pldist: ecological dissimilarities for paired and longitudinal microbiome association analysis

VTwins: inferring causative microbial features from metagenomic data of limited samples

It is difficult to infer causality from high-dimension metagenomic data due to interference from numerous confounders. By imitating the twin studies in genetic research, we develop a straightforward method-virtual twins (VTwins)-to eliminate the confounder effects by transforming the original cohort into a paired cohort of "Twin" samples with distinct phenotypes but matched taxonomic profiles. The results show that VTwins outperforms the conventional approach in the sensitivity of identifying causative features and only requires a 10-fold reduced sample size for recalling disease-associated microbes or pathways, as tested by simulated and empirical data. Benchmark test with other 16 kinds of software further validates the power and applicability of VTwins for handling high-dimension compositional datasets and mining causalities in metagenomic research. In conclusion, VTwins is straightforward and effective in handling high-diversity, high-dimension compositional data, promising applications in mining causalities for metagenomic and potentially other omics data. VTwins is open access and available at https://github.com/mengqingren/VTwins.

Prospective, longitudinal analysis of the gut microbiome in patients with locally advanced rectal cancer predicts response to neoadjuvant concurrent chemoradiotherapy

BACKGROUND

Neoadjuvant concurrent chemoradiotherapy (nCCRT) is a standard treatment for locally advanced rectal cancer (LARC). The gut microbiome may be reshaped by radiotherapy through its effects on microbial composition, mucosal immunity, and the systemic immune system. We sought to clarify dynamic, longitudinal changes in the gut microbiome and blood immunomodulators throughout nCCRT and to explore the relationship of such changes with outcomes after nCCRT.

METHODS

A total of 39 patients with LARC were recruited for this study. Fecal samples and peripheral blood samples were collected from all 39 patients before nCCRT, during nCCRT (at week 3), and after nCCRT (at week 5). The gut microbiota and the microbial community structure were analyzed by 16S rRNA sequencing of the V3-V4 region. Levels of blood immunomodulatory proteins were measured with a Millipore HCKPMAG-11 K kit and Luminex 200 platform (Luminex, USA).

RESULTS

Cross-sectional and longitudinal analyses revealed that the gut microbiome profile and enterotype exhibited characteristic variations that could distinguish patients with good response (AJCC TRG classification 0-1) vs poor response (TRG 2-3) to nCCRT. Sparse partial least squares regression and canonical correspondence analyses showed multivariate associations between specific microbial taxa, host immunomodulatory proteins, immune cells, and outcomes after nCCRT. An integrated model consisting of baseline Clostridium sensu stricto 1 levels, fold changes in Intestinimonas, blood levels of the herpesvirus entry mediator (HVEM/CD270), and lymphocyte counts could predict good vs poor outcome after nCCRT [area under the receiver-operating characteristics curve (AUC)= 0.821; area under the precision-recall curve [AUPR] = 0.911].

CONCLUSIONS

Our results showed that longitudinal variations in specific gut taxa, associated host immune cells, and immunomodulatory proteins before and during nCCRT could be useful for early predictions of the efficacy of nCCRT, which could guide the choice of individualized treatment for patients with LARC.

Impact of Data and Study Characteristics on Microbiome Volatility Estimates

The human microbiome is a dynamic community of bacteria, viruses, fungi, and other microorganisms. Both the composition of the microbiome (the microbes that are present and their relative abundances) and the temporal variability of the microbiome (the magnitude of changes in their composition across time, called volatility) has been associated with human health. However, the effect of unbalanced sampling intervals and differential read depth on the estimates of microbiome volatility has not been thoroughly assessed. Using four publicly available gut and vaginal microbiome time series, we subsampled the datasets to several sampling intervals and read depths and then compared additive, multiplicative, centered log ratio (CLR)-based, qualitative, and distance-based measures of microbiome volatility between the conditions. We find that longer sampling intervals are associated with larger quantitative measures of change (particularly for common taxa), but not with qualitative measures of change or distance-based volatility quantification. A lower sequencing read depth is associated with smaller multiplicative, CLR-based, and qualitative measures of change (particularly for less common taxa). Strategic subsampling may serve as a useful sensitivity analysis in unbalanced longitudinal studies investigating clinical associations with microbiome volatility.

A rarefaction-without-resampling extension of PERMANOVA for testing presence-absence associations in the microbiome

MOTIVATION

PERMANOVA is currently the most commonly used method for testing community-level hypotheses about microbiome associations with covariates of interest. PERMANOVA can test for associations that result from changes in which taxa are present or absent by using the Jaccard or unweighted UniFrac distance. However, such presence-absence analyses face a unique challenge: confounding by library size (total sample read count), which occurs when library size is associated with covariates in the analysis. It is known that rarefaction (subsampling to a common library size) controls this bias but at the potential costs of information loss and the introduction of a stochastic component into the analysis.

RESULTS

Here, we develop a non-stochastic approach to PERMANOVA presence-absence analyses that aggregates information over all potential rarefaction replicates without actual resampling, when the Jaccard or unweighted UniFrac distance is used. We compare this new approach to three possible ways of aggregating PERMANOVA over multiple rarefactions obtained from resampling: averaging the distance matrix, averaging the (element-wise) squared distance matrix and averaging the F-statistic. Our simulations indicate that our non-stochastic approach is robust to confounding by library size and outperforms each of the stochastic resampling approaches. We also show that, when overdispersion is low, averaging the (element-wise) squared distance outperforms averaging the unsquared distance, currently implemented in the R package vegan. We illustrate our methods using an analysis of data on inflammatory bowel disease in which samples from case participants have systematically smaller library sizes than samples from control participants.

AVAILABILITY AND IMPLEMENTATION

We have implemented all the approaches described above, including the function for calculating the analytical average of the squared or unsquared distance matrix, in our R package LDM, which is available on GitHub at https://github.com/yijuanhu/LDM.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Phylogeny-guided microbiome OTU-specific association test (POST)

BACKGROUND

The relationship between host conditions and microbiome profiles, typically characterized by operational taxonomic units (OTUs), contains important information about the microbial role in human health. Traditional association testing frameworks are challenged by the high dimensionality and sparsity of typical microbiome profiles. Phylogenetic information is often incorporated to address these challenges with the assumption that evolutionarily similar taxa tend to behave similarly. However, this assumption may not always be valid due to the complex effects of microbes, and phylogenetic information should be incorporated in a data-supervised fashion.

RESULTS

In this work, we propose a local collapsing test called phylogeny-guided microbiome OTU-specific association test (POST). In POST, whether or not to borrow information and how much information to borrow from the neighboring OTUs in the phylogenetic tree are supervised by phylogenetic distance and the outcome-OTU association. POST is constructed under the kernel machine framework to accommodate complex OTU effects and extends kernel machine microbiome tests from community level to OTU level. Using simulation studies, we show that when the phylogenetic tree is informative, POST has better performance than existing OTU-level association tests. When the phylogenetic tree is not informative, POST achieves similar performance as existing methods. Finally, in real data applications on bacterial vaginosis and on preterm birth, we find that POST can identify similar or more outcome-associated OTUs that are of biological relevance compared to existing methods.

CONCLUSIONS

Using POST, we show that adaptively leveraging the phylogenetic information can enhance the selection performance of associated microbiome features by improving the overall true-positive and false-positive detection. We developed a user friendly R package POSTm which is freely available on CRAN ( https://CRAN.R-project.org/package=POSTm ). Video Abstract.

Detecting sparse microbial association signals adaptively from longitudinal microbiome data based on generalized estimating equations

The association between the compositions of microbial communities and various host phenotypes is an important research topic. Microbiome association research addresses multiple domains, such as human disease and diet. Statistical methods for testing microbiome-phenotype associations have been studied recently to determine their ability to assess longitudinal microbiome data. However, existing methods fail to detect sparse association signals in longitudinal microbiome data. In this paper, we developed a novel method, namely aGEEMIHC, which is a data-driven adaptive microbiome higher criticism analysis based on generalized estimating equations to detect sparse microbial association signals from longitudinal microbiome data. aGEEMiHC adopts generalized estimating equations framework that fully considers the correlation among different observations from the same subject in longitudinal data. To be robust to diverse correlation structures for longitudinal data, aGEEMiHC integrates multiple microbiome higher criticism analyses based on generalized estimating equations with different working correlation structures. Extensive simulation experiments demonstrate that aGEEMiHC can control the type I error correctly and achieve superior performance according to a statistical power comparison. We also applied it to longitudinal microbiome data with various types of host phenotypes to demonstrate the stability of our method. aGEEMiHC is also utilized for real longitudinal microbiome data, and we found a significant association between the gut microbiome and Crohn's disease. In addition, our method ranks the significant factors associated with the host phenotype to provide potential biomarkers.

Intestinal butyrate-metabolizing species contribute to autoantibody production and bone erosion in rheumatoid arthritis

The imbalance between pathogenic and beneficial species of the intestinal microbiome and metabolism in rheumatoid arthritis (RA) remains unclarified. Here, using shotgun-based metagenome sequencing for a treatment-naïve patient cohort and a "quasi-paired cohort" method, we observed a deficiency of butyrate-producing species and an overwhelming number of butyrate consumers in RA patients. These outcomes mainly occurred in patients with positive ACPA, with a mean AUC of 0.94. This panel was also validated in established RA with an AUC of 0.986 in those with joint deformity. In addition, we showed that butyrate promoted Tregs, while suppressing Tconvs and osteoclasts, due to potentiation of the reduction in HDAC expression and down-regulation of proinflammatory cytokine genes. Dietary butyrate supplementation conferred anti-inflammatory benefits in a mouse model by rebalancing TFH cells and Tregs, as well as reducing antibody production. These findings reveal the critical role of butyrate-metabolizing species and suggest the potential of butyrate-based therapies for RA patients.

The Gut Microbiota during a Behavioral Weight Loss Intervention

Altered gut microbiota has been linked to obesity and may influence weight loss. We are conducting an ongoing weight loss trial, comparing daily caloric restriction (DCR) to intermittent fasting (IMF) in adults who are overweight or obese. We report here an ancillary study of the gut microbiota and selected obesity-related parameters at the baseline and after the first three months of interventions. During this time, participants experienced significant improvements in clinical health measures, along with altered composition and diversity of fecal microbiota. We observed significant associations between the gut microbiota features and clinical measures, including weight and waist circumference, as well as changes in these clinical measures over time. Analysis by intervention group found between-group differences in the relative abundance of Akkermansia in response to the interventions. Our results provide insight into the impact of baseline gut microbiota on weight loss responsiveness as well as the early effects of DCR and IMF on gut microbiota.

Association between postmenopausal vulvovaginal discomfort, vaginal microbiota, and mucosal inflammation

BACKGROUND

Half of all postmenopausal women report symptoms of vulvar, vaginal, or urinary discomfort with substantial impact on sexual function and quality of life; underlying mechanisms leading to symptoms are poorly understood.

OBJECTIVE

To examine the possibility that the vaginal microbiota and/or mucosal immune response contributes to the severity of bothersome vaginal symptoms, we conducted a substudy of samples from a randomized trial of vaginal treatment for genitourinary syndrome of menopause to compare these features between women whose symptoms improved and women whose symptoms did not improve.

STUDY DESIGN

This is a secondary analysis of samples collected in a 12-week randomized trial of treatment with vaginal estradiol or moisturizer vs placebo for moderate-severe postmenopausal symptoms of vaginal discomfort. We randomly selected 20 women in each arm with ≥2-point decrease in most bothersome symptom severity (responders) and 20 matched controls with ≤1-point decrease (nonresponders). At 0, 4, and 12 weeks, we characterized vaginal microbiota (16S ribosomal RNA gene sequencing), vaginal fluid metabolites (broad-based metabolomic profiling), vaginal fluid-soluble immune markers (Meso Scale Discovery), pH, and vaginal maturation index. We compared responders with nonresponders at baseline and across all visits using linear mixed models to evaluate associations with microbiota, metabolites, and immune markers, incorporating visit and participant-specific random effects while controlling for treatment arm.

RESULTS

Here, the mean age of women was 61 years (n=120), and most women (92%) were White. At enrollment, no significant differences were observed between responders and nonresponders in age, most bothersome symptom type or severity, microbiota composition or diversity, Lactobacillus dominance, metabolome, or immune markers. There was a significant decrease in diversity of the vaginal microbiota in both responders and nonresponders (P<.001) over 12 weeks. Although this change did not differ by responder status, diversity was associated with treatment arm: more women in the estradiol arm (63%) had Lactobacillus-dominant, lower diversity bacterial communities than women in the moisturizer (35%) or dual placebo (23%) arms (P=.001) at 12 weeks. The metabolome, vaginal maturation index, and measured immune markers were not associated with responder status over the 12 weeks but varied by treatment arm.

CONCLUSION

Postmenopausal vaginal symptom severity was not significantly associated with vaginal microbiota or mucosal inflammatory markers in this small study. Women receiving vaginal estradiol experienced greater abundance of lactobacilli and lower vaginal pH at end of treatment.

Beyond samples: A metric revealing more connections of gut microbiota between individuals

Studies of gut microbiota explore their complicated connections between individuals of different characteristics by applying different metrics to abundance data obtained from fecal samples. Although classic metrics are capable to quantify differences between samples, the microbiome of fecal sample is not a good surrogate for the gut microbiome of individuals because the microbial populations of the distal colon does not adequately represent that of the entire gastrointestinal tract. To overcome the deficiency of classic metrics in which the differences can be measured between the samples analyzed, but not the corresponding populations, we propose a metric for representing composition differences in the gut microbiota of individuals. Our investigation shows this metric outperforms traditional measures for multiple scenarios. For gut microbiota in diverse geographic populations, this metric presents more explainable data variance than others, not only in regular variance analysis but also in principle component analysis and partition analysis of biologic characteristics. With time-series data, the metric further presents a strong correlation with the time interval of serial sampling. Our findings suggest that the metric is robust and powerfully detects the intrinsic variations in gut microbiota. The metric holds promise for revealing more relations between gut microbiota and human health.

A Comprehensive Self-Management Program With Diet Education Does Not Alter Microbiome Characteristics in Women With Irritable Bowel Syndrome

BACKGROUND AND PURPOSE

Changes in diet and lifestyle factors are frequently recommended for persons with irritable bowel syndrome (IBS). It is unknown whether these recommendations alter the gut microbiome and/or whether baseline microbiome predicts improvement in symptoms and quality of life following treatment. Therefore, the purpose of this study was to explore if baseline gut microbiome composition predicted response to a Comprehensive Self-Management (CSM) intervention and if the intervention resulted in a different gut microbiome composition compared to usual care.

METHODS

Individuals aged 18-70 years with IBS symptoms ≥6 months were recruited using convenience sampling. Individuals were excluded if medication use or comorbidities would influence symptoms or microbiome. Participants completed a baseline assessment and were randomized into the eight-session CSM intervention which included dietary education and cognitive behavioral therapy versus usual care. Questionnaires included demographics, quality of life, and symptom diaries. Fecal samples were collected at baseline and 3-month post-randomization for 16S rRNA-based microbiome analysis.

RESULTS

Within the CSM intervention group (n = 30), Shannon diversity, richness, and beta diversity measures at baseline did not predict benefit from the CSM intervention at 3 months, as measured by change in abdominal pain and quality of life. Based on both alpha and beta diversity, the change from baseline to follow-up microbiome bacterial taxa did not differ between CSM (n = 25) and usual care (n = 25).

CONCLUSIONS AND INFERENCES

Baseline microbiome does not predict symptom improvement with CSM intervention. We do not find evidence that the CSM intervention influences gut microbiome diversity or composition over the course of 3 months.

Respiratory microbiome profiles differ by recent hospitalization and nursing home residence in patients on mechanical ventilation

Background

Healthcare-associated pneumonia (HCAP) is a heterogeneous disease. We redefined nursing-home- and hospital-associated infections (NHAI) group by revising existing HCAP risk factors. The NHAI group comprised nursing home residents with a poor functional status, or recent (past 90 days) hospitalization or recent (past 180 days) antibiotic therapy. Our aim was to determine whether respiratory microbiota profiles are related to newly defined NHAI group in critically ill patients on mechanical ventilation.

Methods

The 180 endotracheal aspirates (ETAs) from 60 mechanically ventilated ICU patients (NHAI group, n = 24; non-NHAI group, n = 36) were prospectively collected on days 1, 3 and 7 in a university hospital. The bacterial community profiles of the ETAs were explored by 16S rRNA gene sequencing. A phylogenetic-tree-based microbiome association test (TMAT), generalized linear mixed models (GLMMs), the Wilcoxon test and the reference frame method were used to analyze the association between microbiome abundance and disease phenotype.

Results

The relative abundance of the genus Corynebacterium was significantly higher in the pneumonia than in the non-pneumonia group. The microbiome analysis revealed significantly lower α-diversity in the NHAI group than in the non-NHAI group. In the analysis of β-diversity, the structure of the microbiome also differed significantly between the two groups (weighted UniFrac distance, Adonis, p < 0.001). The abundance of Corynebacterium was significantly higher, and the relative abundances of Granulicatella, Staphylococcus, Streptococcus and Veillonella were significantly lower, in the NHAI group than in the non-NHAI group.

Conclusions

The microbiota signature of the ETAs distinguished between patients with and without risk factors for NHAI. The lung microbiome may serve as a therapeutic target for NHAI group.

A quasi-paired cohort strategy reveals the impaired detoxifying function of microbes in the gut of autistic children

Growing evidence suggests that autism spectrum disorder (ASD) is strongly associated with dysbiosis in the gut microbiome, with the exact mechanisms still unclear. We have proposed a novel analytic strategy-quasi-paired cohort-and applied it to a metagenomic study of the ASD microbiome. By comparing paired samples of ASD and neurotypical subjects, we have identified significant deficiencies in ASD children in detoxifying enzymes and pathways, which show a strong correlation with biomarkers of mitochondrial dysfunction. Diagnostic models based on these detoxifying enzymes accurately distinguished ASD individuals from controls, and the dysfunction score inferred from the model increased with the clinical rating scores of ASD. In summary, our results suggest a previously undiscovered potential role of impaired intestinal microbial detoxification in toxin accumulation and mitochondrial dysfunction, a core component of ASD pathogenesis. These findings pave the way for designing future therapeutic strategies to restore microbial detoxification capabilities for patients with ASD.

Correlation and association analyses in microbiome study integrating multiomics in health and disease

Correlation and association analyses are one of the most widely used statistical methods in research fields, including microbiome and integrative multiomics studies. Correlation and association have two implications: dependence and co-occurrence. Microbiome data are structured as phylogenetic tree and have several unique characteristics, including high dimensionality, compositionality, sparsity with excess zeros, and heterogeneity. These unique characteristics cause several statistical issues when analyzing microbiome data and integrating multiomics data, such as large p and small n, dependency, overdispersion, and zero-inflation. In microbiome research, on the one hand, classic correlation and association methods are still applied in real studies and used for the development of new methods; on the other hand, new methods have been developed to target statistical issues arising from unique characteristics of microbiome data. Here, we first provide a comprehensive view of classic and newly developed univariate correlation and association-based methods. We discuss the appropriateness and limitations of using classic methods and demonstrate how the newly developed methods mitigate the issues of microbiome data. Second, we emphasize that concepts of correlation and association analyses have been shifted by introducing network analysis, microbe-metabolite interactions, functional analysis, etc. Third, we introduce multivariate correlation and association-based methods, which are organized by the categories of exploratory, interpretive, and discriminatory analyses and classification methods. Fourth, we focus on the hypothesis testing of univariate and multivariate regression-based association methods, including alpha and beta diversities-based, count-based, and relative abundance (or compositional)-based association analyses. We demonstrate the characteristics and limitations of each approaches. Fifth, we introduce two specific microbiome-based methods: phylogenetic tree-based association analysis and testing for survival outcomes. Sixth, we provide an overall view of longitudinal methods in analysis of microbiome and omics data, which cover standard, static, regression-based time series methods, principal trend analysis, and newly developed univariate overdispersed and zero-inflated as well as multivariate distance/kernel-based longitudinal models. Finally, we comment on current association analysis and future direction of association analysis in microbiome and multiomics studies.

Precision medicine in perinatal depression in light of the human microbiome

Perinatal depression is the most common complication of pregnancy and affects the mother, fetus, and infant. Recent preclinical studies and a limited number of clinical studies have suggested an influence of the gut microbiome on the onset and course of mental health disorders. In this review, we examine the current state of knowledge regarding genetics, epigenetics, heritability, and neuro-immuno-endocrine systems biology in perinatal mood disorders, with a particular focus on the interaction between these factors and the gut microbiome, which is mediated via the gut-brain axis. We also provide an overview of experimental and analytical methods that are currently available to researchers interested in elucidating the influence of the gut microbiome on mental health disorders during pregnancy and postpartum.