The Madness of Microbiome: Attempting To Find Consensus "Best Practice" for 16S Microbiome Studies

The Effect of Exogenous Sex Steroids on the Vaginal Microbiota: A Systematic Review

Background

Exogenous sex steroids within hormonal contraception and menopausal hormone therapy (MHT) have been used for family planning and management of menopausal symptoms, without consideration of their effects on the vaginal microbiota. This is largely because their use predates our understanding of the importance of the vaginal microbiome on human health. We conducted a systematic review (PROSPERO: CRD42018107730) to determine the influence of exogenous sex steroids, stratified by oestrogen-containing or progestin-only types of contraception, and MHT on the vaginal microbiome, as measured by molecular methods.

Methods

Embase, PubMed and Medline were searched for relevant literature published through to December 1st 2020. Eligible studies reported on the effect of specific exogenous sex steroids on the vaginal microbiome using a molecular method. Data regarding the ‘positive’, ‘negative’ or ‘neutral’ effect of each type of contraceptive or MHT on the vaginal microbiome was extracted and summarised. A positive effect reflected sex steroid exposure that was associated with increased abundance of lactobacilli, a change to, or maintenance of, an optimal vaginal microbiota composition, or a decrease in bacterial diversity (specifically reflecting a low-diversity optimal microbiota state), relative to the control group. An exogenous sex steroid was designated as having a negative effect on the vaginal microbiome if it resulted in opposing effects (i.e. loss of lactobacilli, a non-optimal microbiota state). When no significant change was found, this was considered neutral/inconclusive.

Results

We identified 29 manuscripts reporting on the effect of exogenous sex steroids on the vaginal microbiome; 25 investigating hormonal contraceptives, and 4 investigating MHT. Oestrogen-containing contraception, particularly reflecting the combined oestrogen and progestin-containing contraceptive pill, had a positive effect on the composition of the vaginal microbiota. Progestin-only contraception, particularly reflecting depo-medroxyprogesterone acetate, had mixed effects on the microbiota. Among post-menopausal women using MHT, exogenous oestrogen applied topically was associated with increased prevalence of lactobacilli.

Conclusion

Our findings suggest that oestrogen-containing compounds may promote an optimal vaginal microbiota, which could have clinical applications. The impact of progestin-only contraceptives on the vaginal microbiota is less clear; more data is needed to determine how progestin-only contraceptives contribute to adverse reproductive and sexual health outcomes.

Effect of cecal microbiota transplantation between different broiler breeds on the chick flora in the first week of life

The cecal microbiota plays numerous roles in chicken health and nutrition. Where such microbiota differs between lines exhibiting distinct phenotypes, microbiota transplantation offers scope to dissect the role of gut microbial communities in those traits. However, the composition and stability of transplants over time is relatively ill-defined and varying levels of success have been reported. In this study, we transplanted cecal contents from adult Roslin broilers into chicks from a different broiler line. Within <12 h posthatch, Ross 308 chicks received an oral gavage of cecal contents (n = 26) or a PBS control (n = 24). Cecal contents samples were collected postmortem from birds on d 1, 2, 3, 4, and 7 posthatch. DNA was extracted from these samples and the transplant inoculum and the V4 region of the 16S rRNA gene was amplified and sequenced. The cecal microbiota of chickens receiving the microbiota transplant was significantly different in composition and significantly richer and more diverse, in comparison to control birds. At the final timepoint (d 7), of the 150 Operational Taxonomic Units (OTUs) that were >0.1% abundant (average) in the donor sample, 137 were detected in the treated group (75 were >0.1% abundant (average)) while only 88 were detected in the control group (29 were >0.1% abundant (average)). Our data therefore suggests that stable transplantation of the cecal microbiota between lines is achievable using the methods described in this paper.

Milk microbiome in dairy cattle and the challenges of low microbial biomass and exogenous contamination

Background

The blanket usage of antimicrobials at the end of lactation (or “drying off”) in dairy cattle is under increasing scrutiny due to concerns about antimicrobial resistance. To lower antimicrobial usage in dairy farming, farmers are now encouraged to use “selective dry cow therapy” whereby only cows viewed as at high risk of mastitis are administered antimicrobial agents. It is important to gain a better understanding of how this practice affects the udder-associated microbiota and the potential knock-on effects on antimicrobial-resistant bacterial populations circulating on the farm. However, there are challenges associated with studying low biomass environments such as milk, due to known contamination effects on microbiome datasets. Here, we obtained milk samples from cattle at drying off and at calving to measure potential shifts in bacterial load and microbiota composition, with a critical assessment of contamination effects.

Results

Several samples had no detectable 16S rRNA gene copies and crucially, exogenous contamination was detected in the initial microbiome dataset. The affected samples were removed from the final microbiome analysis, which compromised the experimental design and statistical analysis. There was no significant difference in bacterial load between treatments (P > 0.05), but load was lower at calving than at drying off (P = 0.039). Escherichia coli counts by both sequence and culture data increased significantly in the presence of reduced bacterial load and a decreasing trend of microbiome richness and diversity. The milk samples revealed diverse microbiomes not reflecting a typical infection profile and were largely comprised of gut- and skin-associated taxa, with the former decreasing somewhat after prolonged sealing of the teats.

Conclusions

The drying off period had a key influence on microbiota composition and bacterial load, which appeared to be independent of antimicrobial usage. The interactions between drying off treatment protocol and milk microbiome dynamics are clearly complex, and our evaluations of these interactions were restricted by low biomass samples and contamination effects. Therefore, our analysis will inform the design of future studies to establish whether different selection protocols could be implemented to further minimise antimicrobial usage.

Supplementary Information

The online version contains supplementary material available at 10.1186/s42523-021-00144-x.

Comparison of bacterial community profiles from large intestine specimens, rectal swabs, and stool samples

The human gastrointestinal tract contains a complex and dynamic population of microorganisms, known as the gut microbiota. Although interest in the role of the gut microbiota in human health has increased in recent years, there remains no standard sampling protocol for analyzing these organisms. Here, we aimed to characterize the microbial composition of distinct segments of the large intestine and to determine whether rectal swabs are suitable for identifying colon microbiota. A total of 100 participants who underwent screening colonoscopy from October 2019 to October 2020 were included in this study. Large intestinal samples (ascending colon, descending colon, sigmoid colon, and rectum) were aspirated by colonoscopy. Rectal swabs were collected before colonoscopy, and stool samples were collected before patients began colonoscopy preparation. All samples were subjected to 16S ribosomal RNA gene sequencing. We identified differences in the number of phylum-level operational taxonomic units among large intestinal samples, rectal swabs, and stool. Five major phyla were detected in all samples (Firmicutes, Bacteroides, Proteobacteria, Actinobacteria, Fusobacteria), although their relative abundances varied. Notably, we found that the microbial compositions of rectal swabs were most similar to those of the sigmoid colon and rectum, whereas the microbiota in stool were relatively different than those from the large intestine and rectal swabs. Our results reveal the existence of microbial heterogeneity within different large intestinal compartments and further suggest that rectal swabs are an acceptable and practical tool for gut microbiota analysis. KEY POINTS: • Our findings highlight local microbiome variations within different regions of the large intestine. • Stool samples do not appear to fully recapitulate the gut microbiome. • Our data from a large population-based cohort indicate that rectal swabs can be used to study the gut microbiome.

Building up a clinical microbiota profiling: a quality framework proposal

Extensive characterization of the human microbiota has revealed promising relationships between microbial composition and health or disease, generating interest in biomarkers derived from microbiota profiling. However, microbiota complexity and technical challenges strongly influencing the results limit the generalization of microbiota profiling and question its clinical utility. In addition, no quality management scheme has been adapted to the specificities of microbiota profiling, notably due to the heterogeneity in methods and results. In this review, we discuss possible adaptation of classical quality management tools routinely used in diagnostic laboratories to microbiota profiling and propose a specific framework. Multiple quality controls are needed to cover all steps, from sampling to data processing. Standard operating procedures, primarily developed for wet lab analyses, must be adapted to the use of bioinformatic tools. Finally, requirements for test validation and proficiency testing must take into account expected discrepancies in results due to the heterogeneity of the processes. The proposed quality management framework should support the implementation of routine microbiota profiling by clinical laboratories to support patient care. Furthermore, its use in research laboratories would improve publication reproducibility as well as transferability of methods and results to routine practice.

Seed-Transmitted Bacteria and Fungi Dominate Juvenile Plant Microbiomes

Plant microbiomes play an important role in agricultural productivity, but there is still much to learn about their provenance, diversity, and organization. In order to study the role of vertical transmission in establishing the bacterial and fungal populations of juvenile plants, we used high-throughput sequencing to survey the microbiomes of seeds, spermospheres, rhizospheres, roots, and shoots of the monocot crops maize (B73), rice (Nipponbare), switchgrass (Alamo), Brachiaria decumbens, wheat, sugarcane, barley, and sorghum; the dicot crops tomato (Heinz 1706), coffee (Geisha), common bean (G19833), cassava, soybean, pea, and sunflower; and the model plants Arabidopsis thaliana (Columbia-0) and Brachypodium distachyon (Bd21). Unsterilized seeds were planted in either sterile sand or farm soil inside hermetically sealed jars, and after as much as 60 days of growth, DNA was extracted to allow for amplicon sequence-based profiling of the bacterial and fungal populations that developed. Seeds of most plants were dominated by Proteobacteria and Ascomycetes, with all containing operational taxonomic units (OTUs) belonging to Pantoea and Enterobacter. All spermospheres also contained DNA belonging to Pseudomonas, Bacillus, and Fusarium. Despite having only seeds as a source of inoculum, all plants grown on sterile sand in sealed jars nevertheless developed rhizospheres, endospheres, and phyllospheres dominated by shared Proteobacteria and diverse fungi. Compared to sterile sand-grown seedlings, growth on soil added new microbial diversity to the plant, especially to rhizospheres; however, all 63 seed-transmitted bacterial OTUs were still present, and the most abundant bacteria (Pantoea, Enterobacter, Pseudomonas, Klebsiella, and Massilia) were the same dominant seed-transmitted microbes observed in sterile sand-grown plants. While most plant mycobiome diversity was observed to come from soil, judging by read abundance, the dominant fungi (Fusarium and Alternaria) were also vertically transmitted. Seed-transmitted fungi and bacteria appear to make up the majority of juvenile crop plant microbial populations by abundance, and based on occupancy, there seems to be a pan-angiosperm seed-transmitted core bacterial microbiome. Further study of these seed-transmitted microbes will be important to understand their role in plant growth and health, as well as their fate during the plant life cycle and may lead to innovations for agricultural inoculant development.

Dead or alive: microbial viability treatment reveals both active and inactive bacterial constituents in the fish gut microbiota

AIMS

This study evaluated the microbial viability of fish gut microbiota in both digesta (faecal) and mucosal samples using a modified propidium monoazide (PMA) protocol, followed by 16S ribosomal RNA (rRNA) gene sequencing.

METHODS AND RESULTS

Digesta and gut mucosal samples from farmed yellowtail kingfish (Seriola lalandi) were collected and a modified PMA treatment was applied prior to DNA extraction to differentiate both active and nonviable microbial cells in the samples. All samples were then sequenced using a standard 16S rRNA approach. The digesta and mucosal samples contained significantly different bacterial communities, with a higher diversity observed in digesta samples. In addition, PMA treatment significantly reduced the microbial diversity and richness of digesta and mucosal samples and depleted bacterial constituents typically considered to be important within fish, such as Lactobacillales and Clostridales taxa.

CONCLUSIONS

These findings suggest that important bacterial members may not be active in the fish gut microbiota. In particular, several beneficial lactic acid bacteria (LAB) were identified as nonviable bacterial cells, potentially influencing the functional potential of the fish microbiota.

SIGNIFICANCE AND IMPACTS OF THE STUDY

Standardizing the methods for characterizing the fish microbiota are paramount in order to compare studies. In this study, we showed that both sample type and PMA treatment influence the bacterial communities found in the fish gut microbiota. Our findings also suggest that several microbes previously described in the fish gut may not be active constituents. As a result, these factors should be considered in future studies to better evaluate the active bacterial communities associated with the host.

Integrating the human microbiome in the forensic toolkit: Current bottlenecks and future solutions

Over the last few years, advances in massively parallel sequencing technologies (also referred to next generation sequencing) and bioinformatics analysis tools have boosted our knowledge on the human microbiome. Such insights have brought new perspectives and possibilities to apply human microbiome analysis in many areas, particularly in medicine. In the forensic field, the use of microbial DNA obtained from human materials is still in its infancy but has been suggested as a potential alternative in situations when other human (non-microbial) approaches present limitations. More specifically, DNA analysis of a wide variety of microorganisms that live in and on the human body offers promises to answer various forensically relevant questions, such as post-mortem interval estimation, individual identification, and tissue/body fluid identification, among others. However, human microbiome analysis currently faces significant challenges that need to be considered and overcome via future forensically oriented human microbiome research to provide the necessary solutions. In this perspective article, we discuss the most relevant biological, technical and data-related issues and propose future solutions that will pave the way towards the integration of human microbiome analysis in the forensic toolkit.

Basalt-Hosted Microbial Communities in the Subsurface of the Young Volcanic Island of Surtsey, Iceland

The island of Surtsey was formed in 1963–1967 on the offshore Icelandic volcanic rift zone. It offers a unique opportunity to study the subsurface biosphere in newly formed oceanic crust and an associated hydrothermal-seawater system, whose maximum temperature is currently above 120°C at about 100m below surface. Here, we present new insights into the diversity, distribution, and abundance of microorganisms in the subsurface of the island, 50years after its creation. Samples, including basaltic tuff drill cores and associated fluids acquired at successive depths as well as surface fumes from fumaroles, were collected during expedition 5059 of the International Continental Scientific Drilling Program specifically designed to collect microbiological samples. Results of this microbial survey are investigated with 16S rRNA gene amplicon sequencing and scanning electron microscopy. To distinguish endemic microbial taxa of subsurface rocks from potential contaminants present in the drilling fluid, we use both methodological and computational strategies. Our 16S rRNA gene analysis results expose diverse and distinct microbial communities in the drill cores and the borehole fluid samples, which harbor thermophiles in high abundance. Whereas some taxonomic lineages detected across these habitats remain uncharacterized (e.g., Acetothermiia, Ammonifexales), our results highlight potential residents of the subsurface that could be identified at lower taxonomic rank such as Thermaerobacter, BRH-c8a (Desulfallas-Sporotomaculum), Thioalkalimicrobium, and Sulfurospirillum. Microscopy images reveal possible biotic structures attached to the basaltic substrate. Finally, microbial colonization of the newly formed basaltic crust and the metabolic potential are discussed on the basis of the data.

A comprehensive evaluation of single-end sequencing data analyses for environmental microbiome research

Illumina sequencing platforms have been widely used for amplicon-based environmental microbiome research. Analyses of amplicon data of environmental samples, generated from Illumina MiSeq platform illustrate the reverse (R2) reads in the PE datasets to have low quality towards the 3' end of the reads which affect the sequencing depth of samples and ultimately impact the sample size which may possibly lead to an altered outcome. This study evaluates the usefulness of single-end (SE) sequencing data in microbiome research when the Illumina MiSeq PE dataset shows significantly high number of low-quality reverse reads. In this study, the amplicon data (V1V3, V3V4, V4V5 and V6V8) from 128 environmental (soil) samples, downloaded from SRA, demonstrate the efficiency of single-end (SE) sequencing data analyses in microbiome research. The SE datasets were found to infer the core microbiome structure as comparable to the PE dataset. Conspicuously, the forward (R1) datasets inferred a higher number of taxa as compared to PE datasets for most of the amplicon regions, except V3V4. Thus, analyses of SE sequencing data, especially R1 reads, in environmental microbiome studies could ameliorate the problems arising on sample size of the study due to low quality reverse reads in the dataset. However, care must be taken while interpreting the microbiome structure as few taxa observed in the PE datasets were absent in the SE datasets. In conclusion, this study demonstrates the availability of choices in analyzing the amplicon data without having the need to remove samples with low quality reverse reads.

Concatenation of paired-end reads improves taxonomic classification of amplicons for profiling microbial communities

Background

Taxonomic classification of genetic markers for microbiome analysis is affected by the numerous choices made from sample preparation to bioinformatics analysis. Paired-end read merging is routinely used to capture the entire amplicon sequence when the read ends overlap. However, the exclusion of unmerged reads from further analysis can result in underestimating the diversity in the sequenced microbial community and is influenced by bioinformatic processes such as read trimming and the choice of reference database. A potential solution to overcome this is to concatenate (join) reads that do not overlap and keep them for taxonomic classification. The use of concatenated reads can outperform taxonomic recovery from single-end reads, but it remains unclear how their performance compares to merged reads. Using various sequenced mock communities with different amplicons, read length, read depth, taxonomic composition, and sequence quality, we tested how merging and concatenating reads performed for genus recall and precision in bioinformatic pipelines combining different parameters for read trimming and taxonomic classification using different reference databases.

Results

The addition of concatenated reads to merged reads always increased pipeline performance. The top two performing pipelines both included read concatenation, with variable strengths depending on the mock community. The pipeline that combined merged and concatenated reads that were quality-trimmed performed best for mock communities with larger amplicons and higher average quality sequences. The pipeline that used length-trimmed concatenated reads outperformed quality trimming in mock communities with lower quality sequences but lost a significant amount of input sequences for taxonomic classification during processing. Genus level classification was more accurate using the SILVA reference database compared to Greengenes.

Conclusions

Merged sequences with the addition of concatenated sequences that were unable to be merged increased performance of taxonomic classifications. This was especially beneficial in mock communities with larger amplicons. We have shown for the first time, using an in-depth comparison of pipelines containing merged vs concatenated reads combined with different trimming parameters and reference databases, the potential advantages of concatenating sequences in improving resolution in microbiome investigations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04410-2.

Exploring untapped potential of Streptomyces spp. in Gurbantunggut Desert by use of highly selective culture strategy

Streptomycetes have been, for over 70 years, one of the most abundant sources for the discovery of new antibiotics and clinic drugs. However, in recent decades, it has been more and more difficult to obtain new phylotypes of the genus Streptomyces by using conventional samples and culture strategies. In this study, we combined culture-dependent and culture-independent approaches to better explore the Streptomyces communities in desert sandy soils. Moreover, two different culture strategies termed Conventional Culture Procedure (CCP) and Streptomycetes Culture Procedure (SCP) were employed to evaluate the isolation efficiency of Streptomyces spp. with different intensities of selectivity. The 16S rRNA gene amplicon analysis revealed a very low abundance (0.04-0.37%, average 0.22%) of Streptomyces in all the desert samples, conversely the percentage of Streptomyces spp. obtained by the culture-dependent method was very high (5.20-39.57%, average 27.76%), especially in the rhizospheric sand soils (38.40-39.57%, average 38.99%). Meanwhile, a total of 1589 pure cultures were isolated successfully, dominated by Streptomyces (29.52%), Microvirga (8.06%) and Bacillus (7.68%). In addition, 400 potential new species were obtained, 48 of which belonged to the genus Streptomyces. More importantly, our study demonstrated the SCP strategy which had highly selectivity could greatly expand the number and phylotypes of Streptomyces spp. by almost 4-fold than CCP strategy. These results provide insights on the diversity investigation of desert Streptomyces, and it could be reference for researchers to bring more novel actinobacteria strains from the environment into culture.

Complementary Food Ingredients Alter Infant Gut Microbiome Composition and Metabolism In Vitro

We examined the prebiotic potential of 32 food ingredients on the developing infant microbiome using an in vitro gastroileal digestion and colonic fermentation model. There were significant changes in the concentrations of short-chain fatty-acid metabolites, confirming the potential of the tested ingredients to stimulate bacterial metabolism. The 16S rRNA gene sequencing for a subset of the ingredients revealed significant increases in the relative abundances of the lactate- and acetate-producing Bifidobacteriaceae, Enterococcaceae, and Lactobacillaceae, and lactate- and acetate-utilizing Prevotellaceae, Lachnospiraceae, and Veillonellaceae. Selective changes in specific bacterial groups were observed. Infant whole-milk powder and an oat flour enhanced Bifidobacteriaceae and lactic acid bacteria. A New Zealand-origin spinach powder enhanced Prevotellaceae and Lachnospiraceae, while fruit and vegetable powders increased a mixed consortium of beneficial gut microbiota. All food ingredients demonstrated a consistent decrease in Clostridium perfringens, with this organism being increased in the carbohydrate-free water control. While further studies are required, this study demonstrates that the selected food ingredients can modulate the infant gut microbiome composition and metabolism in vitro. This approach provides an opportunity to design nutrient-rich complementary foods that fulfil infants’ growth needs and support the maturation of the infant gut microbiome.

Learning, visualizing and exploring 16S rRNA structure using an attention-based deep neural network

Recurrent neural networks with memory and attention mechanisms are widely used in natural language processing because they can capture short and long term sequential information for diverse tasks. We propose an integrated deep learning model for microbial DNA sequence data, which exploits convolutional neural networks, recurrent neural networks, and attention mechanisms to predict taxonomic classifications and sample-associated attributes, such as the relationship between the microbiome and host phenotype, on the read/sequence level. In this paper, we develop this novel deep learning approach and evaluate its application to amplicon sequences. We apply our approach to short DNA reads and full sequences of 16S ribosomal RNA (rRNA) marker genes, which identify the heterogeneity of a microbial community sample. We demonstrate that our implementation of a novel attention-based deep network architecture, Read2Pheno, achieves read-level phenotypic prediction. Training Read2Pheno models will encode sequences (reads) into dense, meaningful representations: learned embedded vectors output from the intermediate layer of the network model, which can provide biological insight when visualized. The attention layer of Read2Pheno models can also automatically identify nucleotide regions in reads/sequences which are particularly informative for classification. As such, this novel approach can avoid pre/post-processing and manual interpretation required with conventional approaches to microbiome sequence classification. We further show, as proof-of-concept, that aggregating read-level information can robustly predict microbial community properties, host phenotype, and taxonomic classification, with performance at least comparable to conventional approaches. An implementation of the attention-based deep learning network is available at https://github.com/EESI/sequence_attention (a python package) and https://github.com/EESI/seq2att (a command line tool).

Temporal and nutritional effects on the weaner pig ileal microbiota

BACKGROUND

The porcine gastrointestinal microbiota has been linked to both host health and performance. Most pig gut microbiota studies target faecal material, which is not representative of microbiota dynamics in other discrete gut sections. The weaning transition period in pigs is a key development stage, with gastrointestinal problems being prominent after often sudden introduction to a solid diet. A better understanding of both temporal and nutritional effects on the small intestinal microbiota is required. Here, the development of the porcine ileal microbiota under differing levels of dietary protein was observed over the immediate post-weaning period.

RESULTS

Ileal digesta samples were obtained at post-mortem prior to weaning day (day - 1) for baseline measurements. The remaining pigs were introduced to either an 18% (low) or 23% (high) protein diet on weaning day (day 0) and further ileal digesta sampling was carried out at days 5, 9 and 13 post-weaning. We identified significant changes in microbiome structure (P = 0.01), a reduction in microbiome richness (P = 0.02) and changes in the abundance of specific bacterial taxa from baseline until 13 days post-weaning. The ileal microbiota became less stable after the introduction to a solid diet at weaning (P = 0.036), was highly variable between pigs and no relationship was observed between average daily weight gain and microbiota composition. The ileal microbiota was less stable in pigs fed the high protein diet (P = 0.05), with several pathogenic bacterial genera being significantly higher in abundance in this group. Samples from the low protein and high protein groups did not cluster separately by their CAZyme (carbohydrate-active enzyme) composition, but GH33 exosialidases were found to be significantly more abundant in the HP group (P = 0.006).

CONCLUSIONS

The weaner pig ileal microbiota changed rapidly and was initially destabilised by the sudden introduction to feed. Nutritional composition influenced ileal microbiota development, with the high protein diet being associated with an increased abundance of significant porcine pathogens and the upregulation of GH33 exosialidases-which can influence host-microbe interactions and pathogenicity. These findings contribute to our understanding of a lesser studied gut compartment that is not only a key site of digestion, but also a target for the development of nutritional interventions to improve gut health and host growth performance during the critical weaning transition period.

The microbiome extends host evolutionary potential

The microbiome shapes many host traits, yet the biology of microbiomes challenges traditional evolutionary models. Here, we illustrate how integrating the microbiome into quantitative genetics can help untangle complexities of host-microbiome evolution. We describe two general ways in which the microbiome may affect host evolutionary potential: by shifting the mean host phenotype and by changing the variance in host phenotype in the population. We synthesize the literature across diverse taxa and discuss how these scenarios could shape the host response to selection. We conclude by outlining key avenues of research to improve our understanding of the complex interplay between hosts and microbiomes.

New Approaches to Profile the Microbiome for Treatment of Neurodegenerative Disease

Progressive neurodegenerative diseases represent some of the largest growing treatment challenges for public health in modern society. These diseases mainly progress due to aging and are driven by microglial surveillance and activation in response to changes occurring in the aging brain. The lack of efficacious treatment options for Alzheimer's disease (AD), as the focus of this review, and other neurodegenerative disorders has encouraged new approaches to address neuroinflammation for potential treatments. Here we will focus on the increasing evidence that dysbiosis of the gut microbiome is characterized by inflammation that may carry over to the central nervous system and into the brain. Neuroinflammation is the common thread associated with neurodegenerative diseases, but it is yet unknown at what point and how innate immune function turns pathogenic for an individual. This review will address extensive efforts to identify constituents of the gut microbiome and their neuroactive metabolites as a peripheral path to treatment. This approach is still in its infancy in substantive clinical trials and requires thorough human studies to elucidate the metabolic microbiome profile to design appropriate treatment strategies for early stages of neurodegenerative disease. We view that in order to address neurodegenerative mechanisms of the gut, microbiome and metabolite profiles must be determined to pre-screen AD subjects prior to the design of specific, chronic titrations of gut microbiota with low-dose antibiotics. This represents an exciting treatment strategy designed to balance inflammatory microglial involvement in disease progression with an individual's manifestation of AD as influenced by a coercive inflammatory gut.

Larvae of Deep-Sea Invertebrates Harbor Low-Diversity Bacterial Communities

AbstractMicrobial symbionts are a common life-history character of marine invertebrates and their developmental stages. Communities of bacteria that associate with the eggs, embryos, and larvae of coastal marine invertebrates tend to be species specific and correlate with aspects of host biology and ecology. The richness of bacteria associated with the developmental stages of coastal marine invertebrates spans four orders of magnitude, from single mutualists to thousands of unique taxa. This understanding stems predominately from the developmental stages of coastal species. If they are broadly representative of marine invertebrates, then we may expect deep-sea species to associate with bacterial communities that are similar in diversity. To test this, we used amplicon sequencing to profile the bacterial communities of invertebrate larvae from multiple taxonomic groups (annelids, molluscs, crustaceans) collected from 2500 to 3670 m in depth in near-bottom waters near hydrothermal vents in 3 different regions of the Pacific Ocean (the East Pacific Rise, the Mariana Back-Arc, and the Pescadero Basin). We find that larvae of deep-sea invertebrates associate with low-diversity bacterial communities (~30 bacterial taxa) that lack specificity between taxonomic groups. The diversity of these communities is estimated to be ~7.9 times lower than that of coastal invertebrate larvae, but this result depends on the taxonomic group. Associating with a low-diversity community may imply that deep-sea invertebrate larvae do not have a strong reliance on a microbiome and that the hypothesized lack of symbiotic contributions would differ from expectations for larvae of coastal marine invertebrates.

Metabarcoding options to study eukaryotic endoparasites of birds

There is growing interest in the study of avian endoparasite communities, and metabarcoding is a promising approach to complement more conventional or targeted methods. In the case of eukaryotic endoparasites, phylogenetic diversity is extreme, with parasites from 4 kingdoms and 11 phyla documented in birds. We addressed this challenge by comparing different primer sets across 16 samples from 5 bird species. Samples consisted of blood, feces, and controlled mixes with known proportions of bird and nematode DNA. Illumina sequencing revealed that a 28S primer set used in combination with a custom blocking primer allowed detection of various plasmodiid parasites and filarioid nematodes in the blood, coccidia in the feces, as well as two potentially pathogenic fungal groups. When tested on the controlled DNA mixes, these primers also increased the proportion of nematode DNA by over an order of magnitude. An 18S primer set, originally designed to exclude metazoan sequences, was the most effective at reducing the relative number of avian DNA sequences and was the only one to detect Trypanosoma in the blood. Expectedly, however, it did not allow nematode detection and also failed to detect avian malaria parasites. This study shows that a 28S set including a blocking primer allows detection of several major and very diverse bird parasite clades, while reliable amplification of all major parasite groups may require a combination of markers. It helps clarify options for bird parasite metabarcoding, according to priorities in terms of the endoparasite clades and the ecological questions researchers wish to focus on.

Experimental validation of small mammal gut microbiota sampling from faeces and from the caecum after death

Data on the gut microbiota (GM) of wild animals are key to studies on evolutionary biology (host-GM interactions under natural selection), ecology and conservation biology (GM as a fitness component closely connected to the environment). Wildlife GM sampling often requires non-invasive techniques or sampling from dead animals. In a controlled experiment profiling microbial 16S rRNA in 52 house mice (Mus musculus) from eight families and four genetic backgrounds, we studied the effects of live- and snap-trapping on small mammal GM and evaluated the suitability of microbiota from non-fresh faeces as a proxy for caecal GM. We compared CM from individuals sampled 16-18 h after death with those in live traps and caged controls, and caecal and faecal GM collected from mice in live-traps. Sampling delay did not affect GM composition, validating data from fresh cadavers or snap-trapped animals. Animals trapped overnight displayed a slight but significant difference in GM composition to the caged controls, though the change only had negligible effect on GM diversity, composition and inter-individual divergence. Hence, the trapping process appears not to bias GM profiling. Despite their significant difference, caecal and faecal microbiota were correlated in composition and, to a lesser extent, diversity. Both showed congruent patterns of inter-individual divergence following the natural structure of the dataset. Thus, the faecal microbiome represents a good non-invasive proxy of the caecal microbiome, making it suitable for detecting biologically relevant patterns. However, care should be taken when analysing mixed datasets containing both faecal and caecal samples.

Elucidating biofilm diversity on water lily leaves through 16S rRNA amplicon analysis: Comparison of four DNA extraction kits

PREMISE

Within a broader study on leaf fossilization in freshwater environments, a long-term study on the development and microbiome composition of biofilms on the foliage of aquatic plants has been initiated to understand how microbes and biofilms contribute to leaf decay and preservation. Here, water lily leaves are employed as a study model to investigate the relationship between bacterial microbiomes, biodegradation, and fossilization. We compare four DNA extraction kits to reduce biases in interpretation and to identify the most suitable kit for the extraction of DNA from bacteria associated with biofilms on decaying water lily leaves for 16S rRNA amplicon analysis.

METHODS

We extracted surface-associated DNA from Nymphaea leaves in early stages of decay at two water depth levels using four commercially available kits to identify the most suitable protocol for bacterial extraction, applying a mock microbial community standard to enable a reliable comparison of the kits.

RESULTS

Kit 4, the FastDNA Spin Kit for Soil, resulted in high DNA concentrations with better quality and yielded the most accurate depiction of the mock community. Comparison of the leaves at two water depths showed no significant differences in community composition.

DISCUSSION

The success of Kit 4 may be attributed to its use of bead beating with a homogenizer, which was more efficient in the lysis of Gram-positive bacteria than the manual vortexing protocols used by the other kits. Our results show that microbial composition on leaves during early decay remains comparable and may change only in later stages of decomposition.

Limitations of 16S rRNA Gene Sequencing to Characterize Lactobacillus Species in the Upper Genital Tract

The endometrial cavity is an upper genital tract site previously thought as sterile, however, advances in culture-independent, next-generation sequencing technology have revealed that this low-biomass site harbors a rich microbial community which includes multiple Lactobacillus species. These bacteria are considered to be the most abundant non-pathogenic genital tract commensals. Next-generation sequencing of the female lower genital tract has revealed significant variation amongst microbial community composition with respect to Lactobacillus sp. in samples collected from healthy women and women with urogenital conditions. The aim of this study was to evaluate our ability to characterize members of the genital tract microbial community to species-level taxonomy using variable regions of the 16S rRNA gene. Samples were interrogated for the presence of microbial DNA using next-generation sequencing technology that targets the V5-V8 regions of the 16S rRNA gene and compared to speciation using qPCR. We also performed re-analysis of published data using alternate variable regions of the 16S rRNA gene. In this analysis, we explore next-generation sequencing of clinical genital tract isolates as a method for high throughput identification to species-level of key Lactobacillus sp. Data revealed that characterization of genital tract taxa is hindered by a lack of a consensus protocol and 16S rRNA gene region target allowing comparison between studies.

Is the skin microbiota a modifiable risk factor for breast disease?: A systematic review

PURPOSE

High prevalence, unreliable risk discrimination and poor clinical outcomes are observed in malignant and benign breast diseases (BD). The involvement of microbial communities in the development of BD has become topical, and distal influences of microbial dysregulation in the breast have been well established. Despite advances, the role of the breast skin microbiota in BD remains unclear. Interactions between the skin microbiota and the underlying mucosal immune system are complex. In homeostasis, the skin offers a physical barrier protecting underlying breast tissue from skin commensals and noxious environmental triggers. Our review aims to illuminate the role of the skin microbiota in the development of BD.

METHODS

Adhering to the PRISMA protocol, a systematic review was conducted utilising the Medline and Embase search engines.

RESULTS

Through a comprehensive search of the last ten years, twenty-two studies satisfied the inclusion criteria. Proteobacteria, Firmicutes, Actinobacteria and Bacteroidetes were identified as the most prevalent phyla of both breast tissue and skin in healthy controls and BD. High abundance of skin commensals, specifically some species of Staphylococcus, have been linked in breast cancer and metastases. Similarly, dysregulated microbial abundance is also seen in inflammatory and implant-associated BD. These findings raise the hypothesis that the skin microbiota plays a role in tissue homeostasis and may contribute to a range of breast pathologies. Several mechanisms of microbial transfer to underlying tissue have been proposed, including retrograde transfer through ductal systems, breakdown of the skin barrier, and migration through nipple-aspirate fluid.

CONCLUSION

Our review provides preliminary insights into the skin microbiota as a modifiable risk factor for BD. This raises opportunities for future studies in antimicrobials/probiotics as an adjunct to, or replacement of surgery; a diagnostic and/or prognostic tool for BD; and the possibility of conditioning the microbiota to manage BD.

Characterizing Microbiomes via Sequencing of Marker Loci: Techniques To Improve Throughput, Account for Cross-Contamination, and Reduce Cost

New approaches to characterizing microbiomes via high-throughput sequencing provide impressive gains in efficiency and cost reduction compared to approaches that were standard just a few years ago. However, the speed of method development has been such that staying abreast of the latest technological advances is challenging. Moreover, shifting laboratory protocols to include new methods can be expensive and time consuming. To facilitate adoption of new techniques, we provide a guide and review of recent advances that are relevant for single-locus sequence-based study of microbiomes—from extraction to library preparation—including a primer regarding the use of liquid-handling automation in small-scale academic settings. Additionally, we describe several amendments to published techniques to improve throughput, track contamination, and reduce cost. Notably, we suggest adding synthetic DNA molecules to each sample during nucleic acid extraction, thus providing a method of documenting incidences of cross-contamination. We also describe a dual-indexing scheme for Illumina sequencers that allows multiplexing of many thousands of samples with minimal PhiX input. Collectively, the techniques that we describe demonstrate that laboratory technology need not impose strict limitations on the scale of molecular microbial ecology studies.

IMPORTANCE New methods to characterize microbiomes reduce technology-imposed limitations to study design, but many new approaches have not been widely adopted. Here, we present techniques to increase throughput and reduce contamination alongside a thorough review of current best practices.

Ecosystems monitoring powered by environmental genomics: A review of current strategies with an implementation roadmap

A decade after environmental scientists integrated high-throughput sequencing technologies in their toolbox, the genomics-based monitoring of anthropogenic impacts on the biodiversity and functioning of ecosystems is yet to be implemented by regulatory frameworks. Despite the broadly acknowledged potential of environmental genomics to this end, technical limitations and conceptual issues still stand in the way of its broad application by end-users. In addition, the multiplicity of potential implementation strategies may contribute to a perception that the routine application of this methodology is premature or "in development", hence restraining regulators from binding these tools into legal frameworks. Here, we review recent implementations of environmental genomics-based methods, applied to the biomonitoring of ecosystems. By taking a general overview, without narrowing our perspective to particular habitats or groups of organisms, this paper aims to compare, review and discuss the strengths and limitations of four general implementation strategies of environmental genomics for monitoring: (a) Taxonomy-based analyses focused on identification of known bioindicators or described taxa; (b) De novo bioindicator analyses; (c) Structural community metrics including inferred ecological networks; and (d) Functional community metrics (metagenomics or metatranscriptomics). We emphasise the utility of the three latter strategies to integrate meiofauna and microorganisms that are not traditionally utilised in biomonitoring because of difficult taxonomic identification. Finally, we propose a roadmap for the implementation of environmental genomics into routine monitoring programmes that leverage recent analytical advancements, while pointing out current limitations and future research needs.

Phospholipid fatty acid (PLFA) analysis as a tool to estimate absolute abundances from compositional 16S rRNA bacterial metabarcoding data

Microbial biodiversity monitoring through the analysis of DNA extracted from environmental samples is increasingly popular because it is perceived as being rapid, cost-effective, and flexible concerning the sample types studied. DNA can be extracted from diverse media before high-throughput sequencing of the prokaryotic 16S rRNA gene is used to characterize the taxonomic diversity and composition of the sample (known as metabarcoding). While sources of bias in metabarcoding methodologies are widely acknowledged, previous studies have focused mainly on the effects of these biases within a single substrate type, and relatively little is known of how these vary across substrates. We investigated the effect of substrate type (water, microbial mats, lake sediments, stream sediments, soil and a mock microbial community) on the relative performance of DNA metabarcoding in parallel with phospholipid fatty acid (PLFA) analysis. Quantitative estimates of the biomass of different taxonomic groups in samples were made through the analysis of PLFAs, and these were compared to the relative abundances of microbial taxa estimated from metabarcoding. Furthermore, we used the PLFA-based quantitative estimates of the biomass to adjust relative abundances of microbial groups determined by metabarcoding to provide insight into how the biomass of microbial taxa from PLFA analysis can improve understanding of microbial communities from environmental DNA samples. We used two sets of PLFA biomarkers that differed in their number of PLFAs to evaluate how PLFA biomarker selection influences biomass estimates. Metabarcoding and PLFA analysis provided significantly different views of bacterial composition, and these differences varied among substrates. We observed the most notable differences for the Gram-negative bacteria, which were overrepresented by metabarcoding in comparison to PLFA analysis. In contrast, the relative biomass and relative sequence abundances aligned reasonably well for Cyanobacteria across the tested freshwater substrates. Adjusting relative abundances of microbial taxa estimated by metabarcoding with PLFA-based quantification estimates of the microbial biomass led to significant changes in the microbial community compositions in all substrates. We recommend including independent estimates of the biomass of microbial groups to increase comparability among metabarcoding libraries from environmental samples, especially when comparing communities associated with different substrates.

Microbial colonization of the gastrointestinal tract of dairy calves - a review of its importance and relationship to health and performance

This review aims to explain how microbial colonization of the gastrointestinal tract (GIT) in young dairy calves is related to health and, consequently, to the performance of these animals. The review addresses everything from the fundamental aspects of microbial colonization to the current understanding about the microbiota manipulation to improve performance in adult animals. The ruminal microbiota is the most studied, mainly due to the high interest in the fermentative aspects, the production of short-chain fatty acids, and microbial proteins, and its effects on animal production. However, in recent years, the intestinal microbiota has gained space between studies, mainly due to the relationship to the host health and how it affects performance. Understanding how the GIT's microbiota looks like and how it is colonized may allow future studies to predict the best timing for dietary interventions as a way to manipulate it and, consequently, improve the health and performance of young ruminants.

Unraveling the predictive role of temperature in the gut microbiota of the sea urchin Echinometra sp. EZ across spatial and temporal gradients

Shifts in microbial communities represent a rapid response mechanism for host organisms to respond to changes in environmental conditions. Therefore, they are likely to be important in assisting the acclimatization of hosts to seasonal temperature changes as well as to variation in temperatures across a species' range. The Persian/Arabian Gulf is the world's warmest sea, with large seasonal fluctuations in temperature (20℃ - 37℃) and is connected to the Gulf of Oman which experiences more typical oceanic conditions (<32℃ in the summer). This system is an informative model for understanding how symbiotic microbial assemblages respond to thermal variation across temporal and spatial scales. Here, we elucidate the role of temperature on the microbial gut community of the sea urchin Echinometra sp. EZ and identify microbial taxa that are tightly correlated with the thermal environment. We generated two independent datasets with a high degree of geographic and temporal resolution. The results show that microbial communities vary across thermally variable habitats, display temporal shifts that correlate with temperature, and can become more disperse as temperatures rise. The relative abundances of several ASVs significantly correlate with temperature in both independent datasets despite the >300 km distance between the furthest sites and the extreme seasonal variations. Notably, over 50% of the temperature predictive ASVs identified from the two datasets belonged to the family Vibrionaceae. Together, our results identify temperature as a robust predictor of community-level variation and highlight specific microbial taxa putatively involved in the response to thermal environment.

Leveraging 16S rRNA Microbiome Sequencing Data to Identify Bacterial Signatures for Irritable Bowel Syndrome

Irritable bowel syndrome (IBS) is a chronic gastrointestinal disorder characterized by abdominal pain or discomfort. Previous studies have illustrated that the gut microbiota might play a critical role in IBS, but the conclusions of these studies, based on various methods, were almost impossible to compare, and reproducible microorganism signatures were still in question. To cope with this problem, previously published 16S rRNA gene sequencing data from 439 fecal samples, including 253 IBS samples and 186 control samples, were collected and processed with a uniform bioinformatic pipeline. Although we found no significant differences in community structures between IBS and healthy controls at the amplicon sequence variants (ASV) level, machine learning (ML) approaches enabled us to discriminate IBS from healthy controls at genus level. Linear discriminant analysis effect size (LEfSe) analysis was subsequently used to seek out 97 biomarkers across all studies. Then, we quantified the standardized mean difference (SMDs) for all significant genera identified by LEfSe and ML approaches. Pooled results showed that the SMDs of nine genera had statistical significance, in which the abundance of Lachnoclostridium, Dorea, Erysipelatoclostridium, Prevotella 9, and Clostridium sensu stricto 1 in IBS were higher, while the dominant abundance genera of healthy controls were Ruminococcaceae UCG-005, Holdemanella, Coprococcus 2, and Eubacterium coprostanoligenes group. In summary, based on six published studies, this study identified nine new microbiome biomarkers of IBS, which might be a basis for understanding the key gut microbes associated with IBS, and could be used as potential targets for microbiome-based diagnostics and therapeutics.

Lung microbiome alterations in NSCLC patients

Lung is colonized by a diverse array of microbes and the lung microbiota is profoundly involved in the development of respiratory diseases. There is little knowledge about the role of lung microbiota dysbiosis in lung cancer. In this study, we performed metagenomic sequencing on bronchoalveolar lavage (BAL) from two different sampling methods in non-small cell lung cancer (NSCLC) patients and non-cancer controls. We found the obvious variation between bronchoscopy samples and lobectomy samples. Oral taxa can be found in both bronchoscopy and lobectomy samples and higher abundance of oral taxa can be found in bronchoscopy samples. Although the NSCLC patients had similar microbial communities with non-cancer controls, rare species such as Lactobacillus rossiae, Bacteroides pyogenes, Paenibacillus odorifer, Pseudomonas entomophila, Magnetospirillum gryphiswaldense, fungus Chaetomium globosum et al. showed obvious difference between NSCLC patients and non-cancer controls. Age-, gender-, and smoking-specific species and EGFR expression-related species in NSCLC patients were detected. There results implicated that different lung segments have differential lung microbiome composition. The oral taxa are found in the lobectomy samples suggesting that oral microbiota are the true members of lung microbiota, rather than contamination during bronchoscopy. Lung cancer does not obviously alter the global microbial composition, while rare species are altered more than common species. Certain microbes may be associated with lung cancer progression.

First dynamics of bacterial community during development of Acropora humilis larvae in aquaculture

A symbiosis of bacterial community (sometimes called microbiota) play essential roles in developmental life cycle and health of coral, starting since a larva. For examples, coral bacterial holobionts function nitrogen fixation, carbon supply, sulfur cycling and antibiotic production. Yet, a study of the dynamic of bacteria associated coral larvae development is complicated owning to a vast diversity and culturable difficulty of bacteria; hence this type of study remains unexplored for Acropora humilis larvae in Thai sea. This study represented the first to utilize 16S rRNA gene sequencing to describe the timely bacterial compositions during successfully cultured and reared A. humilis larval transformation in aquaculture (gametes were collected from Sattahip Bay, Chonburi province, Thailand), from gamete spawning (0 h) and fertilization stage (1 h), to embryonic cleavage (8 h), round cell development (28, 39 and 41 h), and planula formation (48 h). The sequencing results as estimated by Good’s coverage at genus level covered 99.65 ± 0.24% of total bacteria. While core phyla of bacteria were observed (Proteobacteria, Actinobacteria, Firmicutes and Bacteroidetes), changes in bacterial population structures and differential predominant core bacterial orders were denoted for each larval developmental stage, from fertilization to embryonic cleavage and subsequently from the embryonic cleavage to round cell development (P = 0.007). For instances, Pseudoalteromonas and Oceanospirillales were found prevalent at 8 h, and Rhizobiales were at 48 h. The bacterial population structures from the round cell stage, particularly at 41 h, showed gradual drift towards those of the planula formation stage, suggesting microbial selection. Overall, this study provides preliminary insights into the dynamics of bacterial community and their potentially functional association (estimated from the bacterial compositions) during the developmental embryonic A. humilis in a cultivation system in Southeast Asia region.

General Unified Microbiome Profiling Pipeline (GUMPP) for Large Scale, Streamlined and Reproducible Analysis of Bacterial 16S rRNA Data to Predicted Microbial Metagenomes, Enzymatic Reactions and Metabolic Pathways

General Unified Microbiome Profiling Pipeline (GUMPP) was developed for large scale, streamlined and reproducible analysis of bacterial 16S rRNA data and prediction of microbial metagenomes, enzymatic reactions and metabolic pathways from amplicon data. GUMPP workflow introduces reproducible data analyses at each of the three levels of resolution (genus; operational taxonomic units (OTUs); amplicon sequence variants (ASVs)). The ability to support reproducible analyses enables production of datasets that ultimately identify the biochemical pathways characteristic of disease pathology. These datasets coupled to biostatistics and mathematical approaches of machine learning can play a significant role in extraction of truly significant and meaningful information from a wide set of 16S rRNA datasets. The adoption of GUMPP in the gut-microbiota related research enables focusing on the generation of novel biomarkers that can lead to the development of mechanistic hypotheses applicable to the development of novel therapies in personalized medicine.

An Economical and Flexible Dual Barcoding, Two-Step PCR Approach for Highly Multiplexed Amplicon Sequencing

In microbiome research, phylogenetic and functional marker gene amplicon sequencing is the most commonly-used community profiling approach. Consequently, a plethora of protocols for the preparation and multiplexing of samples for amplicon sequencing have been developed. Here, we present two economical high-throughput gene amplification and sequencing workflows that are implemented as standard operating procedures at the Joint Microbiome Facility of the Medical University of Vienna and the University of Vienna. These workflows are based on a previously-published two-step PCR approach, but have been updated to either increase the accuracy of results, or alternatively to achieve orders of magnitude higher numbers of samples to be multiplexed in a single sequencing run. The high-accuracy workflow relies on unique dual sample barcoding. It allows the same level of sample multiplexing as the previously-published two-step PCR approach, but effectively eliminates residual read missasignments between samples (crosstalk) which are inherent to single barcoding approaches. The high-multiplexing workflow is based on combinatorial dual sample barcoding, which theoretically allows for multiplexing up to 299,756 amplicon libraries of the same target gene in a single massively-parallelized amplicon sequencing run. Both workflows presented here are highly economical, easy to implement, and can, without significant modifications or cost, be applied to any target gene of interest.

Identifying biases and their potential solutions in human microbiome studies

Advances in DNA sequencing technology have vastly improved the ability of researchers to explore the microbial inhabitants of the human body. Unfortunately, while these studies have uncovered the importance of these microbial communities to our health, they often do not result in similar findings. One possible reason for the disagreement in these results is due to the multitude of systemic biases that are introduced during sequence-based microbiome studies. These biases begin with sample collection and continue to be introduced throughout the entire experiment leading to an observed community that is significantly altered from the true underlying microbial composition. In this review, we will highlight the various steps in typical sequence-based human microbiome studies where significant bias can be introduced, and we will review the current efforts within the field that aim to reduce the impact of these biases. Video abstract.

Phylogeny- and Abundance-Based Metrics Allow for the Consistent Comparison of Core Gut Microbiome Diversity Indices Across Host Species

The filtering of gut microbial datasets to retain high prevalence taxa is often performed to identify a common core gut microbiome that may be important for host biological functions. However, prevalence thresholds used to identify a common core are highly variable, and it remains unclear how they affect diversity estimates and whether insights stemming from core microbiomes are comparable across studies. We hypothesized that if macroecological patterns in gut microbiome prevalence and abundance are similar across host species, then we would expect that increasing prevalence thresholds would yield similar changes to alpha diversity and beta dissimilarity scores across host species datasets. We analyzed eight gut microbiome datasets based on 16S rRNA gene amplicon sequencing and collected from different host species to (1) compare macroecological patterns across datasets, including amplicon sequence variant (ASV) detection rate with sequencing depth and sample size, occupancy-abundance curves, and rank-abundance curves; (2) test whether increasing prevalence thresholds generate universal or host-species specific effects on alpha and beta diversity scores; and (3) test whether diversity scores from prevalence-filtered core communities correlate with unfiltered data. We found that gut microbiomes collected from diverse hosts demonstrated similar ASV detection rates with sequencing depth, yet required different sample sizes to sufficiently capture rare ASVs across the host population. This suggests that sample size rather than sequencing depth tends to limit the ability of studies to detect rare ASVs across the host population. Despite differences in the distribution and detection of rare ASVs, microbiomes exhibited similar occupancy-abundance and rank-abundance curves. Consequently, increasing prevalence thresholds generated remarkably similar trends in standardized alpha diversity and beta dissimilarity across species datasets until high thresholds above 70%. At this point, diversity scores tended to become unpredictable for some diversity measures. Moreover, high prevalence thresholds tended to generate diversity scores that correlated poorly with the original unfiltered data. Overall, we recommend that high prevalence thresholds over 70% are avoided, and promote the use of diversity measures that account for phylogeny and abundance (Balance-weighted phylogenetic diversity and Weighted Unifrac for alpha and beta diversity, respectively), because we show that these measures are insensitive to prevalence filtering and therefore allow for the consistent comparison of core gut microbiomes across studies without the need for prevalence filtering.

Investigation of Potential Brain Microbiome in Alzheimer's Disease: Implications of Study Bias

BACKGROUND

Dysbiotic microbiota in the gastrointestinal tract promotes and aggravates neurodegenerative disorders. Alzheimer's disease (AD) has been shown to correlate to dysbiotic bacteria and the immune, metabolic, and endocrine abnormalities associated with abnormal gut-brain-axis signaling. Recent reports also indicate that brain dysbacteriosis may play a role in AD pathogenesis.

OBJECTIVE

To evaluate the presence and differences of brain-region dependent microbiomes in control and AD subjects and the contribution of study bias.

METHODS

Two independent cohorts of postmortem AD brain samples were collected from separate locations, processed with different extraction protocols and investigated for the presence of bacterial DNA indicative of a brain microbiome with V4 16S next generation sequencing.

RESULTS

In both cohorts, few differences between the control and AD groups were observed in terms of alpha and beta diversities, phyla and genera proportions. Independent of study in both AD and control subjects the most abundant phyla were Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes. Variations in beta diversity between hippocampal and cerebellum samples were observed indicating an impact of brain region on the presence of microbial DNA. Importantly, differences in alpha and beta diversities between the two independent cohorts were found indicating a significant cohort- and processing-dependent effect on the microbiome. Finally, there were cohort-specific correlations between the gut microbiome and subject demographics indicate that postmortem interval may have a significant impact on brain microbiome determination.

CONCLUSIONS

Regardless of the study bias, this study concludes that bacterial DNA can be isolated from the human brain suggesting that a brain microbiome may exist; however, more studies are required to understand the variation in AD.

Beyond taxonomy: Validating functional inference approaches in the context of fish-farm impact assessments

Characterization of microbial assemblages via environmental DNA metabarcoding is increasingly being used in routine monitoring programs due to its sensitivity and cost-effectiveness. Several programs have recently been developed which infer functional profiles from 16S rRNA gene data using hidden-state prediction (HSP) algorithms. These might offer an economic and scalable alternative to shotgun metagenomics. To date, HSP-based methods have seen limited use for benthic marine surveys and their performance in these environments remains unevaluated. In this study, 16S rRNA metabarcoding was applied to sediment samples collected at 0 and ≥1,200 m from Norwegian salmon farms, and three metabolic inference approaches (Paprica, Picrust2 and Tax4Fun2) evaluated against metagenomics and environmental data. While metabarcoding and metagenomics recovered a comparable functional diversity, the taxonomic composition differed between approaches, with genera richness up to 20× higher for metabarcoding. Comparisons between the sensitivity (highest true positive rates) and specificity (lowest true negative rates) of HSP-based programs in detecting functions found in metagenomic data ranged from 0.52 and 0.60 to 0.76 and 0.79, respectively. However, little correlation was observed between the relative abundance of their specific functions. Functional beta-diversity of HSP-based data was strongly associated with that of metagenomics (r ≥ 0.86 for Paprica and Tax4Fun2) and responded similarly to the impact of fish farm activities. Our results demonstrate that although HSP-based metabarcoding approaches provide a slightly different functional profile than metagenomics, partly due to recovering a distinct community, they represent a cost-effective and valuable tool for characterizing and assessing the effects of fish farming on benthic ecosystems.

How Do the Players Play? A Post-Genomic Analysis Paradigm to Understand Aquatic Ecosystem Processes

Culture-independent and meta-omics sequencing methods have shed considerable light on the so-called “microbial dark matter” of Earth’s environmental microbiome, improving our understanding of phylogeny, the tree of life, and the vast functional diversity of microorganisms. This influx of sequence data has led to refined and reimagined hypotheses about the role and importance of microbial biomass, that paradoxically, sequencing approaches alone are unable to effectively test. Post-genomic approaches such as metabolomics are providing more sensitive and insightful data to unravel the fundamental operations and intricacies of microbial communities within aquatic systems. We assert that the implementation of integrated post-genomic approaches, specifically metabolomics and metatranscriptomics, is the new frontier of environmental microbiology and ecology, expanding conventional assessments toward a holistic systems biology understanding. Progressing beyond siloed phylogenetic assessments and cataloging of metabolites, toward integrated analysis of expression (metatranscriptomics) and activity (metabolomics) is the most effective approach to provide true insight into microbial contributions toward local and global ecosystem functions. This data in turn creates opportunity for improved regulatory guidelines, biomarker discovery and better integration of modeling frameworks. To that end, critical aquatic environmental issues related to climate change, such as ocean warming and acidification, contamination mitigation, and macro-organism health have reasonable opportunity of being addressed through such an integrative approach. Lastly, we argue that the “post-genomics” paradigm is well served to proactively address the systemic technical issues experienced throughout the genomics revolution and focus on collaborative assessment of field-wide experimental standards of sampling, bioinformatics and statistical treatments.

Description and comparison of the skin and ear canal microbiota of non-allergic and allergic German shepherd dogs using next generation sequencing

Atopic dermatitis is one of the most common skin diseases in dogs. Pathogenesis is complex and incompletely understood. Skin colonizing bacteria likely play an important role in the severity of this disease. Studying the canine skin microbiota using traditional microbiological methods has many limitations which can be overcome by molecular procedures. The aim of this study was to describe the bacterial microbiota of the skin and ear canals of healthy non-allergic and allergic German shepherd dogs (GSDs) without acute flare or concurrent skin infection and to compare both. Bacterial 16S rRNA gene amplicon sequence data revealed no differences of bacterial community patterns between the different body sites (axilla, front dorsal interdigital skin, groin, and ear canals) in non-allergic dogs. The microbiota at the different body sites of non-allergic GSDs showed no significant differences. Only for the samples obtained from the axilla the bacterial microbiota of allergic dogs was characterized by a lower species richness compared to that of non-allergic dogs and the bacterial community composition of the skin and ear canals of allergic dogs showed body site specific differences compared to non-allergic dogs. Actinobacteria was the most abundant phylum identified from the non-allergic dogs and Proteobacteria from allergic dogs. Macrococcus spp. were more abundant on non-allergic skin while Sphingomonas spp. were more abundant on the allergic skin. Forward step redundancy analysis of metadata indicated that the household the dogs came from had the strongest impact on the composition of the skin microbiome followed by sex, host health status and body site.

Gut microbiota-mediated pesticide toxicity in humans: Methodological issues and challenges in the risk assessment of pesticides

Many in vivo and in vitro studies have shown that pesticides can disrupt the functioning of gut microbiota (GM), which can lead to many diseases in humans. While the tests developed by the Organization of Economic Cooperation and Development (OECD) are expected to capture most apical effects resulting from GM disruptions, exclusion of GM in the risk assessment might mischaracterize hazards or overestimate/underestimate risks, especially when extrapolating results from one species to another species or population with a substantially different GM. On the other hand, direct assessment of GM-mediated effects may face challenges in identifying hazards, since not all GM perturbations will lead to human adverse effects. In this regard, reliable and validated biomarkers for common GM-mediated adverse effects may be very useful in the identification of GM-mediated pesticide toxicity. Nevertheless, proving causality of GM-mediated effects will need modifications of Bradford Hill criteria as well as Koch's postulates, which are more suitable for the "one-pathogen" paradigm. Furthermore, risk assessment of GM-mediated effects may require pesticide toxicokinetics along the gut, possibly through modeling, and the establishment of the involvement of GM in the mechanism of action (MOA) of the pesticide. Risk assessment of GM mediated effects also requires the standardization of experimental approaches as well as the establishment of microbial reference communities, since variations exist among GM in human populations.

Standardization of microbiome studies for urolithiasis: an international consensus agreement

Numerous metagenome-wide association studies (MWAS) for urolithiasis have been published, leading to the discovery of potential interactions between the microbiome and urolithiasis. However, questions remain about the reproducibility, applicability and physiological relevance of these data owing to discrepancies in experimental technique and a lack of standardization in the field. One barrier to interpreting MWAS is that experimental biases can be introduced at every step of the experimental pipeline, including sample collection, preservation, storage, processing, sequencing, data analysis and validation. Thus, the introduction of standardized protocols that maintain the flexibility to achieve study-specific objectives is urgently required. To address this need, the first international consortium for microbiome in urinary stone disease - MICROCOSM - was created and consensus panel members were asked to participate in a consensus meeting to develop standardized protocols for microbiome studies if they had published an MWAS on urolithiasis. Study-specific protocols were revised until a consensus was reached. This consensus group generated standardized protocols, which are publicly available via a secure online server, for each step in the typical clinical microbiome-urolithiasis study pipeline. This standardization creates the benchmark for future studies to facilitate consistent interpretation of results and, collectively, to lead to effective interventions to prevent the onset of urolithiasis, and will also be useful for investigators interested in microbiome research in other urological diseases.

Low microbial biomass within the reproductive tract of mid-lactation dairy cows: A study approach

The microbiome from the reproductive tract is being investigated for its putative effect on fertility, embryo development, and health status of the human or animal host postpartum. Besides the presence of a vaginal microbiome, recent studies have claimed the existence and putative role of the uterine microbiome. Yet, the extremely low bacterial numbers and high eukaryotic/prokaryotic DNA ratio make this a highly challenging environment to study with next-generation sequencing (NGS) techniques. Here, we describe the methodological challenges that are typically encountered when performing an accurate analysis of low microbial biomass samples, illustrated by data of our own observational study. In terms of the research question, we compared the microbial composition throughout different parts of the reproductive tract of clinically healthy, mid-lactation Holstein-Friesian cows. Samples were collected from 5 dairy cows immediately after killing. Swabs were taken from the vagina, and from 4 pre-established locations of the uterine endometrium. In addition to the conventional DNA extraction blank controls, sterile swabs rubbed over disinfected disposable gloves and the disinfected surface of the uterus (tunica serosa) before incision were taken as sampling controls. The DNA extraction, DNA quantification, quantitative PCR of the 16S rRNA genes, and 16S rRNA gene sequencing were performed. In terms of NGS data analysis, we performed prevalence-based filtering of putative contaminant operational taxonomic units (OTU) using the decontam R package. Although the bacterial composition differed between the vagina and uterus, no differences in bacterial community structure (α and β diversity) were found among the different locations in the uterus. At phylum level, uterine samples had a greater relative abundance of Proteobacteria, and a lesser relative abundance of Firmicutes than vaginal samples. The number of shared OTU between vagina and uterus was limited, suggesting the existence of bacterial transmission routes other than the transcervical one to the uterus. The mid-lactation bovine genital tract is a low microbial biomass environment, which makes it difficult to distinguish between its constitutive versus contaminant microbiome. The integration of key controls is therefore strictly necessary to decrease the effect of accidentally introduced contaminant sequences and improve the reliability of results in samples with low microbial biomass.

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

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

Metagenomic shotgun sequencing reveals host species as an important driver of virome composition in mosquitoes

High-throughput nucleic acid sequencing has greatly accelerated the discovery of viruses in the environment. Mosquitoes, because of their public health importance, are among those organisms whose viromes are being intensively characterized. Despite the deluge of sequence information, our understanding of the major drivers influencing the ecology of mosquito viromes remains limited. Using methods to increase the relative proportion of microbial RNA coupled with RNA-seq we characterize RNA viruses and other symbionts of three mosquito species collected along a rural to urban habitat gradient in Thailand. The full factorial study design allows us to explicitly investigate the relative importance of host species and habitat in structuring viral communities. We found that the pattern of virus presence was defined primarily by host species rather than by geographic locations or habitats. Our result suggests that insect-associated viruses display relatively narrow host ranges but are capable of spreading through a mosquito population at the geographical scale of our study. We also detected various single-celled and multicellular microorganisms such as bacteria, alveolates, fungi, and nematodes. Our study emphasizes the importance of including ecological information in viromic studies in order to gain further insights into viral ecology in systems where host specificity is driving both viral ecology and evolution.

Emerging Roles of Gut Virome in Pediatric Diseases

In the last decade, the widespread application of shotgun metagenomics provided extensive characterization of the bacterial "dark matter" of the gut microbiome, propelling the development of dedicated, standardized bioinformatic pipelines and the systematic collection of metagenomic data into comprehensive databases. The advent of next-generation sequencing also unravels a previously underestimated viral population (virome) present in the human gut. Despite extensive efforts to characterize the human gut virome, to date, little is known about the childhood gut virome. However, alterations of the gut virome in children have been linked to pathological conditions such as inflammatory bowel disease, type 1 diabetes, malnutrition, diarrhea and celiac disease.

Meta-analysis suggests the microbiome responds to Evolve and Resequence experiments in Drosophila melanogaster

BACKGROUND

Experimental evolution has a long history of uncovering fundamental insights into evolutionary processes, but has largely neglected one underappreciated component--the microbiome. As eukaryotic hosts evolve, the microbiome may also respond to selection. However, the microbial contribution to host evolution remains poorly understood. Here, we re-analyzed genomic data to characterize the metagenomes from ten Evolve and Resequence (E&R) experiments in Drosophila melanogaster to determine how the microbiome changed in response to host selection.

RESULTS

Bacterial diversity was significantly different in 5/10 studies, primarily in traits associated with metabolism or immunity. Duration of selection did not significantly influence bacterial diversity, highlighting the importance of associations with specific host traits.

CONCLUSIONS

Our genomic re-analysis suggests the microbiome often responds to host selection; thus, the microbiome may contribute to the response of Drosophila in E&R experiments. We outline important considerations for incorporating the microbiome into E&R experiments. The E&R approach may provide critical insights into host-microbiome interactions and fundamental insight into the genomic basis of adaptation.

Formation, characterization and modeling of emergent synthetic microbial communities

Microbial communities colonize plant tissues and contribute to host function. How these communities form and how individual members contribute to shaping the microbial community are not well understood. Synthetic microbial communities, where defined individual isolates are combined, can serve as valuable model systems for uncovering the organizational principles of communities. Using genome-defined organisms, systematic analysis by computationally-based network reconstruction can lead to mechanistic insights and the metabolic interactions between species. In this study, 10 bacterial strains isolated from the Populus deltoides rhizosphere were combined and passaged in two different media environments to form stable microbial communities. The membership and relative abundances of the strains stabilized after around 5 growth cycles and resulted in just a few dominant strains that depended on the medium. To unravel the underlying metabolic interactions, flux balance analysis was used to model microbial growth and identify potential metabolic exchanges involved in shaping the microbial communities. These analyses were complemented by growth curves of the individual isolates, pairwise interaction screens, and metaproteomics of the community. A fast growth rate is identified as one factor that can provide an advantage for maintaining presence in the community. Final community selection can also depend on selective antagonistic relationships and metabolic exchanges. Revealing the mechanisms of interaction among plant-associated microorganisms provides insights into strategies for engineering microbial communities that can potentially increase plant growth and disease resistance. Further, deciphering the membership and metabolic potentials of a bacterial community will enable the design of synthetic communities with desired biological functions.

Diversity structure of the microbial communities in the guts of four neotropical termite species

The termite gut microbiome is dominated by lignocellulose degrading microorganisms. This study describes the intestinal microbiota of four Argentinian higher termite species with different feeding habits: Microcerotermes strunckii (hardwood), Nasutitermes corniger (softwood), Termes riograndensis (soil organic matter/grass) and Cornitermes cumulans (grass) by deep sequencing of amplified 16S rRNA and ITS genes. In addition, we have performed a taxonomic and gut community structure comparison incorporating into the analysis the previously reported microbiomes of additional termite species with varied diets. The bacterial phylum Spirochaetes was dominant in the guts of M. strunckii, N. corniger and C. cumulans, whereas Firmicutes predominated in the T. riograndensis gut microbiome. A single bacterial genus, Treponema (Spirochaetes), was dominant in all termite species, except for T. riograndensis. Both in our own sequenced samples and in the broader comparison, prokaryotic α-diversity was higher in the soil/grass feeders than in the wood feeders. Meanwhile, the β-diversity of prokaryotes and fungi was highly dissimilar among strict wood-feeders, whereas that of soil- and grass-feeders grouped more closely. Ascomycota and Basidiomycota were the only fungal phyla that could be identified in all gut samples, because of the lack of reference sequences in public databases. In summary, higher microbial diversity was recorded in termites with more versatile feeding sources, providing further evidence that diet, along with other factors (e.g., host taxonomy), influences the microbial community assembly in the termite gut.

BEExact: a Metataxonomic Database Tool for High-Resolution Inference of Bee-Associated Microbial Communities

High-throughput 16S rRNA gene sequencing technologies have robust potential to improve our understanding of bee (Hymenoptera: Apoidea)-associated microbial communities and their impact on hive health and disease. Despite recent computation algorithms now permitting exact inferencing of high-resolution exact amplicon sequence variants (ASVs), the taxonomic classification of these ASVs remains a challenge due to inadequate reference databases. To address this, we assemble a comprehensive data set of all publicly available bee-associated 16S rRNA gene sequences, systematically annotate poorly resolved identities via inclusion of 618 placeholder labels for uncultivated microbial dark matter, and correct for phylogenetic inconsistencies using a complementary set of distance-based and maximum likelihood correction strategies. To benchmark the resultant database (BEExact), we compare performance against all existing reference databases in silico using a variety of classifier algorithms to produce probabilistic confidence scores. We also validate realistic classification rates on an independent set of ∼234 million short-read sequences derived from 32 studies encompassing 50 different bee types (36 eusocial and 14 solitary). Species-level classification rates on short-read ASVs range from 80 to 90% using BEExact (with ∼20% due to "bxid" placeholder names), whereas only ∼30% at best can be resolved with current universal databases. A series of data-driven recommendations are developed for future studies. We conclude that BEExact (https://github.com/bdaisley/BEExact) enables accurate and standardized microbiota profiling across a broad range of bee species-two factors of key importance to reproducibility and meaningful knowledge exchange within the scientific community that together, can enhance the overall utility and ecological relevance of routine 16S rRNA gene-based sequencing endeavors.IMPORTANCE The failure of current universal taxonomic databases to support the rapidly expanding field of bee microbiota research has led to many investigators relying on "in-house" reference sets or manual classification of sequence reads (usually based on BLAST searches), often with vague identity thresholds and subjective taxonomy choices. This time-consuming, error- and bias-prone process lacks standardization, cripples the potential for comparative cross-study analysis, and in many cases is likely to incorrectly sway study conclusions. BEExact is structured on and leverages several complementary bioinformatic techniques to enable refined inference of bee host-associated microbial communities without any other methodological modifications necessary. It also bridges the gap between current practical outcomes (i.e., phylotype-to-genus level constraints with 97% operational taxonomic units [OTUs]) and the theoretical resolution (i.e., species-to-strain level classification with 100% ASVs) attainable in future microbiota investigations. Other niche habitats could also likely benefit from customized database curation via implementation of the novel approaches introduced in this study.

The Relationship Between Breast Milk Components and the Infant Gut Microbiota

The assembly of the newborn's gut microbiota during the first months of life is an orchestrated process resulting in specialized microbial ecosystems in the different gut compartments. This process is highly dependent upon environmental factors, and many evidences suggest that early bacterial gut colonization has long-term consequences on host digestive and immune homeostasis but also metabolism and behavior. The early life period is therefore a "window of opportunity" to program health through microbiota modulation. However, the implementation of this promising strategy requires an in-depth understanding of the mechanisms governing gut microbiota assembly. Breastfeeding has been associated with a healthy microbiota in infants. Human milk is a complex food matrix, with numerous components that potentially influence the infant microbiota composition, either by enhancing specific bacteria growth or by limiting the growth of others. The objective of this review is to describe human milk composition and to discuss the established or purported roles of human milk components upon gut microbiota establishment. Finally, the impact of maternal diet on human milk composition is reviewed to assess how maternal diet could be a simple and efficient approach to shape the infant gut microbiota.