The Darkest Place Is under the Candlestick-Healthy Urogenital Tract as a Source of Worldwide Disseminated Extraintestinal Pathogenic Escherichia coli Lineages

Since the discovery of the urinary microbiome, including the identification of Escherichia coli in healthy hosts, its involvement in UTI development has been a subject of high interest. We explored the population diversity and antimicrobial resistance of E. coli (n = 22) in the urogenital microbiome of ten asymptomatic women (representing 50% of the sample tested). We evaluated their genomic relationship with extraintestinal pathogenic E. coli (ExPEC) strains from healthy and diseased hosts, including the ST131 lineage. E. coli prevalence was higher in vaginal samples than in urine samples, and occasionally different lineages were observed in the same individual. Furthermore, B2 was the most frequent phylogenetic group, with the most strains classified as ExPEC. Resistance to antibiotics of therapeutic relevance (e.g., amoxicillin-clavulanate conferred by blaTEM-30) was observed in ExPEC widespread lineages sequence types (ST) 127, ST131, and ST73 and ST95 clonal complexes. Phylogenomics of ST131 and other ExPEC lineages revealed close relatedness with strains from gastrointestinal tract and diseased host. These findings demonstrate that healthy urogenital microbiome is a source of potentially pathogenic and antibiotic resistant E. coli strains, including those causing UTI, e.g., ST131. Importantly, diverse E. coli lineages can be observed per individual and urogenital sample type which is relevant for future studies screening for this uropathogen.

Biomarkers in the Light of the Etiopathology of IC/BPS

In this review, we focused on putatively interesting biomarkers of interstitial cystitis/bladder pain syndrome (IC/BPS) in relation to the etiopathology of this disease. Since its etiopathology is still under discussion, the development of novel biomarkers is critical for the correct classification of the patients in order to open personalized treatment options, on the one hand, and to separate true IC/BPS from the numerous confusable diseases with comparable symptom spectra on the other hand. There is growing evidence supporting the notion that the classical or Hunner-type IC (HIC) and the non-Hunner-type IC (NHIC) are different diseases with different etiopathologies and different pathophysiology at the full-blown state. While genetic alterations indicate close relationship to allergic and autoimmune diseases, at present, the genetic origin of IC/BPS could be identified. Disturbed angiogenesis and impairment of the microvessels could be linked to altered humoral signaling cascades leading to enhanced VEGF levels which in turn could enhance leucocyte and mast cell invasion. Recurrent or chronic urinary tract infection has been speculated to promote IC/BPS. New findings show that occult virus infections occurred in most IC/BPS patients and that the urinary microbiome was altered, supporting the hypothesis of infections as major players in IC/BPS. Environmental and nutritional factors may also influence IC/BPS, at least at a late state (e.g., cigarette smoking can enhance IC/BPS symptoms). The damage of the urothelial barrier could possibly be the result of many different causality chains and mark the final state of IC/BPS, the causes of this development having been introduced years ago. We conclude that the etiopathology of IC/BPS is complex, involving regulatory mechanisms at various levels. However, using novel molecular biologic techniques promise more sophisticated analysis of this pathophysiological network, resulting in a constantly improvement of our understanding of IC/BPS and related diseases.

A metagenomic DNA sequencing assay that is robust against environmental DNA contamination

Metagenomic DNA sequencing is a powerful tool to characterize microbial communities but is sensitive to environmental DNA contamination, in particular when applied to samples with low microbial biomass. Here, we present contamination-free metagenomic DNA sequencing (Coffee-seq), a metagenomic sequencing assay that is robust against environmental contamination. The core idea of Coffee-seq is to tag the DNA in the sample prior to DNA isolation and library preparation with a label that can be recorded by DNA sequencing. Any contaminating DNA that is introduced in the sample after tagging can then be bioinformatically identified and removed. We applied Coffee-seq to screen for infections from microorganisms with low burden in blood and urine, to identify COVID-19 co-infection, to characterize the urinary microbiome, and to identify microbial DNA signatures of inflammatory bowel disease in blood.

The Urinary Microbiome of Older Adults Residing in a Nursing Home Varies with Duration of Residence and Shows Increases in Potential Pathogens

The community of bacteria that colonize the urinary tract, the urinary microbiome, is hypothesized to influence a wide variety of urinary tract conditions. Older adults that reside in nursing homes are frequently diagnosed and treated for urinary tract conditions such as urinary tract infection (UTI). We investigated the urinary microbiome of older adults residing in a nursing home to determine if there are features of the urinary microbiome that are associated specific conditions and exposure in this population. We were also interested in the stability of urinary microbiome over time and in similarities between the urinary and gastrointestinal microbiome. Urine samples were prospectively collected over a period of 10 months from a cohort of 26 older adults (age > 65 years) residing in single nursing home located in Central Massachusetts. Serial samples were obtained from 6 individuals over 10 months and 5 participants were concurrently enrolled in a study of the gastrointestinal microbiome. Information collected on participants included demographics, medical history, duration of residence in the nursing home, frailty, dementia symptoms, urinary symptoms, antibiotic treatment, urinary catherization, and hospitalizations over a 10-month period. Clean catch mid-stream urine samples were collected and stored at -80C. DNA was extracted and 16S rRNA gene sequencing performed. The length of stay in the nursing facility and the Clinical Frailty Scale correlated with significant changes in microbiome composition. An increase in the relative abundance of a putative urinary pathogen, Aerococcus urinae, was the largest factor influencing change that occurred over duration of residence.

The Microbiome’s Function in Disorders of the Urinary Bladder

The introduction of next generation sequencing techniques has enabled the characterization of the urinary tract microbiome, which resulted in the rejection of the long-held notion of urinary bladder sterility. Since the discovery and confirmation of the human bladder microbiome, an increasing number of studies have defined this microbial community and understand better its relationship to urinary pathologies. The composition of microbial communities in the urinary tract is linked to a variety of urinary diseases. The purpose of this review is to provide an overview of current information about the urinary microbiome and diseases as well as the development of novel treatment methods.

Updating urinary microbiome analyses to enhance biologic interpretation

Objective: An approach for assessing the urinary microbiome is 16S rRNA gene sequencing, where a segment of the bacterial genome is amplified and sequenced. Methods used to analyze these data are rapidly evolving, although the research implications are not known. This re-analysis of an existing dataset aimed to determine the impact of updated bioinformatic and statistical techniques. Methods: A prior Pelvic Floor Disorders Network (PFDN) study compared the urinary microbiome in 123 women with mixed urinary incontinence (MUI) and 84 controls. We used the PFDN unprocessed sequencing data of V1-V3 and V4-V6 16S variable regions, processed operational taxonomic unit (OTU) tables, and de-identified clinical data. We processed sequencing data with an updated bioinformatic pipeline, which used DADA2 to generate amplicon sequence variant (ASV) tables. Taxa from ASV tables were compared to OTU tables generated from the original processing; taxa from different variable regions (e.g., V1-V3 versus V4-V6) after updated processing were also compared. After updated processing, data were analyzed with multiple filtering thresholds. Several techniques were tested to cluster samples into microbial communities. Multivariable regression was used to test for associations between microbial communities and MUI, while controlling 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 sequencing data in terms of relative abundance. When different 16S rRNA gene regions were sequenced from the same samples, there were differences noted in recovered taxa. When the original clustering methods were applied to reprocessed sequencing data, we confirmed differences in microbial communities associated with MUI. However, when samples were clustered with a different methodology, microbial communities were no longer associated with MUI. Conclusions: Updated bioinformatic processing techniques recover many different taxa compared to prior techniques, though most of these differences exist in low abundance taxa that occupy a small proportion of the overall microbiome. Detection of high abundance taxa are not significantly impacted by bioinformatic strategy. However, there are different biases for less abundant taxa; these differences as well as downstream clustering methodology and filtering thresholds may affect interpretation of overall results.

Combined Signature of the Urinary Microbiome and Metabolome in Patients With Interstitial Cystitis

Interstitial cystitis (IC) is a clinical syndrome characterized by frequency, urgency, and bladder pain or pelvic pain; however, the underlying pathophysiological mechanisms and diagnostic markers are unknown. In this study, microbiome and metabolome analysis were used to explain the urine signatures of IC patients. Urine samples from 20 IC patients and 22 control groups were analyzed by using 16S rRNA sequence and liquid chromatography coupled with mass spectrometry. Four opportunistic pathogen genera, including Serratia, Brevibacterium, Porphyromonas, and Citrobacter, were significantly upregulated in IC group. The altered metabolite signatures of the metabolome may be related to sphingosine metabolism, amino acid metabolism, and fatty acid biosynthesis. Meanwhile, the associations were observed between different metabolites and microbiomes of IC. The present study suggests that the combined signatures of IC in urine microbiome and metabolome may become its prospective diagnostic markers.