Association between Gut Microbiota and Breast Cancer: Diet as a Potential Modulating Factor.

Breast cancer (BCa) has many well-known risk factors, including age, genetics, lifestyle, and diet; however, the influence of the gut microbiome on BCa remains an emerging area of investigation. This study explores the connection between the gut microbiome, dietary habits, and BCa risk. We enrolled newly diagnosed BCa patients and age-matched cancer-free controls in a case-control study. Comprehensive patient data was collected, including dietary habits assessed through the National Cancer Institute Diet History Questionnaire (DHQ). 16S rRNA amplicon sequencing was used to analyze gut microbiome composition and assess alpha and beta diversity. Microbiome analysis revealed differences in the gut microbiome composition between cases and controls, with reduced microbial diversity in BCa patients. The abundance of three specific microbial genera-Acidaminococus, Tyzzerella, and Hungatella-was enriched in the fecal samples taken from BCa patients. These genera were associated with distinct dietary patterns, revealing significant associations between the presence of these genera in the microbiome and specific HEI2015 components, such as vegetables and dairy for Hungatella, and whole fruits for Acidaminococus. Demographic characteristics were well-balanced between groups, with a significantly higher body mass index and lower physical activity observed in cases, underscoring the role of weight management in BCa risk. Associations between significant microbial genera identified from BCa cases and dietary intakes were identified, which highlights the potential of the gut microbiome as a source of biomarkers for BCa risk assessment. This study calls attention to the complex interplay between the gut microbiome, lifestyle factors including diet, and BCa risk.

A diet rich in fermentable fiber promotes robust changes in the intestinal microbiota, mitigates intestinal permeability, and attenuates autoimmune uveitis.

Therapeutic approaches for noninfectious uveitis have expanded greatly over the past 10 years, but are limited by potential side effects and limited efficacy. Thus, therapeutic approaches that include less toxic, potentially preventative strategies to manage noninfectious uveitis are essential areas of study. Diets rich in fermentable fiber are potentially preventative in various conditions such as metabolic syndrome and type 1 diabetes. We studied the effects of various fermentable dietary fibers in an inducible model of experimental autoimmune uveitis (EAU) and found that they differentially modulated uveitis severity. A high pectin diet was the most protective, reducing clinical disease severity through the induction of regulatory T lymphocytes and the suppression of Th1 and Th17 lymphocytes at peak ocular inflammation in either intestinal or extra-intestinal lymphoid tissues. The high pectin diet also promoted intestinal homeostasis as shown by changes in intestinal morphology and gene expression, as well as intestinal permeability. Pectin-induced modulation of intestinal bacteria appeared to be associated with protective changes in immunophenotype in the intestinal tract, and correlated with reduced uveitis severity. In summary, our current findings support the potential for dietary intervention as a strategy to mitigate noninfectious uveitis severity.

BMI and BPH correlate with urinary microbiome diversity and lower urinary tract symptoms in men.

Several studies have identified bacteria and other microbes in the bladder and lower urinary tract in the absence of infection. In women, the urinary microbiome has been associated with lower urinary tract symptoms (LUTS), however, similar studies have not been undertaken in large cohorts of men. Here we examine the urinary microbiome and its association with LUTS in a subset of 500 men aged 65 to 90 years from the Osteoporotic Fractures in Men (MrOS) study. We identified significant associations between benign prostatic hyperplasia (BPH), age, and body mass index (BMI) with several diversity metrics. Our analysis revealed complex relationships between BMI, BPH, LUTS, and alpha diversity which give insight into the intricate dynamics of the urinary microbiome. By beginning to uncover the interrelationships of BPH, BMI, LUTS, and the urinary microbiome, these results can inform future study design to better understand the heterogeneity of the male urinary microbiome.

Axial spondyloarthritis patients have altered mucosal IgA response to oral and fecal microbiota.

Axial spondyloarthritis (axSpA) is an inflammatory arthritis involving the spine and the sacroiliac joint with extra-articular manifestations in the eye, gut, and skin. The intestinal microbiota has been implicated as a central environmental component in the pathogenesis of various types of spondyloarthritis including axSpA. Additionally, alterations in the oral microbiota have been shown in various rheumatological conditions, such as rheumatoid arthritis (RA). Therefore, the aim of this study was to investigate whether axSpA patients have an altered immunoglobulin A (IgA) response in the gut and oral microbial communities. We performed 16S rRNA gene (16S) sequencing on IgA positive (IgA+) and IgA negative (IgA-) fractions (IgA-SEQ) from feces (n=17 axSpA; n=14 healthy) and saliva (n=14 axSpA; n=12 healthy), as well as on IgA-unsorted fecal and salivary samples. PICRUSt2 was used to predict microbial metabolic potential in axSpA patients and healthy controls (HCs). IgA-SEQ analyses revealed enrichment of several microbes in the fecal (Akkermansia, Ruminococcaceae, Lachnospira) and salivary (Prevotellaceae, Actinobacillus) microbiome in axSpA patients as compared with HCs. Fecal microbiome from axSpA patients showed a tendency towards increased alpha diversity in IgA+ fraction and decreased diversity in IgA- fraction in comparison with HCs, while the salivary microbiome exhibits a significant decrease in alpha diversity in both IgA+ and IgA- fractions. Increased IgA coating of Clostridiales Family XIII in feces correlated with disease severity. Inferred metagenomic analysis suggests perturbation of metabolites and metabolic pathways for inflammation (oxidative stress, amino acid degradation) and metabolism (propanoate and butanoate) in axSpA patients. Analyses of fecal and salivary microbes from axSpA patients reveal distinct populations of immunoreactive microbes compared to HCs using the IgA-SEQ approach. These bacteria were not identified by comparing their relative abundance alone. Predictive metagenomic analysis revealed perturbation of metabolites/metabolic pathways in axSpA patients. Future studies on these immunoreactive microbes may lead to better understanding of the functional role of IgA in maintaining microbial structure and human health.

microshades: An R Package for Improving Color Accessibility and Organization of Microbiome Data.

When creating figures, it is important to consider that individuals with color vision deficiency (CVD) may not perceive all colors. While there are several CVD-friendly color palettes, they are often insufficient for working with microbiome data. Here, we introduce microshades, an R package for creating CVD-accessible microbiome figures.

Updating Urinary Microbiome Analyses to Enhance Biologic Interpretation.

Objective: An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where analysis methods are rapidly evolving. This re-analysis of an existing dataset aimed to determine whether updated bioinformatic and statistical techniques affect clinical inferences. Methods: A prior study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We obtained unprocessed sequencing data from multiple variable regions, processed operational taxonomic unit (OTU) tables from the original analysis, and de-identified clinical data. We re-processed sequencing data with DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to the original OTU tables; taxa from different variable regions after updated processing were also compared. Bayesian graphical compositional regression (BGCR) was used to test for associations between microbial compositions and clinical phenotypes (e.g., MUI versus control) while adjusting for clinical covariates. Several techniques were used to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, again while adjusting for potentially confounding variables. Results: Of taxa identified through updated bioinformatic processing, only 40% were identified originally, though taxa identified through both methods represented >99% of the sequencing data in terms of relative abundance. Different 16S rRNA gene regions resulted in different recovered taxa. With BGCR analysis, there was a low (33.7%) probability of an association between overall microbial compositions and clinical phenotype. However, when microbial data are clustered into bacterial communities, we confirmed that bacterial communities are associated with MUI. Contrary to the originally published analysis, we did not identify different associations by age group, which may be due to the incorporation of different covariates in statistical models. Conclusions: Updated bioinformatic processing techniques recover different taxa compared to earlier techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. While overall microbial compositions are not associated with MUI, we confirmed associations between certain communities of bacteria and MUI. Incorporation of several covariates that are associated with the urinary microbiome improved inferences when assessing for associations between bacterial communities and MUI in multivariable models.

Oral microbiome diversity: The curious case of Corynebacterium sp. isolation.

Oral microbiome sequencing efforts revealed the presence of hundreds of different microbes. Interindividual differences at strain and species resolution suggest that microbiome diversity could lead to mechanistically distinct gene regulation as well as species-related differences in phenotypes. Commonly, gene regulation and related phenotypes are studied in a few selected strains of a particular species with conclusions that are mostly generalized. The aim of this study was to isolate several species of Corynebacterium using an established protocol that led to the previous isolation of C. durum. Characterization of C. durum interspecies interactions revealed a specific mechanism for chain elongation in Streptococcus sanguinis that was the result of corynebacterial fatty acid production and secretion. While the protocol was successfully applied to isolate what we presumed to be additional Corynebacterium based on several phenotypic traits that seem to be identical to C. durum, genome sequencing of the newly isolated strains placed them closer to Actinomyces. Both Corynebacterium and Actinomyces are suborders of the Actinobacteridae and related species. Our study suggests to take several comprehensive strategies into consideration when taxonomically identifying closely related microorganisms. Furthermore, it seems to be important to test common core phenotypes in bacterial ecology to understand the behavior of specific groups of microbes, rather than simply relying upon genome sequence homology to establish relationships in the microbiome.

Network-Based Differences in the Vaginal and Bladder Microbial Communities Between Women With and Without Urgency Urinary Incontinence.

Background: Little is known about the relationship of proximal urogenital microbiomes in the bladder and the vagina and how this contributes to bladder health. In this study, we use a microbial ecology and network framework to understand the dynamics of interactions/co-occurrences of bacteria in the bladder and vagina in women with and without urgency urinary incontinence (UUI). Methods: We collected vaginal swabs and catheterized urine specimens from 20 women with UUI (cases) and 30 women without UUI (controls). We sequenced the V4 region of the bacterial 16S rRNA gene and evaluated using alpha and beta diversity metrics. We used microbial network analysis to detect interactions in the microbiome and the betweenness centrality measure to identify central bacteria in the microbial network. Bacteria exhibiting maximum betweenness centrality are considered central to the microbe-wide networks and likely maintain the overall microbial network structure. Results: There were no significant differences in the vaginal or bladder microbiomes between cases and controls using alpha and beta diversity. Silhouette metric analysis identified two distinct microbiome clusters in both the bladder and vagina. One cluster was dominated by Lactobacillus genus while the other was more diverse. Network-based analyses demonstrated that vaginal and bladder microbial networks were different between cases and controls. In the vagina, there were similar numbers of genera and subgroup clusters in each network for cases and controls. However, cases tend to have more unique bacterial co-occurrences. While Bacteroides and Lactobacillus were the central bacteria with the highest betweenness centrality in controls, Aerococcus had the highest centrality in cases and correlated with bacteria commonly associated with bacterial vaginosis. In the bladder, cases have less than half as many network clusters compared to controls. Lactobacillus was the central bacteria in both groups but associated with several known uropathogens in cases. The number of shared bacterial genera between the bladder and the vagina differed between cases and controls, with cases having larger overlap (43%) compared to controls (29%). Conclusion: Our study shows overlaps in microbial communities of bladder and vagina, with higher overlap in cases. We also identified differences in the bacteria that are central to the overall community structure.

The Impact of Local Estrogen on the Urogenital Microbiome in Genitourinary Syndrome of Menopause: A Randomized-Controlled Trial.

IMPORTANCE: The postmenopausal urinary bladder microbiome is not well defined. OBJECTIVES: The aims of this study were to characterize the effect of vaginal estrogen on the vaginal and urinary bladder microbiome in postmenopausal women and describe any clinical associations with the symptoms of genitourinary syndrome of menopause. STUDY DESIGN: This was a participant-masked, randomized controlled trial comparing the effect of a 12-week course of an estrogen-containing vaginal ring to a placebo vaginal ring. Standardized evaluations were performed at baseline and 12 weeks. Vaginal samples were obtained for pH, vaginal maturation index, and microbiome analysis. Concomitant catheterized urine samples were obtained for microbiome analysis. 16S ribosomal RNA gene sequencing was performed to characterize the resident microbial communities, with Lactobacillus relative abundance as the primary outcome variable. Genitourinary syndrome of menopause symptoms was measured using validated questionnaires (Pelvic Floor Distress Inventory-Short Form, Female Sexual Function Index, Vulvovaginal Symptoms Questionnaire). RESULTS: Of the 39 postmenopausal women randomized, baseline characteristics were similar between arms, with a mean age of 62 years and mean vaginal pH of 5.0. Using intention-to-treat analysis, there were no significant changes in vaginal or urinary Lactobacillus relative abundance. Two participants in each arm removed their ring prior to the end of the study. Eighty percent of participants experienced at least 1 bothersome genitourinary syndrome of menopause symptom. Vulvovaginal dryness and urinary frequency were most common at baseline, whereas painful intercourse and urinary urgency were most common at the final visit, none of which were statistically significant. CONCLUSIONS: Our study did not show a significant change in the bacterial composition of the vaginal or urinary bladder microbiome after either vaginal ring in this relatively asymptomatic postmenopausal population.

Antimetabolite Drugs Exhibit Distinctive Immunomodulatory Mechanisms and Effects on the Intestinal Microbiota in Experimental Autoimmune Uveitis.

Purpose: The purpose of this study was to investigate the effect of antimetabolite drugs on T-cell responses and intestinal microbial composition in autoimmune uveitis. Methods: Experimental autoimmune uveitis (EAU) was induced in C57BL/6J mice treated with 0.00625 mg/mL methotrexate (MTX) or 0.625 mg/mL mycophenolate mofetil (MMF) in drinking water for 4 weeks prior to immunization and 2 weeks thereafter. The effector T cell (Teff) and regulatory T cell (Treg) populations were identified using flow cytometry. The 16S rRNA gene sequencing was applied for gut microbiome characterization. DESeq2 analysis was used to discriminate relative abundances of taxa and PLS-DA to integrate cytometric and microbiome data between groups. Results: Both MTX and MMF abrogated uveitis in EAU without clinical signs of toxicity as compared to water-fed controls. MTX reduced Teff and Treg expansion in peripheral tissues and eyes. MTX decreased alpha diversity, increased Akkermansia, and reduced Lachnoclostridium abundances. Conversely, MMF enhanced Tregs in the mesenteric lymph node and the eyes. In parallel, MMF increased the gut alpha diversity, including an increased abundance of Lachnospiraceae NK4A136 group and a decreased abundance of Lachnospiraceae UCG-001. A significant congruent correlation among intestinal microbial changes, T-cell responses, and clinical scores was observed for both antimetabolites. Conclusions: Although MTX and MMF both abrogated uveitis in EAU, they showed different effects on T-cell subsets and the intestinal bacterial composition. This work indicates unique immunomodulation by each drug and is the first to demonstrate potential microbiota-related mechanisms.

Reporting guidelines for human microbiome research: the STORMS checklist.

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

Species-Level Resolution of Female Bladder Microbiota from 16S rRNA Amplicon Sequencing.

The human bladder contains bacteria, even in the absence of infection. Interest in studying these bacteria and their association with bladder conditions is increasing. However, the chosen experimental method can limit the resolution of the taxonomy that can be assigned to the bacteria found in the bladder. 16S rRNA amplicon sequencing is commonly used to identify bacteria in urinary specimens, but it is typically restricted to genus-level identification. Our primary aim here was to determine if accurate species-level identification of bladder bacteria is possible using 16S rRNA amplicon sequencing. We evaluated the ability of different classification schemes, each consisting of combinations of a reference database, a 16S rRNA gene variable region, and a taxonomic classification algorithm to correctly classify bladder bacteria. We show that species-level identification is possible and that the reference database chosen is the most important component, followed by the 16S variable region sequenced. IMPORTANCE Accurate species-level identification from culture-independent techniques is of importance for microbial niches that are less well characterized, such as that of the bladder. 16S rRNA amplicon sequencing, a common culture-independent way to identify bacteria, is often critiqued for lacking species-level resolution. Here, we extensively evaluate classification schemes for species-level bacterial annotation of 16S amplicon data from bladder bacteria. Our results show that the proper choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show that this improvement can be achieved through the more careful application of existing methods and resources. Species-level information may deepen our understanding of associations between bacteria in the bladder and bladder conditions such as lower urinary tract symptoms and urinary tract infections.

Forming Consensus To Advance Urobiome Research.

Urobiome research has the potential to advance the understanding of a wide range of diseases, including lower urinary tract symptoms and kidney disease. Many scientific areas have benefited from early research method consensus to facilitate the greater, common good. This consensus document, developed by a group of expert investigators currently engaged in urobiome research (UROBIOME 2020 conference participants), aims to promote standardization and advances in this field by the adoption of common core research practices. We propose a standardized nomenclature as well as considerations for specimen collection, preservation, storage, and processing. Best practices for urobiome study design include our proposal for standard metadata elements as part of core metadata collection. Although it is impractical to follow fixed analytical procedures when analyzing urobiome data, we propose guidelines to document and report data originating from urobiome studies. We offer this first consensus document with every expectation of subsequent revision as our field progresses.

The Urinary Microbiome in Postmenopausal Women with Recurrent Urinary Tract Infections.

PURPOSE: The etiology of postmenopausal recurrent urinary tract infection (UTI) is not completely known, but the urinary microbiome is thought to be implicated. We compared the urinary microbiome in menopausal women with recurrent UTIs to age-matched controls, both in the absence of acute infection. MATERIALS AND METHODS: This is a cross-sectional analysis of baseline data from 64 women enrolled in a longitudinal cohort study. All women were using topically applied vaginal estrogen. Women >55 years of age from the following groups were enrolled: 1) recurrent UTIs on daily antibiotic prophylaxis, 2) recurrent UTIs not on antibiotic prophylaxis and 3) age-matched controls without recurrent UTIs. Catheterized urine samples were collected at least 4 weeks after last treatment for UTI and at least 6 weeks after initiation of vaginal estrogen. Samples were evaluated using expanded quantitative urine culture (EQUC) and 16S rRNA gene sequencing. RESULTS: With EQUC, there were no significant differences in median numbers of microbial species isolated among groups (p=0.96), even when considering Lactobacilli (p=0.72). However, there were trends toward different Lactobacillus species between groups. With 16S rRNA sequencing, the majority of urine samples contained Lactobacillaceae, with nonsignificant trends in relative abundance among groups. Using a Bayesian analysis, we identified significant differences in anaerobic taxa associated with phenotypic groups. Most of these differences centered on Bacteroidales and the family Prevotellaceae, although differences were also noted in Actinobacteria and certain genera of Clostridiales. CONCLUSIONS: Associations between anaerobes within the urinary microbiome and postmenopausal recurrent UTI warrants further investigation.

Benchmarking DNA isolation kits used in analyses of the urinary microbiome.

The urinary microbiome has been increasingly characterized using next-generation sequencing. However, many of the technical methods have not yet been specifically optimized for urine. We sought to compare the performance of several DNA isolation kits used in urinary microbiome studies. A total of 11 voided urine samples and one buffer control were divided into 5 equal aliquots and processed in parallel using five commercial DNA isolation kits. DNA was quantified and the V4 segment of the 16S rRNA gene was sequenced. Data were processed to identify the microbial composition and to assess alpha and beta diversity of the samples. Tested DNA isolation kits result in significantly different DNA yields from urine samples. DNA extracted with the Qiagen Biostic Bacteremia and DNeasy Blood & Tissue kits showed the fewest technical issues in downstream analyses, with the DNeasy Blood & Tissue kit also demonstrating the highest DNA yield. Nevertheless, all five kits provided good quality DNA for high throughput sequencing with non-significant differences in the number of reads recovered, alpha, or beta diversity.

Emerging Priorities for Microbiome Research.

Microbiome research has increased dramatically in recent years, driven by advances in technology and significant reductions in the cost of analysis. Such research has unlocked a wealth of data, which has yielded tremendous insight into the nature of the microbial communities, including their interactions and effects, both within a host and in an external environment as part of an ecological community. Understanding the role of microbiota, including their dynamic interactions with their hosts and other microbes, can enable the engineering of new diagnostic techniques and interventional strategies that can be used in a diverse spectrum of fields, spanning from ecology and agriculture to medicine and from forensics to exobiology. From June 19-23 in 2017, the NIH and NSF jointly held an Innovation Lab on Quantitative Approaches to Biomedical Data Science Challenges in our Understanding of the Microbiome. This review is inspired by some of the topics that arose as priority areas from this unique, interactive workshop. The goal of this review is to summarize the Innovation Lab's findings by introducing the reader to emerging challenges, exciting potential, and current directions in microbiome research. The review is broken into five key topic areas: (1) interactions between microbes and the human body, (2) evolution and ecology of microbes, including the role played by the environment and microbe-microbe interactions, (3) analytical and mathematical methods currently used in microbiome research, (4) leveraging knowledge of microbial composition and interactions to develop engineering solutions, and (5) interventional approaches and engineered microbiota that may be enabled by selectively altering microbial composition. As such, this review seeks to arm the reader with a broad understanding of the priorities and challenges in microbiome research today and provide inspiration for future investigation and multi-disciplinary collaboration.

Controlling for Contaminants in Low-Biomass 16S rRNA Gene Sequencing Experiments.

Microbial communities are commonly studied using culture-independent methods, such as 16S rRNA gene sequencing. However, one challenge in accurately characterizing microbial communities is exogenous bacterial DNA contamination, particularly in low-microbial-biomass niches. Computational approaches to identify contaminant sequences have been proposed, but their performance has not been independently evaluated. To identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we created a mock microbial community dilution series. We evaluated four computational approaches to identify and remove contaminants, as follows: (i) filtering sequences present in a negative control, (ii) filtering sequences based on relative abundance, (iii) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam, and (iv) predicting the sequence proportion arising from defined contaminant sources implemented in SourceTracker. As expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 80.1% of the most diluted sample arising from contaminant sequences. Inclusion of contaminant sequences led to overinflated diversity estimates and distorted microbiome composition. All methods for contaminant identification successfully identified some contaminant sequences, which varied depending on the method parameters used and contaminant prevalence. Notably, removing sequences present in a negative control erroneously removed >20% of expected sequences. SourceTracker successfully removed over 98% of contaminants when the experimental environments were well defined. However, SourceTracker misclassified expected sequences and performed poorly when the experimental environment was unknown, failing to remove >97% of contaminants. In contrast, the Decontam frequency method did not remove expected sequences and successfully removed 70 to 90% of the contaminants.IMPORTANCE The relative scarcity of microbes in low-microbial-biomass environments makes accurate determination of community composition challenging. Identifying and controlling for contaminant bacterial DNA are critical steps in understanding microbial communities from these low-biomass environments. Our study introduces the use of a mock community dilution series as a positive control and evaluates four computational strategies that can identify contaminants in 16S rRNA gene sequencing experiments in order to remove them from downstream analyses. The appropriate computational approach for removing contaminant sequences from an experiment depends on prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.