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Eating patterns, i.e. meal frequency and circadian timing of meals, are often modified in weight loss and metabolic healing strategies. However, in-depth research into the effects on the gut microbiome remains scarce, particularly across various colon regions and niches. We identified eating patterns to contribute in shaping the in vitro gut biomass production, metabolism, and microbial community compositions by subjecting four faecal microbiomes to a pattern that is standardized for a dynamic gut model (feeding at 09, 17, and 01 h), a typical Western (breakfast, lunch, and dinner at 09, 13, and 19 h, respectively), and a time-restricted pattern (single meal at 09 h). While eating patterns moderately affected the microbiome (2.4% and 1.8% significant variation in proportional and quantitative microbial compositions, respectively), significant changes were noted in the time-restricted pattern, including increased Bacteroides, Butyricicoccus, Dialister, and Faecalibacterium abundances. Sampling every 4 h revealed no significant circadian fluctuations in biomass production, microbial community compositions, or functionality. Longer fasting times favoured the growth of slower-growing species, such as Akkermansia, Dialister, and Parasutterella over faster-growers, such as Pseudomonas and Stenotrophomonas. Our findings illustrate the importance of recording and considering eating patterns as a gut microbiome determinant in in vivo and in vitro dietary intervention studies., (© The Author(s) 2023. Published by Oxford University Press on behalf of FEMS.)

Background: Transit time is an important modulator of the human gut microbiome. The inability to modify transit time as the sole variable hampers mechanistic in vivo microbiome research. We singled out gut transit time in an unprecedented in vitro approach by subjecting faecal microbial communities from six individuals with either short, medium or long in vivo transit times, to three different colonic transit times of 21, 32 and 63 h in the validated human gut in vitro model, SHIME., Results: Transit time was identified as the single most important driver of microbial cell concentrations (52%), metabolic activity (45%) and quantitative (24%) and proportional (22%) community composition. Deceleration of transit was characterised by a significant decrease of specific Bifidobacterium and Veillonella spp. and increase of specific fibre degrading bacteria and nutrient specialists, such as Bacteroides, Prevotella, Ruminococcus, Bilophila and Akkermansia spp. These microbial communities reached a higher population density and net carbohydrate fermentation, leading to an increased SCFA production at longer transit times. In contrast, the carbohydrate-to-biomass production efficiency was increased at shorter transits, particularly in well-adapted faecal microbiomes from donors with short in vivo transit. Said adaptation was also reflected in the carbohydrate-to-SCFA conversion efficiency which varied with donor, but also colon region and SCFA chain length. A long transit time promoted propionate production, whereas butyrate production and butyrate producers were selectively enriched in the proximal colon at medium transit time., Conclusion: Microbial growth rates and nutrient utilisation efficiency mediate the species-specific gut microbiota response to in vitro transit time variation, which is the main driver of in vitro microbial load, metabolism and community composition. Given the in vivo transit time variation within and between individuals, the personalisation of in vitro transit time based on in vivo data is required to accurately study intra- and inter-individual differences in gut microbiome structure, functionality and interactions with host and environmental modulators. Video Abstract., (© 2023. The Author(s).)

Whether the human fetus and the prenatal intrauterine environment (amniotic fluid and placenta) are stably colonized by microbial communities in a healthy pregnancy remains a subject of debate. Here we evaluate recent studies that characterized microbial populations in human fetuses from the perspectives of reproductive biology, microbial ecology, bioinformatics, immunology, clinical microbiology and gnotobiology, and assess possible mechanisms by which the fetus might interact with microorganisms. Our analysis indicates that the detected microbial signals are likely the result of contamination during the clinical procedures to obtain fetal samples or during DNA extraction and DNA sequencing. Furthermore, the existence of live and replicating microbial populations in healthy fetal tissues is not compatible with fundamental concepts of immunology, clinical microbiology and the derivation of germ-free mammals. These conclusions are important to our understanding of human immune development and illustrate common pitfalls in the microbial analyses of many other low-biomass environments. The pursuit of a fetal microbiome serves as a cautionary example of the challenges of sequence-based microbiome studies when biomass is low or absent, and emphasizes the need for a trans-disciplinary approach that goes beyond contamination controls by also incorporating biological, ecological and mechanistic concepts., (© 2023. Springer Nature Limited.)

Accumulating evidence indicates that gut transit time is a key factor in shaping the gut microbiota composition and activity, which are linked to human health. Both population-wide and small-scale studies have identified transit time as a top covariate contributing to the large interindividual variation in the faecal microbiota composition. Despite this, transit time is still rarely being considered in the field of the human gut microbiome. Here, we review the latest research describing how and why whole gut and segmental transit times vary substantially between and within individuals, and how variations in gut transit time impact the gut microbiota composition, diversity and metabolism. Furthermore, we discuss the mechanisms by which the gut microbiota may causally affect gut motility. We argue that by taking into account the interindividual and intraindividual differences in gut transit time, we can advance our understanding of diet-microbiota interactions and disease-related microbiome signatures, since these may often be confounded by transient or persistent alterations in transit time. Altogether, a better understanding of the complex, bidirectional interactions between the gut microbiota and transit time is required to better understand gut microbiome variations in health and disease., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Mosquitoes are important vectors for many arboviruses. It is becoming increasingly clear that various symbiotic microorganisms (including bacteria and insect-specific viruses; ISVs) in mosquitoes have the potential to modulate the ability of mosquitoes to transmit arboviruses. In this study, we compared the bacteriome and virome (both eukaryotic viruses and bacteriophages) of female adult Aedes aegypti and Culex quinquefasciatus mosquitoes fed with sucrose/water, blood, or blood spiked with Zika virus (ZIKV) or West Nile virus (WNV), respectively. Furthermore, we investigated associations between the microbiota and vector competence. We show that the influence of arboviruses on the mosquito microbiome-and vice versa-is distinct for each combination of arbovirus/mosquito species. The presence of ZIKV resulted in a temporarily increased Aedes ISV diversity. However, this effect was distinct for different ISVs: some ISVs decreased following the blood meal (Aedes aegypti totivirus), whereas other ISVs increased only when the blood contained ZIKV (Guadeloupe mosquito virus). Also, the diversity of the Aedes bacteriome depended on the diet and the presence of ZIKV, with a lower diversity observed for mosquitoes receiving blood without ZIKV. In Cx. quinquefasciatus , some ISVs increased in WNV-infected mosquitoes (Guadeloupe Culex tymo-like virus). Particularly, the presence of Wenzhou sobemo-like virus 3 (WSLV3) was associated with the absence of infectious WNV in mosquito heads, suggesting that WSLV3 might affect vector competence for WNV. Distinct profiles of bacteriophages were identified in Culex mosquitoes depending on diet, despite the lack of clear changes in the bacteriome. Overall, our data demonstrate a complex three-way interaction among arboviruses, resident microbiota, and the host, which is distinct for different arbovirus-mosquito combinations. A better understanding of these interactions may lead to the identification of microbiota able to suppress the ability of arbovirus transmission to humans, and hence improved arbovirus control measures. IMPORTANCE In this study, we first utilized the single mosquito microbiome analysis, demonstrating a complex three-way interaction among arboviruses, resident microbiota, and the host, which is distinct for different arbovirus-mosquito combinations. Some of the previously described "core virus" increased in the mosquitos receiving viral blood meal, like Guadeloupe mosquito virus and Guadeloupe Culex tymo-like virus, suggesting their potential roles in ZIKV and WNV infection. Notably, Wenzhou sobemo-like virus 3 was associated with the absence of infectious WNV in heads of Culex mosquitoes, which might affect vector competence for WNV. A better understanding of these interactions will lead to the identification of microbiota able to suppress the ability of arbovirus transmission to humans, and hence improved arbovirus control measures.

Extensive scientific and clinical microbiome studies have explored contemporary variation and dynamics of the gut microbiome in human health and disease 1-3 , yet the role of long-term life history effects has been underinvestigated. Here, we analyzed the current, quantitative microbiome composition in the older adult Bruneck Study cohort (Italians, Bruneck, n = 304 (male, 154; female, 150); age 65-98 years) with extensive clinical, demographic, lifestyle and nutritional data collected over the past 26 years 4 . Multivariate analysis of historical variables indicated that medication history, historical physical activity, past dietary habits and specific past laboratory blood parameters explain a significant fraction of current quantitative microbiome variation in older adults, enlarging the explanatory power of contemporary covariates by 33.4%. Prediction of current enterotype by a combination of past and contemporary host variables revealed good levels of predictability (area under the curve (AUC), 0.78-0.83), with Prevotella and dysbiotic Bacteroides 2 being the best predicted enterotypes. These findings demonstrate long-term life history effects on the microbiota and provide insights into lifestyle variables and their role in maintaining a healthy gut microbiota in later life., (© 2022. The Author(s).)

Microbial culturing and meta-omic profiling technologies have significantly advanced our understanding of the taxonomic and functional variation of the human microbiome and its impact on host processes. The next increase in resolution will come by understanding the role of low-abundant and less-prevalent bacteria and the study of individual cell behaviors that underlie the complexity of microbial ecosystems. To this aim, single-cell techniques are being rapidly developed to isolate, culture, and characterize the genomes and transcriptomes of individual microbes in complex communities. Here, we discuss how these single-cell technologies are providing unique insights into the biology and behavior of human microbiomes., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 Elsevier Inc. All rights reserved.)

SignificanceMicrobes colonizing the infant gut during the first year(s) of life play an important role in immune system development. We show that after birth the (nearly) sterile gut is rapidly colonized by bacteria and their viruses (phages), which often show a strong cooccurrence. Most viruses infecting the infant do not cause clinical signs and their numbers strongly increase after day-care entrance. The infant diet is clearly reflected by identification of plant-infecting viruses, whereas fungi and parasites are not part of a stable gut microbiota. These temporal high-resolution baseline data about the gut colonization process will be valuable for further investigations of pathogenic viruses, dynamics between phages and their bacterial host, as well as studies investigating infants with a disturbed microbiota.

Previous microbiome and metabolome analyses exploring non-communicable diseases have paid scant attention to major confounders of study outcomes, such as common, pre-morbid and co-morbid conditions, or polypharmacy. Here, in the context of ischemic heart disease (IHD), we used a study design that recapitulates disease initiation, escalation and response to treatment over time, mirroring a longitudinal study that would otherwise be difficult to perform given the protracted nature of IHD pathogenesis. We recruited 1,241 middle-aged Europeans, including healthy individuals, individuals with dysmetabolic morbidities (obesity and type 2 diabetes) but lacking overt IHD diagnosis and individuals with IHD at three distinct clinical stages-acute coronary syndrome, chronic IHD and IHD with heart failure-and characterized their phenome, gut metagenome and serum and urine metabolome. We found that about 75% of microbiome and metabolome features that distinguish individuals with IHD from healthy individuals after adjustment for effects of medication and lifestyle are present in individuals exhibiting dysmetabolism, suggesting that major alterations of the gut microbiome and metabolome might begin long before clinical onset of IHD. We further categorized microbiome and metabolome signatures related to prodromal dysmetabolism, specific to IHD in general or to each of its three subtypes or related to escalation or de-escalation of IHD. Discriminant analysis based on specific IHD microbiome and metabolome features could better differentiate individuals with IHD from healthy individuals or metabolically matched individuals as compared to the conventional risk markers, pointing to a pathophysiological relevance of these features., (© 2022. The Author(s).)

Microbial dysbiosis is an established finding in patients with inflammatory bowel disease (IBD), but host-microbial interactions are poorly understood. We aimed to unravel the effect of microbiota exposure on intestinal epithelial cells. Confluent Transwell® organoid monolayers of eight UC patients and eight non-IBD controls were co-cultured for six hours with microbiota (3x10 8 cells) of UC patients or a healthy volunteer (HV), in the presence or absence of an inflammatory cytokine mix. Transepithelial electrical resistance (TEER), fluorescein isothiocyanate (FITC) dextran measurements, and RNA sequencing were performed on epithelial cells, and 16S rRNA sequencing on microbiota samples before and after co-culture. Transcriptomic response following microbiota exposure was not different between epithelial cells from UC patients or non-IBD controls. Following UC microbiota exposure, but not HV microbiota, a strong decrease in epithelial barrier integrity was observed in both UC and HV epithelial cells by TEER and FITC dextran measurements. Exposure of inflamed epithelium to UC microbiota induced transcriptomic stress pathways including activation of EGR1, MAPK and JAK/STAT signaling, as well as AP-1 family and FOSL transcripts. Stress responses after HV microbiota stimulation were milder. We conclude that not the epithelial cell origin (UC versus non-IBD) but the microbial donor drives transcriptomic responses, as exposure to UC microbiota was sufficient to induce stress responses in all epithelial cells. Further research on therapies to restore the microbial balance, to remove the constant trigger of dysbiosis, is required.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery 1-5 . Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease., (© 2021. The Author(s), under exclusive licence to Springer Nature Limited.)

The particularly interdisciplinary nature of human microbiome research makes the organization and reporting of results spanning epidemiology, biology, bioinformatics, translational medicine and statistics a challenge. Commonly used reporting guidelines for observational or genetic epidemiology studies lack key features specific to microbiome studies. Therefore, a multidisciplinary group of microbiome epidemiology researchers adapted guidelines for observational and genetic studies to culture-independent human microbiome studies, and also developed new reporting elements for laboratory, bioinformatics and statistical analyses tailored to microbiome studies. The resulting tool, called 'Strengthening The Organization and Reporting of Microbiome Studies' (STORMS), is composed of a 17-item checklist organized into six sections that correspond to the typical sections of a scientific publication, presented as an editable table for inclusion in supplementary materials. The STORMS checklist provides guidance for concise and complete reporting of microbiome studies that will facilitate manuscript preparation, peer review, and reader comprehension of publications and comparative analysis of published results., (© 2021. The Author(s), under exclusive licence to Springer Nature America, Inc.)

A recently introduced quantitative framework for gut microbiota analysis indicated that microbial load alterations can be linked to various diseases, making it essential to pinpoint its determinants. We identified nutrient load as a main driver of the quantitative microbial community composition and functionality in vitro by stepwise decreasing standardized feed concentrations from 100% to 33%, 20% and 10% in 5-day intervals. While the proportional composition and metabolic profile were mainly determined by the inter-individual variability (35% and 41%), nutrient load accounted for 58%, 23% and 65% of the observed variation in the microbial load, quantitative composition and net daily metabolite production, respectively. After the 10-fold nutrient reduction, the microbial load decreased by 79.72 ± 9% and 82.96 ± 1.66% in the proximal and distal colon, respectively, while the net total short-chain fatty acid production dropped by 79.42 ± 4.42% and 84.58 ± 2.42%, respectively. The majority of microbial taxa quantitatively decreased, whereas a select group of nutritional specialists, such as Akkermansia muciniphila and Bilophila wadsworthia, and a number of opportunistic pathogens remained unaffected. This shows that nutrient load is an important driver of the human gut microbiome and should be considered in future in vitro and in vivo dietary research., (© The Author(s) 2021. Published by Oxford University Press on behalf of FEMS. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

While metagenomic sequencing has become the tool of preference to study host-associated microbial communities, downstream analyses and clinical interpretation of microbiome data remains challenging due to the sparsity and compositionality of sequence matrices. Here, we evaluate both computational and experimental approaches proposed to mitigate the impact of these outstanding issues. Generating fecal metagenomes drawn from simulated microbial communities, we benchmark the performance of thirteen commonly used analytical approaches in terms of diversity estimation, identification of taxon-taxon associations, and assessment of taxon-metadata correlations under the challenge of varying microbial ecosystem loads. We find quantitative approaches including experimental procedures to incorporate microbial load variation in downstream analyses to perform significantly better than computational strategies designed to mitigate data compositionality and sparsity, not only improving the identification of true positive associations, but also reducing false positive detection. When analyzing simulated scenarios of low microbial load dysbiosis as observed in inflammatory pathologies, quantitative methods correcting for sampling depth show higher precision compared to uncorrected scaling. Overall, our findings advocate for a wider adoption of experimental quantitative approaches in microbiome research, yet also suggest preferred transformations for specific cases where determination of microbial load of samples is not feasible.

Recent population-based 1-4 and clinical studies 5 have identified a range of factors associated with human gut microbiome variation. Murine quantitative trait loci 6 , human twin studies 7 and microbiome genome-wide association studies 1,3,8-12 have provided evidence for genetic contributions to microbiome composition. Despite this, there is still poor overlap in genetic association across human studies. Using appropriate taxon-specific models, along with support from independent cohorts, we show an association between human host genotype and gut microbiome variation. We also suggest that interpretation of applied analyses using genetic associations is complicated by the probable overlap between genetic contributions and heritable components of host environment. Using faecal 16S ribosomal RNA gene sequences and host genotype data from the Flemish Gut Flora Project (n = 2,223) and two German cohorts (FoCus, n = 950; PopGen, n = 717), we identify genetic associations involving multiple microbial traits. Two of these associations achieved a study-level threshold of P = 1.57 × 10 -10 ; an association between Ruminococcus and rs150018970 near RAPGEF1 on chromosome 9, and between Coprococcus and rs561177583 within LINC01787 on chromosome 1. Exploratory analyses were undertaken using 11 other genome-wide associations with strong evidence for association (P < 2.5 × 10 -8 ) and a previously reported signal of association between rs4988235 (MCM6/LCT) and Bifidobacterium. Across these 14 single-nucleotide polymorphisms there was evidence of signal overlap with other genome-wide association studies, including those for age at menarche and cardiometabolic traits. Mendelian randomization analysis was able to estimate associations between microbial traits and disease (including Bifidobacterium and body composition); however, in the absence of clear microbiome-driven effects, caution is needed in interpretation. Overall, this work marks a growing catalogue of genetic associations that will provide insight into the contribution of host genotype to gut microbiome. Despite this, the uncertain origin of association signals will likely complicate future work looking to dissect function or use associations for causal inference analysis.

Despite recent advances in sequencing and culturing, a deep knowledge of the wiring and functioning of the human gut ecosystem and its microbiota as a community is still missing. A holistic mechanistic understanding will require study of the gut microbiota as an interactive and spatially organized biological system, which is difficult to do in complex natural communities. Synthetic gut microbial ecosystems can function as model systems to further current understanding of the composition, stability and functional activities of the microbiota. In this Review, we provide an overview of the current synthetic ecology strategies that can be used towards a more comprehensive understanding of the human gut ecosystem. Such approaches that integrate in vitro experiments using cultured isolates with mathematical modelling will enable the ultimate goal: translating mechanistic and ecological knowledge into novel and effective therapies.

House flies (Musca domestica) are widespread, synanthropic filth flies commonly found on decaying matter, garbage, and feces as well as human food. They have been shown to vector microbes, including clinically relevant pathogens. Previous studies have demonstrated that house flies carry a complex and variable prokaryotic microbiota, but the main drivers underlying this variability and the influence of habitat on the microbiota remain understudied. Moreover, the differences between the external and internal microbiota and the eukaryotic components have not been examined. To obtain a comprehensive view of the fly microbiota and its environmental drivers, we sampled over 400 flies from two geographically distinct countries (Belgium and Rwanda) and three different environments-farms, homes, and hospitals. Both the internal as well as external microbiota of the house flies were studied, using amplicon sequencing targeting both bacteria and fungi. Results show that the house fly's internal bacterial community is very diverse yet relatively consistent across geographic location and habitat, dominated by genera Staphylococcus and Weissella. The external bacterial community, however, varies with geographic location and habitat. The fly fungal microbiota carries a distinct signature correlating with the country of sampling, with order Capnodiales and genus Wallemia dominating Belgian flies and genus Cladosporium dominating Rwandan fly samples. Together, our results reveal an intricate country-specific pattern for fungal communities, a relatively stable internal bacterial microbiota and a variable external bacterial microbiota that depends on geographical location and habitat. These findings suggest that vectoring of a wide spectrum of environmental microbes occurs principally through the external fly body surface, while the internal microbiome is likely more limited by fly physiology.

Current sequencing-based analyses of faecal microbiota quantify microbial taxa and metabolic pathways as fractions of the sample sequence library generated by each analysis. Although these relative approaches permit detection of disease-associated microbiome variation, they are limited in their ability to reveal the interplay between microbiota and host health. Comparative analyses of relative microbiome data cannot provide information about the extent or directionality of changes in taxa abundance or metabolic potential. If microbial load varies substantially between samples, relative profiling will hamper attempts to link microbiome features to quantitative data such as physiological parameters or metabolite concentrations. Saliently, relative approaches ignore the possibility that altered overall microbiota abundance itself could be a key identifier of a disease-associated ecosystem configuration. To enable genuine characterization of host-microbiota interactions, microbiome research must exchange ratios for counts. Here we build a workflow for the quantitative microbiome profiling of faecal material, through parallelization of amplicon sequencing and flow cytometric enumeration of microbial cells. We observe up to tenfold differences in the microbial loads of healthy individuals and relate this variation to enterotype differentiation. We show how microbial abundances underpin both microbiota variation between individuals and covariation with host phenotype. Quantitative profiling bypasses compositionality effects in the reconstruction of gut microbiota interaction networks and reveals that the taxonomic trade-off between Bacteroides and Prevotella is an artefact of relative microbiome analyses. Finally, we identify microbial load as a key driver of observed microbiota alterations in a cohort of patients with Crohn's disease, here associated with a low-cell-count Bacteroides enterotype (as defined through relative profiling).

A complex microbial system consisting of six different interconnected localities was thoroughly investigated at full scale for over a year. The metacommunity concept originating from macro-ecology was used to uncover mechanisms of community assembly by observing microbial interrelationships in and between the different localities via correlation and network analysis. The individual-based observation approach was applied using high-throughput microbial community cytometry in addition to next generation sequencing. We found robust α-diversity values for each of the six localities and high β-diversity values despite directed connectivity between localities, classifying for endpoint assembly of organisms in each locality. Endpoint characteristics were based on subcommunities with high cell numbers whereas those with lower cell numbers were involved in dispersal. Perturbation caused abiotic parameters to alter local community assembly with especially the rare cells announcing community restructuration processes. The mass-effect paradigm as part of the metacommunity concept was identified by an increase in interlocality biotic correlations under perturbation which, however, did not unbalance the predominant species-sorting paradigm in the studied full scale metacommunity. Data as generated in this study might contribute to the development of individual-based models for controlling managed multispecies natural systems in future., (© 2016 Society for Applied Microbiology and John Wiley & Sons Ltd.)

Unlabelled: : When designing a case-control study to investigate differences in microbial composition, it is fundamental to assess the sample sizes needed to detect an hypothesized difference with sufficient statistical power. Our application includes power calculation for (i) a recoded version of the two-sample generalized Wald test of the 'HMP' R-package for comparing community composition, and (ii) the Wilcoxon-Mann-Whitney test for comparing operational taxonomic unit-specific abundances between two samples (optional). The simulation-based power calculations make use of the Dirichlet-Multinomial model to describe and generate abundances. The web interface allows for easy specification of sample and effect sizes. As an illustration of our application, we compared the statistical power of the two tests, with and without stratification of samples. We observed that statistical power increases considerably when stratification is employed, meaning that less samples are needed to detect the same effect size with the same power., Availability and Implementation: The web interface is written in R code using Shiny (RStudio Inc., 2016) and it is available at https://fedematt.shinyapps.io/shinyMB The R code for the recoded generalized Wald test can be found at https://github.com/mafed/msWaldHMP CONTACT: Federico.Mattiello@UGent.be., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.)

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Despite recent progress, the organization and ecological properties of the intestinal microbial ecosystem remain under-investigated. Here, using a manually curated metabolic module framework for (meta-)genomic data analysis, we studied species-function relationships in gut microbial genomes and microbiomes. Half of gut-associated species were found to be generalists regarding overall substrate preference, but we observed significant genus-level metabolic diversification linked to bacterial life strategies. Within each genus, metabolic consistency varied significantly, being low in Firmicutes genera and higher in Bacteroides. Differentiation of fermentable substrate degradation potential contributed to metagenomic functional repertoire variation between individuals, with different enterotypes showing distinct saccharolytic/proteolytic/lipolytic profiles. Finally, we found that module-derived functional redundancy was reduced in the low-richness Bacteroides enterotype, potentially indicating a decreased resilience to perturbation, in line with its frequent association to dysbiosis. These results provide insights into the complex structure of gut microbiome-encoded metabolic properties and emphasize the importance of functional and ecological assessment of gut microbiome variation in clinical studies.

The recent increase in the number of microbial time series studies offers new insights into the stability and dynamics of microbial communities, from the world's oceans to human microbiota. Dedicated time series analysis tools allow taking full advantage of these data. Such tools can reveal periodic patterns, help to build predictive models or, on the contrary, quantify irregularities that make community behavior unpredictable. Microbial communities can change abruptly in response to small perturbations, linked to changing conditions or the presence of multiple stable states. With sufficient samples or time points, such alternative states can be detected. In addition, temporal variation of microbial interactions can be captured with time-varying networks. Here, we apply these techniques on multiple longitudinal datasets to illustrate their potential for microbiome research., (Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Microbes are dominant drivers of biogeochemical processes, yet drawing a global picture of functional diversity, microbial community structure, and their ecological determinants remains a grand challenge. We analyzed 7.2 terabases of metagenomic data from 243 Tara Oceans samples from 68 locations in epipelagic and mesopelagic waters across the globe to generate an ocean microbial reference gene catalog with >40 million nonredundant, mostly novel sequences from viruses, prokaryotes, and picoeukaryotes. Using 139 prokaryote-enriched samples, containing >35,000 species, we show vertical stratification with epipelagic community composition mostly driven by temperature rather than other environmental factors or geography. We identify ocean microbial core functionality and reveal that >73% of its abundance is shared with the human gut microbiome despite the physicochemical differences between these two ecosystems., (Copyright © 2015, American Association for the Advancement of Science.)

Slash-and-burn clearing of forest typically results in increase in soil nutrient availability. However, the impact of these nutrients on the soil microbiome is not known. Using next generation sequencing of 16S rRNA gene and shotgun metagenomic DNA, we compared the structure and the potential functions of bacterial community in forest soils to deforested soils in the Amazon region and related the differences to soil chemical factors. Deforestation decreased soil organic matter content and factors linked to soil acidity and raised soil pH, base saturation and exchangeable bases. Concomitant to expected changes in soil chemical factors, we observed an increase in the alpha diversity of the bacterial microbiota and relative abundances of putative copiotrophic bacteria such as Actinomycetales and a decrease in the relative abundances of bacterial taxa such as Chlamydiae, Planctomycetes and Verrucomicrobia in the deforested soils. We did not observe an increase in genes related to microbial nutrient metabolism in deforested soils. However, we did observe changes in community functions such as increases in DNA repair, protein processing, modification, degradation and folding functions, and these functions might reflect adaptation to changes in soil characteristics due to forest clear-cutting and burning. In addition, there were changes in the composition of the bacterial groups associated with metabolism-related functions. Co-occurrence microbial network analysis identified distinct phylogenetic patterns for forest and deforested soils and suggested relationships between Planctomycetes and aluminium content, and Actinobacteria and nitrogen sources in Amazon soils. The results support taxonomic and functional adaptations in the soil bacterial community following deforestation. We hypothesize that these microbial adaptations may serve as a buffer to drastic changes in soil fertility after slash-and-burning deforestation in the Amazon region., (© 2015 John Wiley & Sons Ltd.)

Antinuclear antibodies are a hallmark feature of generalized autoimmune diseases, including systemic lupus erythematosus and systemic sclerosis. However, the processes underlying the loss of tolerance against nuclear self-constituents remain largely unresolved. Using mice deficient in lymphotoxin and Hox11, we report that approximately 25% of mice lacking secondary lymphoid organs spontaneously develop specific antinuclear antibodies. Interestingly, we find this phenotype is not caused by a defect in central tolerance. Rather, cell-specific deletion and in vivo lymphotoxin blockade link these systemic autoimmune responses to the formation of gut-associated lymphoid tissue in the neonatal period of life. We further demonstrate antinuclear antibody production is influenced by the presence of commensal gut flora, in particular increased colonization with segmented filamentous bacteria, and IL-17 receptor signaling. Together, these data indicate that neonatal colonization of gut microbiota influences generalized autoimmunity in adult life., (© 2015 The Authors.)

Irritable bowel syndrome (IBS) is a heterogeneous functional disorder with a multifactorial etiology that involves the interplay of both host and environmental factors. Among environmental factors relevant for IBS etiology, the diet stands out given that the majority of IBS patients report their symptoms to be triggered by meals or specific foods. The diet provides substrates for microbial fermentation, and, as the composition of the intestinal microbiota is disturbed in IBS patients, the link between diet, microbiota composition, and microbial fermentation products might have an essential role in IBS etiology. In this review, we summarize current evidence regarding the impact of diet and the intestinal microbiota on IBS symptoms, as well as the reported interactions between diet and the microbiota composition. On the basis of the existing data, we suggest pathways (mechanisms) by which diet components, via the microbial fermentation, could trigger IBS symptoms. Finally, this review provides recommendations for future studies that would enable elucidation of the role of diet and microbiota and how these factors may be (inter)related in the pathophysiology of IBS.

The microbiota of the human gut is gaining broad attention owing to its association with a wide range of diseases, ranging from metabolic disorders (e.g. obesity and type 2 diabetes) to autoimmune diseases (such as inflammatory bowel disease and type 1 diabetes), cancer and even neurodevelopmental disorders (e.g. autism). Having been increasingly used in biomedical research, mice have become the model of choice for most studies in this emerging field. Mouse models allow perturbations in gut microbiota to be studied in a controlled experimental setup, and thus help in assessing causality of the complex host-microbiota interactions and in developing mechanistic hypotheses. However, pitfalls should be considered when translating gut microbiome research results from mouse models to humans. In this Special Article, we discuss the intrinsic similarities and differences that exist between the two systems, and compare the human and murine core gut microbiota based on a meta-analysis of currently available datasets. Finally, we discuss the external factors that influence the capability of mouse models to recapitulate the gut microbiota shifts associated with human diseases, and investigate which alternative model systems exist for gut microbiota research., (© 2015. Published by The Company of Biologists Ltd.)

Caspase-14, an important proteinase involved in filaggrin catabolism, is mainly active in terminally differentiating keratinocytes, where it is required for the generation of skin natural moisturizing factors (NMFs). Consequently, caspase-14 deficient epidermis is characterized by reduced levels of NMFs such as urocanic acid and 2-pyrrolidone-5-carboxylic acid. Patients suffering from filaggrin deficiency are prone to develop atopic dermatitis, which is accompanied with increased microbial burden. Among several reasons, this effect could be due to a decrease in filaggrin breakdown products. In this study, we found that caspase-14(-/-) mice show enhanced antibacterial response compared to wild-type mice when challenged with bacteria. Therefore, we compared the microbial communities between wild-type and caspase-14(-/-) mice by sequencing of bacterial 16S ribosomal RNA genes. We observed that caspase-14 ablation leads to an increase in bacterial richness and diversity during steady-state conditions. Although both wild-type and caspase-14(-/-) skin were dominated by the Firmicutes phylum, the Staphylococcaceae family was reduced in caspase-14(-/-) mice. Altogether, our data demonstrated that caspase-14 deficiency causes the imbalance of the skin-resident bacterial communities., (© 2014 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Caspase-deficient mice and wild-type (WT) mice show significant differences in their gut microbiota composition. These differences coincide with the observation that caspase-3-deficient mice carrying a natural caspase-11 mutation (Casp3/11(-/-)) are less sensitive to acute dextran sodium sulfate-induced colitis than WT mice. For these reasons, we investigated the role of the microbiota in the development of colitis by cohousing WT and Casp3/11(-/-) mice. Microbial community fingerprinting by denaturing gradient gel electrophoresis analysis revealed that the similarities in gut microbial composition of WT and Casp3/11(-/-) mice increased after cohousing. In the acute dextran sodium sulfate-induced colitis model, Casp3/11(-/-) mice that were cohoused with WT mice showed increased weight loss and disease activity scores and increased neutrophil infiltration and inflammatory cytokine levels in their colon tissue compared with Casp3/11(-/-) mice that were not cohoused with WT mice. Also, we demonstrate that only the microbiota of the Casp3/11(-/-) mice cohoused with WT mice showed an important increase in Prevotella species. In conclusion, our cohousing experiments revealed that the colitogenic activity of the WT microbiota is transferable to Casp3/11(-/-) mice and that Prevotella species are likely to be involved. By contrast, the relative protection of Casp3/11(-/-) mice against dextran sodium sulfate damage is not transferred to WT mice after cohousing. These results underscore the need for in-depth studies of the bilateral interaction of host genes and microbiota to gain insight into the mechanisms of disease pathogenesis. Our findings also have important implications for the experimental design of disease studies in genetically modified mice and conclusions drawn from them.

Background: Murine models are a crucial component of gut microbiome research. Unfortunately, a multitude of genetic backgrounds and experimental setups, together with inter-individual variation, complicates cross-study comparisons and a global understanding of the mouse microbiota landscape. Here, we investigate the variability of the healthy mouse microbiota of five common lab mouse strains using 16S rDNA pyrosequencing., Results: We find initial evidence for richness-driven, strain-independent murine enterotypes that show a striking resemblance to those in human, and which associate with calprotectin levels, a marker for intestinal inflammation. After enterotype stratification, we find that genetic, caging and inter-individual variation contribute on average 19%, 31.7% and 45.5%, respectively, to the variance in the murine gut microbiota composition. Genetic distance correlates positively to microbiota distance, so that genetically similar strains have more similar microbiota than genetically distant ones. Specific mouse strains are enriched for specific operational taxonomic units and taxonomic groups, while the 'cage effect' can occur across mouse strain boundaries and is mainly driven by Helicobacter infections., Conclusions: The detection of enterotypes suggests a common ecological cause, possibly low-grade inflammation that might drive differences among gut microbiota composition in mammals. Furthermore, the observed environmental and genetic effects have important consequences for experimental design in mouse microbiome research.