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. 2023 Mar 21;9(4):1022–1032. doi: 10.1021/acsinfecdis.3c00027

Urinary Glycosaminoglycans Are Associated with Recurrent UTI and Urobiome Ecology in Postmenopausal Women

Michael L Neugent †, Neha V Hulyalkar †, Ashwani Kumar ‡, Chao Xing ‡,§, Philippe E Zimmern ∥, Vladimir Shulaev ⊥,#, Nicole J De Nisco †,∥,*
PMCID: PMC10111421  PMID: 36942838

Abstract

graphic file with name id3c00027_0007.jpg

Glycosaminoglycans (GAGs) are linear, negatively charged polysaccharides composed of repeating disaccharide units of uronic acid and amino sugars. The luminal surface of the bladder epithelium is coated with a GAG layer. These urothelial GAGs are thought to provide a protective barrier and serve as a potential interaction site with the urinary microbiome (urobiome). Previous studies have profiled urinary GAG composition in mixed cohorts, but the urinary GAG composition in postmenopausal women remains undefined. To investigate the relationship between GAGs and recurrent urinary tract infection (rUTI), we profiled urinary GAGs in a controlled cohort of postmenopausal women. We found that chondroitin sulfate (CS) is the major urinary GAG in postmenopausal women and that urinary CS was elevated in women with active rUTI. We also associated urinary GAGs with urobiome composition and identified bacterial species that significantly associated with urinary GAG concentration. Corynebacterium amycolatum, Porphyromonas somerae, and Staphylococcus pasteuri were positively associated with heparin sulfate or hyaluronic acid, and bacterial species associated with vaginal dysbiosis were negatively correlated with urinary CS. Altogether, this work defines changes in urinary GAG composition associated with rUTI and identifies new associations between urinary GAGs and the urobiome that may play a role in rUTI pathobiology.

Keywords: glycosaminoglycan, recurrent urinary tract infection, urobiome, chondroitin sulfate, heparin sulfate, hyaluronic acid

Recurrent urinary tract infection (rUTI) is a debilitating disease defined as ≥2 symptomatic UTIs in 6 months or ≥3 UTIs in 12 months.1−3 During UTI, uropathogens, such as uropathogenic Escherichia coli (UPEC) or Klebsiella pneumoniae, ascend the urethra and invade the bladder epithelium (urothelium).4,5 One of the first defenses against infection is a luminal layer of glycosaminoglycan (GAG) polysaccharides coating the bladder epithelium.6,7 These luminal polysaccharides are predominantly composed of the GAGs chondroitin sulfate (CS), heparin sulfate (HS), and hyaluronic acid (HA).8 Structurally, GAGs are composed of sequentially bound disaccharide units of glucuronic acid (or iduronic acid) and amino sugars, such as N-acetylglucosamine (GlcNAc) or N-acetylgalactosamine (GalNAc).9 CS and HS are further characterized by sulfated residues (eg., C2, C4, and C6 and/or on the nonacetylated nitrogen).10 The urothelial GAG layer has been hypothesized to be an important interface between the human host and the resident urinary microbiome (urobiome) or invading uropathogens.11−13 For example, Ruggieri et al. reported that acid removal of the GAG layer significantly increased the adherence of UPEC, Klebsiella ozonae, Proteus mirabilis, Pseudomonas aeruginosa, and Enterococcus faecalis in a rabbit model of UTI.14

Interestingly, while UPEC is unable to degrade GAGs, the uropathogenic bacterium P. mirabilis degrades CS, suggesting that some uropathogens may utilize the urothelial GAG layer during urinary tract colonization and invasion.15 Additionally, bacterial genera often observed in the urinary microbiome (urobiome), like Bacteroides and Streptococcus, are known to harbor the ability to metabolize GAGs like heparin and HA into smaller disaccharides.16−20 Although species of the most predominant urobiome genus, Lactobacillus, cannot degrade CS, HS, or HA, Lactobacillus crispatus, Lactobacillus salivaris, and Lactobacillus reuteri have been shown to adhere to HeLa cells in a GAG-dependent manner.15,18,21 These observations suggest that the urothelial GAG layer may serve as a scaffolding site for urobiome species.

Aging and menopause, which are also associated with increased UTI susceptibility, may alter the composition of the urothelial GAG layer. Anand et al. reported that estrogen may play a role in modulating GAG layer thickness and the expression of GAG biosynthetic enzymes.22 Imamov et al. reported in a female interstitial cystitis mouse model that the quantity of urinary GAGs was higher in estrogen receptor-β knock-out (Erβ–/–) versus wild-type mice, suggestive of bladder atrophy and urothelium shedding.23 de Deus et al. observed that hypoestrogenism resulted in a lower sulfated GAG content in rat bladders.24 Furthermore, GAGs like HA have been shown to be as efficacious as vaginal estrogen for the treatment of symptoms of vaginal atrophy and dyspareunia associated with the decline of systemic estrogen levels in postmenopausal women.25,26

Recently, Bratulic et al. reported reference intervals for the free GAG concentration and disaccharide composition in the urine and plasma samples of 308 healthy adults using a high-throughput ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) method.27 In this study, the authors observed higher urinary CS and HS disaccharide concentrations in males than in females.27 This work also investigated differences in urinary GAG composition between binned age groups of mixed biological sex and did not find a significant association between serum and urinary GAG concentration and age. Conversely, Larking et al. reported that mean urine creatine-normalized total GAG concentration was bimodal in women and was elevated in postmenopausal compared to premenopausal women.28 However, the composition of urinary GAGs in postmenopausal women in the context of rUTI and the organization of the urobiome is poorly understood.

Because rUTI disproportionally affects postmenopausal women, we sought to quantitatively measure urinary GAG composition in a controlled, cross-sectional cohort of postmenopausal women designed to capture the cyclic nature of rUTI using a targeted UHPLC MS/MS-based disaccharide analysis.8,29 We observed that CS was the most abundant urinary GAG in postmenopausal women and that the creatinine-normalized urinary CS concentration was higher in women who were experiencing an active rUTI compared to those not experiencing UTI. We further found evidence that a history of rUTI and estrogen hormone therapy (EHT) use are associated with altered sulfonation patterns in urinary GAGs. Using whole genome metagenomic sequencing on the same cohort of women, we recently described changes in both the taxonomic composition and functional potential of the urobiome associated with rUTI.29 We used this previously described metagenomic data set to identify urobiome bacterial species associated with urinary GAG concentration. We found that the abundance of taxa associated with urogenital dysbiosis, such as Atopobium vaginae and Peptoniphilus, negatively correlated with urinary CS concentration. Conversely, Corynebacterium amycolatum, Porphyromonas somerae, and Staphylococcus pasteuri were positively associated with HS or HA. Together, our results suggest potential associations between urinary GAG composition and rUTI, EHT, and urobiome composition.

Results

Human Cohort Design and Clinical Characteristics

To model the pathobiology of rUTI, we previously curated a controlled cross-sectional cohort of postmenopausal women.29 Briefly, postmenopausal women (aged 51–88) were recruited into one of the three cohort groups depending on history of rUTI and current UTI status. Group 1 (No UTI History, n = 25) served as a healthy comparator and consisted of postmenopausal women with no lifetime history of symptomatic UTI. Groups 2 [rUTI History, UTI(−), n = 25] and 3 [rUTI History, UTI(+), n = 25] both consisted of postmenopausal women who had a recent history of rUTI but differed in their current status of UTI—group 2 did not have a symptomatic UTI at the time of sample of collection, while group 3 had a culture-proven symptomatic UTI. To control for common comorbidities that may affect urinary GAG composition or urobiome structure, all postmenopausal women recruited into the cohort passed a strict set of exclusion criteria.29

Targeted LC–MS Assay for Urinary GAGs

To quantitatively measure the urinary GAG disaccharide composition of the assembled postmenopausal cohort, we used a modification of previously published targeted LC–MS/MS methods for the detection of CS, HS, and HA in human urine.8 All samples were spiked with 100 ng of a non-naturally occurring heparin disaccharide. Derivatized samples were analyzed via targeted LC–MS/MS on a Waters Xevo TQS and ACQUITY UPLC using multiple reaction monitoring (MRM) for parent > daughter transitions representing the 17 disaccharides of interest from CS, HS, and HA (Figure 1A,B, Table 1, and Figure S1). We obtained robust and reproducible assay performance over a linear analytical range from 10 to 1000 ng/mL for all analytes of interest (Figure S1).

Figure 1.

Figure 1

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Targeted LC–MS/MS assay for urinary GAGs CS, HS, and HA. (A) Schematic diagram of LC–MS/MS assay workflow from sample preparation, enzymatic digestion, derivatization, and analysis by LC-tandem quadrupole MS. Created with BioRender.com. (B) Chromatogram of eluting GAG disaccharide analytical MRM transitions.

Table 1. GAG Disaccharide Chemical and Analytical Characteristicsa.

  structure standard catalog no. molecular mass precursor ion (m/z) production ion (m/z)
CS Disaccharides
TriSCS ΔUA2S(1,3)GalNAc4S6S CD007 707 405.5 365.5
2S4SCS ΔUA2S(1,3)GalNAc4S CD005 605 732 652
2S6SCS ΔUA2S(1,3)GalNAc6S CD006 605 732 652
4S6SCS ΔUA(1,3)GalNAc4S6S CD004 605 732 652
2SCS ΔUA2S(1,3)GalNAc CD008 503 652 157
4SCS ΔUA(1,3)GalNAc4S CD002 503 652 396
6SCS ΔUA(1,3)GalNAc6S CD003 503 652 396
0SCS ΔUA(1,3)GalNAc CD001 401 572 396
HS Disaccharides
TriSHS ΔUA2S(1,4)GlcNS6S HD001 665 770 690
NS6SHS ΔUA(1,4)GlcNS6S HD004 563 690 610
NS2SHS ΔUA2S(1,4)GlcNS HD002 563 690 610
NSHS ΔUA(1,4)GlcNS HD005 461 610 354
2S6SHS ΔUA2S(1,4)GlcNAc6S HD003 605 732 652
6SHS ΔUA(1,4)GlcNAc6S HD008 503 652 396
2SHS ΔUA2S(1,4)GlcNAc HD007 503 652 157
0SHS ΔUA(1,4)GlcNAc HD006 401 572 396
HA Disaccharide
0SHA ΔUA(1,3)GlcNAc HA02 401 572 396
           
Spike-In ΔUA-2SGlcNCOEt-6S HD009 619 746 666
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a

For each disaccharide belonging to CS, HS, and HA, structural nomenclature, Iduron (Supplier) catalog number, molecular mass, precursor ion, and product ion are supplied. Molecular mass is given in grams per mole, while precursor and product ions refer to mass-to-charge ratio (m/z).

All Three Major GAGs Are Detectable in Postmenopausal Urine, but CS Predominates

We observed all 17 expected disaccharides for CS, HS, and HA.8,30 The most abundant disaccharides were the monosulfate and nonsulfate CS disaccharides 4S-CS (2460.5 ng/mL, CI95% = 2262–3193 ng/mL), 6S-CS (1524 ng/mL CI95% = 1192–1945 ng/mL), and 0S-CS (1472 ng/mL CI95% = 1219–1827 ng/mL) (Figures 1 and 2A,B). We also observed abundant levels of the nonsulfate HS disaccharide, 0S-HS (505.7 ng/mL CI95% = 374.1–648.4 ng/mL), the HA disaccharide, 0S-HA (592.2 ng/mL CI95% = 428.2–716.7 ng/mL), and the disulfate 4S6S disaccharide of CS (236.6 ng/mL CI95% = 188.1–283.7 ng/mL) (Figure 2A,B and Tables S1 and S2).

Figure 2.

Figure 2

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Urinary GAG profile in postmenopausal women with and different UTI histories. (A) Urinary GAG disaccharide concentration in nanograms per milliliter. (B) Urinary GAG disaccharide concentration normalized to urinary creatinine. (C) Correlation of normalized and unnormalized urinary GAG disaccharide concentrations. P-value is generated by t-ratio calculation. (D) Comparison of summed GAG disaccharide distributions across cohort groups (n = 25 in each group, n = 75 total). P-value is generated by two-way analysis of variance with Fischer least significant difference multiple-comparison post-hoc. All boxes are drawn to represent the interquartile range. Median is denoted by the solid horizontal line. Bars are drawn from minimum to maximum of the data range.

To account for hydration status (i.e., urine concentration), urinary metabolites are often normalized to urinary creatinine.31 However, it has been suggested that urinary creatinine excretion rate may not be constant between and within individuals.32 To assess the agreement between raw urinary disaccharide concentration and creatinine-normalized disaccharide concentration, we performed linear regression and correlation analysis between the raw and creatinine-normalized data sets. We observed a strong linear correlation (Spearman ρ = 0.94, P < 10–15) when comparing raw disaccharide concentrations with creatinine-normalized abundance (Figure 2C). We chose to use creatinine-normalized data for downstream analyses due to convention on how urinary metabolites are often reported and the strong linear association we observe between raw and creatinine-normalized concentrations (Table S2).

Consistent with previous observations, our analysis found that the most abundant GAG detected in postmenopausal urine was CS (Figure 2D).8 Interestingly, we observed significantly higher levels of urinary CS in urines from patients with active rUTI (Figure 2D). This observation was most significant when comparing creatinine-normalized urinary CS concentration between the No UTI History and rUTI History, UTI(+) groups (P = 0.0243) and may be indicative of tissue damage and urothelial barrier disruption occurring during UTI.11,33 No difference was found in total urinary HS or HA concentration between cohort groups.

rUTI History Is Associated with Altered Urinary GAG Sulfonation Pattern

Given that we observed a significant positive association between urinary CS concentration and active rUTI, we hypothesized that the sulfonation pattern of urinary GAGs may differ between women with different rUTI histories. To test this hypothesis, we performed a pairwise metabolite enrichment analysis between the groups in our cohort using the nonparametric Wilcoxon rank sum test. When comparing the No UTI History group with the rUTI History, UTI(−) group, we detected the enrichment of disaccharides 2S6S-HS (P = 0.0037) and 4S6S-CS (P = 0.0353) in the rUTI History, UTI(−) group (Figure 3A–C). Similarly, we observed enrichment of the 2S6S-HS disaccharide in the rUTI History, UTI(+) group compared to the No UTI history group (P = 0.0080) (Figure 3D,E). We also observed an elevation of the 2S4S-CS disaccharide in the rUTI History, UTI(+) group urine (P = 0.0472) and an elevation of the monosulfonated 6S-HS disaccharide in No UTI History group urine (P = 0.0450) (Figure 3F,G). These results suggest that rUTI history may be associated with remodeling of urinary GAG sulfonation patterns of HS and CS. Our analysis further detected significant enrichment of HS disaccharides in the rUTI History, UTI(−) group compared to the rUTI History, UTI(+) group (Figure S2A). These differential features included the enrichment of monosulfate 6S- and NS-HS disaccharides (P = 0.0016 and P = 0.0034), the disulfate NS6S- and NS2S-HS disaccharides (P = 0.0102 and P = 0.0353), and the nonsulfate 0S-HS disaccharide (P = 0.0161) in the rUTI History, UTI(−) group (Figure S2B–F). Taken together, these observations provide rationale to hypothesize that rUTI history changes the composition of urinary GAG sulfonation patterns in postmenopausal women.

Figure 3.

Figure 3

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rUTI history is associated with altered urinary GAG disaccharide composition. (A) Volcano plot depicting differential enrichment screening of urinary GAG disaccharides between the No UTI History (n = 25) and rUTI History, UTI(−) groups (n = 25). (B,C) Box plots depicting significant hits from differential enrichment screen. (D) Volcano plot depicting differential enrichment screening of urinary GAG disaccharides between the No UTI History (n = 25) and rUTI History, UTI(+) groups (n = 25). (E–G) Box plots depicting significant hits from differential enrichment screen. All P-values are generated by the nonparametric Wilcoxon rank-sum. All boxes are drawn to represent the interquartile range. Median is denoted by the solid horizontal line. Bars are drawn from minimum to maximum of the data range.

EHT Is Associated with Altered Urinary CS Sulfonation

Many postmenopausal women use EHT to alleviate symptoms of menopausal syndrome.34 We previously showed with this cohort that EHT strongly shapes the taxonomic profile of the postmenopausal urobiome, enriching for protective species, such as L. crispatus.29 Interestingly, previous reports also suggest that EHT thickens the luminal GAG layer of the urothelium and regulates the GAG sulfotransferase enzyme HS6ST1.22,35 Given these observations, we hypothesized that EHT may augment urinary GAGs derived from the urothelium. To test this hypothesis, we compared total urinary CS, HS, and HA abundance among the No UTI History and rUTI History, UTI(−) groups now dichotomized by EHT use. We observed no difference in total urinary CS, HS, or HA abundance (Figure 4A). We further performed a metabolite enrichment analysis of urinary GAG disaccharides among the EHT(+) and EHT(−) groups using the nonparametric Wilcoxon rank sum test (Figure 4B). This analysis identified two urinary CS disaccharides, 4S6S-CS and 2S-CS/HS, as significantly enriched in the EHT(+) patients (P = 0.0174 and P = 0.0346, respectively) (Figure 4C,D). Given the inherent difficulty to distinguish the 2S-CS and 2S-HS disaccharides owing to their identical MRM transition and similar chromatographic retention, we grouped the feature as an aggregate of the two possible disaccharides. While we did not find evidence to support our hypothesis that EHT augments the total amount of urinary GAGs, these data do suggest that EHT may alter the sulfonation patterns of urinary GAGs.

Figure 4.

Figure 4

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EHT is associated with altered urinary CS disaccharide composition. (A) Comparison of summed GAG disaccharide distributions between EHT(+) (n = 29) and EHT(−) (nn = 21) women on the pooled No UTI History and rUTI History, UTI(−). (B) Volcano plot depicting differential enrichment screening of urinary GAG disaccharides between the EHT(+) (n = 29) and EHT(−) (n = 21) women. (C,D). Box plots depicting significant hits from differential enrichment screen. All P-values are generated by the nonparametric Wilcoxon rank-sum. All boxes are drawn to represent the interquartile range. Median is denoted by the solid horizontal line. Bars are drawn from minimum to maximum of the data range.

Urobiome Bacterial Species Associated with Urinary GAGs

The urinary tract hosts a unique microbial ecosystem, members of which belong to the urobiome.4,36−43 A decade of work has studied and defined the urobiome in multiple diseases, including overactive bladder, bladder cancer, urinary incontinence, and rUTI.4,29,44−46 However, host factors contributing to the urobiome ecosystem are poorly understood. We previously performed whole genome metagenomic sequencing of the urobiome in the cohort studied here and reported differential taxonomic and functional characteristics associated with rUTI in postmenopausal women.29 To assess urobiome bacterial ecology associated with urinary GAG composition, we performed a correlative screen of bacterial species with urinary GAG concentration in matched samples. Using the nonparametric Spearman correlation (ρ), we identified five associations between urinary GAGs and urobiome bacterial species that passed both a threshold of statistical significance (P < 0.05) and correlation strength (|ρ| ≥ 0.3) (Table S3). Three positive correlations were identified between HS and C. amycolatum (ρ = 0.345 and P = 0.0024), HA and P. somerae (ρ = 0.319 and P = 0.0053), and HS and S. pasteuri (ρ = 0.309 and P = 0.0071) (Figure 5A). We further observed two significant negative correlations between CS and A. vaginae (ρ = −0.363 and P = 0.0014) and CS and Peptoniphilus rhinitidis (ρ = −0.316 and P = 0.0057). To assess these associations further, we dichotomized the cohort population by the presence or absence of these identified bacterial species and compared GAG abundance between (+) and (−) groups. We observed significantly higher urinary HS in patients with C. amycolatum or S. pasteuri (P = 0.0016 and P = 0.0045) as well as significantly higher urinary HA in patients with P. somerae (P = 0.003) (Figure 5B–D). Significantly lower urinary CS concentrations were observed in patients with A. vaginae and P. rhinitidis detected in their urobiome (P = 0.0012 and P = 0.0068). While not meeting the strict correlation cutoff of |ρ| > 0.3 of our initial correlative screen (ρ = −0.282 and P = 0.0142), we did observe significantly lower urinary CS concentrations in patients with Ruminococcus obeum (renamed Blautia obeum) present in their urobiome (P = 0.0154) (Figure 5G). It should be noted as a limitation of this analysis that the sample size of the patient subset with observed taxa is limited in some instances. For example, S. pasteuri was only detectable in the urobiomes of four patients. Taken together, these data suggest that urinary GAGs associate with the urobiome and thus may play a role in shaping the bacterial ecology of this environment.

Figure 5.

Figure 5

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Urobiome bacterial species are associated with urinary GAG composition. (A) Volcano plot depicting correlation analysis of summed urinary GAGs with urobiome bacterial species detected by whole genome metagenomic sequencing (n = 260 species). For the Spearman correlation, thresholds of ρ ≥ 0.3 and P < 0.05 were chosen to assign significance. P-value is generated by t-ratio calculation. Positive GAG-bacteria correlations are colored blue and negative are colored red. (B–D) Box plots depicting significant positive hits from correlation analysis screen. Patients are dichotomized by the presence(+)/absence(−) of identified species as follows: C. amycolatumn(−) = 60, n(+) = 15; P. someraen(−) = 47, n(+) = 28; and S. pasteurin(−) = 71, n(+) = 4. (E–G) Box plots depicting significant negative hits from correlation analysis screen. Patients have been dichotomized by the presence(+)/absence(−) of identified species as follows: A. vaginaen(−) = 66, n(+) = 9; P. rhinitidisn(−) = 63, n(+) = 12; and R. obeumn(−) = 67, n(+) = 8. P-values depicted in boxplots were generated by the nonparametric Wilcoxon rank-sum. All boxes are drawn to represent the interquartile range. Median is denoted by the solid horizontal line. Bars are drawn from minimum to maximum of the data range.

Discussion

rUTI is a public health problem disproportionately affecting postmenopausal women who currently are underrepresented in biomedical science.4,29 In this population, rUTI can persist for years leading to a dramatic reduction in quality of life, massive financial burden, and life-threatening infections if treatment is unsuccessful.1 Yet, little is known about the pathobiology of rUTI particularly in postmenopausal women. Understanding the urogenital environment is critical to further our understanding of rUTI susceptibility and to develop new therapies. An important defense mechanism and site of host–pathogen or host–microbiome interaction in the urinary tract is the luminal urothelial GAG layer.6,7,35 It is believed that the female urobiome, which has now been identified as a key component of the urogenital environment, interacts with the urothelial GAG layer.36−43 However, little is currently known about how the urobiome as a whole and how individual urobiome species associate with the urothelial GAG layer or urinary GAG composition.

Here, we used targeted MS-based metabolite profiling to quantitatively measure urinary GAGs in a cohort of postmenopausal women designed to study rUTI. Our observations that CS is the major urinary GAG in this cohort are consistent with previous findings and consistent with the composition of the luminal GAG layer of the urothelium.8 We further observe that rUTI alters not only the total amount of urinary GAGs but also the sulfonation patterns of urinary GAGs. These observations may suggest that active rUTI may disrupt the urothelial GAG layer integrity and sulfonation. These data provide rationale to mechanistically study the urothelial GAG layer in rUTI pathobiology. Future work will focus on assessing the transitional and biological relevance of elevated urinary CS during active rUTI as well as the longitudinal stability of these observations.

Interestingly, while we observed an association between urinary CS sulfonation and EHT use, we did not observe differences in total GAG abundance between women using and not using EHT. It should be noted that a limitation of this study is that we assayed free urinary GAGs which may not be fully reflective of urothelial GAG layer thickness, integrity, or composition. Tissue samples would likely be needed to robustly determine if EHT is associated with specific augmentations of the thickness or composition of the urothelial GAG layer. We did, however, observe that EHT use was associated with altered urinary GAG sulfonated disaccharide patterns. We interpret these findings as supportive of previous work described by Anand et al. that showed differential expression of GAG sulfotransferase and biosynthesis enzymes between ovariectomized and Sham mice.22

A body of evidence suggests that both EHT and intravesical administration of GAGs have the potential to reduce the incidence of UTI and rUTI in human subjects.47−50 Ablove et al. reported that intravesical administration of HA and heparin prevented recurrent bacterial cystitis and refractory rUTI, respectively.51,52 Additionally, HA–CS intravesical instillations were found to significantly reduce the incidence of rUTI in women with a history of rUTI.53 These findings are supported by studies in rat models of cystitis that demonstrate a decrease in bladder E. coli burden after HA instillation. Moreover, this work demonstrated that instilled HA was able to coat the urothelium and integrate into the GAG layer, suggesting that therapeutically delivered GAGs may increase barrier function and protect against invasion of E. coli into the urothelium.54 Taken together, these data beg the question of whether EHT and/or intravesically delivered GAGs can be used to therapeutically augment the urothelial GAG layer to treat urological diseases in postmenopausal women.

The urobiome is another potential target for new rUTI therapeutics.4 This understudied microbial environment may harbor intricate interactions between specific host factors and members of the urobiome. However, our knowledge about the interaction of the urobiome with the urothelium and urinary GAGs is limited. Association of Lactobacillus species L. crispatus, Lactobacillus salivarius, and L. reuteri with HeLa cells has been reported to be GAG-dependent.18,21 Interestingly, depletion of the GAG layer in the bladder of a rabbit UTI model led to an increased attachment of UPEC and K. pneumoniae, suggesting that the GAG layer may impede uropathogen adherence to the urothelium.13,55,56 Indeed, it is known that some uropathogens, such as Streptococcus agalactiae and P. mirabilis, can degrade CS.15 We find it interesting to note that all the negative taxa-GAG associations were observed with CS, the most abundant urinary GAG.8 Also, of note, the presence of A. vaginae and species of Peptoniphilus, which were negatively associated with urinary CS, is known to be a signature of dysbiosis in the vagina.29,57−62 In a previous report, we similarly found an enrichment of the genus Peptoniphilus in the urine of women in the rUTI history, UTI(−) group compared to women with No UTI history suggesting that this genus may be similarly associated with dysbiosis in the urinary microbial ecosystem.29 We have also observed an enrichment of A. vaginae in the urobiomes of postmenopausal women who were not taking EHT and had more diverse and presumably more dysbiotic urobiomes.29 Future work will need to investigate mechanistic links between negative associations we observed between A. vaginae, Peptoniphilus, and urinary CS in order to determine if these taxa directly impact or interact with GAGs or are instead part of larger signature of dysbiosis and dysfunction in the urinary tract.

Conclusions

Urinary GAGs are associated with rUTI disease state and urobiome ecology. We hypothesize that urogenital dysbiosis is also associated with changes in the urothelial GAG layer. This hypothesis is evidenced by our observations of negative correlations between urinary CS and taxa known to be signatures of dysbiosis in the vagina. The work presented here provides strong rationale to mechanistically study the impact of the urothelial GAG layer on urobiome composition and structure.

Methods

Cohort Curation

The human cohort presented in this study is approved under IRBs STU032016-006 (University of Texas Southwestern Medical Center) and 19MR0011 (University of Texas at Dallas) and was recruited as previously reported.29 Briefly, participants were recruited with written, informed consent from a single site in the Urology clinic at the University of Texas Southwestern Medical Center between 2018 and 2019. All participants were postmenopausal females who passed a set of exclusion criteria: pre- or perimenopausal, complicated rUTI (i.e., indwelling catheter), pelvic malignancy, history of pelvic radiation within 3 years, pelvic floor procedure within 6 months prior, ongoing chemotherapy, renal insufficiency (creatinine >1.5 mg/dL), >stage 2 prolapse, diabetes mellitus type 1 or 2, neurogenic bladder, upper urinary tract anomaly, and post void residual > 100 mL. Patients were also excluded if antibiotics were used within 4 weeks prior unless a positive urine culture test result was observed. All urine samples were collected via the “clean-catch” midstream method after participant education about the requirements for collection. Urine samples were stored at 4 °C ≤3 h before sample processing and storage at −80 °C.

Urobiome Whole Genome Metagenomics

Urinary whole genome metagenomics was previously reported for the cohort presented here.29 Briefly, metagenomic DNA was isolated from pelleted urine samples using the Zymo Research DNA/RNA microbiome miniprep kit. DNA quality was assessed by Qubit assay and agarose gel electrophoresis. DNA libraries were constructed using the Nextera DNA Flex kit. Whole genome metagenomic sequencing was performed at the University of Texas at Dallas Genome Center using 2 × 150 bp paired-end reads on an Illumina NextSeq500 with a target of ≥50 million paired end reads per sample.

Bioinformatic Analysis

Bioinformatic analysis of metagenomic data was performed and reported previously.29 Taxonomic analysis was performed by first trimming the raw FASTQ files of human-mapping reads with KeadData.63 MethaPhlAn2 was used to generate taxonomic profiles.64 For complete information detailing how these analyses were performed, we refer the reader to the primary report of these data by Neugent et al.29

Urine Desalting and Urinary GAG Concentration

We used a modified version of previously reported methods to quantify urinary GAGs.8 Urine samples (stored at −80 C) were thawed at room temperature. 400 μL of each urine sample was loaded onto a 3 kDa molecule weight cutoff centrifugal filter column unit (Amicon UFC500396) and placed at a 37 °C water bath for 20 min (or until the sample turned clear and devoid of any precipitate). Desalting was carried out by spinning the sample-containing filtration column for 1 h at 14,000g. The column was washed twice with deionized water for 45 min at 14,000g each. Additionally, urine samples spiked with 10 ng/mL CS and HS each were prepared for method validation as positive controls.

Exhaustive Enzymatic Digestion of Urinary GAGs

Following the filter column wash, the casing/collection tubes were replaced prior to digestion. 150 μL of digestion buffer (50 mM ammonium acetate containing 2 mM calcium chloride; pH 7.0) was added to the filter column. Recombinant heparin lyase I, II, and III and recombinant chondroitinase ABC (chondroitin lyase) were added to each sample at 10 mU each and mixed well. Enzymatic digestion was performed at a 37 °C water bath for 2 h. The digestion process was terminated by centrifugation at 14,000g for 1 h. The filter column was washed twice with 100 μL of deionized water, each for 45 min at 14,000g. Deionized water sample was used as a digestion control for the method.

Disaccharide Derivatization with 2-Aminoacridone

Each sample filtrate containing the disaccharide products was spiked with 100 ng of a non-naturally occurring HS disaccharide (ΔUA-2S GlcNOEt-6S), which has a −COCH2CH3 moiety attached to the amine of the glucosamine, as an internal standard (Iduron United Kingdom). This was followed by drying using a vacuum centrifuge. After drying, 10 μL of 0.1 M AMAC in dimethyl sulfoxide (DMSO)/acetic acid (17:3, V/V) was added to each sample and incubated at RT for 10 min 10 μL of 1 M aqueous sodium cyanoborohydride was added, and derivatization was performed at 45 °C for 4 h. The samples were then centrifuged at 14,000g for 5 min to recover the supernatant, and 60 μL of DMSO: acetic acid: deionized water (17:3:20) was added. Samples were stored in a light-resistant amber vial at 4 °C until analyzed by LC–MS/MS.

Liquid Chromatography–Mass Spectrometry

Quantitative measurement of urinary GAG disaccharides was performed via a modification of previously reported methods on a Waters Xevo TQ tandem quadrupole mass spectrometer, in negative ion mode, lined to and ACQUITY UPLC equipped with a CORTECS UPLC T3 2.1 × 150 mm UPLC column with a 1.6 μm particle size.8 5 μL of derivatized sample was injected to run on a 31 min LC gradient starting at 95% 80 mM ammonium acetate pH. 5.8 (solvent A) to 100% methanol (solvent B) at a 0.3 mL/min flowrate. Column temperature was held at 40 °C. Previously reported MRM libraries of GAG disaccharides were validated from pure standards of the 17 disaccharides of interest (Iduron, United Kingdom) and used to monitor analytical transitions (Table 1).8 Data analysis was performed in TargetLynx. Briefly, analytical transition peaks for each disaccharide were integrated, normalized to internal spike-in standard, and mapped to a standard curve to accurately estimate analyte concentration. Urinary disaccharide concentrations were then normalized to urinary creatinine and measured by colorimetric assay (Sigma) to account for urine concentration and hydration status.

Statistical Analysis

Statistical analysis was performed using GraphPad Prism 9 and Microsoft Excel. The nonparametric Wilcoxon rank sum was used for pairwise hypothesis testing. The Spearman correlation was performed as a nonparametric method of assessing the association of two continuous variables. An α of 0.05 was used to control type-I error.

Acknowledgments

The authors would like to thank and acknowledge the patients who participated in this study. This work was supported by grants from the Welch Foundation (AT-2030020200401) and the National Institutes of Health (1R01DK131267-01) to N.J.D. and (1F32DK128975-01A1) to M.L.N. This work was also supported by the Felicia and John Cain Distinguished Chair in Women’s Health to P.E.Z. The TOC graphic was created using BioRender.com.

Supporting Information Available

The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acsinfecdis.3c00027.

  • LC–MS/MS assay response curves for all 17 monitored disaccharide MRM analytical transitions over an analyte concentration range from 10 to 1000 ng/mL; differential disaccharide enrichment analysis between the rUTI History, UTI(−) and rUTI History, UTI(+) groups (PDF)

  • Raw (not creatinine normalized) GAG disaccharide concentrations (ng/ml) (XLSX)

  • Creatinine-normalized GAG disaccharide concentrations (ng/μg creatinine) (XLSX)

  • Correlation analysis between summed GAGs and urobiome bacterial species (XLSX)

Author Contributions

M.L.N. and N.V.H. are equally contributing cofirst authors. Conceptualization, M.L.N., N.V.H., V.S., and N.J.D.; data curation, M.L.N., N.V.H., A.K., C.X., P.E.Z., V.S., and N.J.D.; formal analysis, M.L.N., N.V.H., V.S., and N.J.D.; funding acquisition, M.L.N., P.E.Z., and N.J.D.; investigation, M.L.N. and N.V.H.; methodology, M.L.N., N.V.H., A.K., C.X., P.E.Z., V.S., and N.J.D.; project administration, N.J.D.; resources, P.E.Z., V.S., and N.J.D.; supervision, C.X., P.E.Z., V.S., and N.J.D.; validation, M.L.N., N.V.H., V.S., and N.J.D.; visualization, M.L.N. and N.J.D.; and writing-original draft, M.L.N., N.V.H., and N.J.D.

The authors declare no competing financial interest.

Supplementary Material

id3c00027_si_001.pdf (3.1MB, pdf)
id3c00027_si_002.xlsx (30.8KB, xlsx)
id3c00027_si_003.xlsx (28.5KB, xlsx)
id3c00027_si_004.xlsx (39.3KB, xlsx)

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Associated Data

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Supplementary Materials

id3c00027_si_001.pdf (3.1MB, pdf)
id3c00027_si_002.xlsx (30.8KB, xlsx)
id3c00027_si_003.xlsx (28.5KB, xlsx)
id3c00027_si_004.xlsx (39.3KB, xlsx)

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