Multidomain analyses of a longitudinal human microbiome intestinal cleanout perturbation experiment

The prevalence of constipation in children with new diagnosis of inflammatory bowel disease: A retrospective study

OBJECTIVES

Functional constipation (FC) is a common problem in childhood and the first-line therapy is macrogol. The role of FC in the onset of inflammatory bowel disease (IBD) is poorly understood. Our main aim was to investigate the prevalence of FC in children before the diagnosis of IBD.

METHODS

This is a cross-sectional observational study in pediatric IBD-patients. We collected data on demographics, clinical and endoscopic characteristics at IBD diagnosis, and on the presence of FC and its treatment before IBD diagnosis.

RESULTS

A total of 238 children with IBD, 104 (44%) with Crohn disease (CD), 130 (56%) with ulcerative colitis (UC) and 4 (0.016%) with IBD Unclassified (IBD-U) were enrolled. The mean age was 174 ± 47 months, 56% were male. Forty-seven out of 238 (19.7%) had a FC history before the IBD diagnosis and 31 out of these 47 patients (65%) received macrogol therapy. In the FC group, we found a delay in the diagnosis of IBD compared to the group with no FC [median (interquartile range [IQR]): 5 months (2.5-9.5) and 2 months (0-4), respectively, p ≤ 0.001]. The difference in terms of endoscopic localization was statistically significant in UC patients presenting FC (p = 0.026) with a prevalence of proctitis and left side colitis (30% and 15%, respectively).

CONCLUSION

In conclusion our study highlighted a prevalence of constipation in pediatric IBD patients at diagnosis of 19.7%, which must be taken into account to avoid diagnostic delay and which is associated with limited extent of disease in UC pediatric patients.

Recent alcohol intake impacts microbiota in adult burn patients

Alcohol use is associated with an increased incidence of negative health outcomes in burn patients due to biological mechanisms that include a dysregulated inflammatory response and increased intestinal permeability. This study used phosphatidylethanol (PEth) in blood, a direct biomarker of recent alcohol use, to investigate associations between a recent history of alcohol use and the fecal microbiota, short chain fatty acids, and inflammatory markers in the first week after a burn injury for nineteen participants. Burn patients were grouped according to PEth levels of low or high and differences in the overall fecal microbial community were observed between these cohorts. Two genera that contributed to the differences and had higher relative abundance in the low PEth burn patient group were Akkermansia, a mucin degrading bacteria that improves intestinal barrier function, and Bacteroides, a potentially anti-inflammatory bacteria. There was no statistically significant difference between levels of short chain fatty acids or intestinal permeability across the two groups. To our knowledge, this study represents the first report to evaluate the effects of burn injury and recent alcohol use on early post burn microbiota dysbiosis, inflammatory response, and levels of short chain fatty acids. Future studies in this field are warranted to better understand the factors associated with negative health outcomes and develop interventional trials.

Multi-omic integration of microbiome data for identifying disease-associated modules

Multi-omic studies of the human gut microbiome are crucial for understanding its role in disease across multiple functional layers. Nevertheless, integrating and analyzing such complex datasets poses significant challenges. Most notably, current analysis methods often yield extensive lists of disease-associated features (e.g., species, pathways, or metabolites), without capturing the multi-layered structure of the data. Here, we address this challenge by introducing "MintTea", an intermediate integration-based approach combining canonical correlation analysis extensions, consensus analysis, and an evaluation protocol. MintTea identifies "disease-associated multi-omic modules", comprising features from multiple omics that shift in concord and that collectively associate with the disease. Applied to diverse cohorts, MintTea captures modules with high predictive power, significant cross-omic correlations, and alignment with known microbiome-disease associations. For example, analyzing samples from a metabolic syndrome study, MintTea identifies a module with serum glutamate- and TCA cycle-related metabolites, along with bacterial species linked to insulin resistance. In another dataset, MintTea identifies a module associated with late-stage colorectal cancer, including Peptostreptococcus and Gemella species and fecal amino acids, in line with these species' metabolic activity and their coordinated gradual increase with cancer development. This work demonstrates the potential of advanced integration methods in generating systems-level, multifaceted hypotheses underlying microbiome-disease interactions.

Rookery through rehabilitation: Microbial community assembly in newborn harbour seals after maternal separation

Microbial community assembly remains largely unexplored in marine mammals, despite its potential importance for conservation and management. Here, neonatal microbiota assembly was studied in harbour seals (Phoca vitulina richardii) at a rehabilitation facility soon after maternal separation, through weaning, to the time of release back to their native environment. We found that the gingival and rectal communities of rehabilitated harbour seals were distinct from the microbiotas of formula and pool water, and became increasingly diverse and dissimilar over time, ultimately resembling the gingival and rectal communities of local wild harbour seals. Harbour seal microbiota assembly was compared to that of human infants, revealing the rapid emergence of host specificity and evidence of phylosymbiosis even though these harbour seals had been raised by humans. Early life prophylactic antibiotics were associated with changes in the composition of the harbour seal gingival and rectal communities and surprisingly, with transient increases in alpha diversity, perhaps because of microbiota sharing during close cohabitation with other harbour seals. Antibiotic-associated effects dissipated over time. These results suggest that while early life maternal contact may provide seeding for microbial assembly, co-housing of conspecifics during rehabilitation may help neonatal mammals achieve a healthy host-specific microbiota with features of resilience.

Abrupt perturbation and delayed recovery of the vaginal ecosystem following childbirth

The vaginal ecosystem is closely tied to human health and reproductive outcomes, yet its dynamics in the wake of childbirth remain poorly characterized. Here, we profile the vaginal microbiota and cytokine milieu of participants sampled longitudinally throughout pregnancy and for at least one year postpartum. We show that delivery, regardless of mode, is associated with a vaginal pro-inflammatory cytokine response and the loss of Lactobacillus dominance. By contrast, neither the progression of gestation nor the approach of labor strongly altered the vaginal ecosystem. At 9.5-months postpartum-the latest timepoint at which cytokines were assessed-elevated inflammation coincided with vaginal bacterial communities that had remained perturbed (highly diverse) from the time of delivery. Time-to-event analysis indicated a one-year postpartum probability of transitioning to Lactobacillus dominance of 49.4%. As diversity and inflammation declined during the postpartum period, dominance by L. crispatus, the quintessential health-associated commensal, failed to return: its prevalence before, immediately after, and one year after delivery was 41%, 4%, and 9%, respectively. Revisiting our pre-delivery data, we found that a prior live birth was associated with a lower odds of L. crispatus dominance in pregnant participants-an outcome modestly tempered by a longer ( > 18-month) interpregnancy interval. Our results suggest that reproductive history and childbirth in particular remodel the vaginal ecosystem and that the timing and degree of recovery from delivery may help determine the subsequent health of the woman and of future pregnancies.

A convenient correspondence between k-mer-based metagenomic distances and phylogenetically-informed β-diversity measures

k-mer-based distances are often used to describe the differences between communities in metagenome sequencing studies because of their computational convenience and history of effectiveness. Although k-mer-based distances do not use information about taxon abundances, we show that one class of k-mer distances between metagenomes (the Euclidean distance between k-mer spectra, or EKS distances) are very closely related to a class of phylogenetically-informed β-diversity measures that do explicitly use both the taxon abundances and information about the phylogenetic relationships among the taxa. Furthermore, we show that both of these distances can be interpreted as using certain features of the taxon abundances that are related to the phylogenetic tree. Our results allow practitioners to perform phylogenetically-informed analyses when they only have k-mer data available and provide a theoretical basis for using k-mer spectra with relatively small values of k (on the order of 4-5). They are also useful for analysts who wish to know more of the properties of any method based on k-mer spectra and provide insight into one class of phylogenetically-informed β-diversity measures.

Short-Term Dairy Product Elimination and Reintroduction Minimally Perturbs the Gut Microbiota in Self-Reported Lactose-Intolerant Adults

An outstanding question regarding the human gut microbiota is whether and how microbiota-directed interventions influence host phenotypic traits. Here, we employed a dietary intervention to probe this question in the context of lactose intolerance. To assess the effects of dietary dairy product elimination and (re)introduction on the microbiota and host phenotype, we studied 12 self-reported mildly lactose-intolerant adults with triweekly collection of fecal samples over a 12-week study period: 2 weeks of baseline diet, 4 weeks of dairy product elimination, and 6 weeks of gradual whole cow milk (re)introduction. Of the 12 subjects, 6 reported either no dairy or only lactose-free dairy product consumption. A clinical assay for lactose intolerance, the hydrogen breath test, was performed before and after each of these three study phases, and 16S rRNA gene amplicon sequencing was performed on all fecal samples. We found that none of the subjects showed change in a clinically defined measure of lactose tolerance. Similarly, fecal microbiota structure resisted modification. Although the mean fraction of the genus Bifidobacterium, a group known to metabolize lactose, increased slightly with milk (re)introduction (from 0.0125 to 0.0206; Wilcoxon P = 0.068), the overall structure of each subject’s gut microbiota remained highly individualized and largely stable in the face of diet manipulation.

Arabinoxylan and Pectin Metabolism in Crohn’s Disease Microbiota: An In Silico Study

Inflammatory bowel disease is a chronic disorder including ulcerative colitis and Crohn’s disease (CD). Gut dysbiosis is often associated with CD, and metagenomics allows a better understanding of the microbial communities involved. The objective of this study was to reconstruct in silico carbohydrate metabolic capabilities from metagenome-assembled genomes (MAGs) obtained from healthy and CD individuals. This computational method was developed as a mean to aid rationally designed prebiotic interventions to rebalance CD dysbiosis, with a focus on metabolism of emergent prebiotics derived from arabinoxylan and pectin. Up to 1196 and 1577 MAGs were recovered from CD and healthy people, respectively. MAGs of Akkermansia muciniphila, Barnesiella viscericola DSM 18177 and Paraprevotella xylaniphila YIT 11841 showed a wide range of unique and specific enzymes acting on arabinoxylan and pectin. These glycosidases were also found in MAGs recovered from CD patients. Interestingly, these arabinoxylan and pectin degraders are predicted to exhibit metabolic interactions with other gut microbes reduced in CD. Thus, administration of arabinoxylan and pectin may ameliorate dysbiosis in CD by promoting species with key metabolic functions, capable of cross-feeding other beneficial species. These computational methods may be of special interest for the rational design of prebiotic ingredients targeting at CD.

Nutrition-wide association study of microbiome diversity and composition in colorectal cancer patients

BACKGROUND

The effects of diet on the interaction between microbes and host health have been widely studied. However, its effects on the gut microbiota of patients with colorectal cancer (CRC) have not been elucidated. This study aimed to investigate the association between diet and the overall diversity and different taxa levels of the gut microbiota in CRC patients via the nutrition-wide association approach.

METHODS

This hospital-based study utilized data of 115 CRC patients who underwent CRC surgery in Department of Surgery, Seoul National University Hospital. Spearman correlation analyses were conducted for 216 dietary features and three alpha-diversity indices, Firmicutes/Bacteroidetes ratio, and relative abundance of 439 gut microbial taxonomy. To identify main enterotypes of the gut microbiota, we performed the principal coordinate analysis based on the β-diversity index. Finally, we performed linear regression to examine the association between dietary intake and main microbiome features, and linear discriminant analysis effect size (LEfSe) to identify bacterial taxa phylogenetically enriched in the low and high diet consumption groups.

RESULTS

Several bacteria were enriched in patients with higher consumption of mature pumpkin/pumpkin juice (ρ, 0.31 to 0.41) but lower intake of eggs (ρ, -0.32 to -0.26). We observed negative correlations between Bacteroides fragilis abundance and intake of pork (belly), beef soup with vegetables, animal fat, and fatty acids (ρ, -0.34 to -0.27); an inverse correlation was also observed between Clostridium symbiosum abundance and intake of some fatty acids, amines, and amino acids (ρ, -0.30 to -0.24). Furthermore, high intake of seaweed was associated with a 6% (95% CI, 2% to 11%) and 7% (95% CI, 2% to 11%) lower abundance of Rikenellaceae and Alistipes, respectively, whereas overall beverage consumption was associated with an 10% (95% CI, 2% to 18%) higher abundance of Bacteroidetes, Bacteroidia, and Bacteroidales, compared to that in the low intake group. LEfSe analysis identified phylogenetically enriched taxa associated with the intake of sugars and sweets, legumes, mushrooms, eggs, oils and fats, plant fat, carbohydrates, and monounsaturated fatty acids.

CONCLUSIONS

Our data elucidates the diet-microbe interactions in CRC patients. Additional research is needed to understand the significance of these results in CRC prognosis.

Early indicators of microbial strain dysbiosis in the human gastrointestinal microbial community of certain healthy humans and hospitalized COVID-19 patients

Dysbiosis in the human gastrointestinal microbial community could functionally impact microbial metabolism and colonization resistance to pathogens. To further elucidate the indicators of microbial strain dysbiosis, we have developed an analytic method that detects patterns of presence/absence of selected KEGG metabolic pathways for a selected strain (PKS). Using a metagenomic data set consisting of multiple high-density fecal samples from six normal individuals, we found three had unique PKS for important gut commensal microbes, Bacteroides vulgatus and Bacteroides uniformis, at all sample times examined. Two individuals had multiple shared PKS clusters of B. vulgatus or B. uniformis over time. Analysis of a data set of high-density fecal samples from eight COVID-19 hospitalized patients taken over a short period revealed that two patients had shared PKS clusters for B. vulgatus and one shared cluster for B. uniformis. Our analysis demonstrates that while the majority of normal individuals with no B. vulgatus or B. uniformis strain change over time have unique PKS, in some healthy humans and patients hospitalized with COVID-19, we detected shared PKS clusters at the different times suggesting a slowing down of the intrinsic rates of strain variation that could eventually lead to a dysbiosis in the microbial strain community.

The crewed journey to Mars and its implications for the human microbiome

A human spaceflight to Mars is scheduled for the next decade. In preparation for this unmatched endeavor, a plethora of challenges must be faced prior to the actual journey to Mars. Mission success will depend on the health of its crew and its working capacity. Hence, the journey to Mars will also depend on the microbiome and its far-reaching effects on individual crew health, the spaceship's integrity, and food supply. As human beings rely on their microbiome, these microbes are essential and should be managed to ensure their beneficial effects outweigh potential risks. In this commentary, we focus on the current state of knowledge regarding a healthy (gut) microbiome of space travelers based on research from the International Space Station and simulation experiments on Earth. We further indicate essential knowledge gaps of microbial conditions during long-term space missions in isolated confined space habitats or outposts and give detailed recommendations for microbial monitoring during pre-flight, in-flight, and post-flight. Finally, the conclusion outlines open questions and aspects of space traveler's health beyond the scope of this commentary. Video Abstract.

Dynamic of the human gut microbiome under infectious diarrhea

Despite the widespread implementation of sanitation, immunization and appropriate treatment, infectious diarrheal diseases still inflict a great health burden to children living in low resource settings. Conventional microbiology research in diarrhea have focused on the pathogen's biology and pathogenesis, but initial enteric infections could trigger subsequent perturbations in the gut microbiome, leading to short-term or long-term health effects. Conversely, such pre-existing perturbations could render children more vulnerable to enteropathogen colonization and diarrhea. Recent advances in DNA sequencing and bioinformatic analyses have been integrated in well-designed clinical and epidemiological studies, which allow us to track how the gut microbiome changes from disease onset to recovery. Here, we aim to summarize the current understanding on the diarrheal gut microbiome, stratified into different disease stages. Furthermore, we discuss how such perturbations could have impacts beyond an acute diarrhea episode, specifically on the child's nutritional status and the facilitation of antimicrobial resistance.

Using Community Ecology Theory and Computational Microbiome Methods To Study Human Milk as a Biological System

Human milk is a complex and dynamic biological system that has evolved to optimally nourish and protect human infants. Yet, according to a recent priority-setting review, “our current understanding of human milk composition and its individual components and their functions fails to fully recognize the importance of the chronobiology and systems biology of human milk in the context of milk synthesis, optimal timing and duration of feeding, and period of lactation” (P. Christian et al., Am J Clin Nutr 113:1063–1072, 2021, https://doi.org/10.1093/ajcn/nqab075). We attribute this critical knowledge gap to three major reasons as follows. (i) Studies have typically examined each subsystem of the mother-milk-infant “triad” in isolation and often focus on a single element or component (e.g., maternal lactation physiology or milk microbiome or milk oligosaccharides or infant microbiome or infant gut physiology). This undermines our ability to develop comprehensive representations of the interactions between these elements and study their response to external perturbations. (ii) Multiomics studies are often cross-sectional, presenting a snapshot of milk composition, largely ignoring the temporal variability during lactation. The lack of temporal resolution precludes the characterization and inference of robust interactions between the dynamic subsystems of the triad. (iii) We lack computational methods to represent and decipher the complex ecosystem of the mother-milk-infant triad and its environment. In this review, we advocate for longitudinal multiomics data collection and demonstrate how incorporating knowledge gleaned from microbial community ecology and computational methods developed for microbiome research can serve as an anchor to advance the study of human milk and its many components as a “system within a system.”

Undervalued Pseudo-nifH Sequences in Public Databases Distort Metagenomic Insights into Biological Nitrogen Fixers

Nitrogen fixation, a distinct process incorporating the inactive atmospheric nitrogen into the active biological processes, has been a major topic in biological and geochemical studies. Currently, insights into diversity and distribution of nitrogen-fixing microbes are dependent upon homology-based analyses of nitrogenase genes, especially the nifH gene, which are broadly conserved in nitrogen-fixing microbes. Here, we report the pitfall of using nifH as a marker of microbial nitrogen fixation. We exhaustively analyzed genomes in RefSeq (231,908 genomes) and KEGG (6,509 genomes) and cooccurrence and gene order patterns of nitrogenase genes (including nifH) therein. Up to 20% of nifH-harboring genomes lacked nifD and nifK, which encode essential subunits of nitrogenase, within 10 coding sequences upstream or downstream of nifH or on the same genome. According to a phenotypic database of prokaryotes, no species and strains harboring only nifH possess nitrogen-fixing activities, which shows that these nifH genes are "pseudo"-nifH genes. Pseudo-nifH sequences mainly belong to anaerobic microbes, including members of the class Clostridia and methanogens. We also detected many pseudo-nifH reads from metagenomic sequences of anaerobic environments such as animal guts, wastewater, paddy soils, and sediments. In some samples, pseudo-nifH overwhelmed the number of "true" nifH reads by 50% or 10 times. Because of the high sequence similarity between pseudo- and true-nifH, pronounced amounts of nifH-like reads were not confidently classified. Overall, our results encourage reconsideration of the conventional use of nifH for detecting nitrogen-fixing microbes, while suggesting that nifD or nifK would be a more reliable marker. IMPORTANCE Nitrogen-fixing microbes affect biogeochemical cycling, agricultural productivity, and microbial ecosystems, and their distributions have been investigated intensively using genomic and metagenomic sequencing. Currently, insights into nitrogen fixers in the environment have been acquired by homology searches against nitrogenase genes, particularly the nifH gene, in public databases. Here, we report that public databases include a significant amount of incorrectly annotated nifH sequences (pseudo-nifH). We exhaustively investigated the genomic structures of nifH-harboring genomes and found hundreds of pseudo-nifH sequences in RefSeq and KEGG. Over half of these pseudo-nifH sequences belonged to members of the class Clostridia, which is supposed to be a prominent nitrogen-fixing clade. We also found that the abundance of nitrogen fixers in metagenomes could be overestimated by 1.5 to >10 times due to pseudo-nifH recorded in public databases. Our results encourage reconsideration of the prevalent use of nifH as a marker of nitrogen-fixing microbes.

Changes in Gut Bacterial Translation Occur before Symptom Onset and Dysbiosis in Dextran Sodium Sulfate-Induced Murine Colitis

Longitudinal studies on the gut microbiome that follow the effect of a perturbation are critical in understanding the microbiome's response and succession to disease. Here, we use a dextran sodium sulfate (DSS) mouse model of colitis as a tractable perturbation to study how gut bacteria change their physiology over the course of a perturbation. Using single-cell methods such as flow cytometry, bioorthogonal noncanonical amino acid tagging (BONCAT), and population-based cell sorting combined with 16S rRNA sequencing, we determine the diversity of physiologically distinct fractions of the gut microbiota and how they respond to a controlled perturbation. The physiological markers of bacterial activity studied here include relative nucleic acid content, membrane damage, and protein production. There is a distinct and reproducible succession in bacterial physiology, with an increase in bacteria with membrane damage and diversity changes in the translationally active fraction, both, critically, occurring before symptom onset. Large increases in the relative abundance of Akkermansia were seen in all physiological fractions, most notably in the translationally active bacteria. Performing these analyses within a detailed, longitudinal framework determines which bacteria change their physiology early on, focusing therapeutic efforts in the future to predict or even mitigate relapse in diseases like inflammatory bowel diseases. IMPORTANCE Most studies on the gut microbiome focus on the composition of this community and how it changes in disease. However, how the community transitions from a healthy state to one associated with disease is currently unknown. Additionally, common diversity metrics do not provide functional information on bacterial activity. We begin to address these two unknowns by following bacterial activity over the course of disease progression, using a tractable mouse model of colitis. We find reproducible changes in gut bacterial physiology that occur before symptom onset, with increases in the proportion of bacteria with membrane damage, and changes in community composition of the translationally active bacteria. Our data provide a framework to identify possible windows of intervention and which bacteria to target in microbiome-based therapeutics.

Topical emollient therapy with sunflower seed oil alters the skin microbiota of young children with severe acute malnutrition in Bangladesh: A randomised, controlled study

Background

Topical emollient therapy with sunflower seed oil (SSO) reduces risk of sepsis and mortality in very preterm infants in low- or middle-income countries (LMICs). Proposed mechanisms include modulation of skin and possibly gut barrier function. The skin and gut microbiota play important roles in regulating barrier function, but the effects of emollient therapy on these microbiotas are poorly understood.

Methods

We characterised microbiota structure and diversity with 16S rRNA gene amplicon sequence data and ecological statistics in 20 children with severe acute malnutrition (SAM) aged 2-24 months, at four skin sites and in stool, during a randomised, controlled trial of emollient therapy with SSO in Bangladesh. Microbes associated with therapy were identified with tree-based sparse discriminant analysis.

Results

The skin microbiota of Bangladeshi children with SAM was highly diverse and displayed significant variation in structure as a function of physical distance between sites. Microbiota structure differed between the study groups (P = 0.005), was more diverse in emollient-treated subjects–including on the forehead which did not receive direct treatment–and changed with each day (P = 0.005) at all skin sites. Overall, Prevotellaceae were the most differentially affected by emollient treatment; several genera within this family became more abundant in the emollient group than in the controls across several skin sites. Gut microbiota structure was associated with sample day (P = 0.045) and subject age (P = 0.045), but was not significantly affected by emollient treatment (P = 0.060).

Conclusions

Emollient therapy altered the skin microbiota in a consistent and temporally coherent manner. We speculate that therapy with SSO enhances skin barrier function in part through alterations in the microbiota, and through systemic mechanisms. Strategies to strengthen skin and gut barrier function in populations at risk, such as children in LMICs like Bangladesh, might include deliberate manipulation of their skin microbiota.

Trial registration

ClinicalTrials.gov: NCT02616289.

Microbiota long-term dynamics and prediction of acute graft-versus-host disease in pediatric allogeneic stem cell transplantation

BACKGROUND

Patients undergoing allogeneic hematopoietic stem cell transplantation (HSCT) exhibit changes in their gut microbiota and are experiencing a range of complications, including acute graft-versus-host disease (aGvHD). It is unknown if, when, and under which conditions a re-establishment of microbial and immunological homeostasis occurs. It is also unclear whether microbiota long-term dynamics occur at other body sites than the gut such as the mouth or nose. Moreover, it is not known whether the patients' microbiota prior to HSCT holds clues to whether the patient would suffer from severe complications subsequent to HSCT. Here, we take a holobiont perspective and performed an integrated host-microbiota analysis of the gut, oral, and nasal microbiota in 29 children undergoing allo-HSCT.

RESULTS

The bacterial diversity decreased in the gut, nose, and mouth during the first month and reconstituted again 1-3 months after allo-HSCT. The microbial community composition traversed three phases over 1 year. Distinct taxa discriminated the microbiota temporally at all three body sides, including Enterococcus spp., Lactobacillus spp., and Blautia spp. in the gut. Of note, certain microbial taxa appeared already changed in the patients prior to allo-HSCT as compared with healthy children. Acute GvHD occurring after allo-HSCT could be predicted from the microbiota composition at all three body sites prior to HSCT. The reconstitution of CD4⁺ T cells, T_H17, and B cells was associated with distinct taxa of the gut, oral, and nasal microbiota.

CONCLUSIONS

This study reveals for the first time bacteria in the mouth and nose that may predict aGvHD. Monitoring of the microbiota at different body sites in HSCT patients and particularly through involvement of samples prior to transplantation may be of prognostic value and could assist in guiding personalized treatment strategies. The identification of distinct bacteria that have a potential to predict post-transplant aGvHD might provide opportunities for an improved preventive clinical management, including a modulation of microbiomes. The host-microbiota associations shared between several body sites might also support an implementation of more feasible oral and nasal swab sampling-based analyses. Altogether, the findings suggest that the microbiota and host factors together could provide actionable information to guiding precision medicine. Video Abstract.

A Statistical Perspective on the Challenges in Molecular Microbial Biology

High throughput sequencing (HTS)-based technology enables identifying and quantifying non-culturable microbial organisms in all environments. Microbial sequences have enhanced our understanding of the human microbiome, the soil and plant environment, and the marine environment. All molecular microbial data pose statistical challenges due to contamination sequences from reagents, batch effects, unequal sampling, and undetected taxa. Technical biases and heteroscedasticity have the strongest effects, but different strains across subjects and environments also make direct differential abundance testing unwieldy. We provide an introduction to a few statistical tools that can overcome some of these difficulties and demonstrate those tools on an example. We show how standard statistical methods, such as simple hierarchical mixture and topic models, can facilitate inferences on latent microbial communities. We also review some nonparametric Bayesian approaches that combine visualization and uncertainty quantification. The intersection of molecular microbial biology and statistics is an exciting new venue. Finally, we list some of the important open problems that would benefit from more careful statistical method development.

Clearance of Clostridioides difficile Colonization Is Associated with Antibiotic-Specific Bacterial Changes

The community of microorganisms, or microbiota, in our intestines prevents pathogens like C. difficile from colonizing and causing infection. However, antibiotics can disturb the gut microbiota, which allows C. difficile to colonize. C. difficile infections (CDI) are primarily treated with antibiotics, which frequently leads to recurrent infections because the microbiota has not yet returned to a resistant state.

The gut bacterial community prevents many pathogens from colonizing the intestine. Previous studies have associated specific bacteria with clearing Clostridioides difficile colonization across different community perturbations. However, those bacteria alone have been unable to clear C. difficile colonization. To elucidate the changes necessary to clear colonization, we compared differences in bacterial abundance between communities able and unable to clear C. difficile colonization. We treated mice with titrated doses of antibiotics prior to C. difficile challenge, resulting in no colonization, colonization and clearance, or persistent colonization. Previously, we observed that clindamycin-treated mice were susceptible to colonization but spontaneously cleared C. difficile. Therefore, we investigated whether other antibiotics would show the same result. We found that reduced doses of cefoperazone and streptomycin permitted colonization and clearance of C. difficile. Mice that cleared colonization had antibiotic-specific community changes and predicted interactions with C. difficile. Clindamycin treatment led to a bloom in populations related to Enterobacteriaceae. Clearance of C. difficile was concurrent with the reduction of those blooming populations and the restoration of community members related to the Porphyromonadaceae and Bacteroides. Cefoperazone created a susceptible community characterized by drastic reductions in the community diversity and interactions and a sustained increase in the abundance of many facultative anaerobes. Lastly, clearance in streptomycin-treated mice was associated with the recovery of multiple members of the Porphyromonadaceae, with little overlap in the specific Porphyromonadaceae observed in the clindamycin treatment. Further elucidation of how C. difficile colonization is cleared from different gut bacterial communities will improve C. difficile infection treatments.

IMPORTANCE The community of microorganisms, or microbiota, in our intestines prevents pathogens like C. difficile from colonizing and causing infection. However, antibiotics can disturb the gut microbiota, which allows C. difficile to colonize. C. difficile infections (CDI) are primarily treated with antibiotics, which frequently leads to recurrent infections because the microbiota has not yet returned to a resistant state. The recurrent infection cycle often ends when the fecal microbiota from a presumed resistant person is transplanted into the susceptible person. Although this treatment is highly effective, we do not understand the mechanism. We hope to improve the treatment of CDI through elucidating how the bacterial community eliminates CDI. We found that C. difficile colonized susceptible mice but was spontaneously eliminated in an antibiotic treatment-specific manner. These data indicate that each community had different requirements for clearing colonization. Understanding how different communities clear colonization will reveal targets to improve CDI treatments.

Targeted Manipulation of Abundant and Rare Taxa in the Daphnia magna Microbiota with Antibiotics Impacts Host Fitness Differentially

Host-associated microbes contribute to host fitness, but it is unclear whether these contributions are from rare keystone taxa, numerically abundant taxa, or interactions among community members. Experimental perturbation of the microbiota can highlight functionally important taxa; however, this approach is primarily applied in systems with complex communities where the perturbation affects hundreds of taxa, making it difficult to pinpoint contributions of key community members. Here, we use the ecological model organism Daphnia magna to examine the importance of rare and abundant taxa by perturbing its relatively simple microbiota with targeted antibiotics. We used sublethal antibiotic doses to target either rare or abundant members across two temperatures and then measured key host life history metrics and shifts in microbial community composition. We find that removal of abundant taxa had greater impacts on host fitness than did removal of rare taxa and that the abundances of nontarget taxa were impacted by antibiotic treatment, suggesting that no rare keystone taxa exist in the Daphnia magna microbiota but that microbe-microbe interactions may play a role in host fitness. We also find that microbial community composition was impacted by antibiotics differently across temperatures, indicating that ecological context shapes within-host microbial responses and effects on host fitness.

IMPORTANCE Understanding the contributions of rare and abundant taxa to host fitness is an outstanding question in host microbial ecology. In this study, we use the model zooplankton Daphnia magna and its relatively simple cohort of bacterial taxa to disentangle the roles of distinct taxa in host life history metrics, using a suite of antibiotics to selectively reduce the abundance of functionally important taxa. We also examine how environmental context shapes the importance of these bacterial taxa in host fitness.

Impact of Temporal pH Fluctuations on the Coexistence of Nasal Bacteria in an in silico Community

To manipulate nasal microbiota for respiratory health, we need to better understand how this microbial community is assembled and maintained. Previous work has demonstrated that the pH in the nasal passage experiences temporal fluctuations. Yet, the impact of such pH fluctuations on nasal microbiota is not fully understood. Here, we examine how temporal fluctuations in pH might affect the coexistence of nasal bacteria in in silico communities. We take advantage of the cultivability of nasal bacteria to experimentally assess their responses to pH and the presence of other species. Based on experimentally observed responses, we formulate a mathematical model to numerically investigate the impact of temporal pH fluctuations on species coexistence. We assemble in silico nasal communities using up to 20 strains that resemble the isolates that we have experimentally characterized. We then subject these in silico communities to pH fluctuations and assess how the community composition and coexistence is impacted. Using this model, we then simulate pH fluctuations-varying in amplitude or frequency-to identify conditions that best support species coexistence. We find that the composition of nasal communities is generally robust against pH fluctuations within the expected range of amplitudes and frequencies. Our results also show that cooperative communities and communities with lower niche overlap have significantly lower composition deviations when exposed to temporal pH fluctuations. Overall, our data suggest that nasal microbiota could be robust against environmental fluctuations.

The amphibian microbiome exhibits poor resilience following pathogen-induced disturbance

Infectious pathogens can disrupt the microbiome in addition to directly affecting the host. Impacts of disease may be dependent on the ability of the microbiome to recover from such disturbance, yet remarkably little is known about microbiome recovery after disease, particularly in nonhuman animals. We assessed the resilience of the amphibian skin microbial community after disturbance by the pathogen, Batrachochytrium dendrobatidis (Bd). Skin microbial communities of laboratory-reared mountain yellow-legged frogs were tracked through three experimental phases: prior to Bd infection, after Bd infection (disturbance), and after clearing Bd infection (recovery period). Bd infection disturbed microbiome composition and altered the relative abundances of several dominant bacterial taxa. After Bd infection, frogs were treated with an antifungal drug that cleared Bd infection, but this did not lead to recovery of microbiome composition (measured as Unifrac distance) or relative abundances of dominant bacterial groups. These results indicate that Bd infection can lead to an alternate stable state in the microbiome of sensitive amphibians, or that microbiome recovery is extremely slow—in either case resilience is low. Furthermore, antifungal treatment and clearance of Bd infection had the additional effect of reducing microbial community variability, which we hypothesize results from similarity across frogs in the taxa that colonize community vacancies resulting from the removal of Bd. Our results indicate that the skin microbiota of mountain yellow-legged frogs has low resilience following Bd-induced disturbance and is further altered by the process of clearing Bd infection, which may have implications for the conservation of this endangered amphibian.

Perturbation of the human gastrointestinal tract microbial ecosystem by oral drugs to treat chronic disease results in a spectrum of individual specific patterns of extinction and persistence of dominant microbial strains

BACKGROUND

Oral drugs can have side effects such as diarrhea that indicate the perturbation of the gut microbial community. To further understand the dynamics of perturbation, we have assessed the strain relatedness of samples from previously published data sets from pre and post bowel evacuation, episodes of diarrhea, and administration of oral drugs to treat diabetes and rheumatoid arthritis.

METHODS

We analyzed a total of published five data sets using our strain-tracking tool called Window-based Single Nucleotide Variant (SNV) Similarity (WSS) to identify related strains from the same individual.

RESULTS

Strain-tracking analysis using the first data set from 8 individuals pre and 21-50 days post iso-osmotic bowel wash revealed almost all microbial strains were related in an individual between pre and post samples. Similarly, in a second study, strain-tracking analysis of 4 individuals pre and post sporadic diarrhea revealed the majority of strains were related over time (up to 44 weeks). In contrast, the analysis of a third data set from 22 individuals pre and post 3-day exposure of oral metformin revealed that no individuals had a related strain. In a fourth study, the data set taken at 2 and 4 months from 38 individuals on placebo or metformin revealed individual specific sharing of pre and post strains. Finally, the data set from 18 individuals with rheumatoid arthritis given disease-modifying antirheumatic drugs methotrexate or glycosides of the traditional Chinese medicinal component Tripterygium wilfordii showed individual specific sharing of pre and post strains up to 16 months.

CONCLUSION

Oral drugs used to treat chronic disease can result in individual specific microbial strain change for the majority of species. Since the gut community provides essential functions for the host, our study supports personalized monitoring to assess the status of the dominant microbial strains after initiation of oral drugs to treat chronic disease.

Differently Pre-treated Rapeseed Meals Affect in vitro Swine Gut Microbiota Composition

The aim of the study was to investigate the effect of untreated and processed rapeseed meal (RSM) on fiber degradability by pig gut microbiota and the adaptation of the microbiota to the substrate, by using the Swine Large Intestine in vitro Model (SLIM). A standardized swine gut microbiota was fed for 48 h with pre-digested RSM which was processed enzymatically by a cellulase (CELL), two pectinases (PECT), or chemically by an alkaline (ALK) treatment. Amplicons of the V3-V4 region of the 16S rRNA gene were sequenced to evaluate the gut microbiota composition, whereas short chain fatty acids (SCFA) were measured to assess fiber degradation. Adaptive gPCA showed that CELL and ALK had larger effects on the microbiota composition than PECT1 and PECT2, and all substrates had larger effects than CON. The relative abundance of family Prevotellaceae was significantly higher in CELL treatment compared to other treatments. Regardless of the treatments (including CON), the relative abundance of Dorea, Allisonella, and FamilyXIIIUCG_001 (in the order of Clostridiales) were significantly increased after 24 h, and Parabacteroides, Mogibacterium, Intestinimonas, Oscillibacter, RuminococcaceaeUCG_009, Acidaminococcus, Sutterella, and Citrobacter were significantly higher in abundance at time point 48 compared to the earlier time points. Prevotella 9 had significant positive correlations with propionic and valeric acid, and Mogibacterium positively correlated with acetic and caproic acid. There was no significant difference in SCFA production between untreated and processed RSM. Overall, degradability in the processed RSM was not improved compared to CON. However, the significantly different microbes detected among treatments, and the bacteria considerably correlating with SCFA production might be important findings to determine strategies to shorten the fiber adaptation period of the microbiota, in order to increase feed efficiency in the animal, and particularly in pig production.

A Guide to Diet-Microbiome Study Design

Intense recent interest in understanding how the human gut microbiome influences health has kindled a concomitant interest in linking dietary choices to microbiome variation. Diet is known to be a driver of microbiome variation, and yet the precise mechanisms by which certain dietary components modulate the microbiome, and by which the microbiome produces byproducts and secondary metabolites from dietary components, are not well-understood. Interestingly, despite the influence of diet on the gut microbiome, the majority of microbiome studies published to date contain little or no analysis of dietary intake. Although an increasing number of microbiome studies are now collecting some form of dietary data or even performing diet interventions, there are no clear standards in the microbiome field for how to collect diet data or how to design a diet-microbiome study. In this article, we review the current practices in diet-microbiome analysis and study design and make several recommendations for best practices to provoke broader discussion in the field. We recommend that microbiome studies include multiple consecutive microbiome samples per study timepoint or phase and multiple days of dietary history prior to each microbiome sample whenever feasible. We find evidence that direct effects of diet on the microbiome are likely to be observable within days, while the length of an intervention required for observing microbiome-mediated effects on the host phenotype or host biomarkers, depending on the outcome, may be much longer, on the order of weeks or months. Finally, recent studies demonstrating that diet-microbiome interactions are personalized suggest that diet-microbiome studies should either include longitudinal sampling within individuals to identify personalized responses, or should include an adequate number of participants spanning a range of microbiome types to identify generalized responses.

Testing for dependence on tree structures

Tree-like structures are abundant in the empirical sciences as they can summarize high-dimensional data and show latent structure among many samples in a single framework. Prominent examples include phylogenetic trees or hierarchical clustering derived from genetic data. Currently, users employ ad hoc methods to test for association between a given tree and a response variable, which reduces reproducibility and robustness. In this paper, we introduce treeSeg, a simple to use and widely applicable methodology with high power for testing between all levels of hierarchy for a given tree and the response while accounting for the overall false positive rate. Our method allows for precise uncertainty quantification and therefore, increases interpretability and reproducibility of such studies across many fields of science.

Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical sciences. A common question in many studies is whether there is an association between a response variable measured on each sample and the latent group structure represented by some given tree. Currently, this is addressed on an ad hoc basis, usually requiring the user to decide on an appropriate number of clusters to prune out of the tree to be tested against the response variable. Here, we present a statistical method with statistical guarantees that tests for association between the response variable and a fixed tree structure across all levels of the tree hierarchy with high power while accounting for the overall false positive error rate. This enhances the robustness and reproducibility of such findings.

Fecal microbiota transplantation for the improvement of metabolism in obesity: The FMT-TRIM double-blind placebo-controlled pilot trial

BACKGROUND

There is intense interest about whether modulating gut microbiota can impact systemic metabolism. We investigated the safety of weekly oral fecal microbiota transplantation (FMT) capsules from healthy lean donors and their ability to alter gut microbiota and improve metabolic outcomes in patients with obesity.

METHODS AND FINDINGS

FMT-TRIM was a 12-week double-blind randomized placebo-controlled pilot trial of oral FMT capsules performed at a single US academic medical center. Between August 2016 and April 2018, we randomized 24 adults with obesity and mild-moderate insulin resistance (homeostatic model assessment of insulin resistance [HOMA-IR] between 2.0 and 8.0) to weekly healthy lean donor FMT versus placebo capsules for 6 weeks. The primary outcome, assessed by intention to treat, was change in insulin sensitivity between 0 and 6 weeks as measured by hyperinsulinemic euglycemic clamps. Additional metabolic parameters were evaluated at 0, 6, and 12 weeks, including HbA1c, body weight, body composition by dual-energy X-ray absorptiometry, and resting energy expenditure by indirect calorimetry. Fecal samples were serially collected and evaluated via 16S V4 rRNA sequencing. Our study population was 71% female, with an average baseline BMI of 38.8 ± 6.7 kg/m2 and 41.3 ± 5.1 kg/m2 in the FMT and placebo groups, respectively. There were no statistically significant improvements in insulin sensitivity in the FMT group compared to the placebo group (+5% ± 12% in FMT group versus -3% ± 32% in placebo group, mean difference 9%, 95% CI -5% to 28%, p = 0.16). There were no statistically significant differences between groups for most of the other secondary metabolic outcomes, including HOMA-IR (mean difference 0.2, 95% CI -0.9 to 0.9, p = 0.96) and body composition (lean mass mean difference -0.1 kg, 95% CI -1.9 to 1.6 kg, p = 0.87; fat mass mean difference 1.2 kg, 95% CI -0.6 to 3.0 kg, p = 0.18), over the 12-week study. We observed variable engraftment of donor bacterial groups among FMT recipients, which persisted throughout the 12-week study. There were no significant differences in adverse events (AEs) (10 versus 5, p = 0.09), and no serious AEs related to FMT. Limitations of this pilot study are the small sample size, inclusion of participants with relatively mild insulin resistance, and lack of concurrent dietary intervention.

CONCLUSIONS

Weekly administration of FMT capsules in adults with obesity results in gut microbiota engraftment in most recipients for at least 12 weeks. Despite engraftment, we did not observe clinically significant metabolic effects during the study.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02530385.

Daily Sampling Reveals Personalized Diet-Microbiome Associations in Humans

Diet is a key determinant of human gut microbiome variation. However, the fine-scale relationships between daily food choices and human gut microbiome composition remain unexplored. Here, we used multivariate methods to integrate 24-h food records and fecal shotgun metagenomes from 34 healthy human subjects collected daily over 17 days. Microbiome composition depended on multiple days of dietary history and was more strongly associated with food choices than with conventional nutrient profiles, and daily microbial responses to diet were highly personalized. Data from two subjects consuming only meal replacement beverages suggest that a monotonous diet does not induce microbiome stability in humans, and instead, overall dietary diversity associates with microbiome stability. Our work provides key methodological insights for future diet-microbiome studies and suggests that food-based interventions seeking to modulate the gut microbiota may need to be tailored to the individual microbiome. Trial Registration: ClinicalTrials.gov: NCT03610477.

A Generic Multivariate Framework for the Integration of Microbiome Longitudinal Studies With Other Data Types

Simultaneous profiling of biospecimens using different technological platforms enables the study of many data types, encompassing microbial communities, omics, and meta-omics as well as clinical or chemistry variables. Reduction in costs now enables longitudinal or time course studies on the same biological material or system. The overall aim of such studies is to investigate relationships between these longitudinal measures in a holistic manner to further decipher the link between molecular mechanisms and microbial community structures, or host-microbiota interactions. However, analytical frameworks enabling an integrated analysis between microbial communities and other types of biological, clinical, or phenotypic data are still in their infancy. The challenges include few time points that may be unevenly spaced and unmatched between different data types, a small number of unique individual biospecimens, and high individual variability. Those challenges are further exacerbated by the inherent characteristics of microbial communities-derived data (e.g., sparse, compositional). We propose a generic data-driven framework to integrate different types of longitudinal data measured on the same biological specimens with microbial community data and select key temporal features with strong associations within the same sample group. The framework ranges from filtering and modeling to integration using smoothing splines and multivariate dimension reduction methods to address some of the analytical challenges of microbiome-derived data. We illustrate our framework on different types of multi-omics case studies in bioreactor experiments as well as human studies.

Successful strategies for human microbiome data generation, storage and analyses

Current interest in the potential for clinical use of new tools for improving human health are now focused on techniques for the study of the human microbiome and its interaction with environmental and clinical covariates. This review outlines the use of statistical strategies that have been developed in past studies and can inform successful design and analyses of controlled perturbation experiments performed in the human microbiome. We carefully outline what the data are, their imperfections and how we need to transform, decontaminate and denoise them. We show how to identify the important unknown parameters and how to can leverage variability we see to produce efficient models for prediction and uncertainty quantification. We encourage a reproducible strategy that builds on best practice principles that can be adapted for effective experimental design and reproducible workflows. Nonparametric, data-driven denoising strategies already provide the best strain identification and decontamination methods. Data driven models can be combined with uncertainty quantification to provide reproducible aids to decision making in the clinical context, as long as careful, separate, registered confirmatory testing are undertaken. Here we provide guidelines for effective longitudinal studies and their analyses. Lessons learned along the way are that visualizations at every step can pinpoint problems and outliers, normalization and filtering improve power in downstream testing. We recommend collecting and binding the metadata and covariates to sample descriptors and recording complete computer scripts into an R markdown supplement that can reduce opportunities for human error and enable collaborators and readers to replicate all the steps of the study. Finally, we note that optimizing the bioinformatic and statistical workflow involves adopting a wait-and-see approach that is particularly effective in cases where the features such as 'mass spectrometry peaks' and metagenomic tables can only be partially annotated.

Primate microbiomes over time: Longitudinal answers to standing questions in microbiome research

To date, most insights into the processes shaping vertebrate gut microbiomes have emerged from studies with cross-sectional designs. While this approach has been valuable, emerging time series analyses on vertebrate gut microbiomes show that gut microbial composition can change rapidly from 1 day to the next, with consequences for host physical functioning, health, and fitness. Hence, the next frontier of microbiome research will require longitudinal perspectives. Here we argue that primatologists, with their traditional focus on tracking the lives of individual animals and familiarity with longitudinal fecal sampling, are well positioned to conduct research at the forefront of gut microbiome dynamics. We begin by reviewing some of the most important ecological processes governing microbiome change over time, and briefly summarizing statistical challenges and approaches to microbiome time series analysis. We then introduce five questions of general interest to microbiome science where we think field-based primate studies are especially well positioned to fill major gaps: (a) Do early life events shape gut microbiome composition in adulthood? (b) Do shifting social landscapes cause gut microbial change? (c) Are gut microbiome phenotypes heritable across variable environments? (d) Does the gut microbiome show signs of host aging? And (e) do gut microbiome composition and dynamics predict host health and fitness? For all of these questions, we highlight areas where primatologists are uniquely positioned to make substantial contributions. We review preliminary evidence, discuss possible study designs, and suggest future directions.

Latent variable modeling for the microbiome

The human microbiome is a complex ecological system, and describing its structure and function under different environmental conditions is important from both basic scientific and medical perspectives. Viewed through a biostatistical lens, many microbiome analysis goals can be formulated as latent variable modeling problems. However, although probabilistic latent variable models are a cornerstone of modern unsupervised learning, they are rarely applied in the context of microbiome data analysis, in spite of the evolutionary, temporal, and count structure that could be directly incorporated through such models. We explore the application of probabilistic latent variable models to microbiome data, with a focus on Latent Dirichlet allocation, Non-negative matrix factorization, and Dynamic Unigram models. To develop guidelines for when different methods are appropriate, we perform a simulation study. We further illustrate and compare these techniques using the data of Dethlefsen and Relman (2011, Incomplete recovery and individualized responses of the human distal gut microbiota to repeated antibiotic perturbation. Proceedings of the National Academy of Sciences108, 4554-4561), a study on the effects of antibiotics on bacterial community composition. Code and data for all simulations and case studies are available publicly.

Preliminary feasibility for recruiting and retaining black and white females to provide fecal samples for longitudinal research

As the associations between the gut microbiota and numerous health outcomes become more evident, it is important to conduct longitudinal microbiome research to advance the field beyond the identification of associations. It is also necessary to include individuals who have historically been underrepresented in biomedical research in longitudinal microbiome studies to better understand and eliminate racial/ethnic health disparities. This paper describes our experiences in recruiting and retaining participants for an ongoing, longitudinal microbiome study for which the main results will be reported at a later time. This article provides preliminary evidence of the feasibility of recruiting and retaining a racially diverse sample of females (97% completion for invited participants) for longitudinal microbiome research.

Multitable Methods for Microbiome Data Integration

The simultaneous study of multiple measurement types is a frequently encountered problem in practical data analysis. It is especially common in microbiome research, where several sources of data-for example, 16s-rRNA, metagenomic, metabolomic, or transcriptomic data-can be collected on the same physical samples. There has been a proliferation of proposals for analyzing such multitable microbiome data, as is often the case when new data sources become more readily available, facilitating inquiry into new types of scientific questions. However, stepping back from the rush for new methods for multitable analysis in the microbiome literature, it is worthwhile to recognize the broader landscape of multitable methods, as they have been relevant in problem domains ranging across economics, robotics, genomics, chemometrics, and neuroscience. In different contexts, these techniques are called data integration, multi-omic, and multitask methods, for example. Of course, there is no unique optimal algorithm to use across domains-different instances of the multitable problem possess specific structure or variation that are worth incorporating in methodology. Our purpose here is not to develop new algorithms, but rather to 1) distill relevant themes across different analysis approaches and 2) provide concrete workflows for approaching analysis, as a function of ultimate analysis goals and data characteristics (heterogeneity, dimensionality, sparsity). Towards the second goal, we have made code for all analysis and figures available online at https://github.com/krisrs1128/multitable_review.

Problems with the concept of gut microbiota dysbiosis

The human microbiome research is with the notable exception of fecal transplantation still mostly in a descriptive phase. Part of the difficulty for translating research into medical interventions is due to the large compositional complexity of the microbiome resulting in datasets that need sophisticated statistical methods for their analysis and do not lend to industrial applications. Another part of the difficulty might be due to logical flaws in terminology particularly concerning ‘dysbiosis’ that avoids circular conclusions and is based on sound ecological and evolutionary reasoning. Many case–control studies are underpowered necessitating more meta‐analyses that sort out consistent from spurious dysbiosis–disease associations. We also need for the microbiome a transition from statistical associations to causal relationships with diseases that fulfil a set of modified Koch's postulates for commensals. Disturbingly, the most sophisticated statistical analyses explain only a small percentage of the variance in the microbiome. Microbe–microbe interactions irrelevant to the host and stochastic processes might play a greater role than anticipated. To satisfy the concept of Karl Popper about conjectures and refutations in the scientific process, we should also conduct more experiments that try to refute the role of the commensal gut microbiota for human health and disease.

Timescales of gut microbiome dynamics

Vast communities of microorganisms inhabit the gastrointestinal tracts of humans and other animals. Understanding their initial development, fluctuations in composition, stability over long times, and responses to transient perturbations - in other words their dynamics - is important both for gaining basic insights into these ecosystems and for rationally manipulating them for therapeutic ends. Gut microbiome dynamics, however, remain poorly understood. We review here studies of gut microbiome dynamics in the presence and absence of external perturbations, noting especially the long timescales associated with overall stability and the short timescales associated with various underlying biological processes. Integrating these disparate timescales, we suggest, is an important goal for future work and is necessary for developing a predictive understanding of microbiome dynamics.

Emphasis on the deep or shallow parts of the tree provides a new characterization of phylogenetic distances

Background

Phylogenetically informed distances are commonly used in the analysis of microbiome data, and analysts have many options to choose from. Although all phylogenetic distances share the goal of incorporating the phylogenetic relationships among the bacteria, they do so in different ways and give different pictures of the relationships between the bacterial communities.

Results

We investigate the properties of two classes of phylogenetically informed distances: the Unifrac family, including weighted, unweighted, and generalized Unifrac, and the DPCoA family, which we introduce here. Through several lines of evidence, including a combination of mathematical, data analytic, and computational methods, we show that a major and heretofore unrecognized cleavage in the phylogenetically informed distances is the relative weights placed on the deep and shallow parts of the phylogeny. Specifically, weighted Unifrac and DPCoA place more emphasis on the deep parts of the phylogeny, while unweighted Unifrac places more emphasis on the shallow parts of the phylogeny. Both the Unifrac and the DPCoA families have tunable parameters that can be shown to control how much emphasis the distances place on the deep or shallow parts of the phylogeny.

Conclusions

Our results allow for a more informed choice of distance and give practitioners more insight into the potential differences resulting from different choices of distance.

Electronic supplementary material

The online version of this article (10.1186/s13059-019-1735-y) contains supplementary material, which is available to authorized users.

Community ecology as a framework for human microbiome research

The field of human microbiome research has revealed the intimate co-association of humans with diverse communities of microbes in various habitats in the human body, and the necessity of these microbes for the maintenance of human health. Microbial heterogeneity between humans and across spatial and temporal gradients requires multidimensional datasets and a unifying set of theories and statistical tools to analyze the human microbiome and fully realize the potential of this field. Here we consider the utility of community ecology as a framework for the interrogation and interpretation of the human microbiome.

Spatial and Temporal Analysis of the Stomach and Small-Intestinal Microbiota in Fasted Healthy Humans

Although the microbiota in the proximal gastrointestinal (GI) tract have been implicated in health and disease, much about these microbes remains understudied compared to those in the distal GI tract. This study characterized the microbiota across multiple proximal GI sites over time in healthy individuals. As part of a study of the pharmacokinetics of oral mesalamine administration, healthy, fasted volunteers (n = 8; 10 observation periods total) were orally intubated with a four-lumen catheter with multiple aspiration ports. Samples were taken from stomach, duodenal, and multiple jejunal sites, sampling hourly (≤7 h) to measure mesalamine (administered at t = 0), pH, and 16S rRNA gene-based composition. We observed a predominance of Firmicutes across proximal GI sites, with significant variation compared to stool. The microbiota was more similar within individuals over time than between subjects, with the fecal microbiota being unique from that of the small intestine. The stomach and duodenal microbiota displayed highest intraindividual variability compared to jejunal sites, which were more stable across time. We observed significant correlations in the duodenal microbial composition with changes in pH; linear mixed models identified positive correlations with multiple Streptococcus operational taxonomic units (OTUs) and negative correlations with multiple Prevotella and Pasteurellaceae OTUs. Few OTUs correlated with mesalamine concentration. The stomach and duodenal microbiota exhibited greater compositional dynamics than the jejunum. Short-term fluctuations in the duodenal microbiota were correlated with pH. Given the unique characteristics and dynamics of the proximal GI tract microbiota, it is important to consider these local environments in health and disease states.IMPORTANCE The gut microbiota are linked to a variety of gastrointestinal diseases, including inflammatory bowel disease. Despite this importance, microbiota dynamics in the upper gastrointestinal tract are understudied. Our article seeks to understand what factors impact microbiota dynamics in the healthy human upper gut. We found that the upper gastrointestinal tract contains consistently prevalent bacterial OTUs that dominate the overall community. Microbiota variability is highest in the stomach and duodenum and correlates with pH.

The Human Gut Microbiome: From Association to Modulation

Our understanding of the human gut microbiome continues to evolve at a rapid pace, but practical application of thisknowledge is still in its infancy. This review discusses the type of studies that will be essential for translating microbiome research into targeted modulations with dedicated benefits for the human host.

Dynamic linear models guide design and analysis of microbiota studies within artificial human guts

BACKGROUND

Artificial gut models provide unique opportunities to study human-associated microbiota. Outstanding questions for these models' fundamental biology include the timescales on which microbiota vary and the factors that drive such change. Answering these questions though requires overcoming analytical obstacles like estimating the effects of technical variation on observed microbiota dynamics, as well as the lack of appropriate benchmark datasets.

RESULTS

To address these obstacles, we created a modeling framework based on multinomial logistic-normal dynamic linear models (MALLARDs) and performed dense longitudinal sampling of four replicate artificial human guts over the course of 1 month. The resulting analyses revealed how the ratio of biological variation to technical variation from sample processing depends on sampling frequency. In particular, we find that at hourly sampling frequencies, 76% of observed variation could be ascribed to technical sources, which could also skew the observed covariation between taxa. We also found that the artificial guts demonstrated replicable trajectories even after a recovery from a transient feed disruption. Additionally, we observed irregular sub-daily oscillatory dynamics associated with the bacterial family Enterobacteriaceae within all four replicate vessels.

CONCLUSIONS

Our analyses suggest that, beyond variation due to sequence counting, technical variation from sample processing can obscure temporal variation from biological sources in artificial gut studies. Our analyses also supported hypotheses that human gut microbiota fluctuates on sub-daily timescales in the absence of a host and that microbiota can follow replicable trajectories in the presence of environmental driving forces. Finally, multiple aspects of our approach are generalizable and could ultimately be used to facilitate the design and analysis of longitudinal microbiota studies in vivo.

Daylight exposure modulates bacterial communities associated with household dust

BACKGROUND

Microbial communities associated with indoor dust abound in the built environment. The transmission of sunlight through windows is a key building design consideration, but the effects of light exposure on dust communities remain unclear. We report results of an experiment and computational models designed to assess the effects of light exposure and wavelengths on the structure of the dust microbiome. Specifically, we placed household dust in replicate model "rooms" with windows that transmitted visible, ultraviolet, or no light and measured taxonomic compositions, absolute abundances, and viabilities of the resulting bacterial communities.

RESULTS

Light exposure per se led to lower abundances of viable bacteria and communities that were compositionally distinct from dark rooms, suggesting preferential inactivation of some microbes over others under daylighting conditions. Differences between communities experiencing visible and ultraviolet light wavelengths were relatively minor, manifesting primarily in abundances of dead human-derived taxa. Daylighting was associated with the loss of a few numerically dominant groups of related microorganisms and apparent increases in the abundances of some rare groups, suggesting that a small number of microorganisms may have exhibited modest population growth under lighting conditions. Although biological processes like population growth on dust could have generated these patterns, we also present an alternate statistical explanation using sampling models from ecology; simulations indicate that artefactual, apparent increases in the abundances of very rare taxa may be a null expectation following the selective inactivation of dominant microorganisms in a community.

CONCLUSIONS

Our experimental and simulation-based results indicate that dust contains living bacterial taxa that can be inactivated following changes in local abiotic conditions and suggest that the bactericidal potential of ordinary window-filtered sunlight may be similar to ultraviolet wavelengths across dosages that are relevant to real buildings.

Transient Osmotic Perturbation Causes Long-Term Alteration to the Gut Microbiota

Osmotic diarrhea is a prevalent condition in humans caused by food intolerance, malabsorption, and widespread laxative use. Here, we assess the resilience of the gut ecosystem to osmotic perturbation at multiple length and timescales using mice as model hosts. Osmotic stress caused reproducible extinction of highly abundant taxa and expansion of less prevalent members in human and mouse microbiotas. Quantitative imaging revealed decimation of the mucus barrier during osmotic perturbation, followed by recovery. The immune system exhibited temporary changes in cytokine levels and a lasting IgG response against commensal bacteria. Increased osmolality prevented growth of commensal strains in vitro, revealing one mechanism contributing to extinction. Environmental availability of microbiota members mitigated extinction events, demonstrating how species reintroduction can affect community resilience. Our findings (1) demonstrate that even mild osmotic diarrhea can cause lasting changes to the microbiota and host and (2) lay the foundation for interventions that increase system-wide resilience.

Methods for phylogenetic analysis of microbiome data

How does knowing the evolutionary history of microorganisms affect our analysis of microbiological datasets? Depending on the research question, the common ancestry of microorganisms can be a source of confounding variation, or a scaffolding used for inference. For example, when performing regression on traits, common ancestry is a source of dependence among observations, whereas when searching for clades with correlated abundances, common ancestry is the scaffolding for inference. The common ancestry of microorganisms and their genes are organized in trees-phylogenies-which can and should be incorporated into analyses of microbial datasets. While there has been a recent expansion of phylogenetically informed analytical tools, little guidance exists for which method best answers which biological questions. Here, we review methods for phylogeny-aware analyses of microbiome datasets, considerations for choosing the appropriate method and challenges inherent in these methods. We introduce a conceptual organization of these tools, breaking them down into phylogenetic comparative methods, ancestral state reconstruction and analysis of phylogenetic variables and distances, and provide examples in Supplementary Online Tutorials. Careful consideration of the research question and ecological and evolutionary assumptions will help researchers choose a phylogeny and appropriate methods to produce accurate, biologically informative and previously unreported insights.

Role of priority effects in the early-life assembly of the gut microbiota

Understanding how microbial communities develop is essential for predicting and directing their future states. Ecological theory suggests that community development is often influenced by priority effects, in which the order and timing of species arrival determine how species affect one another. Priority effects can have long-lasting consequences, particularly if species arrival history varies during the early stage of community development, but their importance to the human gut microbiota and host health remains largely unknown. Here, we explore how priority effects might influence microbial communities in the gastrointestinal tract during early childhood and how the strength of priority effects can be estimated from the composition of the microbial species pool. We also discuss factors that alter microbial transmission, such as delivery mode, diet and parenting behaviours such as breastfeeding, which can influence the likelihood of priority effects. An improved knowledge of priority effects has the potential to inform microorganism-based therapies, such as prebiotics and probiotics, which are aimed at guiding the microbiota towards a healthy state.

Phylogenetic convolutional neural networks in metagenomics

BACKGROUND

Convolutional Neural Networks can be effectively used only when data are endowed with an intrinsic concept of neighbourhood in the input space, as is the case of pixels in images. We introduce here Ph-CNN, a novel deep learning architecture for the classification of metagenomics data based on the Convolutional Neural Networks, with the patristic distance defined on the phylogenetic tree being used as the proximity measure. The patristic distance between variables is used together with a sparsified version of MultiDimensional Scaling to embed the phylogenetic tree in a Euclidean space.

RESULTS

Ph-CNN is tested with a domain adaptation approach on synthetic data and on a metagenomics collection of gut microbiota of 38 healthy subjects and 222 Inflammatory Bowel Disease patients, divided in 6 subclasses. Classification performance is promising when compared to classical algorithms like Support Vector Machines and Random Forest and a baseline fully connected neural network, e.g. the Multi-Layer Perceptron.

CONCLUSION

Ph-CNN represents a novel deep learning approach for the classification of metagenomics data. Operatively, the algorithm has been implemented as a custom Keras layer taking care of passing to the following convolutional layer not only the data but also the ranked list of neighbourhood of each sample, thus mimicking the case of image data, transparently to the user.