Expanding the UniFrac Toolbox

Tomato seed extract promotes health of the gut microbiota and demonstrates a potential new way to valorize tomato waste

The current effort to valorize waste byproducts to increase sustainability and reduce agricultural loss has stimulated interest in potential utilization of waste components as health-promoting supplements. Tomato seeds are often discarded in tomato pomace, a byproduct of tomato processing, yet these seeds are known to contain an array of compounds with biological activity and prebiotic potential. Here, extract from tomato seeds (TSE), acquired from pomace, was evaluated for their ability to effect changes on the gut microbiota using an ex vivo strategy. The results found that TSE significantly increased levels of the beneficial taxa Bifidobacteriaceae in a donor-independent manner, from a range of 18.6-24.0% to 27.0-51.6% relative abundance following treatment, yet the specific strain of Bifidobacteriaceae enhanced was inter-individually variable. These structural changes corresponded with a significant increase in total short-chain fatty acids, specifically acetate and propionate, from an average of 13.3 to 22.8 mmol/L and 4.6 to 7.4 mmol/L, respectively. Together, these results demonstrated that TSE has prebiotic potential by shaping the gut microbiota in a donor-independent manner that may be beneficial to human health. These findings provide a novel application for TSE harvested from tomato pomace and demonstrate the potential to further valorize tomato waste products.

Influence of host phylogeny and water physicochemistry on microbial assemblages of the fish skin microbiome

The skin of fish contains a diverse microbiota that has symbiotic functions with the host, facilitating pathogen exclusion, immune system priming, and nutrient degradation. The composition of fish skin microbiomes varies across species and in response to a variety of stressors, however, there has been no systematic analysis across these studies to evaluate how these factors shape fish skin microbiomes. Here, we examined 1922 fish skin microbiomes from 36 studies that included 98 species and nine rearing conditions to investigate associations between fish skin microbiome, fish species, and water physiochemical factors. Proteobacteria, particularly the class Gammaproteobacteria, were present in all marine and freshwater fish skin microbiomes. Acinetobacter, Aeromonas, Ralstonia, Sphingomonas and Flavobacterium were the most abundant genera within freshwater fish skin microbiomes, and Alteromonas, Photobacterium, Pseudoalteromonas, Psychrobacter and Vibrio were the most abundant in saltwater fish. Our results show that different culturing (rearing) environments have a small but significant effect on the skin bacterial community compositions. Water temperature, pH, dissolved oxygen concentration, and salinity significantly correlated with differences in beta-diversity but not necessarily alpha-diversity. To improve study comparability on fish skin microbiomes, we provide recommendations for approaches to the analyses of sequencing data and improve study reproducibility.

Mock community taxonomic classification performance of publicly available shotgun metagenomics pipelines

Shotgun metagenomic sequencing comprehensively samples the DNA of a microbial sample. Choosing the best bioinformatics processing package can be daunting due to the wide variety of tools available. Here, we assessed publicly available shotgun metagenomics processing packages/pipelines including bioBakery, Just a Microbiology System (JAMS), Whole metaGenome Sequence Assembly V2 (WGSA2), and Woltka using 19 publicly available mock community samples and a set of five constructed pathogenic gut microbiome samples. Also included is a workflow for labelling bacterial scientific names with NCBI taxonomy identifiers for better resolution in assessing results. The Aitchison distance, a sensitivity metric, and total False Positive Relative Abundance were used for accuracy assessments for all pipelines and mock samples. Overall, bioBakery4 performed the best with most of the accuracy metrics, while JAMS and WGSA2, had the highest sensitivities. Furthermore, bioBakery is commonly used and only requires a basic knowledge of command line usage. This work provides an unbiased assessment of shotgun metagenomics packages and presents results assessing the performance of the packages using mock community sequence data.

Early-Life Skin Microbial Biomarkers for Eczema Phenotypes in Chinese Toddlers

Eczema is a common inflammatory skin disorder during infancy. Evidence has shown that skin-microbiome fluctuations may precede eczema development, but their predictive value for eczema phenotypes remains unknown. We aimed to investigate the early-life evolution of the skin microbiome and its temporal associations with different pairs of eczema phenotypes (transient versus persistent, atopic versus non-atopic) in Chinese children. We followed 119 term Chinese infants from birth to 24 months old within a Hong Kong birth cohort. The skin microbes at the left antecubital fossa were serially sampled by flocked swabs at 1, 6, and 12 months for bacterial 16S rRNA gene sequencing. The atopic sensitization at 12 months was strongly associated with eczema persisting to 24 months (odds ratio 4.95, 95% confidence interval 1.29-19.01). Compared with those with non-atopic eczema, the children with atopic eczema had reduced alpha diversity at 12 months (p < 0.001) and transiently higher abundance of the genus Janibacter at 6 months (p < 0.001). Our findings suggest that atopic sensitization at 12 months may predict persistent eczema by 24 months, and atopic eczema at 12 months is associated with unique skin microbiome profiles at 6 and 12 months. Non-invasive skin-microbiome profiling may have predictive value for atopic eczema.

Biofilms on Indwelling Artificial Urinary Sphincter Devices Harbor Complex Microbe-Metabolite Interaction Networks and Reconstitute Differentially In Vitro by Material Type

The artificial urinary sphincter (AUS) is an effective treatment option for incontinence due to intrinsic sphincteric deficiency in the context of neurogenic lower urinary tract dysfunction, or stress urinary incontinence following radical prostatectomy. A subset of AUS devices develops infection and requires explant. We sought to characterize biofilm composition of the AUS device to inform prevention and treatment strategies. Indwelling AUS devices were swabbed for biofilm at surgical removal or revision. Samples and controls were subjected to next-generation sequencing and metabolomics. Biofilm formation of microbial strains isolated from AUS devices was reconstituted in a bioreactor mimicking subcutaneous tissue with a medical device present. Mean patient age was 73 (SD 10.2). All eighteen artificial urinary sphincter devices harbored microbial biofilms. Central genera in the overall microbe−metabolite interaction network were Staphylococcus (2620 metabolites), Escherichia/Shigella (2101), and Methylobacterium-Methylorubrum (674). An rpoB mutation associated with rifampin resistance was detected in 8 of 15 (53%) biofilms. Staphylococcus warneri formed greater biofilm on polyurethane than on any other material type (p < 0.01). The results of this investigation, wherein we comprehensively characterized the composition of AUS device biofilms, provide the framework for future identification and rational development of inhibitors and preventive strategies against device-associated infection.

House sparrows do not exhibit a preference for the scent of potential partners with different MHC-I diversity and genetic distances

MHC genes play a fundamental role in immune recognition of pathogens and parasites. Therefore, females may increase offspring heterozygosity and genetic diversity by selecting males with genetically compatible or heterozygous MHC. In birds, several studies suggest that MHC genes play a role in mate choice, and recent evidence suggests that olfaction may play a role in the MHC-II discrimination. However, whether olfaction is involved in MHC-I discrimination in birds remains unknown. Previous studies indicate that house sparrow females with low allelic diversity prefer males with higher diversity in MHC-I alleles. Here, we directly explored whether female and male house sparrows (Passer domesticus) could estimate by scent MHC-I diversity and/or dissimilarity of potential partners. Our results show that neither females nor males exhibit a preference related to MHC-I diversity or dissimilarity of potential partners, suggesting that MHC-I is not detected through olfaction. Further studies are needed to understand the mechanisms responsible for mate discrimination based on MHC-I in birds.

Endospores associated with deep seabed geofluid features in the eastern Gulf of Mexico

Recent studies have reported up to 1.9 × 10²⁹ bacterial endospores in the upper kilometre of deep subseafloor marine sediments, however, little is understood about their origin and dispersal. In cold ocean environments, the presence of thermospores (endospores produced by thermophilic bacteria) suggests that distribution is governed by passive migration from warm anoxic sources possibly facilitated by geofluid flow, such as advective hydrocarbon seepage sourced from petroleum deposits deeper in the subsurface. This study assesses this hypothesis by measuring endospore abundance and distribution across 60 sites in Eastern Gulf of Mexico (EGM) sediments using a combination of the endospore biomarker 2,6-pyridine dicarboxylic acid or 'dipicolinic acid' (DPA), sequencing 16S rRNA genes of thermospores germinated in 50°C sediment incubations, petroleum geochemistry in the sediments and acoustic seabed data from sub-bottom profiling. High endospore abundance is associated with geologically active conduit features (mud volcanoes, pockmarks, escarpments and fault systems), consistent with subsurface fluid flow dispersing endospores from deep warm sources up into the cold ocean. Thermospores identified at conduit sites were most closely related to bacteria associated with the deep biosphere habitats including hydrocarbon systems. The high endospore abundance at geological seep features demonstrated here suggests that recalcitrant endospores and their chemical components (such as DPA) can be used in concert with geochemical and geophysical analyses to locate discharging seafloor features. This multiproxy approach can be used to better understand patterns of advective fluid flow in regions with complex geology like the EGM basin.

Microbiota DNA isolation, 16S rRNA amplicon sequencing, and bioinformatic analysis for bacterial microbiome profiling of rodent fecal samples

Fecal samples are frequently used to characterize bacterial populations of the gastrointestinal tract. A protocol is provided to profile gut bacterial populations using rodent fecal samples. We describe the optimal procedures for collecting rodent fecal samples, isolating genomic DNA, 16S rRNA gene V4 region sequencing, and bioinformatic analyses. This protocol includes detailed instructions and example outputs to ensure accurate, reproducible results and data visualization. Comprehensive troubleshooting and limitation sections address technical and statistical issues that may arise when profiling microbiota. For complete details on the use and execution of this protocol, please refer to Gubert et al. (2022).

Machine Learning and Canine Chronic Enteropathies: A New Approach to Investigate FMT Effects

Fecal microbiota transplantation (FMT) represents a very promising approach to decreasing disease activity in canine chronic enteropathies (CE). However, the relationship between remission mechanisms and microbiome changes has not been elucidated yet. The main objective of this study was to report the clinical effects of oral freeze-dried FMT in CE dogs, comparing the fecal microbiomes of three groups: pre-FMT CE-affected dogs, post-FMT dogs, and healthy dogs. Diversity analysis, differential abundance analysis, and machine learning algorithms were applied to investigate the differences in microbiome composition between healthy and pre-FMT samples, while Canine Chronic Enteropathy Clinical Activity Index (CCECAI) changes and microbial diversity metrics were used to evaluate FMT effects. In the healthy/pre-FMT comparison, significant differences were noted in alpha and beta diversity and a list of differentially abundant taxa was identified, while machine learning algorithms predicted sample categories with 0.97 (random forest) and 0.87 (sPLS-DA) accuracy. Clinical signs of improvement were observed in 74% (20/27) of CE-affected dogs, together with a statistically significant decrease in CCECAI (median value from 5 to 2 median). Alpha and beta diversity variations between pre- and post-FMT were observed for each receiver, with a high heterogeneity in the response. This highlighted the necessity for further research on a larger dataset that could identify different healing patterns of microbiome changes.

Compositionally Aware Phylogenetic Beta-Diversity Measures Better Resolve Microbiomes Associated with Phenotype

Microbiome data have several specific characteristics (sparsity and compositionality) that introduce challenges in data analysis. The integration of prior information regarding the data structure, such as phylogenetic structure and repeated-measure study designs, into analysis, is an effective approach for revealing robust patterns in microbiome data. Past methods have addressed some but not all of these challenges and features: for example, robust principal-component analysis (RPCA) addresses sparsity and compositionality; compositional tensor factorization (CTF) addresses sparsity, compositionality, and repeated measure study designs; and UniFrac incorporates phylogenetic information. Here we introduce a strategy of incorporating phylogenetic information into RPCA and CTF. The resulting methods, phylo-RPCA, and phylo-CTF, provide substantial improvements over state-of-the-art methods in terms of discriminatory power of underlying clustering ranging from the mode of delivery to adult human lifestyle. We demonstrate quantitatively that the addition of phylogenetic information improves effect size and classification accuracy in both data-driven simulated data and real microbiome data.

IMPORTANCE Microbiome data analysis can be difficult because of particular data features, some unavoidable and some due to technical limitations of DNA sequencing instruments. The first step in many analyses that ultimately reveals patterns of similarities and differences among sets of samples (e.g., separating samples from sick and healthy people or samples from seawater versus soil) is calculating the difference between each pair of samples. We introduce two new methods to calculate these differences that combine features of past methods, specifically being able to take into account the principles that most types of microbes are not in most samples (sparsity), that abundances are relative rather than absolute (compositionality), and that all microbes have a shared evolutionary history (phylogeny). We show using simulated and real data that our new methods provide improved classification accuracy of ordinal sample clusters and increased effect size between sample groups on beta-diversity distances.

Beta-diversity distance matrices for microbiome sample size and power calculations - How to obtain good estimates

In microbiome studies, researchers often wish to compare the taxa count distributions between groups of samples. Commonly-used corresponding methods of analysis are built on examining distance matrices, where distances describe the beta-diversity between samples. Analyses then compare the distribution of distances within groups to the distributions between groups. However, when performing a priori sample size or power calculations for such study designs, appropriate within and between group distance distributions can be challenging to obtain. When available, pilot study data, or data from prior studies of similar design should provide realistic distance estimates. However, when these are not available, distances can be extracted from available studies where one can assume similar beta-diversity. Alternatively, distances can be generated by simulation methods. Here, we describe and illustrate these three strategies for obtaining realistic distance matrices. For simulation methods, we illustrate the procedures required starting from existing benchmark data, as well as how to simulate directly from population assumptions. Using data from the American Gut project, we provide tables of observed distances for use by researchers planning their own studies, as well as R codes for generating similar matrices in other datasets. Furthermore, for simulated data, we compare methods, provide R codes, and demonstrate how challenging it is to obtain realistic distance distributions without any benchmark data. This code and illustrative distance tables are provided by the IMPACTT Consortium as a resource to the microbiome research community.

Applications and Comparison of Dimensionality Reduction Methods for Microbiome Data

Dimensionality reduction techniques are a key component of most microbiome studies, providing both the ability to tractably visualize complex microbiome datasets and the starting point for additional, more formal, statistical analyses. In this review, we discuss the motivation for applying dimensionality reduction techniques, the special characteristics of microbiome data such as sparsity and compositionality that make this difficult, the different categories of strategies that are available for dimensionality reduction, and examples from the literature of how they have been successfully applied (together with pitfalls to avoid). We conclude by describing the need for further development in the field, in particular combining the power of phylogenetic analysis with the ability to handle sparsity, compositionality, and non-normality, as well as discussing current techniques that should be applied more widely in future analyses.

Compositional Data Analysis using Kernels in mass cytometry data

MOTIVATION

Cell-type abundance data arising from mass cytometry experiments are compositional in nature. Classical association tests do not apply to the compositional data due to their non-Euclidean nature. Existing methods for analysis of cell type abundance data suffer from several limitations for high-dimensional mass cytometry data, especially when the sample size is small.

RESULTS

We proposed a new multivariate statistical learning methodology, Compositional Data Analysis using Kernels (CODAK), based on the kernel distance covariance (KDC) framework to test the association of the cell type compositions with important predictors (categorical or continuous) such as disease status. CODAK scales well for high-dimensional data and provides satisfactory performance for small sample sizes (n < 25). We conducted simulation studies to compare the performance of the method with existing methods of analyzing cell type abundance data from mass cytometry studies. The method is also applied to a high-dimensional dataset containing different subgroups of populations including Systemic Lupus Erythematosus (SLE) patients and healthy control subjects.

AVAILABILITY AND IMPLEMENTATION

CODAK is implemented using R. The codes and the data used in this manuscript are available on the web at http://github.com/GhoshLab/CODAK/.

CONTACT

prudra@okstate.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics Advances online.

Proteogenomic characterization identifies clinically relevant subgroups of intrahepatic cholangiocarcinoma

We performed proteogenomic characterization of intrahepatic cholangiocarcinoma (iCCA) using paired tumor and adjacent liver tissues from 262 patients. Integrated proteogenomic analyses prioritized genetic aberrations and revealed hallmarks of iCCA pathogenesis. Aflatoxin signature was associated with tumor initiation, proliferation, and immune suppression. Mutation-associated signaling profiles revealed that TP53 and KRAS co-mutations may contribute to iCCA metastasis via the integrin-FAK-SRC pathway. FGFR2 fusions activated the Rho GTPase pathway and could be a potential source of neoantigens. Proteomic profiling identified four patient subgroups (S1-S4) with subgroup-specific biomarkers. These proteomic subgroups had distinct features in prognosis, genetic alterations, microenvironment dysregulation, tumor microbiota composition, and potential therapeutics. SLC16A3 and HKDC1 were further identified as potential prognostic biomarkers associated with metabolic reprogramming of iCCA cells. This study provides a valuable resource for researchers and clinicians to further identify molecular pathogenesis and therapeutic opportunities in iCCA.

Analysing microbiome intervention design studies: Comparison of alternative multivariate statistical methods

The diet plays a major role in shaping gut microbiome composition and function in both humans and animals, and dietary intervention trials are often used to investigate and understand these effects. A plethora of statistical methods for analysing the differential abundance of microbial taxa exists, and new methods are constantly being developed, but there is a lack of benchmarking studies and clear consensus on the best multivariate statistical practices. This makes it hard for a biologist to decide which method to use. We compared the outcomes of generic multivariate ANOVA (ASCA and FFMANOVA) against statistical methods commonly used for community analyses (PERMANOVA and SIMPER) and methods designed for analysis of count data from high-throughput sequencing experiments (ALDEx2, ANCOM and DESeq2). The comparison is based on both simulated data and five published dietary intervention trials representing different subjects and study designs. We found that the methods testing differences at the community level were in agreement regarding both effect size and statistical significance. However, the methods that provided ranking and identification of differentially abundant operational taxonomic units (OTUs) gave incongruent results, implying that the choice of method is likely to influence the biological interpretations. The generic multivariate ANOVA tools have the flexibility needed for analysing multifactorial experiments and provide outputs at both the community and OTU levels; good performance in the simulation studies suggests that these statistical tools are also suitable for microbiome data sets.

Beyond samples: A metric revealing more connections of gut microbiota between individuals

Studies of gut microbiota explore their complicated connections between individuals of different characteristics by applying different metrics to abundance data obtained from fecal samples. Although classic metrics are capable to quantify differences between samples, the microbiome of fecal sample is not a good surrogate for the gut microbiome of individuals because the microbial populations of the distal colon does not adequately represent that of the entire gastrointestinal tract. To overcome the deficiency of classic metrics in which the differences can be measured between the samples analyzed, but not the corresponding populations, we propose a metric for representing composition differences in the gut microbiota of individuals. Our investigation shows this metric outperforms traditional measures for multiple scenarios. For gut microbiota in diverse geographic populations, this metric presents more explainable data variance than others, not only in regular variance analysis but also in principle component analysis and partition analysis of biologic characteristics. With time-series data, the metric further presents a strong correlation with the time interval of serial sampling. Our findings suggest that the metric is robust and powerfully detects the intrinsic variations in gut microbiota. The metric holds promise for revealing more relations between gut microbiota and human health.

Long- and short-read metabarcoding technologies reveal similar spatiotemporal structures in fungal communities

Fungi form diverse communities and play essential roles in many terrestrial ecosystems, yet there are methodological challenges in taxonomic and phylogenetic placement of fungi from environmental sequences. To address such challenges, we investigated spatiotemporal structure of a fungal community using soil metabarcoding with four different sequencing strategies: short-amplicon sequencing of the ITS2 region (300-400 bp) with Illumina MiSeq, Ion Torrent Ion S5 and PacBio RS II, all from the same PCR library, as well as long-amplicon sequencing of the full ITS and partial LSU regions (1200-1600 bp) with PacBio RS II. Resulting community structure and diversity depended more on statistical method than sequencing technology. The use of long-amplicon sequencing enables construction of a phylogenetic tree from metabarcoding reads, which facilitates taxonomic identification of sequences. However, long reads present issues for denoising algorithms in diverse communities. We present a solution that splits the reads into shorter homologous regions prior to denoising, and then reconstructs the full denoised reads. In the choice between short and long amplicons, we suggest a hybrid approach using short amplicons for sampling breadth and depth, and long amplicons to characterize the local species pool for improved identification and phylogenetic analyses.

A zero-inflated non-negative matrix factorization for the deconvolution of mixed signals of biological data

A latent factor model for count data is popularly applied in deconvoluting mixed signals in biological data as exemplified by sequencing data for transcriptome or microbiome studies. Due to the availability of pure samples such as single-cell transcriptome data, the accuracy of the estimates could be much improved. However, the advantage quickly disappears in the presence of excessive zeros. To correctly account for this phenomenon in both mixed and pure samples, we propose a zero-inflated non-negative matrix factorization and derive an effective multiplicative parameter updating rule. In simulation studies, our method yielded the smallest bias. We applied our approach to brain gene expression as well as fecal microbiome datasets, illustrating the superior performance of the approach. Our method is implemented as a publicly available R-package, iNMF.

Transition of microbiota in chicken cecal droppings from commercial broiler farms

Background

Chickens are major sources of human nutrition worldwide, but the chicken intestinal microbiota can be a source of bacterial infection. The microbiota has potential to regulate the colonization of pathogens by competitive exclusion, production of antimicrobial compounds, and stimulation of the mucosal immune system. But information on the microbiota in commercial broiler chickens is limited because of the difficulty of conducting studies at commercial farms. To obtain fundamental information that can be used to control pathogens in chickens, we determined the 6-week dynamics of microbiota in chicken cecal droppings from commercial broiler farms.

Results

Cecal droppings from four chickens were collected once a week from 1 to 6 weeks of age at three commercial broiler farms. A total of 168 samples were collected from 7 flocks and subjected to 16S rRNA amplicon sequencing. Despite the farms have distinctly different climate conditions, the microbiota in the same growth stages were similar among farms. Moreover, as the chickens grew and the feed types were switched, the richness and diversity of the microbiota gradually increased and convergence of the composition of the microbiota was apparent. Notably, minor bacterial taxa (i.e. OTUs with relative abundance < 0.05%) within the microbiota were changed by the chicken age, switching of feed types, and presence of Campylobacter. In particular, the effects of switching of feed types on the microbiota were larger than the effects of age and Campylobacter.

Conclusions

Irrespective of the locations of the farms, the microbiota of chicken cecum, especially minor bacteria, was successively changed more affected by feed types than by ages. Switching of feed types inducing the alteration of the microbiota may be associated with the colonization of pathogens in the chicken gut. These results will also help with extrapolation of studies in experimental animals to those in the commercial farms.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12917-020-02688-7.

Optimal Estimation of Wasserstein Distance on A Tree with An Application to Microbiome Studies

The weighted UniFrac distance, a plug-in estimator of the Wasserstein distance of read counts on a tree, has been widely used to measure the microbial community difference in microbiome studies. Our investigation however shows that such a plug-in estimator, although intuitive and commonly used in practice, suffers from potential bias. Motivated by this finding, we study the problem of optimal estimation of the Wasserstein distance between two distributions on a tree from the sampled data in the high-dimensional setting. The minimax rate of convergence is established. To overcome the bias problem, we introduce a new estimator, referred to as the moment-screening estimator on a tree (MET), by using implicit best polynomial approximation that incorporates the tree structure. The new estimator is computationally efficient and is shown to be minimax rate-optimal. Numerical studies using both simulated and real biological datasets demonstrate the practical merits of MET, including reduced biases and statistically more significant differences in microbiome between the inactive Crohn's disease patients and the normal controls.

The biogeography of Streptomyces in New Zealand enabled by high-throughput sequencing of genus-specific rpoB amplicons

We evaluated Streptomyces biogeography in soils along a 1200 km latitudinal transect across New Zealand (NZ). Streptomyces diversity was examined using high-throughput sequencing of rpoB amplicons generated with a Streptomyces specific primer set. We detected 1287 Streptomyces rpoB operational taxonomic units (OTUs) with 159 ± 92 (average ± SD) rpoB OTUs per site. Only 12% (n = 149) of these OTUs matched rpoB sequences from cultured specimens (99% nucleotide identity cutoff). Streptomyces phylogenetic diversity (Faith's PD) was correlated with soil pH, mean annual temperature and plant community richness (Spearman's r: 0.77, 0.64 and -0.79, respectively; P < 0.05), but not with latitude. In addition, soil pH and plant community richness both explained significant variation in Streptomyces beta diversity. Streptomyces communities exhibited both high dissimilarity and strong dominance of one or a few species at each site. Taken together, these results suggest that dispersal limitation due to competitive interactions limits the colonization success of spores that relocate to new sites. Cultivated Streptomyces isolates represent a major source of clinically useful antibiotics, but only a small fraction of extant diversity within the genus have been identified and most species of Streptomyces have yet to be described.

Oral and intestinal microbial features in pregnant women with hypothyroidism and their correlations with pregnancy outcomes

The purpose of this study was to explore the characteristics of oral and intestinal microbiota of pregnant women with hypothyroidism during pregnancy, and to find the correlations between the changes of flora and pregnancy outcome of pregnant women with hypothyroidism during pregnancy. In this study, oral and intestinal microbial composition was surveyed by using the 16S rRNA sequencing approach in 61 pregnant women (30 with hypothyroidism and 31 normal controls). Sequentially, we validated the differential microbial features by using the quantitative real-time PCR (qPCR) approach in 10 randomly selected pregnant women (5 with hypothyroidism and 5 normal controls). Furthermore, general clinical data and serological indices were added to the analysis to examine the links between oral and intestinal microbiota and pregnancy outcomes. The 16S rRNA results showed that the relative abundances of Gammaproteobacteria were higher in pregnant women in the hypothyroidism group than in those in the control group, whereas the levels of Firmicutes were higher in the control group than in the hypothyroidism group. The serum C-reactive protein level, the weight gain during pregnancy, and the incidence of fetal distress were higher in the hypothyroidism group than in the control group. The QPCR results also showed the same changes of the intestinal microbiota in the two groups. There were significant differences in the oral and intestinal microbiota between pregnant women with hypothyroidism and normal pregnant women. The changes of microbiota is one of the factors influencing the occurrence and development of hypothyroidism during pregnancy.

Differential intestinal and oral microbiota features associated with gestational diabetes and maternal inflammation

Maternal microbiota is involved in many metabolic diseases. However, its role in the pathophysiology of gestational diabetes mellitus (GDM) remains unclear. In this case-control study, we performed a 16S rRNA sequencing-based microbial survey to compare the intestinal and oral microflora at third trimester during pregnancy between 30 GDM and 31 normal controls. Sequentially, a correlation-based network analysis was further performed to explore the interactions among microbiota, maternal and infant blood sugar, and inflammatory markers. Our results show that, compared with controls, the GDM cases showed significant differences in β-diversity and increased Gammaproteobacteria and Hemophilus in intestinal microbiota. Furthermore, the GDM cases showed lower α-diversity, increased Selenomonas and Bifidobacterium, and decreased Fusobacteria and Leptotrichia in oral microbiota. The ROC curve showed the area under the curve to be equal to 0.70 and 0.66 when using oral Leptotrichia or gut Hemophilus, respectively, to predict GDM status. In addition, the components and topography of microbial cooccurrence and coexclusion network were quite distinct by GDM status. In summary, intestinal and oral microorganisms in pregnant women are closely related to the status of GDM in the third trimester of pregnancy. The changes of intestinal and oral microbial features may be noninvasive biomarkers for monitoring the health management of GDM pregnancy.

The influence of host genetics on the microbiome

It is well understood that genetic differences among hosts contribute to variation in pathogen susceptibility and the ability to associate with symbionts. However, it remains unclear just how influential host genes are in shaping the overall microbiome. Studies of both animal and plant microbial communities indicate that host genes impact species richness and the abundances of individual taxa. Analyses of beta diversity (that is, overall similarity), on the other hand, often conclude that hosts play a minor role in shaping microbial communities. In this review, we discuss recent attempts to identify the factors that shape host microbial communities and whether our understanding of these communities is affected by the traits chosen to represent them.

Correlations between microbial population dynamics, bamA gene abundance and performance of anaerobic sequencing batch reactor (ASBR) treating increasing concentrations of phenol

The relevant microorganims driving efficiency changes in anaerobic digestion of phenol remains uncertain. In this study correlations were established between microbial population and the process performance in an anaerobic sequencing batch reactor (ASBR) treating increasing concentrations of phenol (from 120 to 1200 mg L^-1). Sludge samples were taken at different operational stages and microbial community dynamics was analyzed by 16S rRNA sequencing. In addition, bamA gene was quantified in order to evaluate the dynamics of anaerobic aromatic degraders. The microbial community was dominated by Anaerolineae, Bacteroidia, Clostridia, and Methanobacteria classes. Correlation analysis between bamA gene copy number and phenol concentration were highly significant, suggesting that the increase of aromatic degraders targeted by bamA assay was due to an increase in the amount of phenol degraded over time. The incremental phenol concentration affected hydrogenotrophic archaea triggering a linear decrease of Methanobacterium and the growth of Methanobrevibacter. The best performance in the reactor was at 800 mg L^-1 of phenol. At this stage, the highest relative abundances of Syntrophorhabdus, Chloroflexus, Smithella, Methanolinea and Methanosaeta were observed and correlated positively with initial degradation rate, suggesting that these microorganisms are relevant players to maintain a good performance in the ASBR.

Microbial Community Predicts Functional Stability of Microbial Fuel Cells

Stability as evaluated by functional resistance and resilience is critical to the effective operation of environmental biotechnologies. To date, limited tools have been developed that allow operators of these technologies to predict functional responses to environmental and operational disturbances. In the present study, 17 Microbial Fuel Cells (MFCs) were exposed to a low pH perturbation. MFC power dropped 52.7 ± 35.8% during the low pH disturbance. Following the disturbance, 3 MFCs did not recover while 14 took 60.7 ± 58.3 h to recover to previous current output levels. Machine learning models based on genomic data inputs were developed and evaluated on their ability to predict resistance and resilience. Resistance and resilience levels corresponding to risk of deactivation could be classified with 70.47 ± 15.88% and 65.33 ± 19.71% accuracy, respectively. Models predicting resistance and resilience coefficient values projected postperturbation current drops within 6.7-15.8% and recovery times within 5.8-8.7% of observed values. Results suggest that abundances of specific genera are better predictors of resistance while overall microbial community structure more accurately predicts resilience. This approach can be used to assess operational risk and is a first step toward the further understanding and improvement of overall stability of environmental biotechnologies.

Highly Reproducible 16S Sequencing Facilitates Measurement of Host Genetic Influences on the Stickleback Gut Microbiome

Multicellular organisms interact with resident microbes in important ways, and a better understanding of host-microbe interactions is aided by tools such as high-throughput 16S sequencing. However, rigorous evaluation of the veracity of these tools in a different context from which they were developed has often lagged behind. Our goal was to perform one such critical test by examining how variation in tissue preparation and DNA isolation could affect inferences about gut microbiome variation between two genetically divergent lines of threespine stickleback fish maintained in the same laboratory environment. Using careful experimental design and intensive sampling of individuals, we addressed technical and biological sources of variation in 16S-based estimates of microbial diversity. After employing a two-tiered bead beating approach that comprised tissue homogenization followed by microbial lysis in subsamples, we found an extremely minor effect of DNA isolation protocol relative to among-host microbial diversity differences. Abundance estimates for rare operational taxonomic units (OTUs), however, showed much lower reproducibility. Gut microbiome composition was highly variable across fish-even among cohoused siblings-relative to technical replicates, but a subtle effect of host genotype (stickleback line) was nevertheless detected for some microbial taxa.IMPORTANCE Our findings demonstrate the importance of appropriately quantifying biological and technical variance components when attempting to understand major influences on high-throughput microbiome data. Our focus was on understanding among-host (biological) variance in community metrics and its magnitude in relation to within-host (technical) variance, because meaningful comparisons among individuals are necessary in addressing major questions in host-microbe ecology and evolution, such as heritability of the microbiome. Our study design and insights should provide a useful example for others desiring to quantify microbiome variation at biological levels in the face of various technical factors in a variety of systems.

Dysbiosis of gut microbiota in a selected population of Parkinson's patients

INTRODUCTION

In recent years the hypothesis that gut microbiota associates with Parkinson's disease (PD) has gained importance, although it has not been possible to define a specific microbiota composition as a predictive biomarker of this disease. We have investigated dysbiosis of gut microbiota in a selected population of PD patients from Central Italy, and examined the weight of specific confounders and predictors, in order to identify potential correlations with clinical phenotypes.

METHODS

152 fecal samples were collected from 80 patients and 72 healthy controls. Patients were enrolled according to tight inclusion criteria. Microbiota composition was studied through 16s ribosomal RNA gene amplicon sequencing analysis in combination with data on dietary/life habits. Age, loss of weight, and sex were recognized as confounding factors, whereas PD-status, age, Body Mass Index, "eat cereals", "gain of weigth" and "physical activity" as predictors. The presence of Lactobacillaceae, Enterobacteriaceae and Enterococcaceae families was significantly higher in feces from PD patients compared to healthy controls, while Lachnospiraceae were significantly reduced. Lower levels of Lachnospiraceae and higher levels of Enterobacteriaceae families also correlated with increased disease severity and motor impairment (Hoehn & Yahr stage, MDS-UPDRS Part III). Predictive metagenomics indicated a significant variation of genes involved in the metabolism of short chain fatty acids and amino acids, and in lipopolysaccharide biosynthesis.

CONCLUSIONS

PD showed a distinctive microbiota composition. Functional predictions suggest changes in pathways favoring a pro-inflammatory environment in the gastrointestinal tract, and a reduction in the biosynthesis of amino acids acting as precursors of physiological transmitters.

A unified framework for unconstrained and constrained ordination of microbiome read count data

Explorative visualization techniques provide a first summary of microbiome read count datasets through dimension reduction. A plethora of dimension reduction methods exists, but many of them focus primarily on sample ordination, failing to elucidate the role of the bacterial species. Moreover, implicit but often unrealistic assumptions underlying these methods fail to account for overdispersion and differences in sequencing depth, which are two typical characteristics of sequencing data. We combine log-linear models with a dispersion estimation algorithm and flexible response function modelling into a framework for unconstrained and constrained ordination. The method is able to cope with differences in dispersion between taxa and varying sequencing depths, to yield meaningful biological patterns. Moreover, it can correct for observed technical confounders, whereas other methods are adversely affected by these artefacts. Unlike distance-based ordination methods, the assumptions underlying our method are stated explicitly and can be verified using simple diagnostics. The combination of unconstrained and constrained ordination in the same framework is unique in the field and facilitates microbiome data exploration. We illustrate the advantages of our method on simulated and real datasets, while pointing out flaws in existing methods. The algorithms for fitting and plotting are available in the R-package RCM.

Diversity and Dynamics of "Candidatus Endobugula" and Other Symbiotic Bacteria in Chinese Populations of the Bryozoan, Bugula neritina

Bugula neritina is a common invasive cosmopolitan bryozoan that harbors (like many sessile marine invertebrates) a symbiotic bacterial (SB) community. Among the SB of B. neritina, "Candidatus Endobugula sertula" continues to receive the greatest attention, because it is the source of bryostatins. The bryostatins are potent bioactive polyketides, which have been investigated for their therapeutic potential to treat various cancers, Alzheimer's disease, and AIDS. In this study, we compare the metagenomics sequences for the 16S ribosomal RNA gene of the SB communities from different geographic and life cycle samples of Chinese B. neritina. Using a variety of approaches for estimating alpha/beta diversity and taxonomic abundance, we find that the SB communities vary geographically with invertebrate and fish mariculture and with latitude and environmental temperature. During the B. neritina life cycle, we find that the diversity and taxonomic abundances of the SB communities change with the onset of host metamorphosis, filter feeding, colony formation, reproduction, and increased bryostatin production. "Ca. Endobugula sertula" is confirmed as the symbiont of the Chinese "Ca. Endobugula"/B. neritina symbiosis. Our study extends our knowledge about B. neritina symbiosis from the New to the Old World and offers new insights into the environmental and life cycle factors that can influence its SB communities, "Ca. Endobugula," and bryostatins more globally.

Safety and preliminary efficacy of orally administered lyophilized fecal microbiota product compared with frozen product given by enema for recurrent Clostridium difficile infection: A randomized clinical trial

Background

Fecal microbiota transplantation (FMT) via colonoscopy or enema has become a commonly used treatment of recurrent C. difficile infection (CDI).

Aims

To compare the safety and preliminary efficacy of orally administered lyophilized microbiota product compared with frozen product by enema.

Methods

In a single center, adults with ≥ 3 episodes of recurrent CDI were randomized to receive encapsulated lyophilized fecal microbiota from 100–200 g of donor feces (n = 31) or frozen FMT from 100 g of donor feces (n = 34) by enema. Safety during the three months post FMT was the primary study objective. Prevention of CDI recurrence during the 60 days after FMT was a secondary objective. Fecal microbiome changes were examined in first 39 subjects studied.

Results

Adverse experiences were commonly seen in equal frequency in both groups and did not appear to relate to the route of delivery of FMT. CDI recurrence was prevented in 26 of 31 (84%) subjects randomized to capsules and in 30 of 34 (88%) receiving FMT by enema (p = 0.76). Both products normalized fecal microbiota diversity while the lyophilized orally administered product was less effective in repleting Bacteroidia and Verrucomicrobia classes compared to frozen product via enema.

Conclusions

The route of delivery, oral or rectal, did not influence adverse experiences in FMT. In preliminary evaluation, both routes appeared to show equivalent efficacy, although the dose may need to be higher for lyophilized product. Spore-forming bacteria appear to be the most important engrafting organisms in FMT by the oral route using lyophilized product.

Trial registration

ClinicalTrials.gov NCT02449174

Practical considerations for sampling and data analysis in contemporary metagenomics-based environmental studies

Recent advancements in metagenomic-based studies, especially analyses of amplicon-based DNA sequencing targeting taxonomic marker genes, has led to an unprecedented characterization of microbial communities from diverse ecosystems around the world. While originally constrained by a lack of appropriate analytical tools and sequencing depth, new technologies and computational and statistical algorithms have been developed to handle highly dimensional, next-generation sequencing datasets. Both these tools allow for the robust analysis of structural and distributional patterns of microbiota essential for the understanding of microbial ecology and biogeography. Furthermore, consortia of individual laboratories working on large interdisciplinary research programs, like the Human and Earth Microbiome Projects, have developed standardized protocols for DNA extraction, sequencing pipelines, and bioinformatics. These approaches provide large repositories of publicly available data to serve as references for on-going and future, hypothesis-driven studies to better characterize the roles of microbial communities in diverse ecosystems. In this review, we outline the currently available statistical approaches and tools to aid in statistically powered study designs and analyses. Given what is now known about the enormous diversity and variability of the microbial communities in aquatic and terrestrial habitats, we also discuss practical considerations for sample collection. Due to the extensive advances made in the field of metagenomics over the last decade, rigorous, well replicated, hypothesis-driven studies are: 1) needed, 2) now possible, and 3) essential to make best use of sequencing-based technologies to characterize the roles of microbial communities in the structure and function of diverse ecosystems.

Denoising the Denoisers: an independent evaluation of microbiome sequence error-correction approaches

High-depth sequencing of universal marker genes such as the 16S rRNA gene is a common strategy to profile microbial communities. Traditionally, sequence reads are clustered into operational taxonomic units (OTUs) at a defined identity threshold to avoid sequencing errors generating spurious taxonomic units. However, there have been numerous bioinformatic packages recently released that attempt to correct sequencing errors to determine real biological sequences at single nucleotide resolution by generating amplicon sequence variants (ASVs). As more researchers begin to use high resolution ASVs, there is a need for an in-depth and unbiased comparison of these novel “denoising” pipelines. In this study, we conduct a thorough comparison of three of the most widely-used denoising packages (DADA2, UNOISE3, and Deblur) as well as an open-reference 97% OTU clustering pipeline on mock, soil, and host-associated communities. We found from the mock community analyses that although they produced similar microbial compositions based on relative abundance, the approaches identified vastly different numbers of ASVs that significantly impact alpha diversity metrics. Our analysis on real datasets using recommended settings for each denoising pipeline also showed that the three packages were consistent in their per-sample compositions, resulting in only minor differences based on weighted UniFrac and Bray–Curtis dissimilarity. DADA2 tended to find more ASVs than the other two denoising pipelines when analyzing both the real soil data and two other host-associated datasets, suggesting that it could be better at finding rare organisms, but at the expense of possible false positives. The open-reference OTU clustering approach identified considerably more OTUs in comparison to the number of ASVs from the denoising pipelines in all datasets tested. The three denoising approaches were significantly different in their run times, with UNOISE3 running greater than 1,200 and 15 times faster than DADA2 and Deblur, respectively. Our findings indicate that, although all pipelines result in similar general community structure, the number of ASVs/OTUs and resulting alpha-diversity metrics varies considerably and should be considered when attempting to identify rare organisms from possible background noise.

Third-party fecal microbiota transplantation following allo-HCT reconstitutes microbiome diversity

We hypothesized that third-party fecal microbiota transplantation (FMT) may restore intestinal microbiome diversity after allogeneic hematopoietic cell transplantation (allo-HCT). In this open-label single-group pilot study, 18 subjects were enrolled before allo-HCT and planned to receive third-party FMT capsules. FMT capsules were administered no later than 4 weeks after neutrophil engraftment, and antibiotics were not allowed within 48 hours before FMT. Five patients did not receive FMT because of the development of early acute gastrointestinal (GI) graft-versus-host disease (GVHD) before FMT (n = 3), persistent HCT-associated GI toxicity (n = 1), or patient decision (n = 1). Thirteen patients received FMT at a median of 27 days (range, 19-45 days) after HCT. Participants were able to swallow and tolerate all FMT capsules, meeting the primary study endpoint of feasibility. FMT was tolerated well, with 1 treatment-related significant adverse event (abdominal pain). Two patients subsequently developed acute GI GVHD, with 1 patient also having concurrent bacteremia. No additional cases of bacteremia occurred. Median follow-up for survivors is 15 months (range, 13-20 months). The Kaplan-Meier estimates for 12-month overall survival and progression-free survival after FMT were 85% (95% confidence interval, 51%-96%) and 85% (95% confidence interval, 51%-96%), respectively. There was 1 nonrelapse death resulting from acute GI GVHD (12-month nonrelapse mortality, 8%; 95% confidence interval, 0%-30%). Analysis of stool composition and urine 3-indoxyl sulfate concentration indicated improvement in intestinal microbiome diversity after FMT that was associated with expansion of stool-donor taxa. These results indicate that empiric third-party FMT after allo-HCT appears to be feasible, safe, and associated with expansion of recipient microbiome diversity. This trial was registered at www.clinicaltrials.gov as #NCT02733744.

Association of gut microbial communities with plasma lipopolysaccharide-binding protein (LBP) in premenopausal women

The mechanisms by which obesity increases cancer risk are unclear, but some lines of evidence suggest that gut microbial communities (GMC) may contribute to chronic inflammation in obese individuals through raised systemic levels of lipopolysaccharides (LPS). We evaluated associations of the GMC in stool with plasma LPS-binding protein (LBP, a measure of LPS) and C-reactive protein (CRP) concentrations in 110 premenopausal women in the United States. Diet was assessed using 3-day food records and GMCs were evaluated using pyrosequencing of the 16S rRNA gene. OTUs were identified at 97% sequence similarity. Taxonomic classification and functional genes were imputed from 16S rRNA genes, and alpha and beta diversity were assessed using the Shannon index and MRPP, respectively. Multivariable linear regression analysis was used to assess the relation between LBP, specific bacterial genera identified with indicator species analysis, and CRP. Dietary fat intake, particularly saturated fat, and CRP were positively associated with increased LBP. GMC beta diversity, but not alpha diversity, was statistically significantly different between groups using unweighted Unifrac. Several taxa, particularly those in the Clostridia class, were more prevalent in women with low LBP, while Bacteroides were more prevalent in those with high LBP. Genes associated with gram-negative cell wall material synthesis were also associated with LBP and CRP. In contrast, Phascolarctobacterium was associated with lower concentrations of LBP and CRP. We found distinct differences between tertiles of LBP regarding the diversity and composition of the microbiome, as well as differences in functional genes that potentially activate LBP.

Microbiome Sequencing Methods for Studying Human Diseases

Over the last decade, biologists have come to appreciate that the human body is inhabited by thousands of bacterial species in diverse communities unique to each body site. Moreover, due to high-throughput sequencing methods for microbial characterization in a culture-independent manner, it is becoming evident that the microbiome plays an important role in human health and disease. This chapter focuses on the most common form of bacterial microbiome profiling, targeted amplicon sequencing of the 16S ribosomal RNA (rRNA) subunit encoded by 16S rDNA. We discuss important features for designing and performing microbiome experiments on human specimens, including experimental design, sample collection, DNA preparation, and selection of the 16S rDNA sequencing target. We also provide details for designing fusion primers required for targeted amplicon sequencing and selecting the most appropriate high-throughput sequencing platform. We conclude with a review of the fundamental concepts of data analysis and interpretation for these kinds of experiments. Our goal is to provide the reader with the essential knowledge needed to undertake microbiome experiments for application to human disease research questions.

Microbiome Datasets Are Compositional: And This Is Not Optional

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.

Omega-3 fatty acids correlate with gut microbiome diversity and production of N-carbamylglutamate in middle aged and elderly women

Omega-3 fatty acids may influence human physiological parameters in part by affecting the gut microbiome. The aim of this study was to investigate the links between omega-3 fatty acids, gut microbiome diversity and composition and faecal metabolomic profiles in middle aged and elderly women. We analysed data from 876 twins with 16S microbiome data and DHA, total omega-3, and other circulating fatty acids. Estimated food intake of omega-3 fatty acids were obtained from food frequency questionnaires. Both total omega-3and DHA serum levels were significantly correlated with microbiome alpha diversity (Shannon index) after adjusting for confounders (DHA Beta(SE) = 0.13(0.04), P = 0.0006 total omega-3: 0.13(0.04), P = 0.001). These associations remained significant after adjusting for dietary fibre intake. We found even stronger associations between DHA and 38 operational taxonomic units (OTUs), the strongest ones being with OTUs from the Lachnospiraceae family (Beta(SE) = 0.13(0.03), P = 8 × 10^-7). Some of the associations with gut bacterial OTUs appear to be mediated by the abundance of the faecal metabolite N-carbamylglutamate. Our data indicate a link between omega-3 circulating levels/intake and microbiome composition independent of dietary fibre intake, particularly with bacteria of the Lachnospiraceae family. These data suggest the potential use of omega-3 supplementation to improve the microbiome composition.

Hypothesis Testing and Statistical Analysis of Microbiome

After the initiation of Human Microbiome Project in 2008, various biostatistic and bioinformatic tools for data analysis and computational methods have been developed and applied to microbiome studies. In this review and perspective, we discuss the research and statistical hypotheses in gut microbiome studies, focusing on mechanistic concepts that underlie the complex relationships among host, microbiome, and environment. We review the current available statistic tools and highlight recent progress of newly developed statistical methods and models. Given the current challenges and limitations in biostatistic approaches and tools, we discuss the future direction in developing statistical methods and models for the microbiome studies.

Microbiome Datasets Are Compositional: And This Is Not Optional

Datasets collected by high-throughput sequencing (HTS) of 16S rRNA gene amplimers, metagenomes or metatranscriptomes are commonplace and being used to study human disease states, ecological differences between sites, and the built environment. There is increasing awareness that microbiome datasets generated by HTS are compositional because they have an arbitrary total imposed by the instrument. However, many investigators are either unaware of this or assume specific properties of the compositional data. The purpose of this review is to alert investigators to the dangers inherent in ignoring the compositional nature of the data, and point out that HTS datasets derived from microbiome studies can and should be treated as compositions at all stages of analysis. We briefly introduce compositional data, illustrate the pathologies that occur when compositional data are analyzed inappropriately, and finally give guidance and point to resources and examples for the analysis of microbiome datasets using compositional data analysis.