Quantitative microbiome profiling links gut community variation to microbial load

Investigating drug-gut microbiota interactions: reductive and hydrolytic metabolism of oral glucocorticoids by in vitro artificial gut microbiota

Elucidation of the role of gut microbiota in the metabolism of orally administered drugs may improve therapeutic effectiveness and contribute to the development of personalized medicine. In this study, ten different artificial gut microbiota (AGM), obtained by culturing fecal samples in a continuous fermentation system, were challenged for their metabolizing capacity on a panel of six glucocorticoids selected from either prodrugs or drugs. Data from metabolic stability assays highlighted that, while the hydrolysis-mediated conversion of prodrugs to drugs represented only a minor metabolic pathway, significant differences in the stability of parent compounds and in their conversion rates to multiple reductive metabolites were obtained for the selected drugs. In the latter case, a taxonomic composition-dependent ability to convert parent drugs to metabolites was observed. Indeed, the artificial microbial communities dominated by the genus Bacteroides showed the maximal conversion of parent glucocorticoids to several metabolites. Furthermore, the effect of drugs on AGM was also evaluated through shallow shotgun sequencing and flow cytometry-based total bacterial cell count highlighting that these drugs can affect both the taxonomic composition and growth performances of the human gut microbiota.

The protective effect of S-adenosylmethionine on chronic adolescent stress-induced depression-like behaviors by regulating gut microbiota

The efficacy and tolerability of current antidepressants for adolescent depression are inadequate. S-adenosylmethionine (SAMe), known for its effectiveness and minimal side effects in adult depression, remains unstudied in adolescents. This study explored the potential of SAMe to address depression-like behaviors in juvenile rats induced by chronic unpredictable mild stress (CUMS), with a focus on gut microbiome interactions. Adolescent male Wistar rats were subjected to a 4-week CUMS regimen and received daily intraperitoneal injections of 300 mg/kg SAMe. Behavioral assessments included the sucrose preference test, elevated plus maze test, open field test, and Y-maze test. Histopathological changes of the hippocampus and colon were observed by Nissl staining and hematoxylin and eosin staining, respectively. Gut microbiome composition was analyzed using Accurate 16S absolute quantification sequencing. The results showed that SAMe significantly improved behavioral outcomes, reduced histopathological damages in hippocampal neurons and colon tissues, and modulated the gut microbiota of depressed rats. It favorably altered the ratio of Bacteroidetes to Firmicutes, decreased the absolute abundance of Deferribacteres, and adjusted levels of key microbial genera associated with depression-like behaviors. These results suggested that SAMe could effectively counter depression-like behaviors in CUMS-exposed adolescent rats by mitigating hippocampal neuronal and colon damage and modulating the gut microbiota. This supports SAMe as a viable and tolerable treatment option for adolescent depression, highlighting the importance of the gut-brain axis in therapeutic strategies.

Real-time PCR-based quantitative microbiome profiling elucidates the microbial dynamic succession in backslopping fermentation of Taiwanese pickled cabbage

BACKGROUND

Microbiota succession determines the flavor and quality of fermented foods. Quantitative PCR-based quantitative microbiome profiling (QMP) has been applied broadly for microbial analysis from absolute abundance perspectives, transforming microbiota ratios into counts by normalizing 16S ribosomal RNA (16S rRNA) gene sequencing data with gene copies quantified by quantitative PCR. However, the application of QMP in fermented foods is still limited.

RESULTS

QMP elucidated microbial succession of Taiwanese pickled cabbage. In the spontaneous first-round fermentation (FR), the 16S rRNA gene copies of total bacteria increased from 6.1 to 10 log copies mL-1. The dominant lactic acid bacteria genera were successively Lactococcus, Leuconostoc and Lactiplantibacillus. Despite the decrease in the proportion of Lactococcus during the succession, the absolute abundance of Lactococcus still increased. In the backslopping second-round fermentation (SR), the total bacteria 16S rRNA gene copies increased from 7.6 to 9.9 log copies mL-1. The addition of backslopping starter and vinegar rapidly led to a homogenous microbial community dominated by Lactiplantibacillus. The proportion of Lactiplantibacillus remained consistently around 90% during SR, whereas its absolute abundance exhibited a continuous increase. In SR without vinegar, Leuconostoc consistently dominated the fermentation.

CONCLUSION

The present study highlights that compositional analysis would misinterpret microbial dynamics, whereas QMP reflected the real succession profiles and unveiled the essential role of vinegar in promoting Lactiplantibacillus dominance in backslopping fermentation of Taiwanese pickled cabbage. Quantitative microbiome profiling (QMP) was found to be a more promising approach for the detailed observation of microbiome succession in food fermentation compared to compositional analysis. © 2024 Society of Chemical Industry.

Comparison of Daily Versus Admission and Discharge Surveillance Cultures for Multidrug-Resistant Organism Detection in an Intensive Care Unit

BACKGROUND

Admission and discharge screening of patients for asymptomatic gut colonization with multidrug-resistant organisms (MDROs) is a common approach to active surveillance, but its sensitivity for detecting colonization is uncertain.

METHODS

Daily rectal or fecal swab samples and associated clinical data were collected over 12 months from patients in one 25-bed medical intensive care unit (ICU) in Chicago, IL and tested for the following MDROs: vancomycin-resistant enterococci; third-generation cephalosporin-resistant Enterobacterales, including extended-spectrum β-lactamase-producing Enterobacterales; and carbapenem-resistant Enterobacterales. MDRO detection by (1) admission and discharge surveillance cultures or (2) clinical cultures were compared to daily surveillance cultures. Samples underwent 16S rRNA gene sequencing to measure the relative abundance of operational taxonomic units (OTUs) corresponding to each MDRO.

RESULTS

Compared with daily surveillance cultures, admission/discharge cultures detected 91% of prevalent MDRO colonization and 63% of MDRO acquisitions among medical ICU patients. Few (7%) MDRO carriers were identified by clinical cultures alone. Higher relative abundance of MDRO-associated OTUs and specific antibiotic exposures were independently associated with higher probability of MDRO detection by culture.

CONCLUSIONS

Admission and discharge surveillance cultures underestimated MDRO acquisitions in an ICU. These limitations should be considered when designing sampling strategies for epidemiologic studies that use culture-based surveillance.

Harnessing the gut microbiome: a potential biomarker for wild animal welfare

The welfare of wild animal populations is critically important to conservation, with profound implications for ecosystem health, biodiversity, and zoonotic disease transmission. Animal welfare is typically defined as the accumulated affective mental state of an animal over a particular time period. However, the assessment of animal welfare in the wild poses unique challenges, primarily due to the lack of universally applicable biomarkers. This perspective explores the potential role of the gut microbiome, a dynamic and non-invasive biomarker, as a novel avenue for evaluating animal welfare in wild animals. The gut microbiome, through interactions with the host's physiology, behavior, and cognition, offers a promising opportunity to gain insights into the well-being of animals. In this synthesis, we discuss the distinction between fitness and welfare, the complexities of assessing welfare in wild populations, and the linkages between the gut microbiome and aspects of animal welfare such as behavior and cognition. We lastly elucidate how the gut microbiome could serve as a valuable tool for wildlife managers, with the potential to serve as a non-invasive yet informative window into the welfare of wild animals. As this nascent field evolves, it presents unique opportunities to enhance our understanding of the well-being of wild animals and to contribute to the preservation of ecosystems, biodiversity, and human health.

Disclosing α-lactalbumin impact on the intestinal and vaginal microbiota of women suffering from polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is one of the most widespread endocrinopathy affecting women of reproductive age with detrimental effects on life quality and health. Among several mechanisms involved in its aetiopathogenesis, recent studies have also postulated the involvement of the vaginal and intestinal microbiota in the development of this disorder. In this study, an accurate insight into the microbial changes associated with PCOS was performed through a pooled-analysis highlighting that this syndrome is characterized by intestinal and vaginal dysbiosis with a reduction of beneficial microorganisms and a higher proportion of potential pathogens. Based on this observation, we evaluated the ability of a milk-derived protein exerting positive outcomes in the management of PCOS, that is, α-lactalbumin (α-LA), to recover PCOS-related dysbiosis. In vitro experiments revealed that this protein improved the growth performances of members of two health-promoting bacterial genera, that is, Bifidobacterium and Lactobacillus, depleted in both intestinal and vaginal microbiota of PCOS-affected women. In addition, α-LA modulated the taxonomic composition and growth performances of the microbial players of the complex intestinal and vaginal microbiota. Finally, an in vivo pilot study further corroborated these observations. The oral administration of α-LA for 30 days to women with PCOS revealed that this protein may have a role in favouring the growth of health-promoting bacteria yet limiting the proliferation of potential pathogens. Overall, our results could pave the way to the use of α-LA as a valid compound with 'prebiotic effects' to limit/restore the PCOS-related intestinal and vaginal dysbiosis.

High accuracy meets high throughput for near full-length 16S ribosomal RNA amplicon sequencing on the Nanopore platform

Small subunit (SSU) ribosomal RNA (rRNA) gene amplicon sequencing is a foundational method in microbial ecology. Currently, short-read platforms are commonly employed for high-throughput applications of SSU rRNA amplicon sequencing, but at the cost of poor taxonomic classification due to limited fragment lengths. The Oxford Nanopore Technologies (ONT) platform can sequence full-length SSU rRNA genes, but its lower raw-read accuracy has so-far limited accurate taxonomic classification and de novo feature generation. Here, we present a sequencing workflow, termed ssUMI, that combines unique molecular identifier (UMI)-based error correction with newer (R10.4+) ONT chemistry and sample barcoding to enable high throughput near full-length SSU rRNA (e.g. 16S rRNA) amplicon sequencing. The ssUMI workflow generated near full-length 16S rRNA consensus sequences with 99.99% mean accuracy using a minimum subread coverage of 3×, surpassing the accuracy of Illumina short reads. The consensus sequences generated with ssUMI were used to produce error-free de novo sequence features with no false positives with two microbial community standards. In contrast, Nanopore raw reads produced erroneous de novo sequence features, indicating that UMI-based error correction is currently necessary for high-accuracy microbial profiling with R10.4+ ONT sequencing chemistries. We showcase the cost-competitive scalability of the ssUMI workflow by sequencing 87 time-series wastewater samples and 27 human gut samples, obtaining quantitative ecological insights that were missed by short-read amplicon sequencing. ssUMI, therefore, enables accurate and low-cost full-length 16S rRNA amplicon sequencing on Nanopore, improving accessibility to high-resolution microbiome science.

DEFB119 stratifies dysbiosis with distorted networks in the seminal microbiome associated with male infertility

Infertility is associated with the alteration of the seminal microbiome. However, the onset of dysbiosis remains controversial and the involvement of host factors remains elusive. This study investigates the alterations of the seminal microbiome in male infertility and examines the association and function of DEFB119, a reproductive-tract-specific host antimicrobial peptide, on the seminal microbiome and male fertility. While we observed comparable genera, diversity and evenness of bacterial communities, a marked decrease in the modularity of the metacommunities was observed in patients with abnormal spermiogram (n = 57) as compared to the control (n = 30). A marked elevation of DEFB119 was observed in a subpopulation of male infertile patients (n = 5). Elevated seminal DEFB119 was associated with a decrease in the observed genera, diversity and evenness of bacterial communities, and further distortion of the metacommunities. Mediation analysis suggests the involvement of elevated DEFB119 and dysbiosis of the seminal microbiome in mediating the abnormalities in the spermiogram. Functional experiments showed that recombinant DEFB119 significantly decrease the progressive motility of sperm in patients with abnormal spermiogram. Moreover, DEFB119 demonstrated species-specific antimicrobial activity against common seminal and nonseminal species. Our work identifies an important host factor that mediates the host-microbiome interaction and stratifies the seminal microbiome associated with male infertility. These results may lead to a new diagnostic method for male infertility and regimens for formulating the microbiome in the reproductive tract and other organ systems.

Disclosing the key role of Fe/As/Cu in community co-occurrence and microbial recruitment in metallurgical ruins

Mining activities have led to the persistent presence of substantial heavy metals at metallurgical sites. However, the impact of long-term and complex heavy metal pollution in metallurgical ruins on the structure and spatial shift of microbiome remains unclear. In this study, we focused on various types of metallurgical sites to uncover the occurrence of heavy metals in abandoned mines and the response patterns of microbial communities. The results indicate that mining activities have caused severe exceedances of multiple heavy metals, with AsBio, CuBio, and FeBio being the primary factors affecting community structure and function. Co-occurrence network analyses suggest that several genera, including Ellin6515, Cupriavidus, Acidobacteria genus RB41, Vicinamibacteraceae, Blastococcus, and Sphingomonas, may play significant roles in the synergistic metabolism of communities responding to Fe-Cu-As stress. Although random dispersal contributed to community migration, null models emphasized that variable selection predominates in the spatial turnover of community composition. Additionally, metagenomic prediction (PICRUSt2) identified key genes involved in stress and detoxification strategies of heavy metals. The composite heavy metal stress strengthened the relationship between network structure and the potential function of the community, along with critical ecosystem functions. Our findings demonstrated that microbial interactions were crucial for ecosystem management and the ecological consequences of heavy metal pollution remediation.

A realistic benchmark for differential abundance testing and confounder adjustment in human microbiome studies

BACKGROUND

In microbiome disease association studies, it is a fundamental task to test which microbes differ in their abundance between groups. Yet, consensus on suitable or optimal statistical methods for differential abundance testing is lacking, and it remains unexplored how these cope with confounding. Previous differential abundance benchmarks relying on simulated datasets did not quantitatively evaluate the similarity to real data, which undermines their recommendations.

RESULTS

Our simulation framework implants calibrated signals into real taxonomic profiles, including signals mimicking confounders. Using several whole meta-genome and 16S rRNA gene amplicon datasets, we validate that our simulated data resembles real data from disease association studies much more than in previous benchmarks. With extensively parametrized simulations, we benchmark the performance of nineteen differential abundance methods and further evaluate the best ones on confounded simulations. Only classic statistical methods (linear models, the Wilcoxon test, t-test), limma, and fastANCOM properly control false discoveries at relatively high sensitivity. When additionally considering confounders, these issues are exacerbated, but we find that adjusted differential abundance testing can effectively mitigate them. In a large cardiometabolic disease dataset, we showcase that failure to account for covariates such as medication causes spurious association in real-world applications.

CONCLUSIONS

Tight error control is critical for microbiome association studies. The unsatisfactory performance of many differential abundance methods and the persistent danger of unchecked confounding suggest these contribute to a lack of reproducibility among such studies. We have open-sourced our simulation and benchmarking software to foster a much-needed consolidation of statistical methodology for microbiome research.

Highly accurate and sensitive absolute quantification of bacterial strains in human fecal samples

BACKGROUND

Next-generation sequencing (NGS) approaches have revolutionized gut microbiome research and can provide strain-level resolution, but these techniques have limitations in that they are only semi-quantitative, suffer from high detection limits, and generate data that is compositional. The present study aimed to systematically compare quantitative PCR (qPCR) and droplet digital PCR (ddPCR) for the absolute quantification of Limosilactobacillus reuteri strains in human fecal samples and to develop an optimized protocol for the absolute quantification of bacterial strains in fecal samples.

RESULTS

Using strain-specific PCR primers for L. reuteri 17938, ddPCR showed slightly better reproducibility, but qPCR was almost as reproducible and showed comparable sensitivity (limit of detection [LOD] around 104 cells/g feces) and linearity (R2 > 0.98) when kit-based DNA isolation methods were used. qPCR further had a wider dynamic range and is cheaper and faster. Based on these findings, we conclude that qPCR has advantages over ddPCR for the absolute quantification of bacterial strains in fecal samples. We provide an optimized and easy-to-follow step-by-step protocol for the design of strain-specific qPCR assays, starting from primer design from genome sequences to the calibration of the PCR system. Validation of this protocol to design PCR assays for two L. reuteri strains, PB-W1 and DSM 20016 T, resulted in a highly accurate qPCR with a detection limit in spiked fecal samples of around 103 cells/g feces. Applying our strain-specific qPCR assays to fecal samples collected from human subjects who received live L. reuteri PB-W1 or DSM 20016 T during a human trial demonstrated a highly accurate quantification and sensitive detection of these two strains, with a much lower LOD and a broader dynamic range compared to NGS approaches (16S rRNA gene sequencing and whole metagenome sequencing).

CONCLUSIONS

Based on our analyses, we consider qPCR with kit-based DNA extraction approaches the best approach to accurately quantify gut bacteria at the strain level in fecal samples. The provided step-by-step protocol will allow scientists to design highly sensitive strain-specific PCR systems for the accurate quantification of bacterial strains of not only L. reuteri but also other bacterial taxa in a broad range of applications and sample types. Video Abstract.

Carbon-fixing bacteria in diverse groundwaters of karst area: Distribution patterns, ecological interactions, and driving factors

The biological carbon pump in karst areas is of great significance for maintaining the effectiveness of karst carbon sinks. However, the spatial distribution and carbon-fixing potential of microorganisms in different aquifers within karst areas remain poorly understood. In this study, the distribution patterns, ecological roles, and environmental drivers of microbiota associated with CO2 fixation were investigated in karst groundwater (KW), porous groundwater (PW), fractured groundwater (FW), and surface water (SW) within a typical karst watershed, located in Guilin, southwest China. KW, PW, and FW displayed the similar community structure and indicative carbon-fixing bacteria composition, which were dominated by chemoautotrophic bacteria compared to SW. Higher abundances of indicative carbon-fixing bacteria and carbon-fixing genes, as well as richer proportions of microbial-derived DOC, indicated the more significant microbial carbon-fixing potential in KW and PW. At the profile of KW, a carbon-fixing hotspot was discovered at the depths of 0-50 m. Correlation analysis between carbon-fixing bacteria and DOC revealed that the chemoautotrophic process driven by nitrogen and sulfur oxidation predominated the microbial carbon fixation in groundwater. Co-occurrence network analysis demonstrated that carbon-fixing bacteria exhibited cooperation with other bacterial taxa in KW, while competition was the dominant interaction in PW. Moreover, carbon-fixing bacteria was found to lead bacterial assembly more deterministic in KW. The analysis of environmental factors and microbial diversity illustrated that inorganic carbon and redox state drove community variations across groundwaters. Structural equation model (SEM) further confirmed that ORP was the primary factor influencing the carbon fixation potential. This study provides a new insight into biological carbon fixation in karst aquatic systems, which holds significance in the accurate assessment of karst carbon sinks.

Decoding the microbiome for sustainable agriculture

Root-associated microbiota profoundly affect crop health and productivity. Plants can selectively recruit beneficial microbes from the soil and actively balance microbe-triggered plant-growth promotion and stress tolerance enhancement. The cost associated with this is the root-mediated support of a certain number of specific microbes under nutrient limitation. Thus, it is important to consider the dynamic changes in microbial quantity when it comes to nutrient condition-induced root microbiome reassembly. Quantitative microbiome profiling (QMP) has recently emerged as a means to estimate the specific microbial load variation of a root microbiome (instead of the traditional approach quantifying relative microbial abundances) and data from the QMP approach can be more closely correlated with plant development and/or function. However, due to a lack of detailed-QMP data, how soil nutrient conditions affect quantitative changes in microbial assembly of the root-associated microbiome remains poorly understood. A recent study quantified the dynamics of the soybean root microbiome, under unbalanced fertilization, using QMP and provided data on the use of specific synthetic communities (SynComs) for sustaining crop productivity. In this editorial, we explore potential opportunities for utilizing QMP to decode the microbiome for sustainable agriculture.

Microbial Ecology and Metabolism of Emerging Adulthood: Gut Microbiome Insights from a College Freshman Cohort

The human gut microbiome (GM) undergoes dynamic changes throughout life, transitioning from infancy to adulthood. Despite improved understanding over the past years about how genetics, lifestyle, and the external environment impact the GM, limited research has explored the GM's evolution during late-stage adolescence, especially among college students. This study addresses this gap by investigating the longitudinal dynamics of fecal microbial, functional, and metabolomic signatures in a diverse group of first-year, dormitory-housed college students. A total of 485 stool samples from 246 participants were analyzed, identifying four primary GM community types, predominantly led by Bacteroides (66.8% of samples), as well as Blautia and Prevotella. The Prevotella/Bacteroides (P/B) ratio emerged as a robust GM composition indicator, predictively associated with 15 metabolites. Notably, higher P/B ratios correlated negatively with p-cresol sulfate and cholesterol sulfate, implying potential health implications, while positively correlating with kynurenic acid. Distinct GM transition and stability patterns were found from a detailed longitudinal subset of 93 participants over an academic year. Parasutterella and the Ruminococcus gnavus group exhibited positive associations with compositional variability, whereas Faecalibacterium and Eubacterium ventriosum group displayed negative associations, the latter suggesting stabilizing roles in the GM. Most notably, nearly half of the longitudinal cohort experienced GM community shifts, emphasizing long-term GM adaptability. Comparing individuals with stable community types to those undergoing transitions, we observed significant differences in microbial composition and diversity, signifying substantial shifts in the microbiota during transitions. Although diet-related variables contributed to some observed variance, diet did not independently predict the probability of switching between community types within the study's timeframe via multi-state Markov modeling. Furthermore, exploration of stability within dynamic microbiomes among the longitudinal cohort experiencing shifts in community types revealed that microbiome taxa at the genus level exhibited significantly higher total variance than estimated functional and fecal metabolomic features. This suggests tight control of function and metabolism, despite community shifting. Overall, this study highlights the dynamic nature of the late-stage adolescent GM, the role of core taxa, metabolic pathways, the fecal metabolome, and lifestyle and dietary factors, contributing to our understanding of GM assembly and potential health implications during this life phase.

An optimal normalization method for high sparse compositional microbiome data

In many omics data, including microbiome sequencing data, we are only able to measure relative information. Various computational or statistical methods have been proposed to extract absolute (or biologically relevant) information from this relative information; however, these methods are under rather strong assumptions that may not be suitable for multigroup (more than two groups) and/or longitudinal outcome data. In this article, we first introduce the minimal assumption required to extract absolute from relative information. This assumption is less stringent than those imposed in existing methods, thus being applicable to multigroup and/or longitudinal outcome data. We then propose the first normalization method that works under this minimal assumption. The optimality and validity of the proposed method and its beneficial effects on downstream analysis are demonstrated in extensive simulation studies, where existing methods fail to produce consistent performance under the minimal assumption. We also demonstrate its application to real microbiome datasets to determine biologically relevant microbes to a specific disease/condition.

Combined Supplementation of Inulin and Bacillus coagulans Lactospore Demonstrates Synbiotic Potential in the Mucosal Simulator of the Human Intestinal Microbial Ecosystem (M-SHIME®) Model

Prebiotic and probiotic combinations may lead to a synbiotic effect, demonstrating superior health benefits over either component alone. Using the Mucosal Simulator of the Human Intestinal Microbial Ecosystem (M-SHIME®) model, the effects of repeated supplementation with inulin (prebiotic, which is expected to provide a source of nutrition for the live microorganisms in the gut to potentially support optimal digestive health), Bacillus coagulans lactospore (probiotic), and a low and high dose of a synbiotic combination of the two on the gut microbial community activity and composition were evaluated. Test product supplementation increased the health-promoting short-chain fatty acids acetate and butyrate compared with levels recorded during the control period, demonstrating a stimulation of saccharolytic fermentation. This was likely the result of the increased abundance of several saccharolytic bacterial groups, including Megamonas, Bifidobacterium, and Faecalibacterium, following test product supplementation. The stimulation of acetate and butyrate production, as well as the increased abundance of saccharolytic bacterial groups were more evident in treatment week 3 compared with treatment week 1, demonstrating the value of repeated product administration. Further, the synbiotic formulations tended to result in greater changes compared with prebiotic or probiotic alone. Overall, the findings demonstrate a synbiotic potential for inulin and B. coagulans lactospore and support repeated administration of these products, indicating a potential for promoting gut health.

Missing microbes in infants and children in the COVID-19 pandemic: a study of 1,126 participants in Beijing, China

The COVID-19 pandemic has caused many fatalities worldwide and continues to affect the health of the recovered patients in the form of long-COVID. In this study, we compared the gut microbiome of uninfected infants and children before the pandemic began (BEFORE cohort, n=906) to that of after the pandemic (AFTER cohort, n=220) to examine the potential impact of social distancing and life habit changes on infant/children gut microbiome. Based on 16S rRNA sequencing, we found a significant change in microbiome composition after the pandemic, with Bacteroides enterotype increasing to 35.45% from 30.46% before the pandemic. qPCR quantification indicated that the bacterial loads of seven keystone taxa decreased by 91.69%-19.58%. Quantitative microbiome profiling, used to enhance the resolution in detecting microbiome differences, revealed a greater explained variance of pandemic on microbiome compared to gender, as well as a significant decrease in bacterial loads in 15 of the 20 major genera. The random forest age-predictor indicated the gut microbiomes were less mature in the after-pandemic cohort than in the before-pandemic cohort in the children group (3-12 years old) and had features of a significantly younger age (average of 1.86 years). Lastly, body weight and height were significantly lower in the after-pandemic cohort than in the before-pandemic cohort in infants (<1 year of age), which was associated with a decrease in bacterial loads in the fecal microbiome.

The oral-gut microbiome axis in health and disease

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

Rapid Microbial Profiling through Multimodal Biosensors for Transversal Analysis

The intricate interactions between host and microbial communities hold significant implications for biology and medicine. However, traditional microbial profiling methods face limitations in processing time, measurement of absolute abundance, detection of low biomass, discrimination between live and dead cells, and functional analysis. This study introduces a rapid multimodal microbial characterization platform, Multimodal Biosensors for Transversal Analysis (MBioTA), for capturing the taxonomy, viability, and functional genes of the microbiota. The platform incorporates single cell biosensors, scalable microwell arrays, and automated image processing for rapid transversal analysis in as few as 2 h. The multimodal biosensors simultaneously characterize the taxon, viability, and functional gene expression of individual cells. By automating the image processing workflow, the single cell analysis techniques enable the quantification of bacteria with sensitivity down to 0.0075%, showcasing its capability in detecting low biomass samples. We illustrate the applicability of the MBioTA platform through the transversal analysis of the gut microbiota composition, viability, and functionality in a familial Alzheimer's disease mouse model. The effectiveness, rapid turnaround, and scalability of the MBioTA platform will facilitate its application from basic research to clinical diagnostics, potentially revolutionizing our understanding and management of diseases associated with microbe-host interactions.

Synthetic microbial ecology: engineering habitats for modular consortia

Microbiomes, the complex networks of micro-organisms and the molecules through which they interact, play a crucial role in health and ecology. Over at least the past two decades, engineering biology has made significant progress, impacting the bio-based industry, health, and environmental sectors; but has only recently begun to explore the engineering of microbial ecosystems. The creation of synthetic microbial communities presents opportunities to help us understand the dynamics of wild ecosystems, learn how to manipulate and interact with existing microbiomes for therapeutic and other purposes, and to create entirely new microbial communities capable of undertaking tasks for industrial biology. Here, we describe how synthetic ecosystems can be constructed and controlled, focusing on how the available methods and interaction mechanisms facilitate the regulation of community composition and output. While experimental decisions are dictated by intended applications, the vast number of tools available suggests great opportunity for researchers to develop a diverse array of novel microbial ecosystems.

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.

Deep learning for predicting 16S rRNA gene copy number

Culture-independent 16S rRNA gene metabarcoding is a commonly used method for microbiome profiling. To achieve more quantitative cell fraction estimates, it is important to account for the 16S rRNA gene copy number (hereafter 16S GCN) of different community members. Currently, there are several bioinformatic tools available to estimate the 16S GCN values, either based on taxonomy assignment or phylogeny. Here we present a novel approach ANNA16, Artificial Neural Network Approximator for 16S rRNA gene copy number, a deep learning-based method that estimates the 16S GCN values directly from the 16S gene sequence strings. Based on 27,579 16S rRNA gene sequences and gene copy number data from the rrnDB database, we show that ANNA16 outperforms the commonly used 16S GCN prediction algorithms. Interestingly, Shapley Additive exPlanations (SHAP) shows that ANNA16 can identify unexpected informative positions in 16S rRNA gene sequences without any prior phylogenetic knowledge, which suggests potential applications beyond 16S GCN prediction.

Gardnerella diversity and ecology in pregnancy and preterm birth

The vaginal microbiome has been linked to negative health outcomes including preterm birth. Specific taxa, including Gardnerella spp., have been identified as risk factors for these conditions. Historically, microbiome analysis methods have treated all Gardnerella spp. as one species, but the broad diversity of Gardnerella has become more apparent. We explore the diversity of Gardnerella clades and genomic species in the vaginal microbiome of pregnant women and their associations with microbiome composition and preterm birth. Relative abundance of Gardnerella clades and genomic species and other taxa was quantified in shotgun metagenomic sequencing data from three distinct cohorts of pregnant women. We also assessed the diversity and abundance of Gardnerella variants in 16S rRNA gene amplicon sequencing data from seven previously conducted studies in differing populations. Individual microbiomes often contained multiple Gardnerella variants, and the number of clades was associated with increased microbial load, or the ratio of non-human reads to human reads. Taxon co-occurrence patterns were largely consistent across Gardnerella clades and among cohorts. Some variants previously described as rare were prevalent in other cohorts, highlighting the importance of surveying a diverse set of populations to fully capture the diversity of Gardnerella. The diversity of Gardnerella both across populations and within individual vaginal microbiomes has long been unappreciated, as has been the intra-species diversity of many other members of the vaginal microbiome. The broad genomic diversity of Gardnerella has led to its reclassification as multiple species; here we demonstrate the diversity of Gardnerella found within and between vaginal microbiomes.IMPORTANCEThe present study shows that single microbiomes can contain all currently known species of Gardnerella and that multiple similar species can exist within the same environment. Furthermore, surveys of demographically distinct populations suggest that some species appear more commonly in certain populations. Further studies in broad and diverse populations will be necessary to fully understand the ecological roles of each Gardnerella sp., how they can co-exist, and their distinct impacts on microbial communities, preterm birth, and other health outcomes.

Antimicrobial resistance in rural rivers: Comparative study of the Coquet (Northumberland) and Eden (Cumbria) River catchments

Many studies have characterised resistomes in river microbial communities. However, few have compared resistomes in parallel rural catchments that have few point-source inputs of antimicrobial genes (ARGs) and organisms (i.e., AMR) - catchments where one can contrast more nebulous drivers of AMR in rural rivers. Here, we used quantitative microbial profiling (QMP) to compare resistomes and microbiomes in two rural river catchments in Northern England, the Coquet and Eden in Northumberland and Cumbria, respectively, with different hydrological and geographical conditions. The Eden has higher flow rates, higher annual surface runoff, and longer periods of soil saturation, whereas the Coquet is drier and has lower flowrates. QMP analysis showed the Eden contained significantly more abundant microbes associated with soil sources, animal faeces, and wastewater than the Coquet, which had microbiomes like less polluted rivers (Wilcoxon test, p < 0.01). The Eden also had greater ARG abundances and resistome diversity (Kruskal Wallis, p < 0.05), and higher levels of potentially clinically relevant ARGs. The Eden catchment had greater and flashier runoff and more extensive agricultural land use in its middle reach, which explains higher levels of AMR in the river. Hydrological and geographic factors drive AMR in rural rivers, which must be considered in environmental monitoring programmes.

Tibetan Plateau grasslands might increase sequestration of microbial necromass carbon under future warming

Microbial necromass carbon (MNC) can reflect soil carbon (C) sequestration capacity. However, changes in the reserves of MNC in response to warming in alpine grasslands across the Tibetan Plateau are currently unclear. Based on large-scale sampling and published observations, we divided eco-clusters based on dominant phylotypes, calculated their relative abundance, and found that their averaged importance to MNC was higher than most other environmental variables. With a deep learning model based on stacked autoencoder, we proved that using eco-cluster relative abundance as the input variable of the model can accurately predict the overall distribution of MNC under current and warming conditions. It implied that warming could lead to an overall increase in the MNC in grassland topsoil across the Tibetan Plateau, with an average increase of 7.49 mg/g, a 68.3% increase. Collectively, this study concludes that alpine grassland has the tendency to increase soil C sequestration capacity on the Tibetan Plateau under future warming.

Lactobacillus melliventris promotes hive productivity and immune functionality in Bombus terrestris performance in the greenhouse

Bumblebees are important pollinators in agricultural ecosystems, but their abundance is declining globally. There is an urgent need to protect bumblebee health and their pollination services. Bumblebees possess specialized gut microbiota with potential to be used as probiotics to help defend at-risk bumblebee populations. However, evidence for probiotic benefits on bumblebees is lacking. Here, we evaluated how supplementation with Lactobacillus melliventris isolated from bumblebee gut affected the colony development of Bombus terrestris. This native strain colonized robustly and persisted long-term in bumblebees, leading to a significantly higher quality of offspring. Subsequently, the tyrosine pathway was upregulated in the brain and fat body, while the Wnt and mTOR pathways of the gut were downregulated. Notably, the field experiment in the greenhouse revealed the supplementation of L. melliventris led to a 2.5-fold increase in the bumblebee survival rate and a more than 10% increase in the number of flowers visited, indicating a better health condition and pollination ability in field conditions. Our study represents a first screening for the potential use of the native gut member, L. melliventris, as probiotic strains in hive supplement for bumblebee breeding, which may be a practical approach to improve immunity and hive health.

Gut Microbiome Alterations Accompany Metabolic Normalization Following Bariatric Surgery in ROHHAD Syndrome

Rapid onset obesity with hypoventilation, hypothalamic, and autonomic dysregulation (ROHHAD) syndrome in childhood is characterized by abrupt onset weight gain and dysautonomia with variable neuroendocrine involvement. In the absence of definitive disease-modifying therapies, the primary management strategy remains symptom control. This case report describes the first successful correction of obesity, dysautonomia, and metabolic derangement in a patient with ROHHAD following Roux-en-Y gastric bypass. Anthropometrics, metabolic profiling, and stool microbiome composition were assessed in a longitudinal fashion. In the 48-month period following surgery, the patient body mass index (BMI) reduced by 9.5 kg/m2 and metabolic status improved, evidenced in weaning of insulin, and improved glycated hemoglobin, lipid profile, and hepatic enzymes. Chronic diarrhea resolved after surgery and prior to significant weight loss. Evaluation of stool bacterial composition and biomass demonstrated shifts in absolute abundance and taxonomic composition in longitudinal samples following surgery. This case demonstrates the potential efficacy of bariatric surgery in correcting the metabolic disruption of ROHHAD syndrome, producing long-term changes in gut microbiome composition and biomass.

Oral bacteria relative abundance in faeces increases due to gut microbiota depletion and is linked with patient outcomes

The detection of oral bacteria in faecal samples has been associated with inflammation and intestinal diseases. The increased relative abundance of oral bacteria in faeces has two competing explanations: either oral bacteria invade the gut ecosystem and expand (the 'expansion' hypothesis), or oral bacteria transit through the gut and their relative increase marks the depletion of other gut bacteria (the 'marker' hypothesis). Here we collected oral and faecal samples from mouse models of gut dysbiosis (antibiotic treatment and DSS-induced colitis) and used 16S ribosomal RNA sequencing to determine the abundance dynamics of oral bacteria. We found that the relative, but not absolute, abundance of oral bacteria increases, reflecting the 'marker' hypothesis. Faecal microbiome datasets from diverse patient cohorts, including healthy individuals and patients with allogeneic haematopoietic cell transplantation or inflammatory bowel disease, consistently support the 'marker' hypothesis and explain associations between oral bacterial abundance and patient outcomes consistent with depleted gut microbiota. By distinguishing between the two hypotheses, our study guides the interpretation of microbiome compositional data and could potentially identify cases where therapies are needed to rebuild the resident microbiome rather than protect against invading oral bacteria.

Rigorous Donor Selection for Fecal Microbiota Transplantation in Active Ulcerative Colitis: Key Lessons From a Randomized Controlled Trial Halted for Futility

BACKGROUND & AIMS

Rigorous donor preselection on microbiota level, strict anaerobic processing, and repeated fecal microbiota transplantation (FMT) administration were hypothesized to improve FMT induction of remission in ulcerative colitis (UC).

METHODS

The RESTORE-UC trial was a multi-centric, double-blind, sham-controlled, randomized trial. Patients with moderate to severe UC (defined by total Mayo 4-10) were randomly allocated to receive 4 anaerobic-prepared allogenic or autologous donor FMTs. Allogenic donor material was selected after a rigorous screening based on microbial cell count, enterotype, and the abundance of specific genera. The primary endpoint was steroid-free clinical remission (total Mayo ≤2, no sub-score >1) at week 8. A pre-planned futility analysis was performed after 66% (n = 72) of intended inclusions (n = 108). Quantitative microbiome profiling (n = 44) was performed at weeks 0 and 8.

RESULTS

In total, 72 patients were included, of which 66 received at least 1 FMT (allogenic FMT, n = 30 and autologous FMT, n = 36). At week 8, respectively, 3 and 5 patients reached the primary endpoint of steroid-free clinical remission (P = .72), indicating no treatment difference of at least 5% in favor of allogenic FMT. Hence, the study was stopped due to futility. Microbiome analysis showed numerically more enterotype transitions upon allogenic FMT compared with autologous FMT, and more transitions were observed when patients were treated with a different enterotype than their own at baseline (P = .01). Primary response was associated with lower total Mayo scores, lower bacterial cell counts, and higher Bacteroides 2 prevalence at baseline.

CONCLUSION

The RESTORE-UC trial did not meet its primary endpoint of increased steroid-free clinical remission at week 8. Further research should additionally consider patient selection, sterilized sham-control, increased frequency, density, and viability of FMT prior to administration.

CLINICALTRIALS

gov, Number: NCT03110289.

Dysbiosis in inflammatory bowel diseases: egg, not chicken

There is agreement that inflammatory bowel diseases are, both in terms of species composition and function, associated with an altered intestinal microbiome. This is usually described by the term "dysbiosis," but this is a vague definition lacking quantitative precision. In this brief narrative review, the evidence concerning the primary or secondary role of this dysbiotic state is critically evaluated. Among others, the following facts argue against a primary etiological impact: 1) There is no specific dysbiotic microbiome in IBD, 2) the presence or absence of mucosal inflammation has a profound impact on the composition of the microbiome, 3) dysbiosis is not specific for IBD but linked to many unrelated diseases, 4) antibiotics, probiotics, and microbiome transfer have a very limited therapeutic effect, 5) the microbiome in concordant twins is similar to disease-discordant twins, and 6) the microbiome in relatives of IBD patients later developing IBD is altered, but these individuals already display subclinical inflammation.

Co-Supplementation of Baobab Fiber and Arabic Gum Synergistically Modulates the In Vitro Human Gut Microbiome Revealing Complementary and Promising Prebiotic Properties

Arabic gum, a high molecular weight heteropolysaccharide, is a promising prebiotic candidate as its fermentation occurs more distally in the colon, which is the region where most chronic colonic diseases originate. Baobab fiber could be complementary due to its relatively simple structure, facilitating breakdown in the proximal colon. Therefore, the current study aimed to gain insight into how the human gut microbiota was affected in response to long-term baobab fiber and Arabic gum supplementation when tested individually or as a combination of both, allowing the identification of potential complementary and/or synergetic effects. The validated Simulator of the Human Intestinal Microbial Ecosystem (SHIME®), an in vitro gut model simulating the entire human gastrointestinal tract, was used. The microbial metabolic activity was examined, and quantitative 16S-targeted Illumina sequencing was used to monitor the gut microbial composition. Moreover, the effect on the gut microbial metabolome was quantitatively analyzed. Repeated administration of baobab fiber, Arabic gum, and their combination had a significant effect on the metabolic activity, diversity index, and community composition of the microbiome present in the simulated proximal and distal colon with specific impacts on Bifidobacteriaceae and Faecalibacterium prausnitzii. Despite the lower dosage strategy (2.5 g/day), co-supplementation of both compounds resulted in some specific synergistic prebiotic effects, including a biological activity throughout the entire colon, SCFA synthesis including a synergy on propionate, specifically increasing abundance of Akkermansiaceae and Christensenellaceae in the distal colon region, and enhancing levels of spermidine and other metabolites of interest (such as serotonin and ProBetaine).

Quantitative microbiome profiling of honey bee (Apis mellifera) guts is predictive of winter colony loss in northern Virginia (USA)

For the past 15 years, the proportion of honey bee hives that fail to survive winter has averaged ~ 30% in the United States. Winter hive loss has significant negative impacts on agriculture, the economy, and ecosystems. Compared to other factors, the role of honey bee gut microbial communities in driving winter hive loss has received little attention. We investigate the relationship between winter survival and honey bee gut microbiome composition of 168 honey bees from 23 hives, nine of which failed to survive through winter 2022. We found that there was a substantial difference in the abundance and community composition of honey bee gut microbiomes based on hive condition, i.e., winter survival or failure. The overall microbial abundance, as assessed using Quantitative Microbiome Profiling (QMP), was significantly greater in hives that survived winter 2022 than in those that failed, and the average overall abundance of each of ten bacterial genera was also greater in surviving hives. There were no significant differences in alpha diversity based on hive condition, but there was a highly significant difference in beta diversity. The bacterial genera Commensalibacter and Snodgrassella were positively associated with winter hive survival. Logistic regression and random forest machine learning models on pooled ASV counts for the genus data were highly predictive of winter outcome, although model performance decreased when samples from the location with no hive failures were excluded from analysis. As a whole, our results show that the abundance and community composition of honey bee gut microbiota is associated with winter hive loss, and can potentially be used as a diagnostic tool in evaluating hive health prior to the onset of winter. Future work on the functional characterization of the honey bee gut microbiome's role in winter survival is warranted.

Poly(D,l-lactide-co-glycolide) particles are metabolised by the gut microbiome and elevate short chain fatty acids

The production of short chain fatty acids (SCFAs) by the colonic microbiome has numerous benefits for human health, including maintenance of epithelial barrier function, suppression of colitis, and protection against carcinogenesis. Despite the therapeutic potential, there is currently no optimal approach for elevating the colonic microbiome's synthesis of SCFAs. In this study, poly(D,l-lactide-co-glycolide) (PLGA) was investigated for this application, as it was hypothesised that the colonic microbiota would metabolise PLGA to its lactate monomers, which would promote the resident microbiota's synthesis of SCFAs. Two grades of spray dried PLGA, alongside a lactate bolus control, were screened in an advanced model of the human colon, known as the M-SHIME® system. Whilst the high molecular weight (Mw) grade of PLGA was stable in the presence of the microbiota sourced from three healthy humans, the low Mw PLGA (PLGA 2) was found to be metabolised. This microbial degradation led to sustained release of lactate over 48 h and increased concentrations of the SCFAs propionate and butyrate. Further, microbial synthesis of harmful ammonium was significantly reduced compared to untreated controls. Interestingly, both types of PLGA were found to influence the composition of the luminal and mucosal microbiota in a donor-specific manner. An in vitro model of an inflamed colonic epithelium also showed the polymer to affect the expression of pro- and anti-inflammatory markers, such as interleukins 8 and 10. The findings of this study reveal PLGA's sensitivity to enzymatic metabolism in the gut, which could be harnessed for therapeutic elevation of colonic SCFAs.

Standards for fecal microbiota transplant: Tools and therapeutic advances

Fecal microbiota transplantation (FMT) has been demonstrated to be efficacious in preventing recurrent Clostridioides difficile (C. difficile) infections, and is being investigated for treatment of several other diseases including inflammatory bowel disease, cancer, obesity, liver disease, and diabetes. To speed up the translation of FMT into clinical practice as a safe and standardized therapeutic intervention, additional evidence-based technical and regulatory guidance is needed. To this end in May of 2022, the International Alliance for Biological Standardization (IABS) and the BIOASTER Microbiology Technology Institute hosted a second webinar to discuss key issues still impeding the advancement and standardization of FMT. The goal of this two-day webinar was to provide a forum for scientific experts to share and discuss data and key challenges with one another. Discussion included a focus on the evaluation of safety, efficacy, clinical trial design, reproducibility and accuracy in obtained microbiome measurements and data reporting, and the potential for standardization across these areas. It also focused on increasing the application potential and visibility of FMT beyond treating C. difficile infections.

Microbiome confounders and quantitative profiling challenge predicted microbial targets in colorectal cancer development

Despite substantial progress in cancer microbiome research, recognized confounders and advances in absolute microbiome quantification remain underused; this raises concerns regarding potential spurious associations. Here we study the fecal microbiota of 589 patients at different colorectal cancer (CRC) stages and compare observations with up to 15 published studies (4,439 patients and controls total). Using quantitative microbiome profiling based on 16S ribosomal RNA amplicon sequencing, combined with rigorous confounder control, we identified transit time, fecal calprotectin (intestinal inflammation) and body mass index as primary microbial covariates, superseding variance explained by CRC diagnostic groups. Well-established microbiome CRC targets, such as Fusobacterium nucleatum, did not significantly associate with CRC diagnostic groups (healthy, adenoma and carcinoma) when controlling for these covariates. In contrast, the associations of Anaerococcus vaginalis, Dialister pneumosintes, Parvimonas micra, Peptostreptococcus anaerobius, Porphyromonas asaccharolytica and Prevotella intermedia remained robust, highlighting their future target potential. Finally, control individuals (age 22-80 years, mean 57.7 years, standard deviation 11.3) meeting criteria for colonoscopy (for example, through a positive fecal immunochemical test) but without colonic lesions are enriched for the dysbiotic Bacteroides2 enterotype, emphasizing uncertainties in defining healthy controls in cancer microbiome research. Together, these results indicate the importance of quantitative microbiome profiling and covariate control for biomarker identification in CRC microbiome studies.

Non-significant influence between aerobic and anaerobic sample transport materials on gut (fecal) microbiota in healthy and fat-metabolic disorder Thai adults

Background

The appropriate sample handling for human fecal microbiota studies is essential to prevent changes in bacterial composition and quantities that could lead to misinterpretation of the data.

Methods

This study firstly identified the potential effect of aerobic and anaerobic fecal sample collection and transport materials on microbiota and quantitative microbiota in healthy and fat-metabolic disorder Thai adults aged 23-43 years. We employed metagenomics followed by 16S rRNA gene sequencing and 16S rRNA gene qPCR, to analyze taxonomic composition, alpha diversity, beta diversity, bacterial quantification, Pearson's correlation with clinical factors for fat-metabolic disorder, and the microbial community and species potential metabolic functions.

Results

Our study successfully obtained microbiota results in percent and quantitative compositions. Each sample exhibited quality sequences with a >99% Good's coverage index, and a relatively plateau rarefaction curve. Alpha diversity indices showed no statistical difference in percent and quantitative microbiota OTU richness and evenness, between aerobic and anaerobic sample transport materials. Obligate and facultative anaerobic species were analyzed and no statistical difference was observed. Supportively, the beta diversity analysis by non-metric multidimensional scale (NMDS) constructed using various beta diversity coefficients showed resembling microbiota community structures between aerobic and anaerobic sample transport groups (P = 0.86). On the other hand, the beta diversity could distinguish microbiota community structures between healthy and fat-metabolic disorder groups (P = 0.02), along with Pearson's correlated clinical parameters (i.e., age, liver stiffness, GGT, BMI, and TC), the significantly associated bacterial species and their microbial metabolic functions. For example, genera such as Ruminococcus and Bifidobacterium in healthy human gut provide functions in metabolisms of cofactors and vitamins, biosynthesis of secondary metabolites against gut pathogens, energy metabolisms, digestive system, and carbohydrate metabolism. These microbial functional characteristics were also predicted as healthy individual biomarkers by LEfSe scores. In conclusion, this study demonstrated that aerobic sample collection and transport (<48 h) did not statistically affect the microbiota and quantitative microbiota analyses in alpha and beta diversity measurements. The study also showed that the short-term aerobic sample collection and transport still allowed fecal microbiota differentiation between healthy and fat-metabolic disorder subjects, similar to anaerobic sample collection and transport. The core microbiota were analyzed, and the findings were consistent. Moreover, the microbiota-related metabolic potentials and bacterial species biomarkers in healthy and fat-metabolic disorder were suggested with statistical bioinformatics (i.e., Bacteroides plebeius).

A specific enterotype derived from gut microbiome of older individuals enables favorable responses to immune checkpoint blockade therapy

Immunotherapy has revolutionized cancer treatment, but inconsistent responses persist. Our study delves into the intriguing phenomenon of enhanced immunotherapy sensitivity in older individuals with cancers. Through a meta-analysis encompassing 25 small-to-mid-sized trials of immune checkpoint blockade (ICB), we demonstrate that older individuals exhibit heightened responsiveness to ICB therapy. To understand the underlying mechanism, we reanalyze single-cell RNA sequencing (scRNA-seq) data from multiple studies and unveil distinct upregulation of exhausted and cytotoxic T cell markers within the tumor microenvironment (TME) of older patients. Recognizing the potential role of gut microbiota in modulating the efficacy of immunotherapy, we identify an aging-enriched enterotype linked to improved immunotherapy outcomes in older patients. Fecal microbiota transplantation experiments in mice confirm the therapeutic potential of the aging-enriched enterotype, enhancing treatment sensitivity and reshaping the TME. Our discoveries confront the prevailing paradox and provide encouraging paths for tailoring cancer immunotherapy strategies according to an individual's gut microbiome profile.

Characterization of the gut bacterial and viral microbiota in latent autoimmune diabetes in adults

Latent autoimmune diabetes in adults (LADA) is a heterogeneous disease characterized by autoantibodies against insulin producing pancreatic beta cells and initial lack of need for insulin treatment. The aim of the present study was to investigate if individuals with LADA have an altered gut microbiota relative to non-diabetic control subjects, individuals with type 1 diabetes (T1D), and individuals with type 2 diabetes (T2D). Bacterial community profiling was performed with primers targeting the variable region 4 of the 16S rRNA gene and sequenced. Amplicon sequence variants (ASVs) were generated with DADA2 and annotated to the SILVA database. The gut virome was sequenced, using a viral particle enrichment and metagenomics approach, assembled, and quantified to describe the composition of the viral community. Comparison of the bacterial alpha- and beta-diversity measures revealed that the gut bacteriome of individuals with LADA resembled that of individuals with T2D. Yet, specific genera were found to differ in abundance in individuals with LADA compared with T1D and T2D, indicating that LADA has unique taxonomical features. The virome composition reflected the stability of the most dominant order Caudovirales and the families Siphoviridae, Podoviridae, and Inoviridae, and the dominant family Microviridae. Further studies are needed to confirm these findings.

Beyond Normalization: Incorporating Scale Uncertainty in Microbiome and Gene Expression Analysis

Though statistical normalizations are often used in differential abundance or differential expression analysis to address sample-to-sample variation in sequencing depth, we offer a better alternative. These normalizations often make strong, implicit assumptions about the scale of biological systems (e.g., microbial load). Thus, analyses are susceptible to even slight errors in these assumptions, leading to elevated rates of false positives and false negatives. We introduce scale models as a generalization of normalizations so researchers can model potential errors in assumptions about scale. By incorporating scale models into the popular ALDEx2 software, we enhance the reproducibility of analyses while often drastically decreasing false positive and false negative rates. We design scale models that are guaranteed to reduce false positives compared to equivalent normalizations. At least in the context of ALDEx2, we recommend using scale models over normalizations in all practical situations.

Analytical challenges in omics research on asthma and allergy: A National Institute of Allergy and Infectious Diseases workshop

Studies of asthma and allergy are generating increasing volumes of omics data for analysis and interpretation. The National Institute of Allergy and Infectious Diseases (NIAID) assembled a workshop comprising investigators studying asthma and allergic diseases using omics approaches, omics investigators from outside the field, and NIAID medical and scientific officers to discuss the following areas in asthma and allergy research: genomics, epigenomics, transcriptomics, microbiomics, metabolomics, proteomics, lipidomics, integrative omics, systems biology, and causal inference. Current states of the art, present challenges, novel and emerging strategies, and priorities for progress were presented and discussed for each area. This workshop report summarizes the major points and conclusions from this NIAID workshop. As a group, the investigators underscored the imperatives for rigorous analytic frameworks, integration of different omics data types, cross-disciplinary interaction, strategies for overcoming current limitations, and the overarching goal to improve scientific understanding and care of asthma and allergic diseases.

Volatile methyl jasmonate from roots triggers host-beneficial soil microbiome biofilms

The rhizosphere is a niche surrounding plant roots, where soluble and volatile molecules mediate signaling between plants and the associated microbiota. The preferred lifestyle of soil microorganisms is in the form of biofilms. However, less is known about whether root volatile organic compounds (rVOCs) can influence soil biofilms beyond the 2-10 mm rhizosphere zone influenced by root exudates. We report that rVOCs shift the microbiome composition and growth dynamics of complex soil biofilms. This signaling is evolutionarily conserved from ferns to higher plants. Methyl jasmonate (MeJA) is a bioactive signal of rVOCs that rapidly triggers both biofilm and microbiome changes. In contrast to the planktonic community, the resulting biofilm community provides ecological benefits to the host from a distance via growth enhancement. Thus, a volatile host defense signal, MeJA, is co-opted for assembling host-beneficial biofilms in the soil microbiota and extending the sphere of host influence in the rhizosphere.

Effects of fecal microbiota transplantation for recurrent Clostridium difficile infection in children on kidney replacement therapy: a pilot study

BACKGROUND

Recurrent Clostridium difficile infection (rCDI) is a rising problem in children with chronic diseases. Fecal microbiota transplantation (FMT) is a recent alternative for rCDI patients who do not respond to conventional treatment. FMT could have an additional positive effect on the intestinal dysbiosis and accumulation of uremic retention molecules (URM) associated with chronic kidney disease (CKD). Our aim was to investigate the clinical efficacy of FMT for rCDI in children with CKD together with the effect on dysbiosis and URM levels.

METHODS

We analyzed stool and blood samples before and until 3 months after FMT in 3 children between 4 and 8 years old with CKD and rCDI. The microbiome was analyzed by 16 s rRNA sequencing. URM were analyzed with ultra-performance liquid chromatography-tandem mass spectrometry. CRP and fecal calprotectin were analyzed as parameters for systemic and gut inflammation, respectively.

RESULTS

CDI resolved after FMT in all three without adverse events; one patient needed a second FMT. No significant effect on CRP and calprotectin was observed. Stool samples demonstrated a reduced richness and bacterial diversity which did not improve after FMT. We did observe a trend in the decrease of specific URM up to 3 months after FMT.

CONCLUSION

FMT is an effective treatment for rCDI in patients with CKD. Analysis of the microbiome showed an important intestinal dysbiosis that, besides a significant reduction in Clostridium difficile, did not significantly change after FMT. A trend for reduction was seen in some of the measured URM after FMT.

The efficacy of vaginal treatment for non-Lactobacillus dominant endometrial microbiota-A case-control study

AIM

Reduced Lactobacillus occupancy in the uterine microflora has been associated with implantation failure. This study aimed to evaluate a treatment for improving the uterine microflora.

METHODS

This study included patients diagnosed with repeated implantation failure-defined as failure to achieve pregnancy after two or more transfers of viable embryos-who were classified as non-Lactobacillus dominant. Treatment A comprised oral administration of antibiotics for 1 week, followed by oral probiotic butyrate tablets (3 g/day) for approximately 30 days. Treatment B comprised a 1-week course of oral (750 mg/day) and vaginal (250 mg/day) metronidazole, followed by a 1-week intravaginal administration of probiotic capsules (1 capsule/day) and continued oral administration of probiotics (1 capsule/day). Both treatments were compared in terms of efficacy in improving vaginal flora. Improvement was defined as Lactobacillus occupancy >90% or an increase in Lactobacillus occupancy >20%.

RESULTS

Seven (41.2%) of 17 patients in the Treatment A group improved in response to the treatment. Contrastingly, 9 (90.0%) of 10 patients improved in the Treatment B group (p = 0.0127). Following treatment, Lactobacillus occupancy in the Treatment B group (62.9% ± 12.7%) was significantly higher than that in the Treatment A group (5.7% ± 9.8%) (p = 0.0242).

CONCLUSIONS

This study demonstrates the effectiveness of combining antibiotics and probiotics in vaginal formulations for treating abnormal uterine microflora. However, its potential impact on in vitro fertilization outcomes remains unclear and warrants further investigation through larger, more comprehensive studies.

Deep learning methods in metagenomics: a review

The ever-decreasing cost of sequencing and the growing potential applications of metagenomics have led to an unprecedented surge in data generation. One of the most prevalent applications of metagenomics is the study of microbial environments, such as the human gut. The gut microbiome plays a crucial role in human health, providing vital information for patient diagnosis and prognosis. However, analysing metagenomic data remains challenging due to several factors, including reference catalogues, sparsity and compositionality. Deep learning (DL) enables novel and promising approaches that complement state-of-the-art microbiome pipelines. DL-based methods can address almost all aspects of microbiome analysis, including novel pathogen detection, sequence classification, patient stratification and disease prediction. Beyond generating predictive models, a key aspect of these methods is also their interpretability. This article reviews DL approaches in metagenomics, including convolutional networks, autoencoders and attention-based models. These methods aggregate contextualized data and pave the way for improved patient care and a better understanding of the microbiome's key role in our health.

mbDecoda: a debiased approach to compositional data analysis for microbiome surveys

Potentially pathogenic or probiotic microbes can be identified by comparing their abundance levels between healthy and diseased populations, or more broadly, by linking microbiome composition with clinical phenotypes or environmental factors. However, in microbiome studies, feature tables provide relative rather than absolute abundance of each feature in each sample, as the microbial loads of the samples and the ratios of sequencing depth to microbial load are both unknown and subject to considerable variation. Moreover, microbiome abundance data are count-valued, often over-dispersed and contain a substantial proportion of zeros. To carry out differential abundance analysis while addressing these challenges, we introduce mbDecoda, a model-based approach for debiased analysis of sparse compositions of microbiomes. mbDecoda employs a zero-inflated negative binomial model, linking mean abundance to the variable of interest through a log link function, and it accommodates the adjustment for confounding factors. To efficiently obtain maximum likelihood estimates of model parameters, an Expectation Maximization algorithm is developed. A minimum coverage interval approach is then proposed to rectify compositional bias, enabling accurate and reliable absolute abundance analysis. Through extensive simulation studies and analysis of real-world microbiome datasets, we demonstrate that mbDecoda compares favorably with state-of-the-art methods in terms of effectiveness, robustness and reproducibility.

Cluster-specific associations between the gut microbiota and behavioral outcomes in preschool-aged children

BACKGROUND

The gut microbiota is recognized as a regulator of brain development and behavioral outcomes during childhood. Nonetheless, associations between the gut microbiota and behavior are often inconsistent among studies in humans, perhaps because many host-microbe relationships vary widely between individuals. This study aims to stratify children based on their gut microbiota composition (i.e., clusters) and to identify novel gut microbiome cluster-specific associations between the stool metabolomic pathways and child behavioral outcomes.

METHODS

Stool samples were collected from a community sample of 248 typically developing children (3-5 years). The gut microbiota was analyzed using 16S sequencing while LC-MS/MS was used for untargeted metabolomics. Parent-reported behavioral outcomes (i.e., Adaptive Skills, Internalizing, Externalizing, Behavioral Symptoms, Developmental Social Disorders) were assessed using the Behavior Assessment System for Children (BASC-2). Children were grouped based on their gut microbiota composition using the Dirichlet multinomial method, after which differences in the metabolome and behavioral outcomes were investigated.

RESULTS

Four different gut microbiota clusters were identified, where the cluster enriched in both Bacteroides and Bifidobacterium (Ba2) had the most distinct stool metabolome. The cluster characterized by high Bifidobacterium abundance (Bif), as well as cluster Ba2, were associated with lower Adaptive Skill scores and its subcomponent Social Skills. Cluster Ba2 also had significantly lower stool histidine to urocanate turnover, which in turn was associated with lower Social Skill scores in a cluster-dependent manner. Finally, cluster Ba2 had increased levels of compounds involved in Galactose metabolism (i.e., stachyose, raffinose, alpha-D-glucose), where alpha-D-glucose was associated with the Adaptive Skill subcomponent Daily Living scores (i.e., ability to perform basic everyday tasks) in a cluster-dependent manner.

CONCLUSIONS

These data show novel associations between the gut microbiota, its metabolites, and behavioral outcomes in typically developing preschool-aged children. Our results support the concept that cluster-based groupings could be used to develop more personalized interventions to support child behavioral outcomes. Video Abstract.

Identification of bidirectional causal links between gut microbiota and narcolepsy type 1 using Mendelian randomization

STUDY OBJECTIVES

Narcolepsy type 1 (NT1), characterized by cataplexy and orexin deficiency, is a rare and frequently debilitating neurological disorder. It has been noted to have connections with the gut microbiota, yet the exact causal relationships remain unclear.

METHODS

We conducted a comprehensive bidirectional Mendelian randomization (MR) study to rigorously investigate the causal links between the gut microbiota and NT1, utilizing genetic datasets from the MiBioGen consortium and FinnGen consortium, respectively. The inverse-variance weighted (IVW) method was employed to obtain the primary MR estimates, supplemented by several alternative methods as well as sensitivity analyses including Cochran's Q, MR-Egger, MR pleiotropy residual sum and outlier, leave-one-out, and genetic colocalization.

RESULTS

Our findings indicated that an increased relative abundance of five genera including Blautia (p = 4.47E-5), Collinsella (p = 0.036), Gordonibacter (p = 0.047), Hungatella (p = 0.015), and Lachnospiraceae UCG010 (p = 0.027) may be associated with a decreased risk of NT1. Conversely, an increased relative abundance of class Betaproteobacteria (p = 0.032), genus Alloprevotella (p = 0.009), and genus Ruminiclostridium6 (p = 0.029) may potentially heighten the risk of NT1. The onset of NT1 may lead to a decrease in the relative abundance of genus Eubacterium eligens group (p = 0.022), while a increase in the family Family XI (p = 0.009), genus Hungatella (p = 0.005), genus Prevotella (p = 0.013), and unknown genus id.2001 (p = 0.019). These findings remained robust under all sensitivity analyses.

CONCLUSIONS

Our results offer robust evidence for the bidirectional causal links between particular gut microbial taxa and NT1, underscoring the significance of the microbiota-gut-brain axis in the pathological process of NT1.

Considerations for the inclusion of metabarcoding data in the plant protection product risk assessment of the soil microbiome

There is increasing interest in further developing the plant protection product (PPP) environmental risk assessment, particularly within the European Union, to include the assessment of soil microbial community composition, as measured by metabarcoding approaches. However, to date, there has been little discussion as to how this could be implemented in a standardized, reliable, and robust manner suitable for regulatory decision-making. Introduction of metabarcoding-based assessments of the soil microbiome into the PPP risk assessment would represent a significant increase in the degree of complexity of the data that needs to be processed and analyzed in comparison to the existing risk assessment on in-soil organisms. The bioinformatics procedures to process DNA sequences into community compositional data sets currently lack standardization, while little information exists on how these data should be used to generate regulatory endpoints and the ways in which these endpoints should be interpreted. Through a thorough and critical review, we explore these challenges. We conclude that currently, we do not have a sufficient degree of standardization or understanding of the required bioinformatics and data analysis procedures to consider their use in an environmental risk assessment context. However, we highlight critical knowledge gaps and the further research required to understand whether metabarcoding-based assessments of the soil microbiome can be utilized in a statistically and ecologically relevant manner within a PPP risk assessment. Only once these challenges are addressed can we consider if and how we should use metabarcoding as a tool for regulatory decision-making to assess and monitor ecotoxicological effects on soil microorganisms within an environmental risk assessment of PPPs. Integr Environ Assess Manag 2024;20:337-358. © 2023 SETAC.

Dysbiosis and Associated Stool Features Improve Prediction of Response to Biological Therapy in Inflammatory Bowel Disease

BACKGROUND & AIMS

Dysbiosis of the gut microbiota is considered a key contributor to inflammatory bowel disease (IBD) etiology. Here, we investigated potential associations between microbiota composition and the outcomes to biological therapies.

METHODS

The study prospectively recruited 296 patients with active IBD (203 with Crohn's disease, 93 with ulcerative colitis) initiating biological therapy. Quantitative microbiome profiles of pretreatment and posttreatment fecal samples were obtained combining flow cytometry with 16S amplicon sequencing. Therapeutic response was assessed by endoscopy, patient-reported outcomes, and changes in fecal calprotectin. The effect of therapy on microbiome variation was evaluated using constrained ordination methods. Prediction of therapy outcome was performed using logistic regression with 5-fold cross-validation.

RESULTS

At baseline, 65.9% of patients carried the dysbiotic Bacteroides2 (Bact2) enterotype, with a significantly higher prevalence among patients with ileal involvement (76.8%). Microbiome variation was associated with the choice of biological therapy rather than with therapeutic outcome. Only anti-tumor necrosis factor-α treatment resulted in a microbiome shift away from Bact2, concomitant with an increase in microbial load and butyrogen abundances and a decrease in potentially opportunistic Veillonella. Remission rates for patients hosting Bact2 at baseline were significantly higher with anti-tumor necrosis factor-α than with vedolizumab (65.1% vs 35.2%). A prediction model, based on anthropometrics and clinical data, stool features (microbial load, moisture, and calprotectin), and Bact2 detection predicted treatment outcome with 73.9% accuracy for specific biological therapies.

CONCLUSION

Fecal characterization based on microbial load, moisture content, calprotectin concentration, and enterotyping may aid in the therapeutic choice of biological therapy in IBD.