coda4microbiome: compositional data analysis for microbiome cross-sectional and longitudinal studies

Gut microbiome and function are altered for individuals living in high fluoride concentration areas in Pakistan

BACKGROUND

Endemic fluorosis refers to the condition when individuals are exposed to excessive amounts of fluoride ion due to living in a region characterized by elevated levels of fluorine in the drinking water, food, and/or air. In Pakistan, a substantial proportion of the population is thereby affected, posing a public health concern.

OBJECTIVES

Assessing how the gut microbiota and its metabolic profiles are impacted by chronic exposure to fluoride in drinking water (that caused Dental Fluorosis) as well as to perceive how this microbiota is connected to adverse health outcomes prevailing with fluoride exposure.

METHODS

Drinking water (n=27) and biological samples (n=100) of blood, urine and feces were collected from 70 high fluoride exposed (with Dental Fluorosis) and 30 healthy control (without Dental Fluorosis) subjects. Water and urinary fluoride concentrations were determined. Serum/plasma biochemical testing was performed. Fecal DNA extraction, 16S rRNA analysis of microbial taxa, their predicted metabolic function and fecal short chain fatty acids (SCFAs) quantification were carried out.

RESULTS

The study revealed that microbiota taxonomic shifts and their metabolic characterization had been linked to certain host clinical parameters under the chronic fluoride exposure. Some sets of genera showed strong specificity to water and urine fluoride concentrations, Relative Fat Mass index and SCFAs. The SCFAs response in fluoride-exposed samples was observed to be correlated with bacterial taxa that could contribute to adverse health effects.

CONCLUSIONS

Microbial dysbiosis as a result of endemic fluorosis exhibits a structure that is associated with risk of metabolic deregulation and is implicated in various diseases. Our results may form the development of novel interventions and may have utility in diagnosis and monitoring.

Modeling Microbial Communities: Perspective and Challenges

Microbial communities are immensely important due to their widespread presence and profound impact on various facets of life. Understanding these complex systems necessitates mathematical modeling, a powerful tool for simulating and predicting microbial community behavior. This review offers a critical analysis of metabolic modeling and highlights key areas that would greatly benefit from broader discussion and collaboration. Moreover, we explore the challenges and opportunities linked to the intricate nature of these communities, spanning data generation, modeling, and validation. We are confident that ongoing advancements in modeling techniques, such as machine learning, coupled with interdisciplinary collaborations, will unlock the full potential of microbial communities across diverse applications.

EXPLANA: A user-friendly workflow for EXPLoratory ANAlysis and feature selection in cross-sectional and longitudinal microbiome studies

Motivation

Longitudinal microbiome studies (LMS) are increasingly common but have analytic challenges including non-independent data requiring mixed-effects models and large amounts of data that motivate exploratory analysis to identify factors related to outcome variables. Although change analysis (i.e. calculating deltas between values at different timepoints) can be powerful, how to best conduct these analyses is not always clear. For example, observational LMS measurements show natural fluctuations, so baseline might not be a reference of primary interest; whereas, for interventional LMS, baseline is a key reference point, often indicating the start of treatment.

Results

To address these challenges, we developed a feature selection workflow for cross-sectional and LMS that supports numerical and categorical data called EXPLANA (EXPLoratory ANAlysis). Machine-learning methods were combined with different types of change calculations and downstream interpretation methods to identify statistically meaningful variables and explain their relationship to outcomes. EXPLANA generates an interactive report that textually and graphically summarizes methods and results. EXPLANA had good performance on simulated data, with an average area under the curve (AUC) of 0.91 (range: 0.79-1.0, SD = 0.05), outperformed an existing tool (AUC: 0.95 vs. 0.56), and identified novel order-dependent categorical feature changes. EXPLANA is broadly applicable and simplifies analytics for identifying features related to outcomes of interest.

HIV Infection and Exposure Increases Cariogenic Taxa, Reduces Taxonomic Turnover, and Homogenizes Spatial Differentiation for the Supragingival Microbiome

Background

The oral microbiome comprises distinct microbial communities that colonize diverse ecological niches across the oral cavity, the composition of which are influenced by nutrient and substrate availability, host genetics, diet, behavior, age, and other diverse host and environmental factors. Unlike other densely populated human-associated microbial ecosystems (e.g., gut, urogenital), the oral microbiome is regularly and directly exposed to the external environment and is therefore likely less stable over time. Cross sectional studies of the oral microbiome capture a glimpse of this temporal dynamism, yet a full appreciation of the relative stability, robusticity, and spatial structure of the oral environment is necessary to understand the role of microbial communities in promoting health or disease.

Results

Here we investigate the spatial and temporal stability of the oral microbiome over three sampling time points in the context of HIV infection and exposure. Individual teeth were sampled from a cohort of 565 Nigerian children with varying levels of tooth decay severity (i.e., caries disease). We collected 1,960 supragingival plaque samples and characterized the oral microbiome using a metataxonomic approach targeting an approximately 478 bp region of the bacterial rpoC gene. We found that both infection and exposure to HIV have significant effects on the stability of the supragingival plaque microbiome at both the spatial and temporal scale. Specifically, we detect (1) significantly lower taxonomic turnover of the oral community among exposed and infected children compared to unexposed children, (2) we find that HIV infection homogenizes the oral community across the anterior and posterior dentition, and (3) that impaired immunity (i.e., low CD4 count) and low taxonomic turnover over time in children living with HIV is associated with higher frequency of cariogenic taxa including Streptococcus mutans.

Conclusions

Our results document substantial community fluctuations over time in children unexposed to HIV independent of oral health status. This suggests that the oral community, under typical conditions, rapidly adapts to environmental perturbations to maintain homeostasis and that long-term taxonomic rigidity is a signal of community dysfunction, potentially leading to a higher incidence of oral disease including caries.

Genetic hypogonadal (Gnrh1hpg) mouse model uncovers influence of reproductive axis on maturation of the gut microbiome during puberty

The gut microbiome plays a key role in human health and gut dysbiosis is linked to many sex-specific diseases including autoimmune, metabolic, and neurological disorders. Activation of the hypothalamic-pituitary-gonadal (HPG) axis during puberty leads to sexual maturation and development of sex differences through the action of gonadal sex steroids. While the gut microbiome also undergoes sex differentiation, the mechanisms involved remain poorly understood. Using a genetic hypogonadal (hpg) mouse model, we sampled the fecal microbiome of male and female wild-type and hpg mutant mice before and after puberty to determine how microbial taxonomy and function are influenced by age, sex, and the HPG axis. We showed that HPG axis activation during puberty is required for sexual maturation of the gut microbiota composition, community structure, and metabolic functions. We also demonstrated that some sex differences in taxonomic composition and amine metabolism developed independently of the HPG axis, indicating that sex chromosomes are sufficient for certain sex differences in the gut microbiome. In addition, we showed that age, independent of HPG axis activation, led to some aspects of pubertal maturation of the gut microbiota community composition and putative functions. These results have implications for microbiome-based treatments, indicating that sex, hormonal status, and age should be considered when designing microbiome-based therapeutics.

Microbiome compositional data analysis for survival studies

The growing interest in studying the relationship between the human microbiome and our health has also extended to time-to-event studies where researchers explore the connection between the microbiome and the occurrence of a specific event of interest. The analysis of microbiome obtained through high throughput sequencing techniques requires the use of specialized Compositional Data Analysis (CoDA) methods designed to accommodate its compositional nature. There is a limited availability of statistical tools for microbiome analysis that incorporate CoDA, and this is even more pronounced in the context of survival analysis. To fill this methodological gap, we present coda4microbiome for survival studies, a new methodology for the identification of microbial signatures in time-to-event studies. The algorithm implements an elastic-net penalized Cox regression model adapted to compositional covariates. We illustrate coda4microbiome algorithm for survival studies with a case study about the time to develop type 1 diabetes for non-obese diabetic mice. Our algorithm identified a bacterial signature composed of 21 genera associated with diabetes development. coda4microbiome for survival studies is integrated in the R package coda4microbiome as an extension of the existing functions for cross-sectional and longitudinal studies.

Compositional structural brain signatures capture Alzheimer's genetic risk on brain structure along the disease continuum

INTRODUCTION

Traditional brain imaging genetics studies have primarily focused on how genetic factors influence the volume of specific brain regions, often neglecting the overall complexity of brain architecture and its genetic underpinnings.

METHODS

This study analyzed data from participants across the Alzheimer's disease (AD) continuum from the ALFA and ADNI studies. We exploited compositional data analysis to examine relative brain volumetric variations that (i) differentiate cognitively unimpaired (CU) individuals, defined as amyloid-negative (A-) based on CSF profiling, from those at different AD stages, and (ii) associated with increased genetic susceptibility to AD, assessed using polygenic risk scores.

RESULTS

Distinct brain signatures differentiated CU A-individuals from amyloid-positive MCI and AD. Moreover, disease stage-specific signatures were associated with higher genetic risk of AD.

DISCUSSION

The findings underscore the complex interplay between genetics and disease stages in shaping brain structure, which could inform targeted preventive strategies and interventions in preclinical AD.

Sunset Yellow dye effects on gut microbiota, intestinal integrity, and the induction of inflammasomopathy with pyroptotic signaling in male Wistar rats

Although concern persists regarding possible adverse effects of consumption of synthetic azo food dyes, the mechanisms of any such effects remain unclear. We have tested the hypothesis that chronic consumption of the food dye Sunset Yellow (SY) perturbs the composition of the gut microbiota and alters gut integrity. Male rats were administered SY orally for 12 weeks. Analysis of fecal samples before and after dye administration demonstrated SY-induced microbiome dysbiosis. SY treatment reduced the abundance of beneficial taxa such as Treponema 2, Anaerobiospirillum, Helicobacter, Rikenellaceae RC9 gut group, and Prevotellaceae UCG-003, while increasing the abundance of the potentially pathogenic microorganisms Prevotella 2 and Oribacterium. Dysbiosis disrupted gut integrity, altering the jejunal adherens junction complex E-cadherin/β-catenin and decreasing Trefoil Factor (TFF)-3. SY administration elevated LPS serum levels, activated the inflammatory inflammasome cascade TLR4/NLRP3/ASC/cleaved-activated caspase-1 to mature IL-1β and IL-18, and activated caspase-11 and gasdermin-N, indicating pyroptosis and increased intestinal permeability. The possibility that consumption of SY by humans could have effects similar to those that we have observed in rats should be examined.

Longitudinal gut microbiome changes in immune checkpoint blockade-treated advanced melanoma

Multiple clinical trials targeting the gut microbiome are being conducted to optimize treatment outcomes for immune checkpoint blockade (ICB). To improve the success of these interventions, understanding gut microbiome changes during ICB is urgently needed. Here through longitudinal microbiome profiling of 175 patients treated with ICB for advanced melanoma, we show that several microbial species-level genome bins (SGBs) and pathways exhibit distinct patterns from baseline in patients achieving progression-free survival (PFS) of 12 months or longer (PFS ≥12) versus patients with PFS shorter than 12 months (PFS <12). Out of 99 SGBs that could discriminate between these two groups, 20 were differentially abundant only at baseline, while 42 were differentially abundant only after treatment initiation. We identify five and four SGBs that had consistently higher abundances in patients with PFS ≥12 and <12 months, respectively. Constructing a log ratio of these SGBs, we find an association with overall survival. Finally, we find different microbial dynamics in different clinical contexts including the type of ICB regimen, development of immune-related adverse events and concomitant medication use. Insights into the longitudinal dynamics of the gut microbiome in association with host factors and treatment regimens will be critical for guiding rational microbiome-targeted therapies aimed at enhancing ICB efficacy.

Enhanced Feature Selection for Microbiome Data using FLORAL: Scalable Log-ratio Lasso Regression

Identifying predictive biomarkers of patient outcomes from high-throughput microbiome data is of high interest, while existing computational methods do not satisfactorily account for complex survival endpoints, longitudinal samples, and taxa-specific sequencing biases. We present FLORAL (https://vdblab.github.io/FLORAL/), an open-source computational tool to perform scalable log-ratio lasso regression and microbial feature selection for continuous, binary, time-to-event, and competing risk outcomes, with compatibility of longitudinal microbiome data as time-dependent covariates. The proposed method adapts the augmented Lagrangian algorithm for a zero-sum constraint optimization problem while enabling a two-stage screening process for extended false-positive control. In extensive simulation and real-data analyses, FLORAL achieved consistently better false-positive control compared to other lasso-based approaches, and better sensitivity over popular differential abundance testing methods for datasets with smaller sample size. In a survival analysis in allogeneic hematopoietic-cell transplant, we further demonstrated considerable improvement by FLORAL in microbial feature selection by utilizing longitudinal microbiome data over only using baseline microbiome data.

Cotton microbiome profiling and Cotton Leaf Curl Disease (CLCuD) suppression through microbial consortia associated with Gossypium arboreum

The failure of breeding strategies has caused scientists to shift to other means where the new approach involves exploring the microbiome to modulate plant defense mechanisms against Cotton Leaf Curl Disease (CLCuD). The cotton microbiome of CLCuD-resistant varieties may harbor a multitude of bacterial genera that significantly contribute to disease resistance and provide information on metabolic pathways that differ between the susceptible and resistant varieties. The current study explores the microbiome of CLCuD-susceptible Gossypium hirsutum and CLCuD-resistant Gossypium arboreum using 16 S rRNA gene amplification for the leaf endophyte, leaf epiphyte, rhizosphere, and root endophyte of the two cotton species. This revealed that Pseudomonas inhabited the rhizosphere while Bacillus was predominantly found in the phyllosphere of CLCuV-resistant G. arboreum. Using salicylic acid-producing Serratia spp. and Fictibacillus spp. isolated from CLCuD-resistant G. arboreum, and guided by our analyses, we have successfully suppressed CLCuD in the susceptible G. hirsutum through pot assays. The applied strains exhibited less than 10% CLCuD incidence as compared to control group where it was 40% at 40 days post viral inoculation. Through detailed analytics, we have successfully demonstrated that the applied microbes serve as a biocontrol agent to suppress viral disease in Cotton.

Genetic hypogonadal mouse model reveals niche-specific influence of reproductive axis and sex on intestinal microbial communities

BACKGROUND

The gut microbiome has been linked to many diseases with sex bias including autoimmune, metabolic, neurological, and reproductive disorders. While numerous studies report sex differences in fecal microbial communities, the role of the reproductive axis in this differentiation is unclear and it is unknown how sex differentiation affects microbial diversity in specific regions of the small and large intestine.

METHODS

We used a genetic hypogonadal mouse model that does not produce sex steroids or go through puberty to investigate how sex and the reproductive axis impact bacterial diversity within the intestine. Using 16S rRNA gene sequencing, we analyzed alpha and beta diversity and taxonomic composition of fecal and intestinal communities from the lumen and mucosa of the duodenum, ileum, and cecum from adult female (n = 20) and male (n = 20) wild-type mice and female (n = 17) and male (n = 20) hypogonadal mice.

RESULTS

Both sex and reproductive axis inactivation altered bacterial composition in an intestinal section and niche-specific manner. Hypogonadism was significantly associated with bacteria from the Bacteroidaceae, Eggerthellaceae, Muribaculaceae, and Rikenellaceae families, which have genes for bile acid metabolism and mucin degradation. Microbial balances between males and females and between hypogonadal and wild-type mice were also intestinal section-specific. In addition, we identified 3 bacterial genera (Escherichia Shigella, Lachnoclostridium, and Eggerthellaceae genus) with higher abundance in wild-type female mice throughout the intestinal tract compared to both wild-type male and hypogonadal female mice, indicating that activation of the reproductive axis leads to female-specific differentiation of the gut microbiome. Our results also implicated factors independent of the reproductive axis (i.e., sex chromosomes) in shaping sex differences in intestinal communities. Additionally, our detailed profile of intestinal communities showed that fecal samples do not reflect bacterial diversity in the small intestine.

CONCLUSIONS

Our results indicate that sex differences in the gut microbiome are intestinal niche-specific and that sampling feces or the large intestine may miss significant sex effects in the small intestine. These results strongly support the need to consider both sex and reproductive status when studying the gut microbiome and while developing microbial-based therapies.

Reproducible microbiome composition signatures of anxiety and depressive symptoms

The gut microbiome is a significant contributor to mental health, with growing evidence linking its composition to anxiety and depressive disorders. Gut microbiome composition is associated with signs of anxiety and depression both in clinically diagnosed mood disorders and subclinically in the general population and may be influenced by dietary fibre intake and the presence of chronic pain. We provide an update of current evidence on the role of gut microbiome composition in depressive and anxiety disorders or symptoms by reviewing available studies. Analysing data from three independent cohorts (osteoarthritis 1 (OA1); n = 46, osteoarthritis 2 (OA2); n = 58, and healthy controls (CON); n = 67), we identified microbial composition signatures of anxiety and depressive symptoms at genus level and cross-validated our findings performing meta-analyses of our results with results from previously published studies. The genera Bifidobacterium (fixed-effect beta (95% CI) = -0.22 (-0.34, -0.10), p = 3.90e-04) and Lachnospiraceae NK4A136 group (fixed-effect beta (95% CI) = -0.09 (-0.13, -0.05), p = 2.53e-06) were found to be the best predictors of anxiety and depressive symptoms, respectively, across our three cohorts and published literature taking into account demographic and lifestyle covariates, such as fibre intake. The association with anxiety was robust in accounting for heterogeneity between cohorts and supports previous observations of the potential prophylactic effect of Bifidobacterium against anxiety symptoms.

Oral intake of Lactiplantibacillus pentosus LPG1 Produces a Beneficial Regulation of Gut Microbiota in Healthy Persons: A Randomised, Placebo-Controlled, Single-Blind Trial

The search for vegetable-origin probiotic microorganisms is a recent area of interest. This study conducted a phase I clinical trial to assess the effects of oral administration of Lactiplantibacillus pentosus LPG1, a natural strain with probiotic potential isolated from table olive fermentations, on the gut microbiota. The trial was a randomised, placebo-controlled, single-blind study involving 39 healthy volunteers. Group A (n = 20) ingested one capsule/day of L. pentosus LPG1 containing 1 × 10¹⁰ UFC/capsule, while Group B (n = 19) received one capsule/day containing only dextrose (placebo). The capsules were taken during breakfast for 30 consecutive days. Human stool samples were collected from all volunteers at the beginning (baseline) and at the end of the study (post-intervention) and were subjected to 16S rRNA metataxonomic analysis using Illumina MiSeq. Sequencing data at the genus level were statistically analysed using traditional methods and compositional data analysis (CoDA). After treatment, the alpha diversity in Group B (placebo) decreased according to an increase in the Berger and Parker dominance index (p-value < 0.05); moreover, dominance D increased and Simpson 1-D index decreased (p-value < 0.10). The Lactobacillus genus in the faeces was included in the CoDA signature balances (selbal and coda4microbiome) and played a notable role in distinguishing samples from baseline and post-intervention in Group A (LPG1). Additionally, ingesting L. pentosus LPG1 modified the gut microbiota post-intervention, increasing the presence of Parabacteroides and Agathobacter, but reducing Prevotella. These findings suggest that L. pentosus LPG1 is a potentially beneficial gut microbiota modulator in healthy persons.