Bacterial evasion strategies, urothelial biology and new treatments in urinary tract infection

Urinary tract infections (UTI) are among the most prevalent infectious diseases worldwide, leading to significant morbidity and mortality and wreaking a substantial economic cost. Uropathogenic Escherichia coli (UPEC) has been shown to invade the urothelium in murine models of acute UTI, forming intracellular reservoirs that are thought to evade conventional antibiotic treatment and the immune response, allowing recurrence at a later date. However, the role of intracellular infection in chronic UTI causing more subtle lower urinary tract symptoms (LUTS), a particular problem in the growing elderly population, is not well understood. Moreover, the species of bacteria involved remains largely unknown, the model systems used to study them need improvement, and treatment options are not currently optimal. This thesis addresses these important research aims with a view towards improving the situation for LUTS patients who have an underlying UTI. In the first research section, we found strong evidence of intracellular Enterococcus faecalis harboured within urothelial cells shed via an innate immune response from the bladder of LUTS patients. Furthermore, these patient-isolated strains of E. faecalis showed robust invasive properties in a bladder cell line. However, E. coli only formed surface biofilms in these patients, suggesting that the murine UPEC model may not apply to patients with chronic LUTS. In the second section, we addressed the issue that the murine and human urinary bladder differ structurally and functionally, which may be hindering our understanding of UTI pathogenesis in humans. We therefore 4 designed and characterised a human three-dimensional (3D) bladder mimetic differentiated from primary urothelial progenitors, and showed that it closely resembles human tissue. Moreover, infection in this organoid model resulted in outcomes similar to those seen in LUTS patients. In the future we aim to use this 3D culture as a platform for modelling chronic infection and tissue regeneration in the presence of novel therapeutic agents. Finally, in the third section we tackled the issue that traditional oral antibiotic regimens for UTI fail in a high proportion of cases. This recurrence of disease post-treatment could be explained in part by the lack of cellular penetration of orally administered antibiotics, which are not able to accumulate to an effective concentration within intracellular bacterial niches. Meanwhile, oral antibiotics may also lead to antimicrobial resistance and systemic side effects. Using our human urothelial organoid, we tested the ability of novel liposome-coated ultrasound-activated lipid microbubbles to deliver drugs into the cortex of the apical cell layer. Ultrasound-activated intracellular delivery of gentamicin using microbubbles was over twice that achieved by liposomes alone. Moreover, little cell damage was detected and this therapeutic technology exhibited very encouraging antimicrobial activity, showing great promise as a more efficacious alternative to traditional oral antibiotic regimens. In conclusion, these collective results have implications for both the diagnosis and treatment of chronic UTI

Proteinase 3 promotes formation of multinucleated giant cells and granuloma-like structures in patients with granulomatosis with polyangiitis

OBJECTIVES: Granulomatosis with polyangiitis (GPA) and microscopic polyangiitis (MPA) are autoimmune vasculitides associated with antineutrophil cytoplasm antibodies that target proteinase 3 (PR3) or myeloperoxidase (MPO) found within neutrophils and monocytes. Granulomas are exclusively found in GPA and form around multinucleated giant cells (MGCs), at sites of microabscesses, containing apoptotic and necrotic neutrophils. Since patients with GPA have augmented neutrophil PR3 expression, and PR3-expressing apoptotic cells frustrate macrophage phagocytosis and cellular clearance, we investigated the role of PR3 in stimulating giant cell and granuloma formation. METHODS: We stimulated purified monocytes and whole peripheral blood mononuclear cells (PBMCs) from patients with GPA, patients with MPA or healthy controls with PR3 or MPO and visualised MGC and granuloma-like structure formation using light, confocal and electron microscopy, as well as measuring the cell cytokine production. We investigated the expression of PR3 binding partners on monocytes and tested the impact of their inhibition. Finally, we injected zebrafish with PR3 and characterised granuloma formation in a novel animal model. RESULTS: In vitro, PR3 promoted monocyte-derived MGC formation using cells from patients with GPA but not from patients with MPA, and this was dependent on soluble interleukin 6 (IL-6), as well as monocyte MAC-1 and protease-activated receptor-2, found to be overexpressed in the cells of patients with GPA. PBMCs stimulated by PR3 formed granuloma-like structures with central MGC surrounded by T cells. This effect of PR3 was confirmed in vivo using zebrafish and was inhibited by niclosamide, a IL-6-STAT3 pathway inhibitor. CONCLUSIONS: These data provide a mechanistic basis for granuloma formation in GPA and a rationale for novel therapeutic approaches

Urinary ATP as an indicator of infection and inflammation of the urinary tract in patients with lower urinary tract symptoms

BACKGROUND: Adenosine-5'-triphosphate (ATP) is a neurotransmitter and inflammatory cytokine implicated in the pathophysiology of lower urinary tract disease. ATP additionally reflects microbial biomass thus has potential as a surrogate marker of urinary tract infection (UTI). The optimum clinical sampling method for ATP urinalysis has not been established. We tested the potential of urinary ATP in the assessment of lower urinary tract symptoms, infection and inflammation, and validated sampling methods for clinical practice. METHODS: A prospective, blinded, cross-sectional observational study of adult patients presenting with lower urinary tract symptoms (LUTS) and asymptomatic controls, was conducted between October 2009 and October 2012. Urinary ATP was assayed by a luciferin-luciferase method, pyuria counted by microscopy of fresh unspun urine and symptoms assessed using validated questionnaires. The sample collection, storage and processing methods were also validated. RESULTS: 75 controls and 340 patients with LUTS were grouped as without pyuria (n = 100), pyuria 1-9 wbc ?l(-1) (n = 120) and pyuria ?10 wbc ?l(-1) (n = 120). Urinary ATP was higher in association with female gender, voiding symptoms, pyuria greater than 10 wbc ?l(-1) and negative MSU culture. ROC curve analysis showed no evidence of diagnostic test potential. The urinary ATP signal decayed with storage at 23°C but was prevented by immediate freezing at ??-20°C, without boric acid preservative and without the need to centrifuge urine prior to freezing. CONCLUSIONS: Urinary ATP may have a role as a research tool but is unconvincing as a surrogate, clinical diagnostic marker

Improving Social Justice in COVID-19 Health Research: Interim guidelines for reporting health equity in observational studies

The COVID-19 pandemic has highlighted the global imperative to address health inequities. Observational studies are a valuable source of evidence for real-world effects and impacts of implementing COVID-19 policies on the redistribution of inequities. We assembled a diverse global multi-disciplinary team to develop interim guidance for improving transparency in reporting health equity in COVID-19 observational studies. We identified 14 areas in the STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) checklist that need additional detail to encourage transparent reporting of health equity. We searched for examples of COVID-19 observational studies that analysed and reported health equity analysis across one or more social determinants of health. We engaged with Indigenous stakeholders and others groups experiencing health inequities to co-produce this guidance and to bring an intersectional lens. Taking health equity and social determinants of health into account contributes to the clinical and epidemiological understanding of the disease, identifying specific needs and supporting decision-making processes. Stakeholders are encouraged to consider using this guidance on observational research to help provide evidence to close the inequitable gaps in health outcomes

Multi-messenger observations of a binary neutron star merger

On 2017 August 17 a binary neutron star coalescence candidate (later designated GW170817) with merger time 12:41:04 UTC was observed through gravitational waves by the Advanced LIGO and Advanced Virgo detectors. The Fermi Gamma-ray Burst Monitor independently detected a gamma-ray burst (GRB 170817A) with a time delay of ~1.7 s with respect to the merger time. From the gravitational-wave signal, the source was initially localized to a sky region of 31 deg2 at a luminosity distance of 40+8-8 Mpc and with component masses consistent with neutron stars. The component masses were later measured to be in the range 0.86 to 2.26 Mo. An extensive observing campaign was launched across the electromagnetic spectrum leading to the discovery of a bright optical transient (SSS17a, now with the IAU identification of AT 2017gfo) in NGC 4993 (at ~40 Mpc) less than 11 hours after the merger by the One- Meter, Two Hemisphere (1M2H) team using the 1 m Swope Telescope. The optical transient was independently detected by multiple teams within an hour. Subsequent observations targeted the object and its environment. Early ultraviolet observations revealed a blue transient that faded within 48 hours. Optical and infrared observations showed a redward evolution over ~10 days. Following early non-detections, X-ray and radio emission were discovered at the transient’s position ~9 and ~16 days, respectively, after the merger. Both the X-ray and radio emission likely arise from a physical process that is distinct from the one that generates the UV/optical/near-infrared emission. No ultra-high-energy gamma-rays and no neutrino candidates consistent with the source were found in follow-up searches. These observations support the hypothesis that GW170817 was produced by the merger of two neutron stars in NGC4993 followed by a short gamma-ray burst (GRB 170817A) and a kilonova/macronova powered by the radioactive decay of r-process nuclei synthesized in the ejecta

Clinical urine microscopy for urinary tract infections

Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques. Data acquisition 300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists. Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition. Dataset annotation 300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image. Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1). Data structure The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number. Table1 Folder Files Objects Count Pixel Values img 300 Raw data 0-255 bin_mask 300 Background/Foreground 0/1 mult_mask 300 Background/Class 0 Rod 1697 1 RBC/WBC 1056 2 Yeast 41 3 Miscellaneous 550 4 Single EPC 182 5 Small EPC sheet 26 6 Large EPC sheet 10 7 Total 356

Clinical urine microscopy for urinary tract infections

Urinary tract infections (UTI) are a common disorder. Its diagnosis can be made by microscopic examination of voided urine for cellular markers of infection. We present a dataset containing 300 images and 3,562 manually annotated urinary cells labelled into seven classes of clinically significant urinary content. It is an enriched dataset with samples acquired from the unstained and untreated urine of patients with symptomatic UTI. The aim of the dataset is to facilitate UTI diagnosis in nearly all clinical settings by using a simple imaging system which leverages advanced machine learning techniques. Data acquisition 300 urine samples were obtained from patients with symptomatic UTI between April and August 2022 from a specialist LUTS outpatient clinic in central London. Urine samples were collected as natural voids and processed on-site within one hour to mitigate cellular degradation. Brightfield microscopic examination (Olympus BX41F microscope frame, U-5RE quintuple nosepiece, U-LS30 LED illuminator, U-AC Abbe condenser) was performed at x20 objective (Olympus PLCN20x Plan C N Achromat 20x/0.4). A disposable haemocytometer (C Chip™) was used for enumeration of red cells (RBC), white cells (WBC), epithelial cells (EPC), and the presence of other cellular content per 1 µl of urine by two experienced microscopists. Images were acquired using the aforementioned brightfield microscope using a 0.5X C-mount adapter connected to a digital colour camera (Infinity 3S-1UR, Teledyne Lumenera). Images were taken in 16-bit colour in 1392 x 1040 .tif format using Capture and Analyse software. An enriched dataset approach was taken to maximise urinary cellular content in the acquired images. Such data curation was also necessary to overcome class imbalance. Daily Kohler illumination and global white balance was performed to ensure consistency in image acquisition. Dataset annotation 300 images were acquired and manually annotated by first identifying cells of interest as a binary semantic segmentation task. Individual pixels were dichotomously labelled as either informative cells, foreground, or non-informative background. Non-informative background was further constrained by including unidentifiable cells, such as debris or grossly out-of-focus particles. Binary annotation was initially performed using ilastik, an open-source software using a Random Forest classifier for pixel classification, then manually refined at the pixel level to ensure accurate semantic segmentation. This produced a binary mask in 1392 x 1040 .tif format for each corresponding raw colour image. Objects of interest were then manually labelled by two expert microscopists into one of seven clinically significant multi-class categories: rods, RBC/WBC, yeast, miscellaneous, single EPC, small EPC sheet, and large EPC sheet. This produced a multi-class mask in 1392 x 1040 .tif format with a label as pixel value from 0-7, where 0 is background (Table 1). Data structure The dataset is organised into three root folders: img (image), bin_mask (binary mask), and mult_mask (multi-class mask). Each folder has 300 files in .tif format and labelled with an incremental number. Table1 Folder Files Objects Count Pixel Values img 300 Raw data 0-255 bin_mask 300 Background/Foreground 0/1 mult_mask 300 Background/Class 0 Rod 1697 1 RBC/WBC 1056 2 Yeast 41 3 Miscellaneous 550 4 Single EPC 182 5 Small EPC sheet 26 6 Large EPC sheet 10 7 Total 356