Biology subjects

Siddiqui, N. Y.

Publications and source records attributed to Siddiqui, N. Y..

7 recordsLinked to original sources

bioRxiv2026

Advances in 16S Taxonomic Classification: Impacts on Species-Level Resolution of Female Bladder Microbiota

Bacteria are present in the human urinary bladder, even in the absence of infection. These bacteria comprise the urinary microbiome, or urobiome, and have the potential to promote bladder health or contribute to pathology. Despite its importance, studying the urobiome remains challenging given the high proportion of host to bacterial cells and low microbial biomass. The leading method for studying the urobiome has been 16S rRNA amplicon sequencing. This culture-independent approach provides a cost-effective method for identifying bacteria, but taxonomic classification accuracy depends on three key factors: 1) the variable region targeted; 2) the reference database; and 3) the classification algorithm. In 2021, Hoffman et al. demonstrated that species-level classification of bladder microbiota was possible and that the database used was the most important factor, followed by the variable region. Here, we provide an updated analysis evaluating classification schemes using updated taxonomic databases, most notably GreenGenes2, and expand the analysis to full length 16S gene sequencing. We show that updated taxonomic reference databases improve the ability to identify bladder microbiota at the species level and that long-read sequencing of the full length 16S rRNA gene further improves species identification.

bioinformatics↗

bioRxiv2026

The Urogenital Lactobacillus Axis Across Menopause: Connections Between the Vagina, Bladder, and Urinary Metabolome

Alterations in the female urinary microbiome have been linked to several several common urinary conditions that increase in prevalence after menopause. Here we use sequencing and metabolomic data to compare the urinary bladder microenvironments between women who are premenopausal, postmenopausal, and postmenopausal using vaginal estrogen. While the urinary microbiome differs between pre- and postmenopausal women, at least 6 weeks of vaginal estrogen is associated with higher urinary lactobacilli and fewer opportunistic pathogens in the urinary postmenopausal microbiome, which closer approximates what is seen in the premenopausal context. Changes in urinary Lactobacillus species are correlated with vaginal pH and the same Lactobacillus species found in the vaginal microbiome tend to also be identified within the urinary bladder microbiome. Urinary metabolites differ by context (e.g., pre-, post-, and post-menopausal using vaginal estrogen), but the majority of metabolites that drive these differences are unannotated. Of the metabolites with shared associations among different Lactobacillus species, L. iners have consistently inverse relationships compared to L. crispatus, L. gasseri, or L. jensenii.

systems biology↗

bioRxiv2024

A negative binomial latent factor model for paired microbiome sequencing data

MotivationMicrobiome compositional data are often collected from several body sites and exhibit dependency among them. Analyzing microbial compositions from different sites jointly allows for effective borrowing of information by exploiting the underlying cross-site correlation, which can lead to more effective statistical analysis, especially when the sample size at one or both sites is limited. To this end, we introduce a joint model for microbiome compositions at two (or more) sites within the same subjects. Our model incorporates (i) latent factors shared across two body sites to explain the common subject effects and to serve as the source of correlation between the two sites; and (ii) mixtures of latent factors to allow heterogeneity among the samples in their level of cross-site association. The model is illustrated with synthetic data and we apply it in a case study involving samples of the urinary and vaginal microbiome collected from women. ResultsSimulation studies show how common subject effects influence regression analysis results; a stronger association between two sites in the data causes a greater degree of bias in the analysis. The model with latent factors mitigates the bias present in the model without latent factors, whereas the two models perform comparably for the data set without paired associations. In a case study involving samples collected from a study on the female urogenital microbiome with aging (e.g., the UMICRO study), our model leads to the detection of covariate associations of the vaginal and urinary microbiome composition that are otherwise not statistically significant under a similar regression model applied to the two sites separately. Our model also enables prediction of the microbial abundance at one site based on observations from another site. We also consider a model extension that allows the clustering of subjects (samples) and cluster-specific levels of paired association. Under the extended modeling framework, the clusters can be classified according to their association strengths.

bioinformatics↗

bioRxiv2024

Commensal bacteria inhibit viral infections via a tryptophan metabolite

Clinical outcomes following viral exposures exhibit substantial interindividual variability. Although developing evidence suggests commensal bacteria modulate viral infections, the specific bacteria and mechanisms remain underexplored. Here we define a pathway by which viral infections are inhibited by specific tryptophan-catabolizing bacteria. Using HIV as a model, we bioinformatically associated and experimentally validated several bacterial species that inhibited viral replication. This activity required the aromatic amino acid aminotransferase (ArAT) to metabolize tryptophan into 3-indolelactic acid, which agonizes the aryl hydrocarbon receptor (AhR). Given that AhR regulates multiple viral infections, we found that commensal bacteria also inhibit cytomegalovirus (CMV) in an ArAT-dependent manner. Finally, we confirmed that ArAT is associated with protection against disease outcomes in three distinct human cohorts at-risk for HIV, CMV, or symptomatic COVID-19. Taken together, our results provide mechanistic insight into how commensal bacteria impact viral infections, thereby adding to an emerging field focused on host-commensal-virus interactions.

microbiology↗

bioRxiv2020

THE URINARY MICROBIOME IN POSTMENOPAUSAL WOMEN WITH RECURRENT URINARY TRACT INFECTIONS

Recurrent urinary tract infections (UTI) are highly prevalent in postmenopausal women, where vaginal estrogen and prophylactic antibiotics are used for treatment. The etiology of recurrent UTIs is not completely known, but the urinary microbiome is thought to be implicated. Thus, we aimed to compare the "steady state" urinary microbiome in three groups of menopausal women who were all using topically-applied vaginal estrogen: 1) women with recurrent UTIs on daily antibiotic prophylaxis; 2) women with recurrent UTIs not on antibiotic prophylaxis; and 3) age-matched controls without recurrent UTIs. Here we present a cross-sectional analysis of baseline data from 64 women enrolled in a longitudinal cohort study. Catheterized urine samples were collected > 4 weeks after last treatment for UTI. Samples were evaluated using expanded quantitative urine culture (EQUC) and 16S rRNA gene sequencing. With EQUC techniques, there were no significant differences in the median numbers of microbial species isolated among groups (p=0.96), even when considering Lactobacilli (p=0.72). However, there were trends towards different Lactobacillus species between groups. With sequencing the overwhelming majority of urinary samples contained Lactobacilli, with non-significant trends in relative abundance of Lactobacilli among groups. Using a Bayesian regression analysis for compositional data, we identified significant differences in anaerobic taxa that were associated with phenotypic groups. Most of these differences centered on Bacteroidales and the family Prevotellaceae, though differences were also noted in Actinobacteria and certain genera of Clostridiales. Associations between anaerobes within the urinary microbiome and recurrent UTI warrants further investigation. IMPORTANCEIn menopausal women with recurrent urinary tract infections (UTIs) compared to those without, the abundance of Lactobacillus within the urinary microbiome is not significantly different when vaginal estrogen is regularly used. In this population, Lactobacillaceae were identified in 97% of urine samples using culture-independent techniques. However, with expanded urine cultures, women with recurrent UTIs taking daily antibiotics had a disproportionately low amount of L. gasseri/L. acidophilus compared to the other phenotypic groups. These findings support the theory that certain Lactobacillus species may be more important than others in the pathophysiology of postmenopausal recurrent UTIs. Furthermore, when using culture-independent techniques to explore urinary microbiota across phenotypic groups, we identified differences in multiple anaerobic taxa. Taken together, these results suggest that altered ratios of anaerobes and certain Lactobacillus species within the urinary microbiome may be implicated in postmenopausal recurrent UTI.

microbiology↗

bioRxiv2020

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 but non-significant differences in the number of reads recovered, alpha, or beta diversity. 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. The Promega kit recovered fewer Gram positive bacteria compared to other kits. The Promega and DNeasy PowerSoil kits also appear to have some important biases towards over-representing certain Gram negative bacteria of biologic relevance within the urinary microbiome.

microbiology↗

bioRxiv2020

Species level resolution of female bladder microbiota from marker gene surveys

The human bladder contains bacteria 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 is typically restricted to genus-level identification. Our primary aim 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. ImportanceSpecies-level information may deepen our understanding of associations between bladder microbiota and bladder conditions, such as lower urinary tract symptoms and urinary tract infections. The capability to identify bacterial species depends on large databases of sequences, algorithms that leverage statistics and available computer hardware, and knowledge of bacterial genetics and classification. Taken together, this is a daunting body of knowledge to become familiar with before the simple question of bacterial identity can be answered. Our results show the choice of taxonomic database and variable region of the 16S rRNA gene sequence makes species level identification possible. We also show this improvement can be achieved through the more careful application of existing methods and use of existing resources.

bioinformatics↗