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Background: Recently, associations between recurrent urinary tract infections (UTI) and the urinary microbiome (urobiome) composition have been identified in adults. However, little is known about the urobiome in children. We aimed to characterize the urobiome of children with species-level resolution and to identify associations based on UTI history., Study Design: Fifty-four children (31 females and 21 males) from 3 months to 11 years of age participated in the study. Catheterized urine specimens were obtained from children undergoing a clinically indicated voiding cystourethrogram. To improve the analysis of the pediatric urobiome, we used a novel protocol using filters to collect biomass from the urine coupled with synthetic long-read 16S rRNA gene sequencing to obtain culture-independent species-level resolution data. We tested for differences in microbial composition between sex and history of UTIs using non-parametric tests on individual bacteria and alpha diversity measures., Results: We detected bacteria in 61% of samples from 54 children (mean age 40.7 months, 57% females). Similar to adults, urobiomes were distinct across individuals and varied by sex. The urobiome of females showed higher diversity as measured by the inverse Simpson and Shannon indices but not the Pielou evenness index or number of observed species (p = 0.05, p = 0.04, p = 0.35, and p = 0.11, respectively). Additionally, several species were significantly overrepresented in females compared to males, including those from the genera Anaerococcus, Prevotella, and Schaalia (p = 0.03, 0.04, and 0.02, respectively). Urobiome diversity increased with age, driven mainly by males. Comparison of children with a history of 1, 2, or 3+ UTIs revealed that urobiome diversity significantly decreases in the group that experienced 3+ UTIs as measured by the Simpson, Shannon, and Pielou indices (p = 0.03, p = 0.05, p = 0.01). Several bacteria were also found to be reduced in abundance., Discussion: In this study, we confirm that urobiome can be identified from catheter-collected urine specimens in infants as young as 3 months, providing further evidence that the pediatric bladder is not sterile. In addition to confirming variations in the urobiome related to sex, we identify age-related changes in children under 5 years of age, which conflicts with some prior research. We additionally identify associations with a history of UTIs., Conclusions: Our study provides additional evidence that the pediatric urobiome exists. The bacteria in the bladder of children appear to be affected by early urologic events and warrants future research., (Copyright © 2024 Journal of Pediatric Urology Company. Published by Elsevier Ltd. All rights reserved.)

The human urinary bladder hosts a complex microbial community of low biomass referred to as the urobiome. While the composition of the urobiome has been investigated in adults for over a decade now, only a few studies have considered the presence and composition of the urobiome in children. It is critical to explore how the urobiome develops throughout the life span and how it changes in the presence of various health conditions. Therefore, we set to review the available data on pediatric urobiome composition and its development with age and disease. In addition, we focused on identifying and reporting specific gaps in our knowledge of the pediatric urobiome that we hope will be addressed by future studies in this swiftly developing field with fast-improving methods and consensus., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (© 2024 Jeries, Sysoeva, Karstens and Kelly.)

Background: Urinary microbiome (urobiome) studies have previously reported on specific taxa and community differences in women with mixed urinary incontinence compared with controls. Therefore, a hypothesis was made that higher urinary and vaginal microbiome diversity would be associated with increased urinary incontinence severity., Objective: This study aimed to test whether specific urinary or vaginal microbiome community types are associated with urinary incontinence severity in a population of women with mixed urinary incontinence., Study Design: This planned secondary, cross-sectional analysis evaluated associations between the urinary and vaginal microbiomes and urinary incontinence severity in a subset of Effects of Surgical Treatment Enhanced With Exercise for Mixed Urinary Incontinence trial participants with urinary incontinence. Incontinence severity was measured using bladder diaries and Urinary Distress Inventory questionnaires collected at baseline. Catheterized urine samples and vaginal swabs were concurrently collected before treatment at baseline to assess the urinary and vaginal microbiomes. Of note, 16S rRNA V4 to V6 variable regions were sequenced, characterizing bacterial taxa to the genus level using the DADA2 pipeline and SILVA database. Using Dirichlet multinomial mixtures methods, samples were clustered into community types based on core taxa. Associations between community types and severity measures (Urinary Distress Inventory total scores, Urinary Distress Inventory subscale scores, and the number of urinary incontinence episodes [total, urgency, and stress] from the bladder diary) were evaluated using linear regression models adjusted for age and body mass index. In addition, alpha diversity measures for richness (total taxa numbers) and evenness (proportional distribution of taxa abundance) were analyzed for associations with urinary incontinence episodes and community type., Results: Overall, 6 urinary microbiome community types were identified, characterized by varying levels of common genera (Lactobacillus, Gardnerella, Prevotella, Tepidimonas, Acidovorax, Escherichia, and others). The analysis of urinary incontinence severity in 126 participants with mixed urinary incontinence identified a Lactobacillus-dominated reference group with the highest abundance of Lactobacillus (mean relative abundance of 76%). A community characterized by fewer Lactobacilli (mean relative abundance of 19%) and greater alpha diversity was associated with higher total urinary incontinence episodes (2.67 daily leaks; 95% confidence interval, 0.76-4.59; P=.007) and urgency urinary incontinence episodes (1.75 daily leaks; 95% confidence interval, 0.24-3.27; P=.02) than the reference group. No significant association was observed between community type and stress urinary incontinence episodes or Urogenital Distress Inventory total or subscores. The composition of vaginal community types and urinary community types were similar but composed of slightly different bacterial taxa. Vaginal community types were not associated with urinary incontinence severity, as measured by bladder diary or Urogenital Distress Inventory total and subscale scores. Alpha diversity indicated that greater sample richness was associated with more incontinence episodes (observed genera P=.01) in urine. Measures of evenness (Shannon and Pielou) were not associated with incontinence severity in the urinary or vaginal microbiomes., Conclusion: In the urobiome of women with mixed urinary incontinence, a community type with fewer Lactobacilli and more diverse bacteria was associated with more severe urinary incontinence episodes (total and urgency) compared with a community type with high predominance of a single genus, Lactobacillus. Whether mixed urinary incontinence severity is due to lesser predominance of Lactobacillus, greater presence of other non-Lactobacillus genera, or the complement of bacteria consisting of urobiome community types remains to be determined., (Copyright © 2023. Published by Elsevier Inc.)

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.

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., (© 2023. The Author(s).)

Purpose: Low-dose computed tomography (LDCT) for lung cancer screening is effective, although most eligible people are not being screened. Tools that provide personalized future cancer risk assessment could focus approaches toward those most likely to benefit. We hypothesized that a deep learning model assessing the entire volumetric LDCT data could be built to predict individual risk without requiring additional demographic or clinical data., Methods: We developed a model called Sybil using LDCTs from the National Lung Screening Trial (NLST). Sybil requires only one LDCT and does not require clinical data or radiologist annotations; it can run in real time in the background on a radiology reading station. Sybil was validated on three independent data sets: a heldout set of 6,282 LDCTs from NLST participants, 8,821 LDCTs from Massachusetts General Hospital (MGH), and 12,280 LDCTs from Chang Gung Memorial Hospital (CGMH, which included people with a range of smoking history including nonsmokers)., Results: Sybil achieved area under the receiver-operator curves for lung cancer prediction at 1 year of 0.92 (95% CI, 0.88 to 0.95) on NLST, 0.86 (95% CI, 0.82 to 0.90) on MGH, and 0.94 (95% CI, 0.91 to 1.00) on CGMH external validation sets. Concordance indices over 6 years were 0.75 (95% CI, 0.72 to 0.78), 0.81 (95% CI, 0.77 to 0.85), and 0.80 (95% CI, 0.75 to 0.86) for NLST, MGH, and CGMH, respectively., Conclusion: Sybil can accurately predict an individual's future lung cancer risk from a single LDCT scan to further enable personalized screening. Future study is required to understand Sybil's clinical applications. Our model and annotations are publicly available., [Media: see text].

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.

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., (© 2022 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Gill, Stauffer, Asquith, Laderas, Martin, Davin, Schleisman, Ramirez, Ogle, Lindquist, Nguyen, Planck, Shaut, Diamond, Rosenbaum and Karstens.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Siddiqui, Ma, Brubaker, Mao, Hoffman, Dahl, Wang and Karstens.)

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., (Copyright © 2022 American Urogynecologic Society. All rights reserved.)

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., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Nardos, Leung, Dahl, Davin, Asquith, Gregory and Karstens.)

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.

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

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.

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 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.

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., (Copyright © 2020 Cullen, Aneja, Beyhan, Cho, Woloszynek, Convertino, McCoy, Zhang, Anderson, Alvarez-Ponce, Smirnova, Karstens, Dorrestein, Li, Sen Gupta, Cheung, Powers, Zhao and Rosen.)

Acarose is an anti-diabetic drug and exhibits anti-arthritic effects. We hypothesized that acarbose influences the gut microbiota to affect the course of arthritis and tested this hypothesis in a collagen-induced arthritis (CIA) murine model. Acarbose in drinking water was administered via gastric gavage started prior to or at the time of CIA induction. Gut microbiota were evaluated with 16S rRNA gene sequencing from fecal pellets collected prior to arthritis induction, during onset of arthritis, and after treatment. Immune response was evaluated by measuring changes in T helper-17 (Th17) and T regulatory (Treg) cells in the spleen and intestine, as well as serum cytokine levels. Before induction of CIA, acarbose significantly reduced the incidence of arthritis and attenuated clinical severity of arthritis. The frequency of Th17 cells was significantly decreased in the intestinal lamina propria in acarbose treated mice. Mice that were treated with acarbose showed significantly increased CD4 + CD25 + Foxp3 + Treg cells with elevation of Helios and CCR6. A remarkable alteration in microbial community was observed in acarbose treated mice. Bacterial diversity and richness in mice with arthritis were significantly lower than those in acarbose treated groups. The frequency of Firmicutes was significantly reduced after arthritis onset but was restored after treatment with acarbose. The frequency of Lactobacillus , Anaeroplasma , Adlercreutzia , RF39 and Corynebacterium was significantly higher in control groups than in acarbose treated, while Oscillospira , Desulfovibrio and Ruminococcus enriched in acarbose treated group. Miglitol, another α-glucosidase inhibitor showed a similar but less potent anti-arthritic effect to that of acarbose. These data demonstrate that acarbose alleviated CIA through regulation of Th17/Treg cells in the intestinal mucosal immunity, which may have resulted from the impact of acarbose on gut microbial community. Inexpensive antidiabetic drugs with an excellent safety profile are potentially useful for managing rheumatoid arthritis., (Copyright © 2020 Zhang, Song, Zhang, Metea, Schleisman, Karstens, Leung, Zhang, Xu, Liu, Asquith and Chu.)

Objective: HLA alleles affect susceptibility to more than 100 diseases, but the mechanisms that account for these genotype-disease associations are largely unknown. HLA alleles strongly influence predisposition to ankylosing spondylitis (AS) and rheumatoid arthritis (RA). Both AS and RA patients have discrete intestinal and fecal microbiome signatures. Whether these changes are the cause or consequence of the diseases themselves is unclear. To distinguish these possibilities, we examined the effect of HLA-B27 and HLA-DRB1 RA risk alleles on the composition of the intestinal microbiome in healthy individuals., Methods: Five hundred sixty-eight stool and biopsy samples from 6 intestinal sites were collected from 107 healthy unrelated subjects, and stool samples were collected from 696 twin pairs from the TwinsUK cohort. Microbiome profiling was performed using sequencing of the 16S ribosomal RNA bacterial marker gene. All subjects were genotyped using the Illumina CoreExome SNP microarray, and HLA genotypes were imputed from these data., Results: Associations were observed between the overall microbial composition and both the HLA-B27 genotype and the HLA-DRB1 RA risk allele (P = 0.0002 and P = 0.00001, respectively). These associations were replicated using the stool samples from the TwinsUK cohort (P = 0.023 and P = 0.033, respectively)., Conclusion: This study shows that the changes in intestinal microbiome composition seen in AS and RA are at least partially due to effects of HLA-B27 and HLA-DRB1 on the gut microbiome. These findings support the hypothesis that HLA alleles operate to cause or increase the risk of these diseases through interaction with the intestinal microbiome and suggest that therapies targeting the microbiome may be effective in preventing or treating these diseases., (© 2019, American College of Rheumatology.)

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., (Copyright © 2019 Karstens et al.)

Microbiome community composition plays an important role in human health, and while most research to date has focused on high-microbial-biomass communities, low-biomass communities are also important. However, contamination and technical noise make determining the true community signal difficult when biomass levels are low, and the influence of varying biomass on sequence processing methods has received little attention. Here, we benchmarked six methods that infer community composition from 16S rRNA sequence reads, using samples of varying biomass. We included two operational taxonomic unit (OTU) clustering algorithms, one entropy-based method, and three more-recent amplicon sequence variant (ASV) methods. We first compared inference results from high-biomass mock communities to assess baseline performance. We then benchmarked the methods on a dilution series made from a single mock community-samples that varied only in biomass. ASVs/OTUs inferred by each method were classified as representing expected community, technical noise, or contamination. With the high-biomass data, we found that the ASV methods had good sensitivity and precision, whereas the other methods suffered in one area or in both. Inferred contamination was present only in small proportions. With the dilution series, contamination represented an increasing proportion of the data from the inferred communities, regardless of the inference method used. However, correlation between inferred contaminants and sample biomass was strongest for the ASV methods and weakest for the OTU methods. Thus, no inference method on its own can distinguish true community sequences from contaminant sequences, but ASV methods provide the most accurate characterization of community and contaminants. IMPORTANCE Microbial communities have important ramifications for human health, but determining their impact requires accurate characterization. Current technology makes microbiome sequence data more accessible than ever. However, popular software methods for analyzing these data are based on algorithms developed alongside older sequencing technology and smaller data sets and thus may not be adequate for modern, high-throughput data sets. Additionally, samples from environments where microbes are scarce present additional challenges to community characterization relative to high-biomass environments, an issue that is often ignored. We found that a new class of microbiome sequence processing tools, called amplicon sequence variant (ASV) methods, outperformed conventional methods. In samples representing low-biomass communities, where sample contamination becomes a significant confounding factor, the improved accuracy of ASV methods may allow more-robust computational identification of contaminants.

Purpose: We determine the changes in intestinal microbiota and/or disruptions in intestinal homeostasis during uveitis., Methods: Experimental autoimmune uveitis (EAU) was induced in B10.RIII mice with coadministration of interphotoreceptor retinoid-binding protein peptide (IRBP) and killed mycobacterial antigen (MTB) as an adjuvant. Using 16S rRNA gene sequencing, we looked at intestinal microbial differences during the course of uveitis, as well as intestinal morphologic changes, changes in intestinal permeability by FITC-dextran leakage, antimicrobial peptide expression in the gastrointstinal tract, and T lymphocyte prevalence before and at peak intraocular inflammation., Results: We demonstrate that increased intestinal permeability and antimicrobial peptide expression in the intestinal tract coincide in timing with increased effector T cells in the mesenteric lymph nodes, during the early stages of uveitis, before peak inflammation. Morphologic changes in the intestine were most prominent during this phase, but also occurred with adjuvant MTB alone, whereas increased intestinal permeability was found only in IRBP-immunized mice that develop uveitis. We also demonstrate that the intestinal microbiota were altered during the course of uveitis, and that some of these changes are specific to uveitic animals, whereas others are influenced by adjuvant MTB alone. Intestinal permeability peaked at 2 weeks, coincident with an increase in intestinal bacterial strain differences, peak lipocalin production, and peak uveitis., Conclusions: An intestinal dysbiosis accompanies a disruption in intestinal homeostasis in autoimmune uveitis, although adjuvant MTB alone promotes intestinal disruption as well. This may indicate a novel axis for future therapeutic targeting experimentally or clinically.

Many studies have shown that the urinary tract harbours its own microbial community known as the urinary microbiota, which have been implicated in urinary tract disorders. This observation contradicts the long-held notion that urine is a sterile biofluid in the absence of acute infection of the urinary tract. In light of this new discovery, many basic questions that are crucial for understanding the role of the urinary microbiota in human health and disease remain unanswered. Given that the urinary microbiota is an emerging area of study, optimized techniques and protocols to identify microorganisms in the urinary tract are still being established. However, the low microbial biomass and close proximity to higher microbial biomass environments (for example, the vagina) present distinct methodological challenges for microbial community profiling of the urinary microbiota. A clear understanding of the unique technical considerations for obtaining and analysing low microbial biomass samples, as well the influence of key elements of experimental design and computational analysis on downstream interpretation, will improve our ability to interpret and compare results across methods and studies and is relevant for studies profiling the urinary microbiota and other sites of low microbial abundance.

Short chain fatty acids (SCFA) are metabolites of intestinal bacteria resulting from fermentation of dietary fiber. SCFA are protective in various animal models of inflammatory disease. We investigated the effects of exogenous administration of SFCAs, particularly propionate, on uveitis using an inducible model of experimental autoimmune uveitis (EAU). Oral SCFA administration attenuated uveitis severity in a mouse strain-dependent manner through regulatory T cell induction among lymphocytes in the intestinal lamina propria (LPL) and cervical lymph nodes (CLN). SCFA also suppressed effector T cell induction in the CLN and mesenteric lymph nodes (MLN). Alterations in intestinal morphology and gene expression demonstrated in the EAU model prior to the onset of uveitis were blunted by oral SCFA administration. Using a Kaede transgenic mouse, we demonstrated enhanced leukocyte trafficking between the intestine and the eye in EAU. Propionate suppressed T effector cell migration between the intestine and the spleen in EAU Kaede mice. In conclusion, our findings support exogenous administration of SCFAs as a potential treatment strategy for uveitis through the stabilization of subclinical intestinal alterations that occur in inflammatory diseases including uveitis, as well as prevention of trafficking of leukocytes between the gastrointestinal tract and extra-intestinal tissues.

Sex hormones promote immunoregulatory effects on multiple sclerosis. In the current study we evaluated the composition of the gut microbiota and the mucosal-associated regulatory cells in estrogen or sham treated female mice before and after autoimmune encephalomyelitis (EAE) induction. Treatment with pregnancy levels of estrogen induces changes in the composition and diversity of gut microbiota. Additionally, estrogen prevents EAE-associated changes in the gut microbiota and might promote the enrichment of bacteria that are associated with immune regulation. Our results point to a possible cross-talk between the sex hormones and the gut microbiota, which could promote neuroprotection., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Objectives: Traditionally, the urinary tract has been thought to be sterile in the absence of a clinically identifiable infection. However, recent evidence suggests that the urinary tract harbors a variety of bacterial species, known collectively as the urinary microbiome, even when clinical cultures are negative. Whether these bacteria promote urinary health or contribute to urinary tract disease remains unknown. Emerging evidence indicates that a shift in the urinary microbiome may play an important role in urgency urinary incontinence (UUI). The goal of this prospective pilot study was to determine how the urinary microbiome is different between women with and without UUI. We also sought to identify if characteristics of the urinary microbiome are associated with UUI severity., Methods: We collected urine from clinically well-characterized women with UUI (n = 10) and normal bladder function (n = 10) using a transurethral catheter to avoid bacterial contamination from external tissue. To characterize the resident microbial community, we amplified the bacterial 16S rRNA gene by PCR and performed sequencing using Illumina MiSeq. Sequences were processed using the workflow package QIIME. We identified bacteria that had differential relative abundance between UUI and controls using DESeq2 to fit generalized linear models based on the negative binomial distribution. We also identified relationships between the diversity of the urinary microbiome and severity of UUI symptoms with Pearson's correlation coefficient., Results: We successfully extracted and sequenced bacterial DNA from 95% of the urine samples and identified that there is a polymicrobial community in the female bladder in both healthy controls and women with UUI. We found the relative abundance of 14 bacteria significantly differed between control and UUI samples. Furthermore, we established that an increase in UUI symptom severity is associated with a decrease in microbial diversity in women with UUI., Conclusions: Our study provides further characterization of the urinary microbiome in both healthy controls and extensively phenotyped women with UUI. Our results also suggest that the urinary microbiome may play an important role in the pathophysiology of UUI and that the loss of microbial diversity may be associated with clinical severity.

Purpose: To investigate the contribution of the gut microbiota to the pathogenesis of uveitis., Methods: Experimental autoimmune uveitis (EAU) in B10.RIII mice was induced using interphotoreceptor binding protein peptide. Mice were treated with oral or intraperitoneal (IP) antibiotics. Effector (Teff) and regulatory (Treg) T lymphocytes were identified using flow cytometry; 16S rRNA gene sequencing and qPCR were performed on gastrointestinal (GI) contents., Results: Broad-spectrum (four antibiotics given simultaneously) oral, but not IP, antibiotics reduced mean uveitis clinical scores significantly compared with water-treated animals (0.5 vs. 3.0, P < 0.0001 for oral; 3.4 vs. 3.4, P > 0.99 for IP). Both oral metronidazole (P = 0.02) and vancomycin (P < 0.0001) alone decreased inflammation, whereas neomycin (P = 0.7) and ampicillin (P = 0.4) did not change mean uveitis scores. Oral broad-spectrum antibiotics increased Tregs in the GI lamina propria of EAU animals at 1 week, and in extraintestinal lymphoid tissues later, whereas Teff and inflammatory cytokines were reduced. 16S sequencing of GI contents revealed altered microbiota in immunized mice compared with nonimmunized mice, and microbial diversity clustering in EAU mice treated with uveitis-protective antibiotics. Experimental autoimmune uveitis mice also demonstrated gut microbial diversity clustering associated with clinical score severity., Conclusions: Oral antibiotics modulate the severity of inducible EAU by increasing Tregs in the gut and extraintestinal tissues, as well as decreasing effector T cells and cytokines. 16S sequencing suggests that there may be protective and, conversely, potentially uveitogenic, gut microbiota. These findings may lead to a better understanding of how uveitis can be treated or prevented by modulating the gut microbiome.

Aims: To identify atypical brain functional connectivity in women with UUI and detrusor overactivity (DO) and to predict the presence/severity of UUI in individual women using connectivity features., Methods: This is a cross-sectional study comparing brain functional connectivity in women with and without UUI. Validated symptom/quality of life questionnaires were used for phenotyping. Participants are females between ages 40 and 85 with daily UUI with DO (Cases, N = 16) and without UUI (Controls, N = 24). Functional MRI and Resting state connectivity MRI were obtained at empty/ full bladder. Multivariate pattern analysis (MVPA) was used to predict the presence and severity of UUI from connectivity data., Results: There are significant differences in brain activation between cases and controls in eighteen brain regions irrespective of empty or full bladder. These include regions involved in attention (inferior partietal), decision making (inferior and superior frontal gyrus), primary motor and sensory (precentral and postcentral gyrus) functions. Women with UUI showed no change in connectivity with bladder filling in regions involved in interoception (insula), integration of afferent function (anterior cingulate), and decision making (middle frontal). MVPA of connectivity data showed robust classification of an individual woman as case or control (89% sensitivity, 83% specificity). Six connectivity features accurately predicted disease severity (R(2)  = 0.81)., Conclusion: We identified two mechanisms of abnormal bladder control, one involving atypical activation of brain regions, and another atypical functional integration across sensory, emotional, cognitive and motor regions. Connectivity information is robust enough to classify an individual as having UUI or not and to predict symptom severity. Neurourol. Urodynam. 35:564-573, 2016. © 2015 Wiley Periodicals, Inc., (© 2015 Wiley Periodicals, Inc.)

Background: Medication use is associated with falls in many populations, but the relationship between medications and falls in people with multiple sclerosis (MS) is not well understood., Methods: The number and types of medications used by 248 ambulatory adults with MS in the United States (n = 53) and Australia (n = 195) were assessed. Participants completed fall diaries for 6 months. Associations between number and type of medications reported and falls, adjusting for age, disease severity, comorbidities, sex, and country, were evaluated using multiple logistic regression., Results: Participants reported taking a median of three medications and two supplements. A total of 143 participants (58%) fell at least once in the 6 months, and 110 (44%) experienced one or more injurious falls. The adjusted relative odds of a fall or an injurious fall increased by 13% (P = .048) and 11% (P = .049), respectively, for each medication and by 43% (P = .015) and 55% (P = .001) for each neurologically active medication. Reported use of MS disease-modifying therapy was associated with 48% decreased odds of falling (P = .035) but not significantly decreased odds of injurious falls., Conclusions: Reporting use of more medications and more neurologically active medications is associated with falls and injurious falls in people with MS. Close evaluation of the need for each medication, with associated minimization of neurologically active medications in patients with MS, may help prevent falls. Use of MS disease-modifying therapies may be associated with fewer falls. This relationship needs further evaluation.