Sensitivity and correlation of hypervariable regions in 16S rRNA genes in phylogenetic analysis

Comparative evaluation of three archaeal primer pairs for exploring archaeal communities in deep-sea sediments and permafrost soils

16S rRNA gene profiling is a powerful method for characterizing microbial communities; however, no universal primer pair can target all bacteria and archaea, resulting in different primer pairs which may impact the diversity profile obtained. Here, we evaluated three pairs of high-throughput sequencing primers for characterizing archaeal communities from deep-sea sediments and permafrost soils. The results show that primer pair Arch519/Arch915 (V4-V5 regions) produced the highest alpha diversity estimates, followed by Arch349f/Arch806r (V3-V4 regions) and A751f/AU1204r (V5-V7 regions) in both sample types. The archaeal taxonomic compositions and the relative abundance estimates of archaeal communities are influenced by the primer pairs. Beta diversity of the archaeal community detected by the three primer pairs reveals that primer pairs Arch349f/Arch806r and Arch519f/Arch915r are biased toward detection of Halobacteriales, Methanobacteriales and MBG-E/Hydrothermarchaeota, whereas the primer pairs Arch519f/Arch915r and A751f/UA1204r are biased to detect MBG-B/Lokiarchaeota, and the primers pairs Arch349f/Arch806r and A751f/UA1204r are biased to detect Methanomicrobiales and Methanosarcinales. The data suggest that the alpha and beta diversities of archaeal communities as well as the community compositions are influenced by the primer pair choice. This finding provides researchers with valuable experimental insight for selection of appropriate archaeal primer pairs to characterize archaeal communities.

Dietary interventions, intestinal microenvironment, and obesity: a systematic review

CONTEXT

Obesity has been linked to the intestinal microenvironment. Diet plays an important role in obesity and has been associated with microbiota.

OBJECTIVE

This systematic review sought to evaluate the scientific evidence on the effect of dietary modification, including supplementation with prebiotics and probiotics, on microbiota diversity in obesity.

DATA SOURCES

A systematic search was performed in the MEDLINE and EMBASE databases. Studies were considered eligible if they were clinical trials evaluating dietary intervention and microbiota, body weight, or clinical parameters in obesity.

DATA EXTRACTION

Data were extracted by 2 independent reviewers.

RESULTS

From 168 articles identified, 20 were included (n = 931 participants). Increased phyla abundance after food interventions was the main finding in relation to microbiota. Regarding the impact of interventions, increased insulin sensitivity, reduced levels of inflammatory markers, and reduced body mass index were shown in several studies.

CONCLUSIONS

Interventions that modulate microbiota, especially prebiotics, show encouraging results in treating obesity, improving insulin levels, inflammatory markers, and body mass index. Because the studies included in this review were heterogeneous, it is difficult to achieve conclusive and definitive results.

A town on fire! Integrating 16S rRNA gene amplicon analyses into an undergraduate microbiology lecture class

Microbiology increasingly relies upon bioinformatics to understand complex microbial interactions. Nevertheless, biology undergraduates often lack the basic quantitative and computer-based skills required for bioinformatics analyses. To address these issues, the course module 'A Town on Fire! 16S rRNA Gene Amplicon Analysis of Microbial Communities Overlying the Centralia, PA Mine Fire' was developed for an undergraduate microbiology lecture course. In this module, microbiology students used Quantitative Insights into Microbial Ecology to perform taxonomic, phylogenetic and statistical analyses on bacterial communities from three hot mine fire-impacted surface soils using 16S rRNA gene amplicon sequences. Pre- and post-module assessment data for each of 2 years were compiled, and indirect assessment indicated that students' confidence regarding their ability to perform bioinformatics analyses, as well as their ability to interpret bioinformatics data both increased, as did their enthusiasm for bioinformatics. Direct assessment demonstrated that students' understanding of topics that they actually used in the module, such as the statistical analyses that underlie bioinformatics investigations and the ability to infer phylogenetic relationships, improved during the module, but that their underlying understanding of techniques that they did not directly perform, such as sequencing and library construction, did not.

High-Throughput Sequencing of the 16S rRNA Gene as a Survey to Analyze the Microbiomes of Free-Living Ciliates Paramecium

Ciliates are the largest group of ubiquitous aquatic bacterivorous protists, and many species are easily cultivated. However, only few studies reported prokaryotic communities naturally associated with ciliate cells. Herein, we analyzed the microbiome composition of several strains of Paramecium (Ciliophora) originating from different locations and belonging to two morpho-species by high-throughput sequencing (HTS) of the 16S rRNA gene. Possible reasons of HTS results bias were addressed comparing DNA libraries obtained using different primers and different number of ciliate cells. Microbiomes associated with ciliates and their environments were always significantly different by prokaryotic taxonomic composition and bacterial richness. There were also pronounced differences between Paramecium strains. Interestingly, potentially pathogenic bacteria were revealed in Paramecium microbiomes.

Cyanobacterial diversity in mat sample obtained from hypersaline desert, Rann of Kachchh

Rann of Kachchh (RoK) is a unique geoformation, which is exposed to dynamic environmental changes such as salinity, temperature, and nutrients throughout the year. In this study, the pooled mat sample was examined for the cyanobacterial community structure using culture-dependent and culture-independent approaches. Taxonomic profiling was studied using amplicon sequencing that revealed the enrichment of Pseudanabaenales and Oscillatoriales by QIIME and MG-RAST, respectively. Other abundant orders were represented by Chroococcales, Nostocales, and unclassified cyanobacteria by both approaches. Nine cyanobacterial cultures were isolated from mat samples showing 90-98% similarities with available sequences in GenBank. The culture-dependent study suggested that mat was dominated by cyanobacterial orders such as Oscillatoriales-filamentous and Chroococcales-unicellular. Our results from the culture-dependent approach also indicated that despite high similarities in gene sequences, six cyanobacteria fall into the separate clade in the phylogenetic analysis that could be signs of evolution due to an extreme environment. Cultured isolates are correlated well with abundant taxa from amplicon sequencing. Further, protein profiling was done specifically for phycobiliproteins which will be helpful to elucidate their roles in light harvesting and energy transfer mechanism in the unique environment of RoK.

Rapid metagenomics analysis of EMS vehicles for monitoring pathogen load using nanopore DNA sequencing

Pathogen monitoring, detection and removal are essential to public health and outbreak management. Systems are in place for monitoring the microbial load of hospitals and public health facilities with strategies to mitigate pathogen spread. However, no such strategies are in place for ambulances, which are tasked with transporting at-risk individuals in immunocompromised states. As standard culturing techniques require a laboratory setting, and are time consuming and labour intensive, our approach was designed to be portable, inexpensive and easy to use based on the MinION third-generation sequencing platform from Oxford Nanopore Technologies. We developed a transferable sampling-to-analysis pipeline to characterize the microbial community in emergency medical service vehicles. Our approach identified over sixty-eight organisms in ambulances to the genera level, with a proportion of these being connected with health-care associated infections, such as Clostridium spp. and Staphylococcus spp. We also monitored the microbiome of different locations across three ambulances over time, and examined the dynamic community of microorganisms found in emergency medical service vehicles. Observed differences identified hot spots, which may require heightened monitoring and extensive cleaning. Through metagenomics analysis it is also possible to identify how microorganisms spread between patients and colonize an ambulance over time. The sequencing results aid in the development of practices to mitigate disease spread, while also providing a useful tool for outbreak prediction through ongoing analysis of the ambulance microbiome to identify new and emerging pathogens. Overall, this pipeline allows for the tracking and monitoring of pathogenic microorganisms of epidemiological interest, including those related to health-care associated infections.

Does the endometrial cavity have a molecular microbial signature?

Recent molecular studies concluded that the endometrium has a resident microbiota dominated by Lactobacillus spp. and is therefore similar to that of the vagina. These findings were largely derived from endometrial samples obtained through a transcervical catheter and thus prone to contamination. Herein, we investigated the molecular microbial profiles of mid-endometrial samples obtained through hysterectomy and compared them with those of the cervix, vagina, rectum, oral cavity, and controls for background DNA contamination. Microbial profiles were examined through 16S rRNA gene qPCR and sequencing. Universal bacterial qPCR of total 16S rDNA revealed a bacterial load exceeding that of background DNA controls in the endometrium of 60% (15/25) of the study subjects. Bacterial profiles of the endometrium differed from those of the oral cavity, rectum, vagina, and background DNA controls, but not of the cervix. The bacterial profiles of the endometrium and cervix were dominated by Acinetobacter, Pseudomonas, Cloacibacterium, and Comamonadaceae. Both 16S rRNA gene sequencing and Lactobacillus species-specific (L. iners & L crispatus) qPCR showed that Lactobacillus was rare in the endometrium. In conclusion, if there is a microbiota in the middle endometrium, it is not dominated by Lactobacillus as was previously concluded, yet further investigation using culture and microscopy is necessary.

Community Structures of Bacteria, Archaea, and Eukaryotic Microbes in the Freshwater Glacier Lake Yukidori-Ike in Langhovde, East Antarctica

Since most studies about community structures of microorganisms in Antarctic terrestrial lakes using molecular biological tools are mainly focused on bacteria, limited information is available about archaeal and eukaryotic microbial diversity. In this study, the biodiversity of microorganisms belonging to all three domains in a typical Antarctic freshwater glacier lake (Yukidori-Ike) was revealed using small subunit ribosomal RNA (SSU rRNA) clone library analysis. The bacterial clones were grouped into 102 operational taxonomic units (OTUs) and showed significant biodiversity. Betaproteobacteria were most frequently detected, followed by Cyanobacteria, Bacteroidetes, and Firmicutes as major lineages. In contrast to the bacterial diversity, much lower archaeal diversity, consisting of only two OTUs of methanogens, was observed. In the eukaryotic microbial community consisting of 20 OTUs, Tardigradal DNA was remarkably frequently detected. Genera affiliated with the phyla Ciliophora, Cryptomycota, Chlorophyta, Bacillariophyta, and Apusozoa were also detected. The biodiversity and species compositions of the whole microbial community of Lake Yukidori-Ike are similar to those of freshwater environments in temperate regions but are different from saline lakes in Antarctica, indicating that the salinity seems to affect the microbial composition more than the temperature.

Human microbial ecology and the rising new medicine

The first life forms on earth were Prokaryotic, and the evolution of all Eukaryotic life occurred with the help of bacteria. Animal-associated microbiota also includes members of the archaea, fungi, protists, and viruses. The genomes of this host-associated microbial life are called the microbiome. Across the mammalian tree, microbiomes guarantee the development of immunity, physiology, and resistance to pathogens. In humans, all surfaces and cavities are colonized by a microbiome, maintained by a careful balance between the host response and its colonizers-thus humans are considered now supraorganisms. These microbiomes supply essential ecosystem services that benefit health through homeostasis, and the loss of the indigenous microbiota leads to dysbiosis, which can have significant consequences to disease. This educational review aims to describe the importance of human microbial ecology, explain the ecological terms applied to the study of the human microbiome, developments within the cutting-edge microbiome field, and implications to diagnostic and treatment.

Phylogenetic determinants of toxin gene distribution in genomes of Brevibacillus laterosporus

Brevibacillus laterosporus is a globally ubiquitous, spore forming bacterium, strains of which have shown toxic activity against invertebrates and microbes and several have been patented due to their commercial potential. Relatively little is known about this bacterium. Here, we examined the genomes of six published and five newly determined genomes of B. laterosporus, with an emphasis on the relationships between known and putative toxin encoding genes, as well as the phylogenetic relationships between strains. Phylogenetically, strain relationships are similar using average nucleotide identity (ANI) values and multi-gene approaches, although PacBio sequencing revealed multiple copies of the 16S rDNA gene which lessened utility at the strain level. Based on ANI values, the New Zealand isolates were distant from other isolates and may represent a new species. While all of the genomes examined shared some putative toxicity or virulence related proteins, many specific genes were only present in a subset of strains.

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We examined genomes of 11 Brevibacillus laterosporus, a bacterium which is antagonistic to invertebrates and/or microbes

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Multiple phylogenetic methods showed New Zealand isolates more distant than all other isolates

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Each genome could contain 11–13 copies of the 16S rDNA gene, some of which were not identical

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Many putative toxin encoding genes were present in the genomes, but the toxin complement varied from isolate to isolate

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Variation in occurrence of toxin-encoding genes indicates the potential to find strains with new combinations of activities

The role of tissue type, sampling and nucleic acid purification methodology on the inferred composition of Pacific oyster (Crassostrea gigas) microbiome

AIMS

This study evaluated methods to sample and extract nucleic acids from Pacific oysters to accurately determine the microbiome associated with different tissues.

METHODS AND RESULTS

Samples were collected from haemolymph, gill, gut and adductor muscle, using swabs and homogenates of solid tissues. Nucleic acids were extracted from fresh and frozen samples using three different commercial kits. The bacterial DNA yield varied between methods (P < 0·05) and each tissue harboured a unique microbiota, except for gill and muscle. Higher bacterial DNA yields were obtained by swabbing compared to tissue homogenates and from fresh tissues compared to frozen tissues, without impacting the bacterial community composition estimated by 16S rRNA gene (V1-V3 region) sequencing. Despite the higher bacterial DNA yields with QIAamp® DNA Microbiome Kit, the E.Z.N.A.^® Mollusc DNA Kit identified twice as many operational taxonomic units (OTUs) and eliminated PCR inhibition from gut tissues.

CONCLUSIONS

Sampling and nucleic acid purification substantially affected the quantity and diversity of bacteria identified in Pacific oyster microbiome studies and a fit-for-purpose strategy is recommended.

SIGNIFICANCE AND IMPACT OF THE STUDY

Accurate identification of Pacific oyster microbial diversity is instrumental for understanding the polymicrobial aetiology of Pacific oyster mortality diseases which greatly impact oyster production.

HMP16SData: Efficient Access to the Human Microbiome Project Through Bioconductor

Phase 1 of the Human Microbiome Project (HMP) investigated 18 body subsites of 242 healthy American adults to produce the first comprehensive reference for the composition and variation of the "healthy" human microbiome. Publicly available data sets from amplicon sequencing of two 16S ribosomal RNA variable regions, with extensive controlled-access participant data, provide a reference for ongoing microbiome studies. However, utilization of these data sets can be hindered by the complex bioinformatic steps required to access, import, decrypt, and merge the various components in formats suitable for ecological and statistical analysis. The HMP16SData package provides count data for both 16S ribosomal RNA variable regions, integrated with phylogeny, taxonomy, public participant data, and controlled participant data for authorized researchers, using standard integrative Bioconductor data objects. By removing bioinformatic hurdles of data access and management, HMP16SData enables epidemiologists with only basic R skills to quickly analyze HMP data.

Effects of tillage managements and maize straw returning on soil microbiome using 16S rDNA sequencing

Agricultural practices could affect bacterial diversity and community structure by altering soil physical and chemical properties. Straw returning and tillage practices are widely used in agriculture, however, the effects of these agricultural practices on microbiomes are still unclear. In the present study, we compared the 18 bacterial communities of soil with different straw returning and tillage treatment combinations. The V3-V4 regions of the 16S ribosomal RNA were amplified and analyzed by high-throughput sequencing technology. The results showed that the bacterial communities were consistently dominated by Acidobacteria, Proteobacteria, Actinobacteria, and Chloroflexi. Short-term straw returning and tillage practices significantly altered the diversity, relative abundance and functions of the soil microbiome. Soil subjected to rotary tillage and straw returning (RTS) combination possessed the highest bacterial diversity and lowest ratio of G+/G- bacteria, indicating that RTS could be an efficient integrated management system to improve microbiome in the short term. Double verifications based on relative abundance and network analysis, revealed close relationships of Mycobacterium and Methylibium with RTS, indicating they could serve as biomarkers for RTS. Investigating microbial changes under different agricultural practices will provide valuable foundations for land sustainable utilization and increase crop yields.

Long-Term Temperature Stress in the Coral Model Aiptasia Supports the "Anna Karenina Principle" for Bacterial Microbiomes

The understanding of host-microbial partnerships has become a hot topic during the last decade as it has been shown that associated microbiota play critical roles in the host physiological functions and susceptibility to diseases. Moreover, the microbiome may contribute to host resilience to environmental stressors. The sea anemone Aiptasia is a good laboratory model system to study corals and their microbial symbiosis. In this regard, studying its bacterial microbiota provides a better understanding of cnidarian metaorganisms as a whole. Here, we investigated the bacterial communities of different Aiptasia host-symbiont combinations under long-term heat stress in laboratory conditions. Following a 16S rRNA gene sequencing approach we were able to detect significant differences in the bacterial composition and structure of Aiptasia reared at different temperatures. A higher number of taxa (i.e., species richness), and consequently increased α-diversity and β-dispersion, were observed in the microbiomes of heat-stressed individuals across all host strains and experimental batches. Our findings are in line with the recently proposed Anna Karenina principle (AKP) for animal microbiomes, which states that dysbiotic or stressed organisms have a more variable and unstable microbiome than healthy ones. Microbial interactions affect the fitness and survival of their hosts, thus exploring the AKP effect on animal microbiomes is important to understand host resilience. Our data contributes to the current knowledge of the Aiptasia holobiont and to the growing field of study of host-associated microbiomes.

Invited review: Application of meta-omics to understand the dynamic nature of the rumen microbiome and how it responds to diet in ruminants

Ruminants are unique among livestock due to their ability to efficiently convert plant cell wall carbohydrates into meat and milk. This ability is a result of the evolution of an essential symbiotic association with a complex microbial community in the rumen that includes vast numbers of bacteria, methanogenic archaea, anaerobic fungi and protozoa. These microbes produce a diverse array of enzymes that convert ingested feedstuffs into volatile fatty acids and microbial protein which are used by the animal for growth. Recent advances in high-throughput sequencing and bioinformatic analyses have helped to reveal how the composition of the rumen microbiome varies significantly during the development of the ruminant host, and with changes in diet. These sequencing efforts are also beginning to explain how shifts in the microbiome affect feed efficiency. In this review, we provide an overview of how meta-omics technologies have been applied to understanding the rumen microbiome, and the impact that diet has on the rumen microbial community.

Uncovering the hidden marine sponge microbiome by applying a multi-primer approach

Marine sponges (phylum Porifera) are hosts to microorganisms that make up to 40–60% of the mesohyl volume. The challenge is to characterise this microbial diversity more comprehensively. To accomplish this, a new method was for the first time proposed to obtain sequence coverage of all the variable regions of the 16S rRNA gene to analyze the amplicon-based microbiomes of four representative sponge species belonging to different orders. The five primer sets targeting nine variable regions of the 16S rRNA gene revealed a significant increase in microbiome coverage of 29.5% of phylum level OTUs and 35.5% class level OTUs compared to the community revealed by the commonly used V4 region-specific primer set alone. Among the resulting OTUs, 52.6% and 61.3% were unaffiliated, including candidate OTUs, at the phylum and class levels, respectively, which demonstrated a substantially superior performance in uncovering taxonomic ‘blind spots’. Overall, a more complete sponge microbiome profile was achieved by this multi-primer approach, given the significant improvement of microbial taxonomic coverage and the enhanced capacity to uncover novel microbial taxa. This multi-primer approach represents a fundamental and practical change from the conventional single primer set amplicon-based microbiome approach, and can be broadly applicable to other microbiome studies.

Rapid and reasonable molecular identification of bacteria and fungi in microbiological diagnostics using rapid real-time PCR and Sanger sequencing

Analyses of short subunit gene sequences have been established for taxonomic classification and identification of bacteria and fungi. To produce partial bacterial ribosomal 16S rRNA and rpoB and fungal ribosomal ITS/LSU gene sequences for DNA sequencing, real-time PCR assays supplemented with the nucleic acid stain SYBR Green were created. Generation of PCR products was monitored based on amplification and melting curves. The PCR products were subsequently subjected to Sanger sequencing on demand for identification of bacteria and fungi in routine microbiological diagnostics within a period of two days. From a total of 78 bacterial isolates 40 (51%) or 67 (86%) could be identified at species level using only partial 16S rRNA or additionally rpoB gene sequences based on BLASTN (NCBI) database queries, respectively. Using partial 16S rRNA and rpoB gene sequencing unambiguous assignment was not possible for the closely related species of the Bacillus (B.) cereus group, Bordetella (B.) pertussis/ B. parapertussis/ B. bronchiseptica, Brucella spp., Enterobacter cloacae complex, Escherichia/ Shigella spp., Staphylococcus (S.) hyicus/ S. agnetis and Yersinia (Y.) pseudotuberculosis/ Y. pestis. However, partial rpoB gene sequencing succeeded in identifying 27 bacterial isolates at species level in addition to 16S rRNA gene sequencing. Regarding ITS/LSU gene sequencing, best results could be achieved by ITS gene sequencing followed by LSU gene sequencing, resulting in 32 (63%) and 21 (43%) of a total of 51 fungal isolates that could be identified at species level, respectively. Insufficient identification at species level was observed for the genera Apiotrichum, Aspergillus, Cladosporium, Cryptococcus, Microsporum, Nannizziopsis, Penicillium, Trichosporon, and Tolypocladium included in this study. The concept of this procedure is suitable for rapid and reasonable molecular identification of bacteria and fungi within two days and is therefore applicable in routine microbiological diagnostic laboratories.

Understanding and overcoming the pitfalls and biases of next-generation sequencing (NGS) methods for use in the routine clinical microbiological diagnostic laboratory

Recent advancements in next-generation sequencing (NGS) have provided the foundation for modern studies into the composition of microbial communities. The use of these NGS methods allows for the detection and identification of (‘difficult-to-culture’) microorganisms using a culture-independent strategy. In the field of routine clinical diagnostics however, the application of NGS is currently limited to microbial strain typing for epidemiological purposes only, even though the implementation of NGS for microbial community analysis may yield clinically important information. This lack of NGS implementation is due to many different factors, including issues relating to NGS method standardization and result reproducibility. In this review article, the authors provide a general introduction to the most widely used NGS methods currently available (i.e., targeted amplicon sequencing and shotgun metagenomics) and the strengths and weaknesses of each method is discussed. The focus of the publication then shifts toward 16S rRNA gene NGS methods, which are currently the most cost-effective and widely used NGS methods for research purposes, and are therefore more likely to be successfully implemented into routine clinical diagnostics in the short term. In this respect, the experimental pitfalls and biases created at each step of the 16S rRNA gene NGS workflow are explained, as well as their potential solutions. Finally, a novel diagnostic microbiota profiling platform (‘MYcrobiota’) is introduced, which was developed by the authors by taking into consideration the pitfalls, biases, and solutions explained in this article. The development of the MYcrobiota, and future NGS methodologies, will help pave the way toward the successful implementation of NGS methodologies into routine clinical diagnostics.

'RA and the microbiome: do host genetic factors provide the link?

Rheumatoid arthritis (RA) is a chronic autoimmune disease, characterised by painful synovium inflammation, bony erosions, immune activation and the circulation of autoantibodies. Despite recent advances in therapeutics enabling disease suppression, there is a considerable demand for alternative therapeutic strategies as well as optimising those available at present. The relatively low concordance rate between monozygotic twins, 20–30% contrasts with heritability estimates of ∼65%, indicating a substantive role of other risk factors in RA pathogenesis. There is established evidence that RA has an infective component to its aetiology. More recently, differences in the commensal microbiota in RA compared to controls have been identified. Studies have shown that the gut, oral and lung microbiota is different in new onset treatment naïve, and established RA patients, compared to controls. Key taxonomic associations are an increase in abundance of Porphyromonas gingivalis and Prevotella copri in RA patients, compared to healthy controls. Host genetics may provide the link between disease and the microbiome. Genetic influence may be mediated by the host immune system; a differential response to RA associated taxa is suggested. The gut microbiome contains elements which are as much as 30% heritable. A better understanding of the influence of host genetics will shed light onto the role of the microbiome in RA. Here we review the role of the microbiome in RA through the lens of host genetics, and consider future research areas addressing microbiome study design and bioinformatics approaches.

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Rheumatoid arthritis (RA) affects 1% of the population and is highly debilitating.

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RA is ~65% heritable, yet the concordance rate between monozygotic twins is just 20–30%, indicating a substantive role of other risk factors.

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Studies have shown that the gut, oral and lung microbiome is different in treatment naïve and established RA patients, compared to controls.

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Current findings suggest an important influence of host genetics on the microbiome, which may contribute to RA via the host immune system.

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Associations of the microbiome with RA described thus far are confounded by host genetics, and future studies need to take account of this.

Considerations When Designing a Microbiome Study: Implications for Nursing Science

Nurse scientists play an important role in studying complex relationships among human genetics, environmental factors, and the microbiome, all of which can contribute to human health and disease. Therefore, it is essential that they have the tools necessary to execute a successful microbiome research study. The purpose of this article is to highlight important methodological factors for nurse scientists to consider when designing a microbiome study. In addition to considering factors that influence host-associated microbiomes (i.e., microorganisms associated with organisms such as humans, mice, and rats), this manuscript highlights study designs and methods for microbiome analysis. Exemplars are presented from nurse scientists who have incorporated microbiome methods into their program of research. This review is intended to be a resource to guide nursing-focused microbiome research and highlights how study of the microbiome can be incorporated to answer research questions.

The effect of 16S rRNA region choice on bacterial community metabarcoding results

In this work, we compare the resolution of V2-V3 and V3-V4 16S rRNA regions for the purposes of estimating microbial community diversity using paired-end Illumina MiSeq reads, and show that the fragment, including V2 and V3 regions, has higher resolution for lower-rank taxa (genera and species). It allows for a more precise distance-based clustering of reads into species-level OTUs. Statistically convergent estimates of the diversity of major species (defined as those that together are covered by 95% of reads) can be achieved at the sample sizes of 10000 to 15000 reads. The relative error of the Shannon index estimate for this condition is lower than 4%.

A census-based estimate of Earth's bacterial and archaeal diversity

The global diversity of Bacteria and Archaea, the most ancient and most widespread forms of life on Earth, is a subject of intense controversy. This controversy stems largely from the fact that existing estimates are entirely based on theoretical models or extrapolations from small and biased data sets. Here, in an attempt to census the bulk of Earth's bacterial and archaeal ("prokaryotic") clades and to estimate their overall global richness, we analyzed over 1.7 billion 16S ribosomal RNA amplicon sequences in the V4 hypervariable region obtained from 492 studies worldwide, covering a multitude of environments and using multiple alternative primers. From this data set, we recovered 739,880 prokaryotic operational taxonomic units (OTUs, 16S-V4 gene clusters at 97% similarity), a commonly used measure of microbial richness. Using several statistical approaches, we estimate that there exist globally about 0.8–1.6 million prokaryotic OTUs, of which we recovered somewhere between 47%–96%, representing >99.98% of prokaryotic cells. Consistent with this conclusion, our data set independently "recaptured" 91%–93% of 16S sequences from multiple previous global surveys, including PCR-independent metagenomic surveys. The distribution of relative OTU abundances is consistent with a log-normal model commonly observed in larger organisms; the total number of OTUs predicted by this model is also consistent with our global richness estimates. By combining our estimates with the ratio of full-length versus partial-length (V4) sequence diversity in the SILVA sequence database, we further estimate that there exist about 2.2–4.3 million full-length OTUs worldwide. When restricting our analysis to the Americas, while controlling for the number of studies, we obtain similar richness estimates as for the global data set, suggesting that most OTUs are globally distributed. Qualitatively similar results are also obtained for other 16S similarity thresholds (90%, 95%, and 99%). Our estimates constrain the extent of a poorly quantified rare microbial biosphere and refute recent predictions that there exist trillions of prokaryotic OTUs.

A massive survey of Earth's Bacteria and Archaea reveals that their diversity is orders of magnitude lower than previously thought. The study also indicates that extinctions played an important role in prokaryotic evolution.

The global diversity of Bacteria and Archaea ("prokaryotes"), the most ancient and most widespread forms of life on Earth, is subject to high uncertainty. Here, to estimate the global diversity of prokaryotes, we analyzed a large number of 16S ribosomal RNA gene sequences, found in all prokaryotes and commonly used to catalogue prokaryotic diversity. Sequences were obtained from a multitude of environments across thousands of geographic locations worldwide. From this data set, we recovered 739,880 prokaryotic operational taxonomic units (OTUs), i.e., 16S gene clusters sharing 97% similarity, roughly corresponding to prokaryotic species. Using several statistical approaches and through comparison with existing databases and previous independent surveys, we estimate that there exist globally between 0.8 and 1.6 million prokaryotic OTUs. When restricting our analysis to the Americas, while controlling for the number of studies, we obtain similar estimates as for the global data set, suggesting that most OTUs are not restricted to a single continent but are instead globally distributed. Our estimates constrain the extent of a commonly hypothesized but poorly quantified rare prokaryotic biosphere and refute recent predictions that there exists trillions of prokaryotic OTUs. Our findings also indicate that, contrary to common speculation, extinctions may strongly influence global prokaryotic diversity.

Mapping the Surface Microbiome and Metabolome of Brown Seaweed Fucus vesiculosus by Amplicon Sequencing, Integrated Metabolomics and Imaging Techniques

The brown alga Fucus vesiculosus is a keystone marine species, which is subject to heavy surface colonisation. This study was designed to analyse the surface epibiome of F. vesiculosus in conjunction with the composition and spatial distribution of its surface metabolome. The amplicon sequencing, SEM and CARD-FISH imaging studies showed Alphaproteobacteria to predominate the epibiotic bacteria. Fungi of the class Eurotiomycetes were visualised for the first time on an algal surface. An untargeted metabolomics approach using molecular networks, in silico prediction and manual dereplication showed the differential metabolome of the surface and the whole tissue extracts. In total, 50 compounds were putatively dereplicated by UPLC-MS/MS, 37 of which were previously reported from both seaweeds and microorganisms. Untargeted spatial metabolomics by DESI-Imaging MS identified the specific localisation and distribution of various primary and secondary metabolites on surface imprints and in algal cross sections. The UPLC-MS, DESI-IMS and NMR analyses failed to confirm the presence of any surface-associated metabolite, except for mannitol, which were previously reported from F. vesiculosus. This is the first study analysing the seaweed surface microbiome in conjunction with untargeted surface metabolomics and spatial metabolomics approaches.

Benchmarking taxonomic assignments based on 16S rRNA gene profiling of the microbiota from commonly sampled environments

Background

Taxonomic profiling of ribosomal RNA (rRNA) sequences has been the accepted norm for inferring the composition of complex microbial ecosystems. Quantitative Insights Into Microbial Ecology (QIIME) and mothur have been the most widely used taxonomic analysis tools for this purpose, with MAPseq and QIIME 2 being two recently released alternatives. However, no independent and direct comparison between these four main tools has been performed. Here, we compared the default classifiers of MAPseq, mothur, QIIME, and QIIME 2 using synthetic simulated datasets comprised of some of the most abundant genera found in the human gut, ocean, and soil environments. We evaluate their accuracy when paired with both different reference databases and variable sub-regions of the 16S rRNA gene.

Findings

We show that QIIME 2 provided the best recall and F-scores at genus and family levels, together with the lowest distance estimates between the observed and simulated samples. However, MAPseq showed the highest precision, with miscall rates consistently <2%. Notably, QIIME 2 was the most computationally expensive tool, with CPU time and memory usage almost 2 and 30 times higher than MAPseq, respectively. Using the SILVA database generally yielded a higher recall than using Greengenes, while assignment results of different 16S rRNA variable sub-regions varied up to 40% between samples analysed with the same pipeline.

Conclusions

Our results support the use of either QIIME 2 or MAPseq for optimal 16S rRNA gene profiling, and we suggest that the choice between the two should be based on the level of recall, precision, and/or computational performance required.

Lactobacillus elicits a 'Marmite effect' on the chicken cecal microbiome

The poultry industry has traditionally relied on the use of antibiotic growth promoters (AGPs) to improve production efficiency and minimize infection. With the recent drive to eliminate the use of AGPs, novel alternatives are urgently required. Recently attention has turned to the use of synthetic communities that may be used to ‘seed’ the developing microbiome. The current challenge is identifying keystone taxa whose influences in the gut can be leveraged for probiotic development. To help define such taxa we present a meta-analysis of 16S rRNA surveys of 1572 cecal microbiomes generated from 19 studies. Accounting for experimental biases, consistent with previous studies, we find that AGP exposure can result in reduced microbiome diversity. Network community analysis defines groups of taxa that form stable clusters and further reveals Lactobacillus to elicit a polarizing effect on the cecal microbiome, exhibiting relatively equal numbers of positive and negative interactions with other taxa. Our identification of stable taxonomic associations provides a valuable framework for developing effective microbial consortia as alternatives to AGPs.

Joint analysis of multiple phenotypes in association studies using allele-based clustering approach for non-normal distributions

In the study of complex diseases, several correlated phenotypes are usually measured. There is also increasing evidence showing that testing the association between a single-nucleotide polymorphism (SNP) and multiple-dependent phenotypes jointly is often more powerful than analyzing only one phenotype at a time. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. In this paper, we develop an Allele-based Clustering Approach (ACA) for the joint analysis of multiple non-normal phenotypes in association studies. In ACA, we consider the alleles at a SNP of interest as a dependent variable with two classes, and the correlated phenotypes as predictors to predict the alleles at the SNP of interest. We perform extensive simulation studies to evaluate the performance of ACA and compare the power of ACA with the powers of Adaptive Fisher's Combination test, Trait-based Association Test that uses Extended Simes procedure, Fisher's Combination test, the standard MANOVA, and the joint model of Multiple Phenotypes. Our simulation studies show that the proposed method has correct type I error rates and is much more powerful than other methods for some non-normal distributions.

Exploring biogeographic patterns of bacterioplankton communities across global estuaries

Estuaries provide an ideal niche to study structure and function of bacterioplankton communities owing to the presence of a multitude of environmental stressors. Bacterioplankton community structures from nine global estuaries were compared to understand their broad‐scale biogeographic patterns. Bacterioplankton community structure from four estuaries of Sundarbans, namely Mooriganga, Thakuran, Matla, and Harinbhanga, was elucidated using Illumina sequencing. Bacterioplankton communities from these estuaries were compared against available bacterioplankton sequence data from Columbia, Delaware, Jiulong, Pearl, and Hangzhou estuaries. All nine estuaries were dominated by Proteobacteria. Other abundant phyla included Bacteroidetes, Firmicutes, Acidobacteria, Actinobacteria, Cyanobacteria, Planctomycetes, and Verrucomicrobia. The abundant bacterial phyla showed a ubiquitous presence across the estuaries. At class level, the overwhelming abundance of Gammaproteobacteria in the estuaries of Sundarbans and Columbia estuary clearly stood out amidst high abundance of Alphaproteobacteria observed in the other estuaries. Abundant bacterial families including Rhodobacteriaceae, Shingomonadaceae, Acidobacteriaceae, Vibrionaceae, and Xanthomondaceae also showed ubiquitous presence in the studied estuaries. However, rare taxa including Chloroflexi, Tenericutes, Nitrospirae, and Deinococcus‐Thermus showed clear site‐specific distribution patterns. Such distribution patterns were also reinstated by nMDS ordination plots. Such clustering patterns could hint toward the potential role of environmental parameters and substrate specificity which could result in distinct bacterioplankton communities at specific sites. The ubiquitous presence of abundant bacterioplankton groups along with their strong correlation with surface water temperature and dissolved nutrient concentrations indicates the role of such environmental parameters in shaping bacterioplankton community structure in estuaries. Overall, studies on biogeographic patters of bacterioplankton communities can provide interesting insights into ecosystem functioning and health of global estuaries.

A multi-amplicon 16S rRNA sequencing and analysis method for improved taxonomic profiling of bacterial communities

Metagenomic sequencing of bacterial samples has become the gold standard for profiling microbial populations, but 16S rRNA profiling remains widely used due to advantages in sample throughput, cost, and sensitivity even though the approach is hampered by primer bias and lack of specificity. We hypothesized that a hybrid approach, that combined targeted PCR amplification with high-throughput sequencing of multiple regions of the genome, would capture many of the advantages of both approaches. We developed a method that identifies and quantifies members of bacterial communities through simultaneous analysis of multiple variable regions of the bacterial 16S rRNA gene. The method combines high-throughput microfluidics for PCR amplification, short read DNA sequencing, and a custom algorithm named MVRSION (Multiple 16S Variable Region Species-Level IdentificatiON) for optimizing taxonomic assignment. MVRSION performance was compared to single variable region analyses (V3 or V4) of five synthetic mixtures of human gut bacterial strains using existing software (QIIME), and the results of community profiling by shotgun sequencing (COPRO-Seq) of fecal DNA samples collected from gnotobiotic mice colonized with a defined, phylogenetically diverse consortium of human gut bacterial strains. Positive predictive values for MVRSION ranged from 65%-91% versus 44%-61% for single region QIIME analyses (p < .01, p < .001), while the abundance estimate r² for MVRSION compared to COPRO-Seq was 0.77 vs. 0.46 and 0.45 for V3-QIIME and V4-QIIME, respectively. MVRSION represents a generally applicable tool for taxonomic classification that is superior to single-region 16S rRNA methods, resource efficient, highly scalable for assessing the microbial composition of up to thousands of samples concurrently, with multiple applications ranging from whole community profiling to targeted tracking of organisms of interest in diverse habitats as a function of specified variables/perturbations.

Structural and Functional Analysis of the Gut Microbiome for Toxicologists

Characterizing the reciprocal interactions between toxicants, the gut microbiota, and the host, holds great promise for improving our mechanistic understanding of toxic endpoints. Advances in culture-independent sequencing analysis (e.g., 16S rRNA gene amplicon sequencing) combined with quantitative metabolite profiling (i.e., metabolomics) have provided new ways of studying the gut microbiome and have begun to illuminate how toxicants influence the structure and function of the gut microbiome. Developing a standardized protocol is important for establishing robust, reproducible, and importantly, comparative data. This protocol can be used as a foundation for examining the gut microbiome via sequencing-based analysis and metabolomics. Two main units follow: (1) analysis of the gut microbiome via sequencing-based approaches; and (2) functional analysis of the gut microbiome via metabolomics. © 2018 by John Wiley & Sons, Inc.

High-resolution ISR amplicon sequencing reveals personalized oral microbiome

BACKGROUND

Sequencing of the 16S rRNA gene has been the standard for studying the composition of microbial communities. While it allows identification of bacteria at the level of species, this method does not usually provide sufficient information to resolve communities at the sub-species level. Species-level resolution is not adequate for studies of transmission or stability or for exploring subspecies variation in disease association. Strain level analysis using whole metagenome shotgun sequencing has significant limitations that can make it unsuitable for large-scale studies. Achieving sufficient depth of sequencing can be cost-prohibitive, and even with adequate coverage, deconvoluting complex communities such as the oral microbiota is computationally very challenging. Thus, there is a need for high-resolution, yet cost-effective, high-throughput methods for characterizing microbial communities.

RESULTS

Significant improvement in resolution for amplicon-based bacterial community analysis was achieved by combining amplicon sequencing of a high-diversity marker gene, the ribosomal 16-23S intergenic spacer region (ISR), with a probabilistic error modeling based denoising algorithm, DADA2. The resolving power of this new approach was compared to that of both standard and high-resolution 16S-based approaches using a set of longitudinal subgingival plaque samples. The ISR strategy resulted in a 5.2-fold increase in community resolution compared to reference-based 16S rRNA gene analysis and showed 100% accuracy in predicting the correct source of a clinical sample. Individuals' microbial communities were highly personalized, and although they exhibited some drift in membership and levels over time, that difference was always smaller than the differences between any two subjects, even after 1 year. The construction of an ISR database from publicly available genomic sequences allowed us to explore genomic variation within species, resulting in the identification of multiple variants of the ISR for most species.

CONCLUSIONS

The ISR approach resulted in significantly improved resolution of communities and revealed a highly personalized human oral microbiota that was stable over 1 year. Multiple ISR types were observed for all species examined, demonstrating a high level of subspecies variation in the oral microbiota. The approach is high-throughput, high-resolution yet cost-effective, allowing subspecies-level community fingerprinting at a cost comparable to that of 16S rRNA gene amplicon sequencing. It will be useful for a range of applications that require high-resolution identification of organisms, including microbial tracking, community fingerprinting, and potentially for identification of virulence-associated strains.

Dynamic bacterial and fungal microbiomes during sweet sorghum ensiling impact bioethanol production

Significant low-cost biofuel production volumes could be achieved from commercial-scale silage by redirecting lactic acid fermentation to ethanol production. A temporal metagenomic analysis on ensiled sweet sorghum inoculated with an ethanologenic yeast has been conducted to understand the underlying microbial processes during bioethanol production. Individual silage buckets approximating silage piles were prepared with freshly harvested material and supplemented with ethanologenic yeast, sulfuric acid or both. The ensiling progress was assessed using high performance liquid chromatography, microbial taxonomic identification and abundance. The combined treatment with Saccharomyces and acid led to a steady reduction of bacterial abundance and microbial diversity with Lactobacillus becoming the dominant genus during the late timepoints. Furthermore, the addition of acid to inhibit bacterial growth hindered Saccharomyces ability to compete with native yeasts like Candida. Knowledge of the response of the in-situ microbial community to the various treatments during ensiling will help improve current methodologies for bioethanol production.

Exploring the potential of marine microbes in clinical management of Alzheimer's disease: A road map for bioprospecting and identifying promising isolates

Pervasiveness of Alzheimer's disease (AD) across the globe is on rise, devitalizing the essential brain functions of the afflicted individual. Multiple neurological pathways viz., cholinergic, amyloidogenic and tau protein pathways underlying the disease and interdependence make it more complex to develop effective treatment strategies. Existing drug treatments for Alzheimer's disease majorly belong to the class of cholinergic inhibitors which improve the behavioral symptoms. But there are no drugs that could arrest the disease progression. Inhibition of beta secretase enzyme could prevent the deposition of amyloid plaques in the neurons, thereby arresting the disease progression. Search for novel drugs to treat the underlying pathogenesis of the disease is pivotal in this day and age. The source of most active lead molecules discovered recently is from the nature. Marine ecosystem provides a plethora of pharmacologically lead molecules from various living organisms inhabiting the sea. Among all, marine microbes are the most under-explored and indispensable source of many bioactive metabolites. Studies have been reported on potent metabolites from marine microbes which could inhibit the key enzymes involved in the AD pathogenesis. The advancement in microbial bioprospecting and molecular biology techniques have eased the process of cultivation and identification of microbes, isolation of novel bioactive metabolites of clinical use. Exploring such marine natural resources for pharmacological lead molecules could give a breakthrough in the drug discovery domain for treating AD such debilitating diseases. In this review, a comprehensive account of bioprospecting methods and reports of marine microbial isolates are discussed.

One Health Relationships Between Human, Animal, and Environmental Microbiomes: A Mini-Review

The One Health concept stresses the ecological relationships between human, animal, and environmental health. Much of the One Health literature to date has examined the transfer of pathogens from animals (e.g., emerging zoonoses) and the environment to humans. The recent rapid development of technology to perform high throughput DNA sequencing has expanded this view to include the study of entire microbial communities. Applying the One Health approach to the microbiome allows for consideration of both pathogenic and non-pathogenic microbial transfer between humans, animals, and the environment. We review recent research studies of such transmission, the molecular and statistical methods being used, and the implications of such microbiome relationships for human health. Our review identified evidence that the environmental microbiome as well as the microbiome of animals in close contact can affect both the human microbiome and human health outcomes. Such microbiome transfer can take place in the household as well as the workplace setting. Urbanization of built environments leads to changes in the environmental microbiome which could be a factor in human health. While affected by environmental exposures, the human microbiome also can modulate the response to environmental factors through effects on metabolic and immune function. Better understanding of these microbiome interactions between humans, animals, and the shared environment will require continued development of improved statistical and ecological modeling approaches. Such enhanced understanding could lead to innovative interventions to prevent and manage a variety of human health and disease states.

Horizontal gene transfer of three co-inherited methane monooxygenase systems gave rise to methanotrophy in the Proteobacteria

The critical role that bacterial methanotrophs have in regulating the environmental concentrations of the potent greenhouse gas, methane, under aerobic conditions is dependent on monooxygenase enzymes which oxidise the substrate as both a carbon and energy source. Despite the importance of these organisms, the evolutionary origins of aerobic methane oxidation capability and its relationship to proteobacterial evolution is not well understood. Here we investigated the phylogenetic relationship of proteobacterial methanotrophs with related, non-methanotrophic bacteria using 16S rRNA and the evolution of two forms of methane monooxygenase: membrane bound (pMMO and pXMO) and cytoplasmic (sMMO). Through analysis we have concluded that extant proteobacterial methanotrophs evolved from up to five ancestral species, and that all three methane monooxygenase systems, pMMO, pXMO and sMMO, were likely present in the ancestral species (although pXMO and sMMO are not present in most of the present day methanotrophs). Here we propose that the three monooxygenase systems entered the ancestral species by horizontal gene transfer, with these likely to have pre-existing physiological and metabolic attributes that supported conversion to methanotrophy. Further, we suggest that prior to these enzyme systems developing methane oxidation capabilities, the membrane-bound and cytoplasmic monooxygenases were already both functionally and phylogenetically associated. These results not only suggest that sMMO and pXMO have a far greater role in methanotrophic evolution than previously understood but also implies that the co-inheritance of membrane bound and cytoplasmic monooxygenases have roles additional to that of supporting methanotrophy.

Potentially toxic elements to maize in agricultural soils-microbial approach of rhizospheric and bulk soils and phytoaccumulation

Maize fields near Mae Tao Creek in Pha Te Village, Tak Province, Thailand are contaminated with Zn, Cd, and Pb. This research studied the interaction between levels of the metals contaminating the soil and maize development, heavy metal accumulation in the seeds, and the soil bacterial community structure. Our field experiment was carried out in five plots with metal contents that gradually decreased from a high level near the creek to a lower level further into the land: Zn 380-4883 mg kg^-1, Cd 6-85 mg kg^-1, and Pb 34-154 mg kg^-1. Cultivation and isolation on nutrient agar (NA) was utilized to study the culturable bacterial community, and polymerase chain reaction denaturing gradient gel electrophoresis (PCR-DGGE) was utilized for the unculturable bacterial communities. All statistical analyses clearly indicated that rainfall and irrigation were the main factors affecting total Zn concentration and bioavailable Zn, Cd, and Pb in the field. The variation in the contents of the heavy metals was weakly correlated with the culturable bacterial community indices (Shannon-Wiener, evenness and richness), but the contents resulted in a difference in the overall diversity of the bacteria in the soil. The richness, numbers of culturable rhizobacteria, and maize growth stage significantly affected the amount of Zn and Cd that accumulated in the roots. In addition, maize accumulated a high level of Zn in the seeds, while the low contents of Cd and Pb in the seeds were below our limit of detection. The results obtained could be informative for the management of maize cultivation in the area.

Differential Modulation of Endophytic Microbiome of Ginger in the Presence of Beneficial Organisms, Pathogens and Both as Identified by DGGE Analysis

Endophytic microorganisms play a significant role in plants response to beneficial organisms and pathogens. In the current study, endophytic microorganisms from Zingiber officinale were screened for in vitro inhibition against Pythium myriotylum. From this, Burkholderia vietnamiensis ZoB74 was selected as an organism with remarkable antifungal effect. Further, the study focussed on analysis of in vivo changes in endophytic bacterial community of Z. officinale in presence of selected organisms and the pathogen P. myriotylum by PCR-DGGE. 16S rDNA sequencing of bacterial community after DGGE has resulted in the identification of a group of uncultured bacteria as the predominant microbial community of rhizome under various conditions of treatment. High frequency dominance of these endophytic bacteria suggests their role in disease resistance to soft rot in ginger. This also revealed the variation of endophytic microbiome of Z. officinale under biotic stress. Hence the study provides molecular insight into uncultured microbiome and its stress-inducible variation in ginger rhizome.

Oropharyngeal microbiome evaluation highlights Neisseria abundance in active celiac patients

We previously profiled duodenal microbiome in active (a-), gluten-free diet (GFD) celiac disease (CD) patients and controls finding higher levels of the Proteobacterium Neisseria flavescens in a-CD patients than in the other two groups. Here, we investigate the oropharyngeal microbiome in CD patients and controls to evaluate whether this niche share microbial composition with the duodenum. We characterized by 16S rRNA gene sequencing the oropharyngeal microbiome in 14 a-CD, 22 GFD patients and 20 controls. Bacteroidetes, Proteobacteria and Firmicutes differed significantly between the three groups. In particular, Proteobacteria abounded in a-CD and Neisseria species mostly accounted for this abundance (p < 0.001), whereas Bacteroidetes were more present in control and GFD microbiomes. Culture-based oropharyngeal microbiota analysis confirmed the greater abundance of Proteobacteria and of Neisseria species in a-CD. Microbial functions prediction indicated a greater metabolic potential for degradation of aminoacids, lipids and ketone bodies in a-CD microbiome than in control and GFD microbiomes, in which polysaccharide metabolism predominated. Our results suggest a continuum of a-CD microbial composition from mouth to duodenum. We may speculate that microbiome characterization in the oropharynx, which is a less invasive sampling than the duodenum, could contribute to investigate the role of dysbiosis in CD pathogenesis.

Characterized non-transient microbiota from stinkbug (Nezara viridula) midgut deactivates soybean chemical defenses

The Southern green stinkbug (N. viridula) feeds on developing soybean seeds in spite of their strong defenses against herbivory, making this pest one of the most harmful to soybean crops. To test the hypothesis that midgut bacterial community allows stinkbugs to tolerate chemical defenses of soybean developing seeds, we identified and characterized midgut microbiota of stinkbugs collected from soybean crops, different secondary plant hosts or insects at diapause on Eucalyptus trees. Our study demonstrated that while more than 54% of N. viridula adults collected in the field had no detectable bacteria in the V1-V3 midgut ventricles, the guts of the rest of stinkbugs were colonized by non-transient microbiota (NTM) and transient microbiota not present in stinkbugs at diapause. While transient microbiota Bacillus sp., Micrococcus sp., Streptomyces sp., Staphylococcus sp. and others had low abundance, NTM microbiota was represented by Yokenella sp., Pantoea sp. and Enterococcus sp. isolates. We found some isolates that showed in vitro β-glucosidase and raffinase activities plus the ability to degrade isoflavonoids and deactivate soybean protease inhibitors. Our results suggest that the stinkbugs´ NTM microbiota may impact on nutrition, detoxification and deactivation of chemical defenses, and Enterococcus sp., Yokenella sp. and Pantoea sp. strains might help stinkbugs to feed on soybean developing seeds in spite of its chemical defenses.

Deep learning models for bacteria taxonomic classification of metagenomic data

Background

An open challenge in translational bioinformatics is the analysis of sequenced metagenomes from various environmental samples. Of course, several studies demonstrated the 16S ribosomal RNA could be considered as a barcode for bacteria classification at the genus level, but till now it is hard to identify the correct composition of metagenomic data from RNA-seq short-read data. 16S short-read data are generated using two next generation sequencing technologies, i.e. whole genome shotgun (WGS) and amplicon (AMP); typically, the former is filtered to obtain short-reads belonging to a 16S shotgun (SG), whereas the latter take into account only some specific 16S hypervariable regions. The above mentioned two sequencing technologies, SG and AMP, are used alternatively, for this reason in this work we propose a deep learning approach for taxonomic classification of metagenomic data, that can be employed for both of them.

Results

To test the proposed pipeline, we simulated both SG and AMP short-reads, from 1000 16S full-length sequences. Then, we adopted a k-mer representation to map sequences as vectors into a numerical space. Finally, we trained two different deep learning architecture, i.e., convolutional neural network (CNN) and deep belief network (DBN), obtaining a trained model for each taxon. We tested our proposed methodology to find the best parameters configuration, and we compared our results against the classification performances provided by a reference classifier for bacteria identification, known as RDP classifier. We outperformed the RDP classifier at each taxonomic level with both architectures. For instance, at the genus level, both CNN and DBN reached 91.3% of accuracy with AMP short-reads, whereas RDP classifier obtained 83.8% with the same data.

Conclusions

In this work, we proposed a 16S short-read sequences classification technique based on k-mer representation and deep learning architecture, in which each taxon (from phylum to genus) generates a classification model. Experimental results confirm the proposed pipeline as a valid approach for classifying bacteria sequences; for this reason, our approach could be integrated into the most common tools for metagenomic analysis. According to obtained results, it can be successfully used for classifying both SG and AMP data.

Electronic supplementary material

The online version of this article (10.1186/s12859-018-2182-6) contains supplementary material, which is available to authorized users.

Bacterial microbiota assemblage in Aedes albopictus mosquitoes and its impacts on larval development

Interactions between bacterial microbiota and mosquitoes play an important role in mosquitoes' capacity to transmit pathogens. However, microbiota assemblages within mosquitoes and the impact of microbiota in environments on mosquito development and survival remain unclear. This study examined microbiota assemblages and the effects of aquatic environment microbiota on the larval development of the Aedes albopictus mosquito, an important dengue virus vector. Life table studies have found that reducing bacterial load in natural aquatic habitats through water filtering and treatment with antibiotics significantly reduced the larva-to-adult emergence rate. This finding was consistent in two types of larval habitats examined-discarded tires and flowerpots, suggesting that bacteria play a crucial role in larval development. Pyrosequencing of the bacterial 16S rRNA gene was used to determine the diversity of bacterial communities in larval habitats and the resulting numbers of mosquitoes under both laboratory and field conditions. The microbiota profiling identified common shared bacteria among samples from different years; further studies are needed to determine whether these bacteria represent a core microbiota. The highest microbiota diversity was found in aquatic habitats, followed by mosquito larvae, and the lowest in adult mosquitoes. Mosquito larvae ingested their bacterial microbiota and nutrients from aquatic habitats of high microbiota diversity. Taken together, the results support the observation that Ae. albopictus larvae are able to utilize diverse bacteria from aquatic habitats and that live bacteria from aquatic habitats play an important role in larval mosquito development and survival. These findings provide new insights into bacteria's role in mosquito larval ecology.

The gut microbiome of nonhuman primates: Lessons in ecology and evolution

The mammalian gastrointestinal (GI) tract is home to trillions of bacteria that play a substantial role in host metabolism and immunity. While progress has been made in understanding the role that microbial communities play in human health and disease, much less attention has been given to host-associated microbiomes in nonhuman primates (NHPs). Here we review past and current research exploring the gut microbiome of NHPs. First, we summarize methods for characterization of the NHP gut microbiome. Then we discuss variation in gut microbiome composition and function across different NHP taxa. Finally, we highlight how studying the gut microbiome offers new insights into primate nutrition, physiology, and immune system function, as well as enhances our understanding of primate ecology and evolution. Microbiome approaches are useful tools for studying relevant issues in primate ecology. Further study of the gut microbiome of NHPs will offer new insight into primate ecology and evolution as well as human health.

The role of the gut microbiota on animal model reproducibility

The gut microbiota is composed of approximately 1010-1014 cells, including fungi, bacteria, archaea, protozoa, viruses, and bacteriophages; their genes and their various metabolites were found throughout the gastrointestinal tract. It has co-evolved with each species to assist with day to day bodily functions, such as digestion, metabolism of xenobiotics, development of mucosal immunity and immunomodulation, and protection against invading pathogens. Because of the significant beneficial impact that gut microbiota may have, there is interest in learning more about it and translating these findings into clinical therapies. Results from recent studies characterizing the gut microbiota of various species have demonstrated the range of influences that may affect gut microbiota diversity, including animal strain, obesity, types of enrichment used, bedding and housing methods, treatment with antimicrobials, vendor source, specific animal housing, diet, and intercurrent disease. Relatively little is known about the functional consequences of alterations of the gut microbiota and exactly how changes in richness and diversity of the microbiota translate into changes in health and susceptibility to disease. Furthermore, questions have been raised as to whether germ-free or even ultraclean, barrier-raised mice are relevant models of human disease, given their significantly reduced gut microbiota diversity and complexity compared with conventionally housed mice. In addition, evidence suggests that the specific anatomical location selected for assessing the gut microbiota has a highly significant effect on study outcomes, in that bacterial phyla change significantly along the gastrointestinal tract. This paper will explore animal model reproducibility in light of this information about the gut microbiota.

Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants

The production of biogas by anaerobic digestion (AD) of agricultural residues, organic wastes, animal excrements, municipal sludge, and energy crops has a firm place in sustainable energy production and bio-economy strategies. Focusing on the microbial community involved in biomass conversion offers the opportunity to control and engineer the biogas process with the objective to optimize its efficiency. Taxonomic profiling of biogas producing communities by means of high-throughput 16S rRNA gene amplicon sequencing provided high-resolution insights into bacterial and archaeal structures of AD assemblages and their linkages to fed substrates and process parameters. Commonly, the bacterial phyla Firmicutes and Bacteroidetes appeared to dominate biogas communities in varying abundances depending on the apparent process conditions. Regarding the community of methanogenic Archaea, their diversity was mainly affected by the nature and composition of the substrates, availability of nutrients and ammonium/ammonia contents, but not by the temperature. It also appeared that a high proportion of 16S rRNA sequences can only be classified on higher taxonomic ranks indicating that many community members and their participation in AD within functional networks are still unknown. Although cultivation-based approaches to isolate microorganisms from biogas fermentation samples yielded hundreds of novel species and strains, this approach intrinsically is limited to the cultivable fraction of the community. To obtain genome sequence information of non-cultivable biogas community members, metagenome sequencing including assembly and binning strategies was highly valuable. Corresponding research has led to the compilation of hundreds of metagenome-assembled genomes (MAGs) frequently representing novel taxa whose metabolism and lifestyle could be reconstructed based on nucleotide sequence information. In contrast to metagenome analyses revealing the genetic potential of microbial communities, metatranscriptome sequencing provided insights into the metabolically active community. Taking advantage of genome sequence information, transcriptional activities were evaluated considering the microorganism's genetic background. Metaproteome studies uncovered enzyme profiles expressed by biogas community members. Enzymes involved in cellulose and hemicellulose decomposition and utilization of other complex biopolymers were identified. Future studies on biogas functional microbial networks will increasingly involve integrated multi-omics analyses evaluating metagenome, transcriptome, proteome, and metabolome datasets.

Bacterial Communities Changes during Food Waste Spoilage

Food waste is an important component of municipal solid waste worldwide. There are various ways to treat or utilize food waste, such as, biogas fermentation, animal feed, etc. but pathogens and mycotoxins that accumulate in the process of spoilage can present a health hazard. However, spoilage of food waste has not yet been studied, and there are no reports of the bacterial communities present in this waste. In this research, food waste was collected and placed at two different temperatures. We investigated the spoilage microbiota by using culture-independent methods and measured the possible mycotoxins may appear in the spoilage process. The results showed that lactic acid bacteria are the most important bacteria in the food waste community, regardless of the temperature. Few microbial pathogens and aflatoxins were found in the spoilage process. This suggests that if food waste is stored at a relatively low temperature and for a short duration, there will be less risk for utilization.

Metagenomic Approaches to Investigate the Contribution of the Vineyard Environment to the Quality of Wine Fermentation: Potentials and Difficulties

The winemaking is a complex process that begins in the vineyard and ends at consumption moment. Recent reports have shown the relevance of microbial populations in the definition of the regional organoleptic and sensory characteristics of a wine. Metagenomic approaches, allowing the exhaustive identification of microorganisms present in complex samples, have recently played a fundamental role in the dissection of the contribution of the vineyard environment to wine fermentation. Systematic approaches have explored the impact of agronomical techniques, vineyard topologies, and climatic changes on bacterial and fungal populations found in the vineyard and in fermentations, also trying to predict or extrapolate the effects on the sensorial characteristics of the resulting wine. This review is aimed at highlighting the major technical and experimental challenges in dissecting the contribution of the vineyard and native environments microbiota to the wine fermentation process, and how metagenomic approaches can help in understanding microbial fluxes and selections across the environments and specimens related to wine fermentation.

Metagenome, metatranscriptome, and metaproteome approaches unraveled compositions and functional relationships of microbial communities residing in biogas plants

The production of biogas by anaerobic digestion (AD) of agricultural residues, organic wastes, animal excrements, municipal sludge, and energy crops has a firm place in sustainable energy production and bio-economy strategies. Focusing on the microbial community involved in biomass conversion offers the opportunity to control and engineer the biogas process with the objective to optimize its efficiency. Taxonomic profiling of biogas producing communities by means of high-throughput 16S rRNA gene amplicon sequencing provided high-resolution insights into bacterial and archaeal structures of AD assemblages and their linkages to fed substrates and process parameters. Commonly, the bacterial phyla Firmicutes and Bacteroidetes appeared to dominate biogas communities in varying abundances depending on the apparent process conditions. Regarding the community of methanogenic Archaea, their diversity was mainly affected by the nature and composition of the substrates, availability of nutrients and ammonium/ammonia contents, but not by the temperature. It also appeared that a high proportion of 16S rRNA sequences can only be classified on higher taxonomic ranks indicating that many community members and their participation in AD within functional networks are still unknown. Although cultivation-based approaches to isolate microorganisms from biogas fermentation samples yielded hundreds of novel species and strains, this approach intrinsically is limited to the cultivable fraction of the community. To obtain genome sequence information of non-cultivable biogas community members, metagenome sequencing including assembly and binning strategies was highly valuable. Corresponding research has led to the compilation of hundreds of metagenome-assembled genomes (MAGs) frequently representing novel taxa whose metabolism and lifestyle could be reconstructed based on nucleotide sequence information. In contrast to metagenome analyses revealing the genetic potential of microbial communities, metatranscriptome sequencing provided insights into the metabolically active community. Taking advantage of genome sequence information, transcriptional activities were evaluated considering the microorganism's genetic background. Metaproteome studies uncovered enzyme profiles expressed by biogas community members. Enzymes involved in cellulose and hemicellulose decomposition and utilization of other complex biopolymers were identified. Future studies on biogas functional microbial networks will increasingly involve integrated multi-omics analyses evaluating metagenome, transcriptome, proteome, and metabolome datasets.