Adjusting microbiome profiles for differences in microbial load by spike-in bacteria

Multi-factor analysis of the quality of cellar mud of Luzhou-flavor liquor in Yibin production area

The aim of this study was to conduct a thorough scientific investigation into the similarities and differences in the quality of the cellars of different Luzhou-flavor liquor wineries in Yibin production area and the reasons for them. This study analyzed cellar mud samples from five wineries in Yibin production area. The analysis of volatile flavor compounds was carried out using headspace solid-phase microextraction and gas chromatography-mass spectrometry. The bacterial and archaeal community structures and their correlations were analyzed by high-throughput sequencing. The study indicates that the Distillery A had the highest levels of ammonium nitrogen and effective phosphorus, Distillery F had the highest humus levels, and Distillery I had the highest pH levels. The community structure of the principal bacterial and archaeal communities in the five subterranean clays exhibited similarity, and all samples were dominated by Firmicutes as the primary bacterial group. However, there was variation in bacterial abundance. The cellar mud also has obvious regional differences, and there are three genera of differentially dominant archaea in the archaea. In summary, significant differences were observed in the physicochemical indexes of bacterial and archaeal abundance across all five samples. These differences led to variations in both the content and composition of volatile constituents.

Harnessing human microbiomes for disease prediction

The human microbiome has been increasingly recognized as having potential use for disease prediction. Predicting the risk, progression, and severity of diseases holds promise to transform clinical practice, empower patient decisions, and reduce the burden of various common diseases, as has been demonstrated for cardiovascular disease or breast cancer. Combining multiple modifiable and non-modifiable risk factors, including high-dimensional genomic data, has been traditionally favored, but few studies have incorporated the human microbiome into models for predicting the prospective risk of disease. Here, we review research into the use of the human microbiome for disease prediction with a particular focus on prospective studies as well as the modulation and engineering of the microbiome as a therapeutic strategy.

Multiple Sclerosis and Clostridium perfringens Epsilon Toxin: Is There a Relationship?

Recent research has suggested a link between multiple sclerosis and the gut microbiota. This prospective pilot study aimed to investigate the composition of the gut microbiota in MS patients, the presence of Clostridium perfringens epsilon toxin in the serum of MS patients, and the influence of disease-modifying drugs (DMDs) on epsilon toxin levels and on the microbiota. Epsilon toxin levels in blood were investigated by two methods, a qualitative ELISA and a highly sensitive quantitative ELISA. Neither epsilon toxin nor antibodies against it were detected in the analyzed serum samples. 16S ribosomal RNA sequencing was applied to obtain insights into the composition of the gut microbiota of MS patients. No significant differences in the quantity, diversity, and the relative abundance of fecal microbiota were observed in the gut microbiota of MS patients receiving various DMDs, including teriflunomide, natalizumab, ocrelizumab, and fingolimod, or no therapy. The present study did not provide evidence supporting the hypothesis of a causal relationship between Clostridium perfringens epsilon toxin and multiple sclerosis.

A new spike-in-based method for quantitative metabarcoding of soil fungi and bacteria

Metabarcoding is a powerful tool to characterize biodiversity in biological samples. The interpretation of taxonomic profiles from metabarcoding data has been hindered by their compositional nature. Several strategies have been proposed to transform compositional data into quantitative data, but they have intrinsic limitations. Here, I propose a workflow based on bacterial and fungal cellular internal standards (spike-ins) for absolute quantification of the microbiota in soil samples. These standards were added to the samples before DNA extraction in amounts estimated after qPCRs, to target around 1-2% coverage in the sequencing run. In bacteria, proportions of spike-in reads in the sequencing run were very similar (< 2-fold change) to those predicted by the qPCR assessment, and for fungi they differed up to 40-fold. The low variation among replicates highlights the reproducibility of the method. Estimates based on multiple bacterial spike-ins were highly correlated (r = 0.99). Procrustes analysis evidenced significant biological effects on the community composition when normalizing compositional data. A protocol based on qPCR estimation of input amounts of cellular spikes is proposed as a cheap and reliable strategy for quantitative metabarcoding of biological samples.

Quantitative analysis of the effects of essential oil mouthrinses on clinical plaque microbiome: a parallel-group, randomized trial

BACKGROUND

The rich diversity of microorganisms in the oral cavity plays an important role in the maintenance of oral health and development of detrimental oral health conditions. Beyond commonly used qualitative microbiome metrics, such as relative proportions or diversity, both the species-level identification and quantification of bacteria are key to understanding clinical disease associations. This study reports the first-time application of an absolute quantitative microbiome analysis using spiked DNA standards and shotgun metagenome sequencing to assess the efficacy and safety of product intervention on dental plaque microbiome.

METHODS

In this parallel-group, randomized clinical trial, essential oil mouthrinses, including LISTERINE® Cool Mint Antiseptic (LCM), an alcohol-containing prototype mouthrinse (ACPM), and an alcohol-free prototype mouthrinse (AFPM), were compared against a hydroalcohol control rinse on clinical parameters and the oral microbiome of subjects with moderate gingivitis. To enable a sensitive and clinically meaningful measure of bacterial abundances, species were categorized according to their associations with oral conditions based on published literature and quantified using known amounts of spiked DNA standards.

RESULTS

Multivariate analysis showed that both LCM and ACPM shifted the dysbiotic microbiome composition of subjects with gingivitis to a healthier state after 4 weeks of twice-daily use, resembling the composition of subjects with clinically healthy oral conditions recruited for observational reference comparison at baseline. The essential oil-containing mouthrinses evaluated in this study showed statistically significant reductions in clinical gingivitis and plaque measurements when compared to the hydroalcohol control rinse after 6 weeks of use.

CONCLUSIONS

By establishing a novel quantitative method for microbiome analysis, this study sheds light on the mechanisms of LCM mouthrinse efficacy on oral microbial ecology, demonstrating that repeated usage non-selectively resets a gingivitis-like oral microbiome toward that of a healthy oral cavity.

TRIAL REGISTRATION

The trial was registered on ClinicalTrials.gov on 10/06/2021. The registration number is NCT04921371.

Prevotella copri and microbiota members mediate the beneficial effects of a therapeutic food for malnutrition

Microbiota-directed complementary food (MDCF) formulations have been designed to repair the gut communities of malnourished children. A randomized controlled trial demonstrated that one formulation, MDCF-2, improved weight gain in malnourished Bangladeshi children compared to a more calorically dense standard nutritional intervention. Metagenome-assembled genomes from study participants revealed a correlation between ponderal growth and expression of MDCF-2 glycan utilization pathways by Prevotella copri strains. To test this correlation, here we use gnotobiotic mice colonized with defined consortia of age- and ponderal growth-associated gut bacterial strains, with or without P. copri isolates closely matching the metagenome-assembled genomes. Combining gut metagenomics and metatranscriptomics with host single-nucleus RNA sequencing and gut metabolomic analyses, we identify a key role of P. copri in metabolizing MDCF-2 glycans and uncover its interactions with other microbes including Bifidobacterium infantis. P. copri-containing consortia mediated weight gain and modulated energy metabolism within intestinal epithelial cells. Our results reveal structure-function relationships between MDCF-2 and members of the gut microbiota of malnourished children with potential implications for future therapies.

Strain specific differences in vitamin D3 response: impact on gut homeostasis

Vitamin D3 regulates a variety of biological processes irrespective of its well-known importance for calcium metabolism. Epidemiological and animal studies indicate a role in immune regulation, intestinal barrier function and microbiome diversity. Here, we analyzed the impact of different vitamin D3- containing diets on C57BL/6 and BALB/c mice, with a particular focus on gut homeostasis and also investigated effects on immune cells in vitro. Weak regulatory effects were detected on murine T cells. By trend, the active vitamin D3 metabolite 1,25-dihydroxyvitamin D3 suppressed IFN, GM-CSF and IL-10 cytokine secretion in T cells of C57BL/6 but not BALB/c mice, respectively. Using different vitamin D3-fortified diets, we found a tissue-specific enrichment of mainly CD11b+ myeloid cells but not T cells in both mouse strains e.g. in spleen and Peyer's Patches. Mucin Reg3γ and Batf expression, as well as important proteins for gut homeostasis, were significantly suppressed in the small intestine of C57BL76 but not BALB/c mice fed with a high-vitamin D3 containing diet. Differences between both mouse stains were not completely explained by differences in vitamin D3 receptor expression which was strongly expressed in epithelial cells of both strains. Finally, we analyzed gut microbiome and again an impact of vitamin D3 was detected in C57BL76 but not BALB/c. Our data suggest strain-specific differences in vitamin D3 responsiveness under steady state conditions which may have important implications when choosing a murine disease model to study vitamin D3 effects.

A tripartite bacterial-fungal-plant symbiosis in the mycorrhiza-shaped microbiome drives plant growth and mycorrhization

BACKGROUND

Plant microbiomes play crucial roles in nutrient cycling and plant growth, and are shaped by a complex interplay between plants, microbes, and the environment. The role of bacteria as mediators of the 400-million-year-old partnership between the majority of land plants and, arbuscular mycorrhizal (AM) fungi is still poorly understood. Here, we test whether AM hyphae-associated bacteria influence the success of the AM symbiosis.

RESULTS

Using partitioned microcosms containing field soil, we discovered that AM hyphae and roots selectively assemble their own microbiome from the surrounding soil. In two independent experiments, we identified several bacterial genera, including Devosia, that are consistently enriched on AM hyphae. Subsequently, we isolated 144 pure bacterial isolates from a mycorrhiza-rich sample of extraradical hyphae and isolated Devosia sp. ZB163 as root and hyphal colonizer. We show that this AM-associated bacterium synergistically acts with mycorrhiza on the plant root to strongly promote plant growth, nitrogen uptake, and mycorrhization.

CONCLUSIONS

Our results highlight that AM fungi do not function in  isolation and that the plant-mycorrhiza symbiont can recruit beneficial bacteria that support the symbiosis. Video Abstract.

Bacteria and bacteriophage consortia are associated with protective intestinal metabolites in patients receiving stem cell transplantation

The microbiome is a predictor of clinical outcome in patients receiving allogeneic hematopoietic stem cell transplantation (allo-SCT). Microbiota-derived metabolites can modulate these outcomes. How bacteria, fungi and viruses contribute to the production of intestinal metabolites is still unclear. We combined amplicon sequencing, viral metagenomics and targeted metabolomics from stool samples of patients receiving allo-SCT (n = 78) and uncovered a microbiome signature of Lachnospiraceae and Oscillospiraceae and their associated bacteriophages, correlating with the production of immunomodulatory metabolites (IMMs). Moreover, we established the IMM risk index (IMM-RI), which was associated with improved survival and reduced relapse. A high abundance of short-chain fatty acid-biosynthesis pathways, specifically butyric acid via butyryl-coenzyme A (CoA):acetate CoA-transferase (BCoAT, which catalyzes EC 2.8.3.8) was detected in IMM-RI low-risk patients, and virome genome assembly identified two bacteriophages encoding BCoAT as an auxiliary metabolic gene. In conclusion, our study identifies a microbiome signature associated with protective IMMs and provides a rationale for considering metabolite-producing consortia and metabolite formulations as microbiome-based therapies.

A metagenomics method for the quantitative detection of bacterial pathogens causing hospital-associated and ventilator-associated pneumonia

IMPORTANCE

The management of ventilator-associated pneumonia and hospital-acquired pneumonia requires rapid and accurate quantitative detection of the infecting pathogen. To this end, we propose a metagenomic sequencing assay that includes the use of an internal sample processing control for the quantitative detection of 20 relevant bacterial species from bronchoalveolar lavage samples.

A quantitative sequencing method using synthetic internal standards including functional and phylogenetic marker genes

The method of spiking synthetic internal standard genes (ISGs) to samples for amplicon sequencing, generating sequences and converting absolute gene numbers from read counts has been used only for phylogenetic markers and has not been applied to functional markers. In this study, we developed ISGs, including gene sequences of the 16S rRNA, pmoA, encoding a subunit of particulate methane monooxygenase and amoA, encoding a subunit of ammonia monooxygenase. We added ISGs to the samples, amplified the target genes and performed amplicon sequencing. For the mock community, the copy numbers converted from read counts using ISGs were equivalent to those obtained by the quantitative real-time polymerase chain reaction (4.0 × 104 versus 4.1 × 104 and 3.0 × 103 versus 4.0 × 103 copies μL-DNA-1 for 16S rRNA and pmoA genes, respectively), but we also identified underestimation, possibly due to primer coverage (7.8 × 102 versus 3.7 × 103  μL-DNA-1 for amoA gene). We then applied this method to environmental samples and analysed phylogeny, functional diversity and absolute quantities. One Methylocystis population was most abundant in the sludge samples [16S rRNA gene (3.8 × 109 copies g-1 ) and the pmoA gene (2.3 × 109 copies g-1 )] and were potentially interrelated. This study demonstrates that ISG spiking is useful for evaluating sequencing data processing and quantifying functional markers.

Restrictive Versus Permissive Use of Broad-spectrum Antibiotics in Patients Receiving Allogeneic Stem Cell Transplantation and With Early Fever Due to Cytokine Release Syndrome: Evidence for Beneficial Microbiota Protection Without Increase in Infectious Complications

BACKGROUND

Intestinal microbiome contributes to the pathophysiology of acute gastrointestinal (GI) graft-versus-host disease (GvHD) and loss of microbiome diversity influences the outcome of patients after allogeneic stem cell transplantation (SCT). Systemic broad-spectrum antibiotics have been identified as a major cause of early intestinal dysbiosis.

METHODS

In 2017, our transplant unit at the university hospital in Regensburg changed the antibiotic strategy from a permissive way with initiation of antibiotics in all patients with neutropenic fever independent of the underlying cause and risk to a restrictive use in cases with high likelihood of cytokine release syndrome (eg, after anti-thymocyte globulin [ATG] therapy). We analyzed clinical data and microbiome parameters obtained 7 days after allogeneic SCT from 188 patients with ATG therapy transplanted in 2015/2016 (permissive cohort, n = 101) and 2918/2019 (restrictive cohort, n = 87).

RESULTS

Restrictive antibiotic treatment postponed the beginning of antibiotic administration from 1.4 ± 7.6 days prior to 1.7 ± 5.5 days after SCT (P = .01) and significantly reduced the duration of antibiotic administration by 5.8 days (P < .001) without increase in infectious complications. Furthermore, we observed beneficial effects of the restrictive strategy compared with the permissive way on microbiome diversity (urinary 3-indoxylsulfate, P = .01; Shannon and Simpson indices, P < .001) and species abundance 7 days post-transplant as well as a positive trend toward a reduced incidence of severe GI GvHD (P = .1).

CONCLUSIONS

Our data indicate that microbiota protection can be achieved by a more careful selection of neutropenic patients qualifying for antibiotic treatment during allogeneic SCT without increased risk of infectious complications.

Quantitative Amplicon Sequencing Is Necessary to Identify Differential Taxa and Correlated Taxa Where Population Sizes Differ

High-throughput, multiplexed-amplicon sequencing has become a core tool for understanding environmental microbiomes. As researchers have widely adopted sequencing, many open-source analysis pipelines have been developed to compare microbiomes using compositional analysis frameworks. However, there is increasing evidence that compositional analyses do not provide the information necessary to accurately interpret many community assembly processes. This is especially true when there are large gradients that drive distinct community assembly processes. Recently, sequencing has been combined with Q-PCR (among other sources of total quantitation) to generate "Quantitative Sequencing" (QSeq) data. QSeq more accurately estimates the true abundance of taxa, is a more reliable basis for inferring correlation, and, ultimately, can be more reliably related to environmental data to infer community assembly processes. In this paper, we use a combination of published data sets, synthesis, and empirical modeling to offer guidance for which contexts QSeq is advantageous. As little as 5% variation in total abundance among experimental groups resulted in more accurate inference by QSeq than compositional methods. Compositional methods for differential abundance and correlation unreliably detected patterns in abundance and covariance when there was greater than 20% variation in total abundance among experimental groups. Whether QSeq performs better for beta diversity analysis depends on the question being asked, and the analytic strategy (e.g., what distance metric is being used); for many questions and methods, QSeq and compositional analysis are equivalent for beta diversity analysis. QSeq is especially useful for taxon-specific analysis; QSeq transformation and analysis should be the default for answering taxon-specific questions of amplicon sequence data. Publicly available bioinformatics pipelines should incorporate support for QSeq transformation and analysis.

Depth-Dependent Distribution of Prokaryotes in Sediments of the Manganese Crust on Nazimov Guyots of the Magellan Seamounts

Deep ocean polymetallic nodules, rich in cobalt, nickel, and titanium which are commonly used in high-technology and biotechnology applications, are being eyed for green energy transition through deep-sea mining operations. Prokaryotic communities underneath polymetallic nodules could participate in deep-sea biogeochemical cycling, however, are not fully described. To address this gap, we collected sediment cores from Nazimov guyots, where polymetallic nodules exist, to explore the diversity and vertical distribution of prokaryotic communities. Our 16S rRNA amplicon sequencing data, quantitative PCR results, and phylogenetic beta diversity indices showed that prokaryotic diversity in the surficial layers (0-8 cm) was > 4-fold higher compared to deeper horizons (8-26 cm), while heterotrophs dominated in all sediment horizons. Proteobacteria was the most abundant taxon (32-82%) across all sediment depths, followed by Thaumarchaeota (4-37%), Firmicutes (2-18%), and Planctomycetes (1-6%). Depth was the key factor controlling prokaryotic distribution, while heavy metals (e.g., iron, copper, nickel, cobalt, zinc) can also influence significantly the downcore distribution of prokaryotic communities. Analyses of phylogenetic diversity showed that deterministic processes governing prokaryotic assembly in surficial layers, contrasting with stochastic influences in deep layers. This was further supported from the detection of a more complex prokaryotic co-occurrence network in the surficial layer which suggested more diverse prokaryotic communities existed in the surface vs. deeper sediments. This study expands current knowledge on the vertical distribution of benthic prokaryotic diversity in deep sea settings underneath polymetallic nodules, and the results reported might set a baseline for future mining decisions.

Overview of data preprocessing for machine learning applications in human microbiome research

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Climate change drives loss of bacterial gut mutualists at the expense of host survival in wild meerkats

Climate change and climate-driven increases in infectious disease threaten wildlife populations globally. Gut microbial responses are predicted to either buffer or exacerbate the negative impacts of these twin pressures on host populations. However, examples that document how gut microbial communities respond to long-term shifts in climate and associated disease risk, and the consequences for host survival, are rare. Over the past two decades, wild meerkats inhabiting the Kalahari have experienced rapidly rising temperatures, which is linked to the spread of tuberculosis (TB). We show that over the same period, the faecal microbiota of this population has become enriched in Bacteroidia and impoverished in lactic acid bacteria (LAB), a group of bacteria including Lactococcus and Lactobacillus that are considered gut mutualists. These shifts occurred within individuals yet were compounded over generations, and were better explained by mean maximum temperatures than mean rainfall over the previous year. Enriched Bacteroidia were additionally associated with TB exposure and disease, the dry season and poorer body condition, factors that were all directly linked to reduced future survival. Lastly, abundances of LAB taxa were independently and positively linked to future survival, while enriched taxa did not predict survival. Together, these results point towards extreme temperatures driving an expansion of a disease-associated pathobiome and loss of beneficial taxa. Our study provides the first evidence from a longitudinally sampled population that climate change is restructuring wildlife gut microbiota, and that these changes may amplify the negative impacts of climate change through the loss of gut mutualists. While the plastic response of host-associated microbiotas is key for host adaptation under normal environmental fluctuations, extreme temperature increases might lead to a breakdown of coevolved host-mutualist relationships.

Inducible CRISPR-targeted "knockdown" of human gut Bacteroides in gnotobiotic mice discloses glycan utilization strategies

Understanding how members of the human gut microbiota prioritize nutrient resources is one component of a larger effort to decipher the mechanisms defining microbial community robustness and resiliency in health and disease. This knowledge is foundational for development of microbiota-directed therapeutics. To model how bacteria prioritize glycans in the gut, germfree mice were colonized with 13 human gut bacterial strains, including seven saccharolytic Bacteroidaceae species. Animals were fed a Western diet supplemented with pea fiber. After community assembly, an inducible CRISPR-based system was used to selectively and temporarily reduce the absolute abundance of Bacteroides thetaiotaomicron or B. cellulosilyticus by 10- to 60-fold. Each knockdown resulted in specific, reproducible increases in the abundances of other Bacteroidaceae and dynamic alterations in their expression of genes involved in glycan utilization. Emergence of these "alternate consumers" was associated with preservation of community saccharolytic activity. Using an inducible system for CRISPR base editing in vitro, we disrupted translation of transporters critical for utilizing dietary polysaccharides in Phocaeicola vulgatus, a B. cellulosilyticus knockdown-responsive taxon. In vitro and in vivo tests of the resulting P. vulgatus mutants allowed us to further characterize mechanisms associated with its increased fitness after knockdown. In principle, the approach described can be applied to study utilization of a range of nutrients and to preclinical efforts designed to develop therapeutic strategies for precision manipulation of microbial communities.

Long-term biodegradation of crude oil in high-arctic backshore sediments: The Baffin Island Oil Spill (BIOS) after nearly four decades

With an on-going disproportional warming of the Arctic Ocean and the reduction of the sea ice cover, the risk of an accidental oil spill from ships or future oil exploration is increasing. It is hence important to know how crude oil weathers in this environment and what factors affect oil biodegradation in the Arctic. However, this topic is currently poorly studied. In the 1980s, the Baffin Island Oil Spill (BIOS) project carried out a series of simulated oil spills in the backshore zone of beaches located on Baffin Island in the Canadian High Arctic. In this study two BIOS sites were re-visited, offering the unique opportunity to study the long-term weathering of crude oil under Arctic conditions. Here we show that residual oil remains present at these sites even after almost four decades since the original oiling. Oil at both BIOS sites appears to have attenuated very slowly with estimated loss rates of 1.8-2.7% per year. The presence of residual oil continues to significantly affect sediment microbial communities at the sites as manifested by a significantly decreased diversity, differences in the abundance of microorganisms and an enrichment of putative oil-degrading bacteria in oiled sediments. Reconstructed genomes of putative oil degraders suggest that only a subset is specifically adapted for growth under psychrothermic conditions, further reducing the time for biodegradation during the already short Arctic summers. Altogether, this study shows that crude oil spilled in the Arctic can persist and significantly affect the Arctic ecosystem for a long time, in the order of several decades.

Uterine Microbiome: Does the Sampling Technique Matter?

Studies have proven the significance of microbial communities in various parts of the human body for health. In recent years it has been discovered that the uterine cavity is not sterile, and endometrium has its own microbiome which appears to have an impact on female fertility and gynecological pathologies. Lactobacillus has shown to dominate the microbial profile in the uterus and is considered an indicator of a healthy uterine environment. Yet, many argue that the Lactobacillus dominance is due to vaginal contamination during the sampling process. To date there is no clearly defined healthy endometrial microbial profile, which is largely due to the fact that determining the microbial community from the endometrium is complicated, and there is currently no consensus on sampling methods for the endometrial microbiome. As a result, this restricts ability to replicate discoveries made in other cohorts. Here we aim to give an overview of the sampling methods used and discuss what impedes the endometrial microbiome studies as well as how to reach a consensus on the study design. This knowledge could be incorporated into the future research and the knowledge on endometrial microbiome could be included into the diagnostics and treatment of female reproductive health.

RSim: A reference-based normalization method via rank similarity

Microbiome sequencing data normalization is crucial for eliminating technical bias and ensuring accurate downstream analysis. However, this process can be challenging due to the high frequency of zero counts in microbiome data. We propose a novel reference-based normalization method called normalization via rank similarity (RSim) that corrects sample-specific biases, even in the presence of many zero counts. Unlike other normalization methods, RSim does not require additional assumptions or treatments for the high prevalence of zero counts. This makes it robust and minimizes potential bias resulting from procedures that address zero counts, such as pseudo-counts. Our numerical experiments demonstrate that RSim reduces false discoveries, improves detection power, and reveals true biological signals in downstream tasks such as PCoA plotting, association analysis, and differential abundance analysis.

Multimodal immune cell phenotyping in GI biopsies reveals microbiome-related T cell modulations in human GvHD

Acute graft-versus-host disease (aGvHD) is a significant complication after allogeneic hematopoietic stem cell transplantation (aHSCT), but major factors determining disease severity are not well defined yet. By combining multiplexed tissue imaging and single-cell RNA sequencing on gastrointestinal biopsies from aHSCT-treated individuals with fecal microbiome analysis, we link high microbiome diversity and the abundance of short-chain fatty acid-producing bacteria to the sustenance of suppressive regulatory T cells (Tregs). Furthermore, aGvHD severity strongly associates with the clonal expansion of mainly CD8 T cells, which we find distributed over anatomically distant regions of the gut, persistent over time, and inversely correlated with the presence of suppressive Tregs. Overall, our study highlights the pathophysiological importance of expanded CD8 T cell clones in the progression of aGvHD toward more severe clinical manifestations and strongly supports the further development of microbiome interventions as GvHD treatment via repopulation of the gut Treg niche to suppress inflammation.

Comparison of DNA extraction methods for 16S rRNA gene sequencing in the analysis of the human gut microbiome

The gut microbiome is widely analyzed using high-throughput sequencing, such as 16S rRNA gene amplicon sequencing and shotgun metagenomic sequencing (SMS). DNA extraction is known to have a large impact on the metagenomic analyses. The aim of this study was to compare DNA extraction protocols for 16S sequencing. In that context, four commonly used DNA extraction methods were compared for the analysis of the gut microbiota. Commercial versions were evaluated against modified protocols using a stool preprocessing device (SPD, bioMérieux) upstream DNA extraction. Stool samples from nine healthy volunteers and nine patients with a Clostridium difficile infection were extracted with all protocols and 16S sequenced. Protocols were ranked using wet- and dry-lab criteria, including quality controls of the extracted genomic DNA, alpha-diversity, accuracy using a mock community of known composition and repeatability across technical replicates. SPD improved overall efficiency of three of the four tested protocols compared with their commercial version, in terms of DNA extraction yield, sample alpha-diversity, and recovery of Gram-positive bacteria. The best overall performance was obtained for the S-DQ protocol, SPD combined with the DNeasy PowerLyser PowerSoil protocol from QIAGEN. Based on this evaluation, we strongly believe that the use of such stool preprocessing device improves both the standardization and the quality of the DNA extraction in the human gut microbiome studies.

EMBED: Essential MicroBiomE Dynamics, a dimensionality reduction approach for longitudinal microbiome studies

Dimensionality reduction offers unique insights into high-dimensional microbiome dynamics by leveraging collective abundance fluctuations of multiple bacteria driven by similar ecological perturbations. However, methods providing lower-dimensional representations of microbiome dynamics both at the community and individual taxa levels are not currently available. To that end, we present EMBED: Essential MicroBiomE Dynamics, a probabilistic nonlinear tensor factorization approach. Like normal mode analysis in structural biophysics, EMBED infers ecological normal modes (ECNs), which represent the unique orthogonal modes capturing the collective behavior of microbial communities. Using multiple real and synthetic datasets, we show that a very small number of ECNs can accurately approximate microbiome dynamics. Inferred ECNs reflect specific ecological behaviors, providing natural templates along which the dynamics of individual bacteria may be partitioned. Moreover, the multi-subject treatment in EMBED systematically identifies subject-specific and universal abundance dynamics that are not detected by traditional approaches. Collectively, these results highlight the utility of EMBED as a versatile dimensionality reduction tool for studies of microbiome dynamics.

A Refined, Controlled 16S rRNA Gene Sequencing Approach Reveals Limited Detection of Cerebrospinal Fluid Microbiota in Children with Bacterial Meningitis

Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. Additionally, these refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads, such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples with low bacterial loads by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. Several bench and computational approaches were taken to address potential sources of error for low bacterial load samples. We compared DNA yields and sequencing results after applying three different DNA extraction approaches to an artificially constructed mock-bacterial community. We also compared two postsequencing computational contaminant removal strategies, decontam R and full contaminant sequence removal. All three extraction techniques followed by decontam R yielded similar results for the mock community. We then applied these methods to 22 CSF samples from children diagnosed with meningitis, which has low bacterial loads relative to other clinical infection samples. The refined 16S HTS pipelines identified the cultured bacterial genus as the dominant organism for only 3 of these samples. We found that all three DNA extraction techniques followed by decontam R generated similar DNA yields for mock communities at the low bacterial loads representative of CSF samples. However, the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches. Although we did not find current DNA-based diagnostics to be useful for pediatric meningitis samples, the utility of these methods for CSF shunt infection remains undefined. Future advances in sample processing methods to minimize or eliminate contamination will be required to improve the sensitivity and specificity of these methods for pediatric meningitis. IMPORTANCE Advances in both laboratory and computational components of high-throughput 16S amplicon sequencing (16S HTS) have markedly increased its sensitivity and specificity. These refinements have better delineated the limits of sensitivity, and contributions of contamination to these limits, for 16S HTS that are particularly relevant for samples with low bacterial loads such as human cerebrospinal fluid (CSF). The objectives of this work were to (i) optimize the performance of 16S HTS in CSF samples by defining and addressing potential sources of error, and (ii) perform refined 16S HTS on CSF samples from children diagnosed with bacterial meningitis and compare results with those from microbiological cultures. We found that the limits of detection imposed by reagent contaminants and methodologic bias precluded the accurate detection of bacteria in CSF from children with culture-confirmed meningitis using these approaches, despite rigorous controls and sophisticated computational approaches.

Genome-wide mapping of gene-microbe interactions in the murine lung microbiota based on quantitative microbial profiling

BACKGROUND

Mammalian lungs comprise a complex microbial ecosystem that interacts with host physiology. Previous research demonstrates that the environment significantly contributes to bacterial community structure in the upper and lower respiratory tract. However, the influence of host genetics on the makeup of lung microbiota remains ambiguous, largely due to technical difficulties related to sampling, as well as challenges inherent to investigating low biomass communities. Thus, innovative approaches are warranted to clarify host-microbe interactions in the mammalian lung.

RESULTS

Here, we aimed to characterize host genomic regions associated with lung bacterial traits in an advanced intercross mouse line (AIL). By performing quantitative microbial profiling (QMP) using the highly precise method of droplet digital PCR (ddPCR), we refined 16S rRNA gene amplicon-based traits to identify and map candidate lung-resident taxa using a QTL mapping approach. In addition, the two abundant core taxa Lactobacillus and Pelomonas were chosen for independent microbial phenotyping using genus-specific primers. In total, this revealed seven significant loci involving eight bacterial traits. The narrow confidence intervals afforded by the AIL population allowed us to identify several promising candidate genes related to immune and inflammatory responses, cell apoptosis, DNA repair, and lung functioning and disease susceptibility. Interestingly, one genomic region associated with Lactobacillus abundance contains the well-known anti-inflammatory cytokine Il10, which we confirmed through the analysis of Il10 knockout mice.

CONCLUSIONS

Our study provides the first evidence for a role of host genetic variation contributing to variation in the lung microbiota. This was in large part made possible through the careful curation of 16S rRNA gene amplicon data and the incorporation of a QMP-based methods. This approach to evaluating the low biomass lung environment opens new avenues for advancing lung microbiome research using animal models.

Mock community as an in situ positive control for amplicon sequencing of microbiotas from the same ecosystem

Metataxonomy has become the standard for characterizing the diversity and composition of microbial communities associated with multicellular organisms and their environment. Currently available protocols for metataxonomy assume a uniform DNA extraction, amplification and sequencing efficiency for all sample types and taxa. It has been suggested that the addition of a mock community (MC) to biological samples before the DNA extraction step could aid identification of technical biases during processing and support direct comparisons of microbiota composition, but the impact of MC on diversity estimates of samples is unknown. Here, large and small aliquots of pulverized bovine fecal samples were extracted with no, low or high doses of MC, characterized using standard Illumina technology for metataxonomics, and analysed with custom bioinformatic pipelines. We demonstrated that sample diversity estimates were distorted only if MC dose was high compared to sample mass (i.e. when MC > 10% of sample reads). We also showed that MC was an informative in situ positive control, permitting an estimation of the sample 16S copy number, and detecting sample outliers. We tested this approach on a range of sample types from a terrestrial ecosystem, including rhizosphere soil, whole invertebrates, and wild vertebrate fecal samples, and discuss possible clinical applications.

The Effects of Phytase and Non-Starch Polysaccharide-Hydrolyzing Enzymes on Trace Element Deposition, Intestinal Morphology, and Cecal Microbiota of Growing-Finishing Pigs

This study investigated the effects of supplementing phytase and non-starch polysaccharide-degrading enzymes (NSPases) to corn-soybean meal-based diet on the growth performance, trace element deposition, and intestinal health of growing-finishing pigs. Fifty pigs were randomly assigned into the control (basal diet), phytase (basal diet + 100 g/t of phytase), β-mannanase (basal diet + 40 g/t of β-mannanase), β-glucanase (basal diet + 100 g/t of β-glucanase), and xylanase (basal diet + 100 g/t of xylanase) groups. The results show that the supplementation of phytase and NSPases had no impacts (p  >  0.05) on the growth performance of pigs. Compared with the control group, pigs fed with xylanase had higher (p < 0.05) Zn concentrations in the ileum and muscle and those fed with phytase had higher (p < 0.05) Zn concentrations in the ileum. Phytase and xylanase supplementation decreased (p < 0.05) fecal Zn concentrations in pigs compared with the control group (p < 0.05). In addition, phytase, β-mannanase, β-glucanase, and xylanase supplementation up-regulated (p < 0.05) the FPN1 expression, whereas xylanase up-regulated (p < 0.05) the Znt1 expression in the duodenum of pigs compared with the control group. Moreover, phytase, β-glucanase, and xylanase supplementation up-regulated (p < 0.05) the jejunal Znt1 expression compared with the control group. The intestinal morphology results show that the phytase, β-mannanase, and xylanase groups had increased villus heights (VHs), an increased villus height-crypt depth ratio (VH:CD), and decreased crypt depths (CDs) in the duodenum, whereas phytase, β-mannanase, β-glucanase, and xylanase groups had decreased VH and VH:CD, and increased CD in the jejunum compared with the control group (p < 0.05). Pigs fed with exogenous enzymes had decreased bacterial diversity in the cecum. The dietary supplementation of NSPases increased the relative abundance of Firmicutes and decreased spirochaetes (p < 0.05). Compared with the control group, dietary NSPase treatment decreased (p < 0.05) the opportunistic pathogens, such as Treponema_2 and Eubacterium_ruminantium. Moreover, the relative abundances of Lachnospiraceae_XPB1014 and Lachnospiraceae were enriched in the β-glucanase and β-mannanase groups (p < 0.05), respectively. In conclusion, phytase and xylanase supplementation may promote zinc deposition in pigs. Additionally, the supplementation of NSPases may improve the gut health of pigs by modulating the intestinal morphology and microbiota.

QMD: A new method to quantify microbial absolute abundance differences between groups

A new method, quantification of microbial absolute abundance differences (QMD), was proposed to estimate the microbial absolute abundance changes of each taxon under different conditions based on the microbial relative abundance. Compared with other methods, QMD displayed greater confidence in understanding microbiome dynamics between groups. We also provide QMD software to investigate common deviations and achieve a better understanding of microbiota changes under different conditions.

Absolute quantification of viable bacteria abundances in food by next-generation sequencing: Quantitative NGS of viable microbes

Next-generation sequencing (NGS) is an important tool for taxonomical bacteria identification. Recent technological developments have led to its improvement and availability. Despite the undeniable advantages of this approach, it has several limitations and shortcomings. The usual outcome of microbiota sequencing is a relative abundance of bacterial taxa. The information about bacteria viability or enumeration is missing. However, this knowledge is crucial for many applications. In the current study, we elaborated the complete workflow for the absolute quantification of living bacteria based on 16S rRNA gene amplicon sequencing. A fluorescent PMAxx reagent penetrating a damaged cell membrane was used to discriminate between the total and viable bacterial population. Bacteria enumeration was estimated by the spike-in technique or qPCR quantification. For method optimization, twenty bacterial species were taken, and the results of the workflow were validated by widely accepted methodologies: flow cytometry, microbiological plating, and viability-qPCR. Despite the minor discrepancy between all methods used, they all showed compatible results. Finally, we tested the workflow with actual food samples and received a good correlation between the methods regarding the estimation of the number of viable bacteria. Overall, the elaborated and integrated NGS approach could be the next step in perceiving a holistic picture of a sample microbiota.

Tracking defined microbial communities by multicolor flow cytometry reveals tradeoffs between productivity and diversity

Cross feeding between microbes is ubiquitous, but its impact on the diversity and productivity of microbial communities is incompletely understood. A reductionist approach using simple microbial communities has the potential to detect cross feeding interactions and their impact on ecosystem properties. However, quantifying abundance of more than two microbes in a community in a high throughput fashion requires rapid, inexpensive assays. Here, we show that multicolor flow cytometry combined with a machine learning-based classifier can rapidly quantify species abundances in simple, synthetic microbial communities. Our approach measures community structure over time and detects the exchange of metabolites in a four-member community of fluorescent Bacteroides species. Notably, we quantified species abundances in co-cultures and detected evidence of cooperation in polysaccharide processing and competition for monosaccharide utilization. We also observed that co-culturing on simple sugars, but not complex sugars, reduced microbial productivity, although less productive communities maintained higher community diversity. In summary, our multicolor flow cytometric approach presents an economical, tractable model system for microbial ecology using well-studied human bacteria. It can be extended to include additional species, evaluate more complex environments, and assay response of communities to a variety of disturbances.

A westernized diet changed the colonic bacterial composition and metabolite concentration in a dextran sulfate sodium pig model for ulcerative colitis

Introduction

Ulcerative colitis (UC) is characterized by chronic inflammation in the colonic epithelium and has a blurred etiology. A western diet and microbial dysbiosis in the colon were reported to play a role in UC development. In this study, we investigated the effect of a westernized diet, i.e., increasing fat and protein content by including ground beef, on the colonic bacterial composition in a dextran sulfate sodium (DexSS) challenged pig study.

Methods

The experiment was carried out in three complete blocks following a 2×2 factorial design including 24 six-week old pigs, fed either a standard diet (CT) or the standard diet substituted with 15% ground beef to simulate a typical westernized diet (WD). Colitis was induced in half of the pigs on each dietary treatment by oral administration of DexSS (DSS and WD+DSS, respectively). Samples from proximal and distal colon and feces were collected.

Results and discussion

Bacterial alpha diversity was unaffected by experimental block, and sample type. In proximal colon, WD group had similar alpha diversity to CT group and the WD+DSS group showed the lowest alpha diversity compared to the other treatment groups. There was a significant interaction between western diet and DexSS for beta diversity, based on Bray-Curtis dissimilarly. The westernized diet and DexSS resulted in three and seven differentially abundant phyla, 21 and 65 species, respectively, mainly associated with the Firmicutes and Bacteroidota phyla followed by Spirochaetota, Desulfobacterota, and Proteobacteria. The concentration of short-chain fatty acids (SCFA) was lowest in the distal colon. Treatment had a slight effect on the estimates for microbial metabolites that might have valuable biological relevance for future studies. The concentration of putrescine in the colon and feces and that of total biogenic amines was highest in the WD+DSS group. We conclude that a westernized diet could be a potential risk factor and an exacerbating agent for UC by reducing the abundance of SCFA-producing bacteria, increasing the abundance of pathogens such as Helicobacter trogontum, and by increasing the concentration of microbial proteolytic-derived metabolites in the colon.

An experimental oil spill at a tidal freshwater wetland along the St. Lawrence River re-visited after 21 years

In 1999, a tidal wetland located along the St. Lawrence River close to Ste. Croix de Lotbinière (Quebec, Eastern Canada) was the site of an experimental oil spill. Test plots were established and subjected to an experimental crude oil spill to evaluate natural attenuation, nutrient amendment and vegetation cropping as countermeasures. In 2020, this study re-visited the test plots to investigate residual oil and habitat recovery. Only concentrations of mid-chain length n-alkanes (C10-C36), but not of polycyclic aromatic hydrocarbons (PAHs), were significantly above detection limit, and were detected in both test plot and control sediments. Hydrocarbon, total organic carbon, nitrogen and phosphate contents did not differ significantly between test plot and control sediments. Microbial analyses did not detect significant differences in microbial load, microbial diversity or microbial community composition between test plot and control sediments. Key genes for the aerobic and anaerobic degradation of n-alkanes as well as for the aerobic degradation of PAHs were detected in all sediment samples. Associated gene abundances did not differ significantly between test plot and control sediments. This study shows that oil-exposed test plot sediments of the Ste. Croix wetland can be considered completely recovered after 21 years irrespective of the performed countermeasure.

Effect of bacterial DNA enrichment on detection and quantification of bacteria in an infected tissue model by metagenomic next-generation sequencing

Before implementing metagenomic next-generation sequencing (mNGS) in the routine diagnostic laboratory, several challenges need to be resolved. To address strengths and limitations of mNGS in bacterial detection and quantification in samples with overwhelming host DNA abundance, we used the pig muscle tissue spiked with a home-made bacterial mock community, consisting of four species from different phyla. From the spiked tissue, we extracted DNA using: (i) a procedure based on mechanical/chemical lysis (no bacterial DNA enrichment); (ii) the Ultra-Deep Microbiome Prep (Molzym) kit for bacterial DNA enrichment; and (iii) the same enrichment kit but replacing the original proteinase K treatment for tissue solubilization by a collagenases/thermolysin digestion and cell filtration. Following mNGS, we determined bacterial: 'host' read ratios and taxonomic abundance profiles. We calculated the load of each mock-community member by combining its read counts with read counts and microscopically-determined cell counts of other co-spiked bacteria. In unenriched samples, bacterial quantification and taxonomic profiling were fairly accurate but at the expense of the sensitivity of detection. The removal of 'host' DNA by the modified enrichment protocol substantially improved bacterial detection in comparison to the other two extraction procedures and generated less distorted taxonomic profiles as compared to the original enrichment protocol.

Microbial DNA extraction of high-host content and low biomass samples: Optimized protocol for nasopharynx metagenomic studies

Introduction

Low microbial biomass and high human DNA content in nasopharyngeal aspirate samples hinder comprehensive characterization of microbiota and resistome. We obtained samples from premature infants, a group with increased risk of developing respiratory disorders and infections, and consequently frequent exposure to antibiotics. Our aim was to devise an optimal protocol for handling nasopharyngeal aspirate samples from premature infants, focusing on host DNA depletion and microbiome and resistome characterization.

Methods

Three depletion and three DNA extraction protocols were compared, using RT-PCR and whole metagenome sequencing to determine the efficiency of human DNA removal, taxonomic profiling and assignment of antibiotic resistance genes. Protocols were tested using mock communities, as well as pooled and individual patient samples.

Results

The only extraction protocol to retrieve the expected DNA yield from mock community samples was based on a lytic method to improve Gram positive recovery (MasterPure™). Host DNA content in non-depleted aliquots from pooled patient samples was 99%. Only samples depleted with MolYsis™ showed satisfactory, but varied reduction in host DNA content, in both pooled and individual patient samples, allowing for microbiome and resistome characterisation (host DNA content from 15% to 98%). Other depletion protocols either retrieved too low total DNA yields, preventing further analysis, or failed to reduce host DNA content. By using Mol_MasterPure protocol on aliquots from pooled patient samples, we increased the number of bacterial reads by 7.6 to 1,725.8-fold compared to non-depleted reference samples. PCR results were indicative of achieved microbial enrichment. Individual patient samples processed with Mol_MasterPure protocol varied greatly in total DNA yield, host DNA content (from 40% to 98%), species and antibiotic resistance gene richness.

Discussion

Despite high human DNA and low microbial biomass content in nasopharynx aspirates of preterm infants, we were able to reduce host DNA content to levels compatible with downstream shotgun metagenomic analysis, including bacterial species identification and coverage of antibiotic resistance genes. Whole metagenomic sequencing of microbes colonizing the nasopharynx may contribute to explaining the possible role of airway microbiota in respiratory conditions and reveal carriage of antibiotic resistance genes.

synDNA-a Synthetic DNA Spike-in Method for Absolute Quantification of Shotgun Metagenomic Sequencing

Microbiome studies have the common goal of determining which microbial taxa are present, respond to specific conditions, or promote phenotypic changes in the host. Most of these studies rely on relative abundance measurements to drive conclusions. Inherent limitations of relative values are the inability to determine whether an individual taxon is more or less abundant and the magnitude of this change between the two samples. These limitations can be overcome by using absolute abundance quantifications, which can allow for a more complete understanding of community dynamics by measuring variations in total microbial loads. Obtaining absolute abundance measurements is still technically challenging. Here, we developed synthetic DNA (synDNA) spike-ins that enable precise and cost-effective absolute quantification of microbiome data by adding defined amounts of synDNAs to the samples. We designed 10 synDNAs with the following features: 2,000-bp length, variable GC content (26, 36, 46, 56, or 66% GC), and negligible identity to sequences found in the NCBI database. Dilution pools were generated by mixing the 10 synDNAs at different concentrations. Shotgun metagenomic sequencing showed that the pools of synDNAs with different percentages of GC efficiently reproduced the serial dilution, showing high correlation (r = 0.96; R² ≥ 0.94) and significance (P < 0.01). Furthermore, we demonstrated that the synDNAs can be used as DNA spike-ins to generate linear models and predict with high accuracy the absolute number of bacterial cells in complex microbial communities. IMPORTANCE The synDNAs designed in this study enable accurate and reproducible measurements of absolute amount and fold changes of bacterial species in complex microbial communities. The method proposed here is versatile and promising as it can be applied to bacterial communities or genomic features like genes and operons, in addition to being easily adaptable by other research groups at a low cost. We also made the synDNAs' sequences and the plasmids available to encourage future application of the proposed method in the study of microbial communities.

An improved workflow for accurate and robust healthcare environmental surveillance using metagenomics

BACKGROUND

Effective surveillance of microbial communities in the healthcare environment is increasingly important in infection prevention. Metagenomics-based techniques are promising due to their untargeted nature but are currently challenged by several limitations: (1) they are not powerful enough to extract valid signals out of the background noise for low-biomass samples, (2) they do not distinguish between viable and nonviable organisms, and (3) they do not reveal the microbial load quantitatively. An additional practical challenge towards a robust pipeline is the inability to efficiently allocate sequencing resources a priori. Assessment of sequencing depth is generally practiced post hoc, if at all, for most microbiome studies, regardless of the sample type. This practice is inefficient at best, and at worst, poor sequencing depth jeopardizes the interpretation of study results. To address these challenges, we present a workflow for metagenomics-based environmental surveillance that is appropriate for low-biomass samples, distinguishes viability, is quantitative, and estimates sequencing resources.

RESULTS

The workflow was developed using a representative microbiome sample, which was created by aggregating 120 surface swabs collected from a medical intensive care unit. Upon evaluating and optimizing techniques as well as developing new modules, we recommend best practices and introduce a well-structured workflow. We recommend adopting liquid-liquid extraction to improve DNA yield and only incorporating whole-cell filtration when the nonbacterial proportion is large. We suggest including propidium monoazide treatment coupled with internal standards and absolute abundance profiling for viability assessment and involving cultivation when demanding comprehensive profiling. We further recommend integrating internal standards for quantification and additionally qPCR when we expect poor taxonomic classification. We also introduce a machine learning-based model to predict required sequencing effort from accessible sample features. The model helps make full use of sequencing resources and achieve desired outcomes. Video Abstract CONCLUSIONS: This workflow will contribute to more accurate and robust environmental surveillance and infection prevention. Lessons gained from this study will also benefit the continuing development of methods in relevant fields.

Challenges and perspectives of quantitative microbiome profiling in food fermentations

Spontaneously fermented foods are consumed and appreciated for thousands of years although they are usually produced with fluctuated productivity and quality, potentially threatening both food safety and food security. To guarantee consistent fermentation productivity and quality, it is essential to control the complex microbiota, the most crucial factor in food fermentations. The prerequisite for the control is to comprehensively understand the structure and function of the microbiota. How to quantify the actual microbiota is of paramount importance. Among various microbial quantitative methods evolved, quantitative microbiome profiling, namely to quantify all microbial taxa by absolute abundance, is the best method to understand the complex microbiota, although it is still at its pioneering stage for food fermentations. Here, we provide an overview of microbial quantitative methods, including the development from conventional methods to the advanced quantitative microbiome profiling, and the application examples of these methods. Moreover, we address potential challenges and perspectives of quantitative microbiome profiling methods, as well as future research needs for the ultimate goal of rational and optimal control of microbiota in spontaneous food fermentations. Our review can serve as reference for the traditional food fermentation sector for stable fermentation productivity, quality and safety.

Host biology, ecology and the environment influence microbial biomass and diversity in 101 marine fish species

Fish are the most diverse and widely distributed vertebrates, yet little is known about the microbial ecology of fishes nor the biological and environmental factors that influence fish microbiota. To identify factors that explain microbial diversity patterns in a geographical subset of marine fish, we analyzed the microbiota (gill tissue, skin mucus, midgut digesta and hindgut digesta) from 101 species of Southern California marine fishes, spanning 22 orders, 55 families and 83 genera, representing ~25% of local marine fish diversity. We compare alpha, beta and gamma diversity while establishing a method to estimate microbial biomass associated with these host surfaces. We show that body site is the strongest driver of microbial diversity while microbial biomass and diversity is lowest in the gill of larger, pelagic fishes. Patterns of phylosymbiosis are observed across the gill, skin and hindgut. In a quantitative synthesis of vertebrate hindguts (569 species), we also show that mammals have the highest gamma diversity when controlling for host species number while fishes have the highest percent of unique microbial taxa. The composite dataset will be useful to vertebrate microbiota researchers and fish biologists interested in microbial ecology, with applications in aquaculture and fisheries management.

Supplemental Oxygen Alters the Airway Microbiome in Cystic Fibrosis

Features of the airway microbiome in persons with cystic fibrosis (pwCF) are correlated with disease progression. Microbes have traditionally been classified for their ability to tolerate oxygen. It is unknown whether supplemental oxygen, a common medical intervention, affects the airway microbiome of pwCF. We hypothesized that hyperoxia significantly impacts the pulmonary microbiome in cystic fibrosis. In this study, we cultured spontaneously expectorated sputum from pwCF in artificial sputum medium under 21%, 50%, and 100% oxygen conditions using a previously validated model system that recapitulates microbial community composition in uncultured sputum. Culture aliquots taken at 24, 48, and 72 h, along with uncultured sputum, underwent shotgun metagenomic sequencing with absolute abundance values obtained with the use of spike-in bacteria. Raw sequencing files were processed using the bioBakery pipeline to determine changes in taxonomy, predicted function, antimicrobial resistance genes, and mobile genetic elements. Hyperoxia reduced absolute microbial load, species richness, and diversity. Hyperoxia reduced absolute abundance of specific microbes, including facultative anaerobes such as Rothia and some Streptococcus species, with minimal impact on canonical CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. The effect size of hyperoxia on predicted functional pathways was stronger than that on taxonomy. Large changes in microbial cooccurrence networks were noted. Hyperoxia exposure perturbs airway microbial communities in a manner well tolerated by key pathogens. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting. IMPORTANCE The airway microbiome in persons with cystic fibrosis (pwCF) is correlated with lung function and disease severity. Supplemental oxygen use is common in more advanced CF, yet its role in perturbing airway microbial communities is unknown. By culturing sputum samples from pwCF under normal and elevated oxygen conditions, we found that increased oxygen led to reduced total numbers and diversity of microbes, with relative sparing of common CF pathogens such as Pseudomonas aeruginosa and Staphylococcus aureus. Supplemental oxygen use may enable the growth of lung pathogens and should be further studied in the clinical setting.

Absolute quantification and genome-centric analyses elucidate the dynamics of microbial populations in anaerobic digesters

Anaerobic digestion (AD) relies on myriads of functions performed by complex microbial communities in customized settings, thus, a comprehensive investigation on the AD microbiome is central to the fine-tuned control. Most current AD microbiome studies are based on relative abundance, which hinders the interpretation of microbes' dynamics and inter-sample comparisons. Here, we developed an absolute quantification (AQ) approach that integrated cellular spike-ins with metagenomic sequencing to elucidate microbial community variations and population dynamics in four anaerobic digesters. Using this method, 253 microbes were defined as decaying populations with decay rates ranging from -0.05 to -5.85 d^-1, wherein, a population from Flavobacteriaceae family decayed at the highest rates of -3.87 to -5.85 d^-1 in four digesters. Meanwhile, 25 microbes demonstrated the growing trend in the AD processes with growth rates ranging from 0.11 to 1.77 d^-1, and genome-centric analysis assigned some of the populations to the functional niches of hydrolysis, short-chain fatty acids metabolism, and methane generation. Additionally, we observed that the specific activity of methanogens was lower in the prolonged digestion stage, and redundancy analysis revealed that the feedstock composition and the digestion duration were the two key parameters in governing the AD microbial compositions.

Contribution of Symptomatic, Herbal Treatment Options to Antibiotic Stewardship and Microbiotic Health

Epithelial surfaces in humans are home to symbiotic microbes (i.e., microbiota) that influence the defensive function against pathogens, depending on the health of the microbiota. Healthy microbiota contribute to the well-being of their host, in general (e.g., via the gut-brain axis), and their respective anatomical site, in particular (e.g., oral, urogenital, skin, or respiratory microbiota). Despite efforts towards a more responsible use of antibiotics, they are often prescribed for uncomplicated, self-limiting infections and can have a substantial negative impact on the gut microbiota. Treatment alternatives, such as non-steroidal anti-inflammatory drugs, may also influence the microbiota; thus, they can have lasting adverse effects. Herbal drugs offer a generally safe treatment option for uncomplicated infections of the urinary or respiratory tract. Additionally, their microbiota preserving properties allow for a more appropriate therapy of uncomplicated infections, without contributing to an increase in antibiotic resistance or disturbing the gut microbiota. Here, herbal treatments may be a more appropriate therapy, with a generally favorable safety profile.

Airborne fungi and human exposure in different areas of composting facilities

Airborne fungi can pose serious health concerns in humans; however, the area-specific abundance and composition of airborne fungal microbiota discharged from composting facilities remain unclear. In the present study, we collected air samples from composting, packaging, office, and downwind areas of four commercial composting facilities. The characteristics of airborne fungi, including pathogen/allergen-containing genera, and their corresponding human exposure in different areas of composting facilities were analyzed using high-throughput sequencing and ddPCR. High fungal concentrations and richness were detected in the air of the packaging area. In all four areas, Ascomycota, Basidiomycota, and Mucoromycota were observed to be the primary fungal phyla, with Cladosporium, Alternaria, and Aspergillus as the consistently dominant fungal genera. A large number of endemic airborne fungi were found in the composting and packaging areas, which also shared the most common airborne fungi as well as pathogen/allergen-containing genera. The packaging area contributed substantially to airborne fungi in the office and downwind areas. Area-specific human exposure to broad airborne fungal compositions was revealed, especially regarding the pathogen/allergen-containing genera. Current results provide valuable data for a comprehensive understanding of area-specific airborne fungi in composting facilities and highlight the importance of assessing the inhaled exposure to airborne fungi in evaluating their following health risks.

Progress in the application of metagenomic next-generation sequencing in pediatric infectious diseases

Infectious diseases are the major cause of children's deaths all over the world. With the development of evidence-based medicine, etiological diagnosis becomes more and more important. Since traditional methods have been unable to meet the needs of diagnosis and treatment, metagenomic next-generation sequencing (mNGS) gradually shows its unique advantages for pathogen diagnosis. This article aimed to introduce the application of mNGS technology in the diagnosis and treatment of neonatal and puerile infectious diseases by providing some examples.

A multiview model for relative and absolute microbial abundances

The absolute abundance of bacterial taxa in human host-associated environments plays a critical role in reproductive and gastrointestinal health. However, obtaining the absolute abundance of many bacterial species is typically prohibitively expensive. In contrast, relative abundance data for many species are comparatively cheap and easy to collect (e.g., with universal primers for the 16S rRNA gene). In this paper, we propose a method to jointly model relative abundance data for many taxa and absolute abundance data for a subset of taxa. Our method provides point and interval estimates for the absolute abundance of all taxa. Crucially, our proposal accounts for differences in the efficiency of taxon detection in the relative and absolute abundance data. We show that modeling taxon-specific efficiencies substantially reduces the estimation error for absolute abundance, and controls the coverage of interval estimators. We demonstrate the performance of our proposed method via a simulation study, a study of the effect of HIV acquisition on microbial abundances, and a sensitivity study where we jackknife the taxa with observed absolute abundances.

Gut microbiota individuality is contingent on temporal scale and age in wild meerkats

Inter-individual differences in gut microbiota composition are hypothesized to generate variation in host fitness-a premise for the evolution of host-gut microbe symbioses. However, recent evidence suggests that gut microbial communities are highly dynamic, challenging the notion that individuals harbour unique gut microbial phenotypes. Leveraging a long-term dataset of wild meerkats, we reconcile these concepts by demonstrating that the relative importance of identity for shaping gut microbiota phenotypes depends on the temporal scale. Across meerkat lifespan, year-to-year variation overshadowed the effects of identity and social group in predicting gut microbiota composition, with identity explaining on average less than 2% of variation. However, identity was the strongest predictor of microbial phenotypes over short sampling intervals (less than two months), predicting on average 20% of variation. The effect of identity was also dependent on meerkat age, with the gut microbiota becoming more individualized and stable as meerkats aged. Nevertheless, while the predictive power of identity was negligible after two months, gut microbiota composition remained weakly individualized compared to that of other meerkats for up to 1 year. These findings illuminate the degree to which individualized gut microbial signatures can be expected, with important implications for the time frames over which gut microbial phenotypes may mediate host physiology, behaviour and fitness in natural populations.

Association of the gut microbiome and metabolome with wheeze frequency in childhood asthma

BACKGROUND

While the microbiome has an established role in asthma development, less is known about its contribution to morbidity in children with asthma.

OBJECTIVE

In this ancillary study of the Vitamin D Antenatal Asthma Reduction Trial (VDAART), we analyzed the gut microbiome and metabolome of wheeze frequency in children with asthma.

METHODS

Bacterial 16S ribosomal RNA microbiome and untargeted metabolomic profiling were performed on fecal samples collected from 3-year-old children with parent-reported physician-diagnosed asthma. We analyzed wheeze frequency by calculating the proportion of quarterly questionnaires administered between ages 3 and 5 years in which parents reported the child had wheezed (wheeze proportion). Taxa and metabolites associated with wheeze were analyzed by identifying log fold changes with respect to wheeze frequency and correlation/linear regression analyses, respectively. Microbe-metabolite and microbe-microbe correlation networks were compared between subjects with high and low wheeze proportion.

RESULTS

Specific taxa, including the genus Veillonella and histidine pathway metabolites, were enriched in subjects with high wheeze proportion. Among wheeze-associated taxa, Veillonella and Oscillospiraceae UCG-005, which was inversely associated with wheeze, were correlated with the greatest number of fecal metabolites. Microbial networks were similar between subjects with low versus high wheeze frequency.

CONCLUSION

Gut microbiome features are associated with wheeze frequency in children with asthma, suggesting an impact of the gut microbiome on morbidity in childhood asthma.

Microbial liberation of N-methylserotonin from orange fiber in gnotobiotic mice and humans

Plant fibers in byproduct streams produced by non-harsh food processing methods represent biorepositories of diverse, naturally occurring, and physiologically active biomolecules. To demonstrate one approach for their characterization, mass spectrometry of intestinal contents from gnotobiotic mice, plus in vitro studies, revealed liberation of N-methylserotonin from orange fibers by human gut microbiota members including Bacteroides ovatus. Functional genomic analyses of B. ovatus strains grown under permissive and non-permissive N-methylserotonin "mining" conditions revealed polysaccharide utilization loci that target pectins whose expression correlate with strain-specific liberation of this compound. N-methylserotonin, orally administered to germ-free mice, reduced adiposity, altered liver glycogenesis, shortened gut transit time, and changed expression of genes that regulate circadian rhythm in the liver and colon. In human studies, dose-dependent, orange-fiber-specific fecal accumulation of N-methylserotonin positively correlated with levels of microbiome genes encoding enzymes that digest pectic glycans. Identifying this type of microbial mining activity has potential therapeutic implications.