validating these hypotheses in complex, biologically relevant experimental models. In this review we discuss existing analysis and validation methods, and propose best-practice approaches required to enable the next phase of microbiome research.

Arsenic dynamics and flux assessment under drying-wetting irrigation and enhanced microbial diversity in paddy soils: A four year study in Bengal delta plain

Arsenic (As) assessment in agricultural soils and corresponding crops is necessary from the global health safety perspective. To the best of our knowledge, we are reporting for the first time, As flux determining parametric equations for paddy field with seasonal rice cultivation under conventional flooding and dry-wet irrigation approaches. Rigorous field experiments and measuring quantitative parameters, flushed out or percolated into the deeper soil As flux was assessed. A wintery (boro)-monsoonal (aman) study from 2016 to 2019 has been conducted showing the efficiency of dry-wet irrigation on reduction of soil As bioavailability. The reduction in boro was 52.4% in 2016 to 64.8% in 2019 while in aman, it was 61% in 2016 to 74.9% in 2019. Low bioavailability was correlated to plant's internal vascular structure that was found more rigid and firm in dry-wet field grown plants. Observed soil physico-chemical parameters clearly influenced As bioavailability as well as soil microbial community. Assessment of microbial diversity using metagenomics under altered water regime was done by population analysis, relative abundance, species richness, Krona chart comparison. Dry-wet field was found to be more diverse and enriched in microbial community than that of the flooded soil indicating an affective reduction of As bioavailability under biotic-abiotic factors.

Phylogenetic Core Groups: a promising concept in search of a consistent methodological framework : Comment to ``A conceptual framework for the phylogenetically-constrained assembly of microbial communities''

In this comment, we analyse the conceptual framework proposed by Aguirre de Cárcer (Microbiome 7:142, 2019), introducing the novel concept of Phylogenetic Core Groups (PCGs). This notion aims to complement the traditional classification in operational taxonomic units (OTUs), widely used in microbial ecology, to provide a more intrinsic taxonomical classification which avoids the use of pre-determined thresholds. However, to introduce this concept, the author frames his proposal in a wider theoretical framework based on a conceptualization of selection that we argue is a tautology. This blurs the subsequent formulation of an assembly principle for microbial communities, favouring that some contradictory examples introduced to support the framework appear aligned in their conclusions. And more importantly, under this framework and its derived methodology, it is not possible to infer PCGs from data in a consistent way. We reanalyse the proposal to identify its logical and methodological flaws and, through the analysis of synthetic scenarios, we propose a number of methodological refinements to contribute towards the determination of PCGs in a consistent way. We hope our analysis will promote the exploration of PCGs as a potentially valuable tool, helping to bridge the gap between environmental conditions and community composition in microbial ecology. Video Abstract.

Anaerobic bacterial degradation of protein and lipid macromolecules in subarctic marine sediment

Microorganisms in marine sediments play major roles in marine biogeochemical cycles by mineralizing substantial quantities of organic matter from decaying cells. Proteins and lipids are abundant components of necromass, yet the taxonomic identities of microorganisms that actively degrade them remain poorly resolved. Here, we revealed identities, trophic interactions, and genomic features of bacteria that degraded 13C-labeled proteins and lipids in cold anoxic microcosms containing sulfidic subarctic marine sediment. Supplemented proteins and lipids were rapidly fermented to various volatile fatty acids within 5 days. DNA-stable isotope probing (SIP) suggested Psychrilyobacter atlanticus was an important primary degrader of proteins, and Psychromonas members were important primary degraders of both proteins and lipids. Closely related Psychromonas populations, as represented by distinct 16S rRNA gene variants, differentially utilized either proteins or lipids. DNA-SIP also showed 13C-labeling of various Deltaproteobacteria within 10 days, indicating trophic transfer of carbon to putative sulfate-reducers. Metagenome-assembled genomes revealed the primary hydrolyzers encoded secreted peptidases or lipases, and enzymes for catabolism of protein or lipid degradation products. Psychromonas species are prevalent in diverse marine sediments, suggesting they are important players in organic carbon processing in situ. Together, this study provides new insights into the identities, functions, and genomes of bacteria that actively degrade abundant necromass macromolecules in the seafloor.

Current challenges and best-practice protocols for microbiome analysis

Analyzing the microbiome of diverse species and environments using next-generation sequencing techniques has significantly enhanced our understanding on metabolic, physiological and ecological roles of environmental microorganisms. However, the analysis of the microbiome is affected by experimental conditions (e.g. sequencing errors and genomic repeats) and computationally intensive and cumbersome downstream analysis (e.g. quality control, assembly, binning and statistical analyses). Moreover, the introduction of new sequencing technologies and protocols led to a flood of new methodologies, which also have an immediate effect on the results of the analyses. The aim of this work is to review the most important workflows for 16S rRNA sequencing and shotgun and long-read metagenomics, as well as to provide best-practice protocols on experimental design, sample processing, sequencing, assembly, binning, annotation and visualization. To simplify and standardize the computational analysis, we provide a set of best-practice workflows for 16S rRNA and metagenomic sequencing data (available at https://github.com/grimmlab/MicrobiomeBestPracticeReview).

Anthropogenic impact on the historical phytoplankton community of Lake Constance reconstructed by multimarker analysis of sediment-core environmental DNA

During the 20th century, many lakes in the Northern Hemisphere were affected by increasing human population and urbanization along their shorelines and catchment, resulting in aquatic eutrophication. Ecosystem monitoring commenced only after the changes became apparent, precluding any examination of timing and dynamics of initial community change in the past and comparison of pre- and postimpact communities. Peri-Alpine Lake Constance (Germany) underwent a mid-century period of eutrophication followed by re-oligotrophication since the 1980s and is now experiencing warm temperatures. We extended the period for which monitoring data of indicator organisms exist by analysing historical environmental DNA (eDNA) from a sediment core dating back some 110 years. Using three metabarcoding markers-for microbial eukaryotes, diatoms and cyanobacteria-we revealed two major breakpoints of community change, in the 1930s and the mid-1990s. In our core, the latest response was exhibited by diatoms, which are classically used as palaeo-bioindicators for the trophic state of lakes. Following re-oligotrophication, overall diversity values reverted to similar ones of the early 20th century, but multivariate analysis indicated that the present community is substantially dissimilar. Community changes of all three groups were strongly correlated to phosphorus concentration changes, whereas significant relationships to temperature were only observed when we did not account for temporal autocorrelation. Our results indicate that each microbial group analysed exhibited a unique response, highlighting the particular strength of multimarker analysis of eDNA, which is not limited to organisms with visible remains and can therefore discover yet unknown responses and abiotic-biotic relationships.

On the way to specificity - Microbiome reflects sponge genetic cluster primarily in highly structured populations

Most animals, including sponges (Porifera), have species-specific microbiomes. Which genetic or environmental factors play major roles structuring the microbial community at the intraspecific level in sponges is, however, largely unknown. In this study, we tested whether geographic location or genetic structure of conspecific sponges influences their microbial assembly. For that, we used three sponge species with different rates of gene flow, and collected samples along their entire distribution range (two from the Mediterranean and one from the Southern Ocean) yielding a total of 393 samples. These three sponge species have been previously analysed by microsatellites or single nucleotide polymorphisms, and here we investigate their microbiomes by amplicon sequencing of the microbial 16S rRNA gene. The sponge Petrosia ficiformis, with highly isolated populations (low gene flow), showed a stronger influence of the host genetic distance on the microbial composition than the spatial distance. Host-specificity was therefore detected at the genotypic level, with individuals belonging to the same host genetic cluster harbouring more similar microbiomes than distant ones. On the contrary, the microbiome of Ircinia fasciculata and Dendrilla antarctica - both with weak population structure (high gene flow) - seemed influenced by location rather than by host genetic distance. Our results suggest that in sponge species with high population structure, the host genetic cluster influence the microbial community more than the geographic location.

Oyster Calcifying Fluid Harbors Persistent and Dynamic Autochthonous Bacterial Populations That May Aid in Shell Formation

The eastern oyster (Crassostrea virginica) is a keystone species in estuarine environments but faces threats to shell formation associated with warming temperatures and acidification. Extrapallial fluid (EF), which is responsible for shell formation, harbors diverse and abundant microbial communities. Commensal microbial communities are vital to host health and fitness, yet long-term studies investigating temporal responses of the EF microbiome and its function in oyster fitness are lacking. In this study, bacterial communities of oyster EF and the water column were characterized monthly from October 2010 to September 2011. We investigated the selection, composition, and dynamics of resident and transient community members, evaluated the impact of temperature on EF microbial communities, and examined the functional role of the EF microbiome. Oyster EF communities were significantly different from the water column and were enriched for several taxa, including the Deltaproteobacteria, Epsilonproteobacteria, and Gammaproteobacteria. Overall, 94 resident members were identified in oyster EF. These members were persistent and abundant, comprising on average 33% of EF communities. Resident EF communities formed high-temperature and low-temperature groups and were more abundant overall at colder temperatures. Oyster EF resident communities were predicted to be enriched for dissimilatory nitrate reduction, nitrogen fixation, nitrification, and sulfite reductase genes. Sulfate and nitrate reduction may have a synergistic effect on calcium carbonate precipitation and indirectly aid in shell formation. Therefore, the potential role of the oyster EF microbiome in shell formation warrants further investigation as oysters and other shellfish face the future impacts of ocean warming and acidification.

Biological observations in microbiota analysis are robust to the choice of 16S rRNA gene sequencing processing algorithm: case study on human milk microbiota

BACKGROUND

In recent years, the microbiome field has undergone a shift from clustering-based methods of operational taxonomic unit (OTU) designation based on sequence similarity to denoising algorithms that identify exact amplicon sequence variants (ASVs), and methods to identify contaminating bacterial DNA sequences from low biomass samples have been developed. Although these methods improve accuracy when analyzing mock communities, their impact on real samples and downstream analysis of biological associations is less clear.

RESULTS

Here, we re-processed our recently published milk microbiota data using Qiime1 to identify OTUs, and Qiime2 to identify ASVs, with or without contaminant removal using decontam. Qiime2 resolved the mock community more accurately, primarily because Qiime1 failed to detect Lactobacillus. Qiime2 also considerably reduced the average number of ASVs detected in human milk samples (364 ± 145 OTUs vs. 170 ± 73 ASVs, p < 0.001). Compared to the richness, the estimated diversity measures had a similar range using both methods albeit statistically different (inverse Simpson index: 14.3 ± 8.5 vs. 15.6 ± 8.7, p = 0.031) and there was strong consistency and agreement for the relative abundances of the most abundant bacterial taxa, including Staphylococcaceae and Streptococcaceae. One notable exception was Oxalobacteriaceae, which was overrepresented using Qiime1 regardless of contaminant removal. Downstream statistical analyses were not impacted by the choice of algorithm in terms of the direction, strength, and significance of associations of host factors with bacterial diversity and overall community composition.

CONCLUSION

Overall, the biological observations and conclusions were robust to the choice of the sequencing processing methods and contaminant removal.

Hill-based dissimilarity indices and null models for analysis of microbial community assembly

BACKGROUND

High-throughput amplicon sequencing of marker genes, such as the 16S rRNA gene in Bacteria and Archaea, provides a wealth of information about the composition of microbial communities. To quantify differences between samples and draw conclusions about factors affecting community assembly, dissimilarity indices are typically used. However, results are subject to several biases, and data interpretation can be challenging. The Jaccard and Bray-Curtis indices, which are often used to quantify taxonomic dissimilarity, are not necessarily the most logical choices. Instead, we argue that Hill-based indices, which make it possible to systematically investigate the impact of relative abundance on dissimilarity, should be used for robust analysis of data. In combination with a null model, mechanisms of microbial community assembly can be analyzed. Here, we also introduce a new software, qdiv, which enables rapid calculations of Hill-based dissimilarity indices in combination with null models.

RESULTS

Using amplicon sequencing data from two experimental systems, aerobic granular sludge (AGS) reactors and microbial fuel cells (MFC), we show that the choice of dissimilarity index can have considerable impact on results and conclusions. High dissimilarity between replicates because of random sampling effects make incidence-based indices less suited for identifying differences between groups of samples. Determining a consensus table based on count tables generated with different bioinformatic pipelines reduced the number of low-abundant, potentially spurious amplicon sequence variants (ASVs) in the data sets, which led to lower dissimilarity between replicates. Analysis with a combination of Hill-based indices and a null model allowed us to show that different ecological mechanisms acted on different fractions of the microbial communities in the experimental systems.

CONCLUSIONS

Hill-based indices provide a rational framework for analysis of dissimilarity between microbial community samples. In combination with a null model, the effects of deterministic and stochastic community assembly factors on taxa of different relative abundances can be systematically investigated. Calculations of Hill-based dissimilarity indices in combination with a null model can be done in qdiv, which is freely available as a Python package ( https://github.com/omvatten/qdiv ). In qdiv, a consensus table can also be determined from several count tables generated with different bioinformatic pipelines. Video Abstract.

Diversity within species: interpreting strains in microbiomes

Studying within-species variation has traditionally been limited to culturable bacterial isolates and low-resolution microbial community fingerprinting. Metagenomic sequencing and technical advances have enabled culture-free, high-resolution strain and subspecies analyses at high throughput and in complex environments. This holds great scientific promise but has also led to an overwhelming number of methods and terms to describe infraspecific variation. This Review aims to clarify these advances by focusing on the diversity within bacterial and archaeal species in the context of microbiomics. We cover foundational microevolutionary concepts relevant to population genetics and summarize how within-species variation can be studied and stratified directly within microbial communities with a focus on metagenomics. Finally, we describe how common applications of within-species variation can be achieved using metagenomic data. We aim to guide the selection of appropriate terms and analytical approaches to facilitate researchers in benefiting from the increasing availability of large, high-resolution microbiome genetic sequencing data.

Strain-level epidemiology of microbial communities and the human microbiome

The biological importance and varied metabolic capabilities of specific microbial strains have long been established in the scientific community. Strains have, in the past, been largely defined and characterized based on microbial isolates. However, the emergence of new technologies and techniques has enabled assessments of their ecology and phenotypes within microbial communities and the human microbiome. While it is now more obvious how pathogenic strain variants are detrimental to human health, the consequences of subtle genetic variation in the microbiome have only recently been exposed. Here, we review the operational definitions of strains (e.g., genetic and structural variants) as they can now be identified from microbial communities using different high-throughput, often culture-independent techniques. We summarize the distribution and diversity of strains across the human body and their emerging links to health maintenance, disease risk and progression, and biochemical responses to perturbations, such as diet or drugs. We list methods for identifying, quantifying, and tracking strains, utilizing high-throughput sequencing along with other molecular and “culturomics” technologies. Finally, we discuss implications of population studies in bridging experimental gaps and leading to a better understanding of the health effects of strains in the human microbiome.

Changes in the core endophytic mycobiome of carrot taproots in response to crop management and genotype

Fungal endophytes can influence production and post-harvest challenges in carrot, though the identity of these microbes as well as factors affecting their composition have not yet been determined, which prevents growers from managing these organisms to improve crop performance. Consequently, we characterized the endophytic mycobiome in the taproots of three carrot genotypes that vary in resistance to two pathogens grown in a trial comparing organic and conventional crop management using Illumina sequencing of the internal transcribed spacer (ITS) gene. A total of 1,480 individual operational taxonomic units (OTUs) were identified. Most were consistent across samples, indicating that they are part of a core mycobiome, though crop management influenced richness and diversity, likely in response to differences in soil properties. There were also differences in individual OTUs among genotypes and the nematode resistant genotype was most responsive to management system indicating that it has greater control over its endophytic mycobiome, which could potentially play a role in resistance. Members of the Ascomycota were most dominant, though the exact function of most taxa remains unclear. Future studies aimed at overcoming difficulties associated with isolating fungal endophytes are needed to identify these microbes at the species level and elucidate their specific functional roles.

High-Fat Diet Induced Alteration of Mice Microbiota and the Functional Ability to Utilize Fructooligosaccharide for Ethanol Production

High-fat diet (HFD) leads to enhancement in various parameters of mice like weight, fasting glucose levels, adipose tissue, and also the liver weight in male C57 BL/6 J mice. Additionally, high-fat diet causes severe liver damage with significant increase in the level of aspartate amino transferase (AST) and alanine transaminase (ALT). The variations in microbiota induced by different diet were analyzed by Illumina MiSeq platform with sequencing of 16S ribosomal RNA (rRNA) gene, and QIIME pipeline was used. The population of Proteobacteria was found to be higher in HFD cecum sample as compared to other treatments. Microbiota analysis suggests that phylum Proteobacteria and Firmicutes were found to be higher in high-fat diet groups as compared to mice fed with normal diet (ND). At the genus level, Bacteroides showed higher population in HFD diet. Bacterial strains belonging to Enterobacteriaceae like Escherichia, Klebsiella, and Shigella were also dominant in HFD treatments. Furthermore, we explored the effects of ethanol production in vitro with supplementation of dietary fibers following inoculation of ND and HFD microbiotas. HFD microbiota of cecum and feces showed high level (P < 0.05) of ethanol production with 2% fructooligosaccharide (FOS) as compared to 2% galactomannan. Microbial fermentation also generated short-chain fatty acids (SCFAs), such as acetate, propionate, and butyrate. High levels (P < 0.05) of propionate were found after fermentation of FOS with HFD cecum and feces microbiota. The present study highlights the HFD-induced population of phylum Proteobacteria and genus Bacteroides for ethanol production using FOS as a dietary supplement, and these findings may imply on the harmful effect of HFD even at the microbiota level.

Characterization of Bacterial and Fungal Assemblages From Historically Contaminated Metalliferous Soils Using Metagenomics Coupled With Diffusion Chambers and Microbial Traps

The majority of environmental microbiomes are not amenable to cultivation under standard laboratory growth conditions and hence remain uncharacterized. For environmental applications, such as bioremediation, it is necessary to isolate microbes performing the desired function, which may not necessarily be the fast growing or the copiotroph microbiota. Toward this end, cultivation and isolation of microbial strains using diffusion chambers (DC) and/or microbial traps (MT) have both been recently demonstrated to be effective strategies because microbial enrichment is facilitated by soil nutrients and not by synthetically defined media, thus simulating their native habitat. In this study, DC/MT chambers were established using soils collected from two US Department of Energy (DOE) sites with long-term history of heavy metal contamination, including mercury (Hg). To characterize the contamination levels and nutrient status, soils were first analyzed for total mercury (THg), methylmercury (MeHg), total carbon (TC), total nitrogen (TN), and total phosphorus (TP). Multivariate statistical analysis on these measurements facilitated binning of soils under high, medium and low levels of contamination. Bacterial and fungal microbiomes that developed within the DC and MT chambers were evaluated using comparative metagenomics, revealing Chthoniobacter, Burkholderia and Bradyrhizobium spp., as the predominant bacteria while Penicillium, Thielavia, and Trichoderma predominated among fungi. Many of these core microbiomes were also retrieved as axenic isolates. Furthermore, canonical correspondence analysis (CCA) of biogeochemical measurements, metal concentrations and bacterial communities revealed a positive correlation of Chthoniobacter/Bradyrhizobium spp., to THg whereas Burkholderia spp., correlated with MeHg. Penicillium spp., correlated with THg whereas Trichoderma spp., and Aspergillus spp., correlated with MeHg, from the MT approach. This is the first metagenomics-based assessment, isolation and characterization of soil-borne bacterial and fungal communities colonizing the diffusion chambers (DC) and microbial traps (MT) established with long-term metal contaminated soils. Overall, this study provides proof-of-concept for the successful application of DC/MT based assessment of mercury resistant (HgR) microbiomes in legacy metal-contaminated soils, having complex contamination issues. Overall, this study brings out the significance of microbial communities and their relevance in context to heavy metal cycling for better stewardship and restoration of such historically contaminated systems.

Stabilization of extensive fine-scale diversity by ecologically driven spatiotemporal chaos

It has recently become apparent that the diversity of microbial life extends far below the species level to the finest scales of genetic differences. Remarkably, extensive fine-scale diversity can coexist spatially. How is this diversity stable on long timescales, despite selective or ecological differences and other evolutionary processes? Most work has focused on stable coexistence or assumed ecological neutrality. We present an alternative: extensive diversity maintained by ecologically driven spatiotemporal chaos, with no assumptions about niches or other specialist differences between strains. We study generalized Lotka-Volterra models with antisymmetric correlations in the interactions inspired by multiple pathogen strains infecting multiple host strains. Generally, these exhibit chaos with increasingly wild population fluctuations driving extinctions. But the simplest spatial structure, many identical islands with migration between them, stabilizes a diverse chaotic state. Some strains (subspecies) go globally extinct, but many persist for times exponentially long in the number of islands. All persistent strains have episodic local blooms to high abundance, crucial for their persistence as, for many, their average population growth rate is negative. Snapshots of the abundance distribution show a power law at intermediate abundances that is essentially indistinguishable from the neutral theory of ecology. But the dynamics of the large populations are much faster than birth-death fluctuations. We argue that this spatiotemporally chaotic "phase" should exist in a wide range of models, and that even in rapidly mixed systems, longer-lived spores could similarly stabilize a diverse chaotic phase.

Cryoconite Hole Location in East-Antarctic Untersee Oasis Shapes Physical and Biological Diversity

Antarctic cryoconite holes (CHs) are mostly perennially ice-lidded and sediment-filled depressions that constitute important features on glaciers and ice sheets. Once being hydrologically connected, these microbially dominated mini-ecosystems provide nutrients and biota for downstream environments. For example, the East Antarctic Anuchin Glacier gradually melts into the adjacent perennially ice-covered Lake Untersee, and CH biota from this glacier contribute up to one third of the community composition in benthic microbial mats within the lake. However, biogeochemical features of these CHs and associated spatial patterns across the glacier are still unknown. Here we hypothesized about the CH minerogenic composition between the different sources such as the medial moraine and other zones. Further, we intended to investigate if the depth of the CH mirrors the CH community composition, organic matter (OM) content and would support productivity. In this study we show that both microbial communities and biogeochemical parameters in CHs were significantly different between the zones medial moraine and the glacier terminus. Variations in microbial community composition are the result of factors such as depth, diameter, organic matter, total carbon, particle size, and mineral diversity. More than 90% of all ribosomal sequence variants (RSV) reads were classified as Proteobacteria, Cyanobacteria, Bacteroidetes, Actinobacteria, and Acidobacteria. Archaea were detected in 85% of all samples and exclusively belonged to the classes Halobacteria, Methanomicrobia, and Thermoplasmata. The most abundant genus was Halorubrum (Halobacteria) and was identified in nine RSVs. The core microbiome for bacteria comprised 30 RSVs that were affiliated with Cyanobacteria, Bacteroidetes, Actinobacteria, and Proteobacteria. The archaeal fraction of the core microbiome consisted of three RSVs belonging to unknown genera of Methanomicrobiales and Thermoplasmatales and the genus Rice_Cluster_I (Methanocellales). Further, mean bacterial carbon production in cryoconite was exceptionally low and similar rates have not been reported elsewhere. However, bacterial carbon production insignificantly trended toward higher rates in shallow CHs and did not seem to be supported by accumulation of OM and nutrients, respectively, in deeper holes. OM fractions were significantly different between shallower CHs along the medial moraine and deeper CHs at the glacier terminus. Overall, our findings suggest that wind-blown material originating south and southeast of the Anuchin Glacier and deposits from a nunatak are assumed to be local inoculation sources. High sequence similarities between samples from the Untersee Oasis and other Antarctic sites further indicate long-range atmospheric transport mechanisms that complement local inoculation sources.

Interaction between glyphosate and dissolved phosphorus on bacterial and eukaryotic communities from river biofilms

Since the capacity of river biofilms to degrade glyphosate has been proven to increase when the availability of dissolved phosphorus (P) in water decreases, the present study investigates the diversity responses of bacterial and eukaryotic microbial communities from biofilms in a search for glyphosate-degrader candidates. Glyphosate and P interactions were observed for eukaryotic communities, the highest community richness and diversity being preserved at low concentrations of glyphosate and P. This trend marked by glyphosate was also observed in the structure of eukaryotic communities. Therefore, phosphorus and glyphosate had a synergistic effect in decreasing the richness and diversity of eukaryotes species in biofilms. However, species richness and diversity in bacterial communities were not affected by glyphosate, though shifts in the structure of these communities were concomitant with the degradation of the herbicide. Bacterial communities capable of using glyphosate as P source were characterized by increases in the relative abundance of certain Bacteroidetes, Chloroflexi, Cyanobacteria, Planctomycetes and alpha-Proteobacteria members. Glyphosate-degrader candidates found in natural river biofilms can be further isolated for better understanding of glyphosate degradation pathways, and used as bioremediation strategies in heavily contaminated sites.

Urinary Microbiota-Are We Ready for Prime Time? A Literature Review of Study Methods' Critical Steps in Avoiding Contamination and Minimizing Biased Results

Within the last few years, there have been an increased number of clinical studies involving urinary microbiota. Low-biomass microbiome sequencing (e.g., urine, lung, placenta, blood) is easily biased by contamination or cross-contamination. So far, a few critical steps, from sampling urine to processing and analyzing, have been described (e.g., urine collection modality, sample volume size, snap freezing, negative controls usage, laboratory risks for contamination assessment, contamination of negative results reporting, exploration and discussion of the impact of contamination for the final results, etc.) We performed a literature search (Pubmed, Scopus and Embase) and reviewed the published articles related to urinary microbiome, evaluating how the aforementioned critical steps to obtain unbiased, reliable results have been taken or have been reported. We identified different urinary microbiome evaluation protocols, with non-homogenous reporting systems, which can make gathering results into consistent data for similar topics difficult and further burden the already so complex emerging field of urinary microbiome. We concluded that to ease the progress in this field, a joint approach from researchers, authors and publishers would be necessary in order to create mandatory reporting systems which would allow to recognize pitfalls and avoid compromising a promising field of research.

Microbiome analysis of healthy and diseased sponges Lubomirskia baicalensis by using cell cultures of primmorphs

Endemic sponges (Demosponges, Lubomirskiidae) dominate the fauna of the littoral zone of Lake Baikal. These freshwater sponges live in symbiosis with diverse eukaryotes and prokaryotes, including chlorophyll-containing microalgae. Within the last 5 years, the incidence of sponge disease and mortality events in Lake Baikal has increased. The etiology and ecology of these events remain unknown, in part because of the lack of models to study sponge-microbe interactions. In this work, we tested the use of primmorph cell cultures of Lubomirskia baicalensis as a tool for investigating the microbiomes of sponges. We infected primmorphs, cultured in vitro, with samples from diseased sponges and observed, by microscopy, disease symptoms, including loss of green symbionts, associated with mass die-off events. Subsequent sequencing of 16S rRNA gene fragments revealed that the microbiome community of healthy sponge and primmorphs formed a group separate from the community of diseased sponges and infected primmorphs. This confirms the suitability of the primmorph cell culture as a model sponge system. We also discovered mass mortality of green symbionts (Chlorophyta) was associated with a shift in the microbial communities of sponges/primmorphs. Microbes in diseased sponges, and infected primmorphs, belonged mainly to the phyla Bacteroidetes and Proteobacteria and these families Flavobacteriaceae, Burkholderiaceae, and Moraxellaceae. Primmorphs cell culture may provide a model to study interactions between these bacteria and their host and elucidate the cause of mass mortality events.

Construction of habitat-specific training sets to achieve species-level assignment in 16S rRNA gene datasets

BACKGROUND

The low cost of 16S rRNA gene sequencing facilitates population-scale molecular epidemiological studies. Existing computational algorithms can resolve 16S rRNA gene sequences into high-resolution amplicon sequence variants (ASVs), which represent consistent labels comparable across studies. Assigning these ASVs to species-level taxonomy strengthens the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies and further facilitates data comparison across studies.

RESULTS

To achieve this, we developed a broadly applicable method for constructing high-resolution training sets based on the phylogenic relationships among microbes found in a habitat of interest. When used with the naïve Bayesian Ribosomal Database Project (RDP) Classifier, this training set achieved species/supraspecies-level taxonomic assignment of 16S rRNA gene-derived ASVs. The key steps for generating such a training set are (1) constructing an accurate and comprehensive phylogenetic-based, habitat-specific database; (2) compiling multiple 16S rRNA gene sequences to represent the natural sequence variability of each taxon in the database; (3) trimming the training set to match the sequenced regions, if necessary; and (4) placing species sharing closely related sequences into a training-set-specific supraspecies taxonomic level to preserve subgenus-level resolution. As proof of principle, we developed a V1-V3 region training set for the bacterial microbiota of the human aerodigestive tract using the full-length 16S rRNA gene reference sequences compiled in our expanded Human Oral Microbiome Database (eHOMD). We also overcame technical limitations to successfully use Illumina sequences for the 16S rRNA gene V1-V3 region, the most informative segment for classifying bacteria native to the human aerodigestive tract. Finally, we generated a full-length eHOMD 16S rRNA gene training set, which we used in conjunction with an independent PacBio single molecule, real-time (SMRT)-sequenced sinonasal dataset to validate the representation of species in our training set. This also established the effectiveness of a full-length training set for assigning taxonomy of long-read 16S rRNA gene datasets.

CONCLUSION

Here, we present a systematic approach for constructing a phylogeny-based, high-resolution, habitat-specific training set that permits species/supraspecies-level taxonomic assignment to short- and long-read 16S rRNA gene-derived ASVs. This advancement enhances the ecological and/or clinical relevance of 16S rRNA gene-based microbiota studies. Video Abstract.

Stability and nuclear localization of yeast telomerase depend on protein components of RNase P/MRP

RNase P and MRP are highly conserved, multi-protein/RNA complexes with essential roles in processing ribosomal and tRNAs. Three proteins found in both complexes, Pop1, Pop6, and Pop7 are also telomerase-associated. Here, we determine how temperature sensitive POP1 and POP6 alleles affect yeast telomerase. At permissive temperatures, mutant Pop1/6 have little or no effect on cell growth, global protein levels, the abundance of Est1 and Est2 (telomerase proteins), and the processing of TLC1 (telomerase RNA). However, in pop mutants, TLC1 is more abundant, telomeres are short, and TLC1 accumulates in the cytoplasm. Although Est1/2 binding to TLC1 occurs at normal levels, Est1 (and hence Est3) binding is highly unstable. We propose that Pop-mediated stabilization of Est1 binding to TLC1 is a pre-requisite for formation and nuclear localization of the telomerase holoenzyme. Furthermore, Pop proteins affect TLC1 and the RNA subunits of RNase P/MRP in very different ways.