Biology subjects

Karstens, L.

Publications and source records attributed to Karstens, L..

11 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↗

bioRxiv2025

Complex carbohydrates catabolism capacity of bladder microbiota inhabiting healthy and overactive female bladders

Overactive bladder syndrome (OAB) is a poorly understood symptom complex that affects 40% of females over the age of 40, with clinical features including urinary urgency and incontinence. In addition to inflammation, oxidative stress, nerve damage and reduced blood flow, alterations in the urinary microbiome (urobiome), specifically in bladder bacterial diversity, have been reported to be associated with OAB. Bladder bacteria are members of the urobiome along with viruses, archaea, fungi, and protozoans. The urobiome metabolism, particularly in relationship to host complex sugars (glycans), has been investigated recently in terms of glycosaminoglycan (GAG) utilization. Nevertheless, other urinary free oligosaccharides (FOS) have not yet been explored in both OAB and urobiome contexts. Similarly, a comprehensive search of microbial genes involved in host glycan metabolism in the bladder of adult females with or without OAB has not yet been reported. In this study, we investigated urinary FOS by mass spectrometry in women without OAB (asymptomatic controls), with OAB without incontinence (dry OAB), or with OAB with incontinence (wet OAB or urgency urinary incontinence, UUI). We also questioned the ability of commensal bladder bacteria to digest these FOS and other glycans, using bioinformatic tools to query publicly available bladder genomes isolated from affected and unaffected adult females to identify genes that encode polysaccharide lyases (PL) and glycoside hydrolases (GH). Our results show that FOS are present in a similar level in affected and unaffected controls with a few exceptions: ten FOS were found to differ between the OAB dry groups and either the control (four) or UUI (six) groups. Our results indicate that bladder microbiota from adult females both with and without OAB have the genetic capacity to digest host glycans and dietary sugars with subtle differences. Bladder bacteria isolated from females with OAB possess more GH/PL genes for host mucins, whereas bladder bacteria from controls possess more GH/PL genes for GAG digestion. In the control group, specifically, the genus Streptococcus possessed genes for the PL8 and GH88 enzymes, known to be involved in host GAG digestion. These novel bioinformatic data can enable future biochemical exploration of the urobiomes metabolism toward specific host glycans, such as GAGs, mucins O-glycans and N-glycans.

biochemistry↗

bioRxiv2025

Differences in cell-associated and cell-free microbial DNA in blood

In the absence of infection, blood has previously been understood to be free of microbes. However, with advances in sequencing technology this notion has been challenged, prompting new investigations into microbial DNA within the blood of both healthy and diseased individuals. To comprehensively survey microbial DNA in blood, we separated blood into fractions (plasma, red blood cells, and buffy coat) and assessed if the microbial-DNA is cell-free by the addition of DNase to a subset of each fraction. We measured 16S rRNA gene copy number with digital droplet PCR and identified the taxonomic origin of the microbial DNA with synthetic full-length 16S rRNA gene sequencing. As a use case, we examine microbial DNA from the blood of 5 men without prostate cancer (PC), 5 men with low-grade PC, and 5 men with high-grade PC. Our study demonstrates that the majority of microbial DNA is cell-free, indicating that it is not representative of proliferating microbes. Our analyses also revealed buffy coat had the lowest number of 16S rRNA gene copies yet highest number of genera of the fractions (median 23.3 copies/{micro}L and 10 genera) and thus may be a useful fraction to study moving forward. Additionally, microbial DNA in blood may have utility as a biomarker, as we detected disease-associated compositional differences in the plasma and buffy coat fractions. This study lays the groundwork for rigorously studying microbial DNA in blood, however larger studies are needed to confirm our disease-association findings. ImportanceThe concept of a "blood microbiome" has sparked debate in recent years, with questions about whether microbes truly exist in circulation. This study provides a crucial evaluation of the fractions of blood and their capacity to harbor microbial DNA, offering important context for prior and future research. By using DNase to differentiate between cell-associated and cell-free microbial DNA, we show that while microbial DNA is present in blood, it is sparse and heterogenous. These findings highlight the need for rigorous study design that carefully considers both positive and negative controls, as well as the specific blood fractions being examined.

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↗

bioRxiv2023

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.

systems biology↗

bioRxiv2021

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

ObjectiveTo investigate whether axial spondyloarthritis (AxSpA) patients have an altered immunoglobulin A (IgA) response in the gut and oral microbial communities. MethodsWe 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=17 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). ResultsIgA-SEQ 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 trend towards increased alpha diversity of the IgA+ fraction and decreased diversity in the 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 correlated with disease severity. Inferred metagenomic analysis suggests perturbation of metabolites and metabolic pathways for inflammation (oxidative phosphorylation, glutathione metabolism) and metabolism (propanoate and butanoate metabolism) in AxSpA patients. ConclusionsAnalyses of fecal and salivary microbes from AxSpA patients reveal distinct populations of immunoreactive microbes using novel IgA-SEQ approach, which were not captured by comparing their relative abundance with HCs. 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.

immunology↗

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↗

bioRxiv2019

HLA alleles associated with risk of ankylosing spondylitis and rheumatoid arthritis influence the gut microbiome

ObjectivesHLA alleles affect susceptibility to more than 100 diseases, but the mechanisms to 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 faecal microbiome signatures. Whether these changes are cause or consequence of the diseases themselves is unclear. To distinguish these possibilities, we examine the effect of HLA-B27 and HLA-DRB1 RA-risk alleles on the composition of the intestinal microbiome in healthy individuals.\n\nMethods568 samples from 6 intestinal sites were collected from 107 otherwise healthy unrelated subjects and stool samples from 696 twin pairs from the TwinsUK cohort. Microbiome profiling was performed using sequencing of the 16S rRNA bacterial marker gene. All patients were genotyped using the Illumina CoreExome SNP microarray, and HLA genotypes were imputed from these data.\n\nResultsAssociation was observed between HLA-B27 genotype, and RA-risk HLA-DRB1 alleles, and overall microbial composition (P=0.0002 and P=0.00001 respectively). These associations were replicated in the TwinsUK cohort stool samples (P=0.023 and P=0.033 respectively).\n\nConclusionsThis 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 -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 their prevention and/or treatment.

microbiology↗

bioRxiv2018

Controlling for contaminants in low biomass 16S rRNA gene sequencing experiments

BackgroundMicrobial 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. This is particularly problematic for sites of low microbial biomass such as the urinary tract, placenta, and lower airway. Computational approaches have been proposed as a post-processing step to identify and remove potential contaminants, but their performance has not been independently evaluated.\n\nTo identify the impact of decreasing microbial biomass on polymicrobial 16S rRNA gene sequencing experiments, we used a serial dilution of a mock microbial community. We evaluated two computational approaches to identify and remove contaminants: 1) identifying sequences that have an inverse correlation with DNA concentration implemented in Decontam and 2) predicting the proportion of experimental sample arising from defined contaminant sources implemented in SourceTracker.\n\nResultsAs expected, the proportion of contaminant bacterial DNA increased with decreasing starting microbial biomass, with 79.12% of the most dilute sample arising from contaminant sequences. Inclusion of contaminant sequences in analyses leads to overinflated diversity estimates (up to 12 times greater than the expected values) and distorts microbiome composition. SourceTracker successfully removed over 98% of contaminants when the experimental environments are well defined. However, SourceTracker performed poorly when the experimental environment is unknown, failing to remove the majority of contaminants. Decontam successfully removed 74-91% of contaminants regardless of prior knowledge of the experimental environment.\n\nConclusionsOur study indicates that computational methods can reduce the amount of contaminants in 16S rRNA gene sequencing experiments. The appropriate computational approach for removing contaminant sequences from an experiment depends on the prior knowledge about the microbial environment under investigation and can be evaluated with a dilution series of a mock microbial community.

bioinformatics↗