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(1) Background: Indwelling ureteral stents are commonly used urological devices to maintain ureteral patency, yet they have been associated with complications such as infections. Some studies have shown that bacteria adhere to and create an antimicrobial-resistant biofilm on stents. One factor that may impact biofilm formation is the original condition informing stent placement, such as kidney stones and renal allografts. Both kidney stones and renal allografts are independently associated with infection, yet the differential stent microbiomes of these populations remain poorly characterized. Our objective was to characterize these microbiomes in order to inform urological health practice and help prevent ureteral stent-associated infections. (2) Methods: Stents were collected from kidney stone and renal transplant recipients undergoing routine cystoscopic stent removal. Microbial DNA was extracted from stents and analyzed using 16S Next Generation Sequencing. Descriptive statistics, alpha diversity, and beta diversity methods were used for statistical analysis. (3) Results: The microbiome of ureteral stents in kidney stone and transplant patients is composed of unique species, each with different biofilm-forming abilities. (4) Conclusions: Our findings demonstrate that the microbiome of stents differs based on preceding condition. It is important to conduct future studies that explore this microbiome further to understand what type of stent-associated infection someone may develop based on their initial condition.

Interstitial cystitis/bladder pain syndrome (IC/BPS) is defined as an unpleasant sensation perceived to be related to the bladder with associated urinary symptoms. Due to difficulties discriminating pelvic visceral sensation, IC/BPS likely represents multiple phenotypes with different etiologies that present with overlapping symptomatic manifestations, which complicates clinical management. We hypothesized that unique bladder pain phenotypes or “symptomatic clusters” would be identifiable using machine learning analysis (unsupervised clustering) of validated patient-reported urinary and pain measures. Patients (n = 145) with pelvic pain/discomfort perceived to originate in the bladder and lower urinary tract symptoms answered validated questionnaires [OAB Questionnaire (OAB-q), O'Leary-Sant Indices (ICSI/ICPI), female Genitourinary Pain Index (fGUPI), and Pelvic Floor Disability Index (PFDI)]. In comparison to asymptomatic controls (n = 69), machine learning revealed three bladder pain phenotypes with unique, salient features. The first group chiefly describes urinary frequency and pain with the voiding cycle, in which bladder filling causes pain relieved by bladder emptying. The second group has fluctuating pelvic discomfort and straining to void, urinary frequency and urgency without incontinence, and a sensation of incomplete emptying without urinary retention. Pain in the third group was not associated with voiding, instead being more constant and focused on the urethra and vagina. While not utilized as a feature for clustering, subjects in the second and third groups were significantly younger than subjects in the first group and controls without pain. These phenotypes defined more homogeneous patient subgroups which responded to different therapies on chart review. Current approaches to the management of heterogenous populations of bladder pain patients are often ineffective, discouraging both patients and providers. The granularity of individual phenotypes provided by unsupervised clustering approaches can be exploited to help objectively define more homogeneous patient subgroups. Better differentiation of unique phenotypes within the larger group of pelvic pain patients is needed to move toward improvements in care and a better understanding of the etiologies of these painful symptoms.

OBJECTIVES/GOALS: In young women, there is significant symptomatic overlap among lower urinary tract conditions, including bladder and pelvic pain, leading to misdiagnosis and delayed care. The epidemiology of pelvic pain suggests a microbial involvement, but previous studies have not definitively identified specific bacteria associated with pain diagnoses. METHODS/STUDY POPULATION: We examined urinary bacterial associations with specific symptom clusters, not diagnoses. Catheterized urinary samples were obtained from 78 pre-menopausal controls and cases with bladder and pelvic pain. 16S next-generation sequencing (NGS) characterized urinary microbial populations; validated questionnaires quantified symptom type and severity. K means unsupervised clustering analysis of NGS data assigned subjects to urotypes based on the urinary bacterial community state types. Quantitative PCR (qPCR) confirmed the NGS results and provided objective concentrations for critical taxa. Linear regression analysis confirmed the associations of bacterial concentrations and specific symptoms. RESULTS/ANTICIPATED RESULTS: In a pilot study of 35 reproductive-age women with a variety of complaints NGS revealed four urotypes that correlated with symptomatology. Isolated urgency incontinence was rare; the majority of subjects with symptoms complained of genitourinary pain. Bladder-specific pain (worse with filling, relieved by voiding) was associated with Lactobacillus iners. Asymptomatic patients almost universally had a non-iners, Lactobacillus-predominant microbiota. Vaginal and urethral pain unrelated to voiding correlated with increasing Enterobacteriaceae, primarily Escherichia coli. Detection of these species by qPCR in a validation population (n = 43) was highly predictive of each phenotype (P < 0.00001). Pathologic bacteria were associated with the severity of specific pain symptoms. DISCUSSION/SIGNIFICANCE OF IMPACT: Our results implicate a microbial role in genitourinary pain. We describe clinically-useful bacterial biomarkers for specific pelvic and bladder pain phenotypes. This objective, rapid, and inexpensive testing to classify bladder and pelvic pain would allow more accurate diagnosis and improve treatment. CONFLICT OF INTEREST DESCRIPTION: Dr. Anger is an expert witness for Boston Scientific. Dr. Eilber is an investigator and expert witness for Boston Scientific, an investigator for Aquinox, and a consultant for Boston Scientific and Allergan. Dr. Ackerman is an expert witness for Cynosure.

IntroductionRecent data suggest the urinary tract hosts a microbial community of varying composition, even in the absence of infection. Culture-independent methodologies, such as next-generation sequencing of conserved ribosomal DNA sequences, provide an expansive look at these communities, identifying both common commensals and fastidious organisms. A fundamental challenge has been the isolation of DNA representative of the entire resident microbial community, including fungi.Materials and methodsWe evaluated multiple modifications of commonly-used DNA extraction procedures using standardized male and female urine samples, comparing resulting overall, fungal and bacterial DNA yields by quantitative PCR. After identifying protocol modifications that increased DNA yields (lyticase/lysozyme digestion, bead beating, boil/freeze cycles, proteinase K treatment, and carrier DNA use), all modifications were combined for systematic confirmation of optimal protocol conditions. This optimized protocol was tested against commercially available methodologies to compare overall and microbial DNA yields, community representation and diversity by next-generation sequencing (NGS).ResultsOverall and fungal-specific DNA yields from standardized urine samples demonstrated that microbial abundances differed significantly among the eight methods used. Methodologies that included multiple disruption steps, including enzymatic, mechanical, and thermal disruption and proteinase digestion, particularly in combination with small volume processing and pooling steps, provided more comprehensive representation of the range of bacterial and fungal species. Concentration of larger volume urine specimens at low speed centrifugation proved highly effective, increasing resulting DNA levels and providing greater microbial representation and diversity.ConclusionsAlterations in the methodology of urine storage, preparation, and DNA processing improve microbial community profiling using culture-independent sequencing methods. Our optimized protocol for DNA extraction from urine samples provided improved fungal community representation. Use of this technique resulted in equivalent representation of the bacterial populations as well, making this a useful technique for the concurrent evaluation of bacterial and fungal populations by NGS.