Red blood cell-derived arginase release in hemolytic uremic syndrome.

BACKGROUND: Hemolysis is a cardinal feature of hemolytic uremic syndrome (HUS) and during hemolysis excess arginase 1 is released from red blood cells. Increased arginase activity leads to reduced L-arginine, as it is converted to urea and L-ornithine, and thereby reduced nitric oxide bioavailability, with secondary vascular injury. The objective of this study was to investigate arginase release in HUS patients and laboratory models and correlate arginase levels to hemolysis and kidney injury. METHODS: Two separate cohorts of patients (n = 47 in total) with HUS associated with Shiga toxin-producing enterohemorrhagic E. coli (EHEC) and pediatric controls (n = 35) were investigated. Two mouse models were used, in which mice were either challenged intragastrically with E. coli O157:H7 or injected intraperitoneally with Shiga toxin 2. An in vitro model of thrombotic microangiopathy was developed in which Shiga toxin 2- and E. coli O157 lipopolysaccharide-stimulated human blood cells combined with ADAMTS13-deficient plasma were perfused over glomerular endothelial cells. Two group statistical comparisons were performed using the Mann-Whitney test, multiple groups were compared using the Kruskal-Wallis test followed by Dunn's procedure, the Wilcoxon signed rank test was used for paired data, or linear regression for continuous variables. RESULTS: HUS patients had excessively high plasma arginase 1 levels and activity (conversion of L-arginine to urea and L-ornithine) during the acute phase, compared to remission and controls. Arginase 1 levels correlated with lactate dehydrogenase activity, indicating hemolysis, as well as the need for dialysis treatment. Patients also exhibited high levels of plasma alpha-1-microglobulin, a heme scavenger. Both mouse models exhibited significantly elevated plasma arginase 1 levels and activity. Plasma arginase 1 levels correlated with lactate dehydrogenase activity, alpha-1-microglobulin and urea levels, the latter indicative of kidney dysfunction. In the in vitro model of thrombotic microangiopathy, bioactive arginase 1 was released and levels correlated to the degree of hemolysis. CONCLUSIONS: Elevated red blood cell-derived arginase was demonstrated in HUS patients and in relevant in vivo and in vitro models. The excessively high arginase levels correlated to the degree of hemolysis and kidney dysfunction. Thus, arginase inhibition should be investigated in HUS.

Artificial intelligence for detection of prostate cancer in biopsies during active surveillance.

OBJECTIVES: To evaluate a cancer detecting artificial intelligence (AI) algorithm on serial biopsies in patients with prostate cancer on active surveillance (AS). PATIENTS AND METHODS: A total of 180 patients in the Prostate Cancer Research International Active Surveillance (PRIAS) cohort were prospectively monitored using pre-defined criteria. Diagnostic and re-biopsy slides from 2011 to 2020 (n = 4744) were scanned and analysed by an in-house AI-based cancer detection algorithm. The algorithm was analysed for sensitivity, specificity, and for accuracy to predict need for active treatment. Prognostic properties of cancer size, prostate-specific antigen (PSA) level and PSA density at diagnosis were evaluated. RESULTS: The sensitivity and specificity of the AI algorithm was 0.96 and 0.73, respectively, for correct detection of cancer areas. Original pathology report diagnosis was used as the reference method. The area of cancer estimated by the pathologists correlated highly with the AI detected cancer size (r = 0.83). By using the AI algorithm, 63% of the slides would not need to be read by a pathologist as they were classed as benign, at the risk of missing 0.55% slides containing cancer. Biopsy cancer content and PSA density at diagnosis were found to be prognostic of whether the patient stayed on AS or was discontinued for active treatment. CONCLUSION: The AI-based biopsy cancer detection algorithm could be used to reduce the pathologists' workload in an AS cohort. The detected cancer amount correlated well with the cancer length measured by the pathologist and the algorithm performed well in finding even small areas of cancer. To our knowledge, this is the first report on an AI-based algorithm in digital pathology used to detect cancer in a cohort of patients on AS.

Deep learning prediction of quantitative coronary angiography values using myocardial perfusion images with a CZT camera.

PURPOSE: Evaluate the prediction of quantitative coronary angiography (QCA) values from MPI, by means of deep learning. METHODS: 546 patients (67% men) undergoing stress 99mTc-tetrofosmin MPI in a CZT camera in the upright and supine position were included (1092 MPIs). Patients were divided into two groups: ICA group included 271 patients who performed an ICA within 6 months of MPI and a control group with 275 patients with low pre-test probability for CAD and a normal MPI. QCA analyses were performed using radiologic software and verified by an expert reader. Left ventricular myocardium was segmented using clinical nuclear cardiology software and verified by an expert reader. A deep learning model was trained using a double cross-validation scheme such that all data could be used as test data as well. RESULTS: Area under the receiver-operating characteristic curve for the prediction of QCA, with > 50% narrowing of the artery, by deep learning for the external test cohort: per patient 85% [95% confidence interval (CI) 84%-87%] and per vessel; LAD 74% (CI 72%-76%), RCA 85% (CI 83%-86%), LCx 81% (CI 78%-84%), and average 80% (CI 77%-83%). CONCLUSION: Deep learning can predict the presence of different QCA percentages of coronary artery stenosis from MPIs.

Early Terminal Complement Blockade and C6 Deficiency Are Protective in Enterohemorrhagic Escherichia coli-Infected Mice.

Complement activation occurs during enterohemorrhagic Escherichia coli (EHEC) infection and may exacerbate renal manifestations. In this study, we show glomerular C5b-9 deposits in the renal biopsy of a child with EHEC-associated hemolytic uremic syndrome. The role of the terminal complement complex, and its blockade as a therapeutic modality, was investigated in a mouse model of E. coli O157:H7 infection. BALB/c mice were treated with monoclonal anti-C5 i.p. on day 3 or 6 after intragastric inoculation and monitored for clinical signs of disease and weight loss for 14 d. All infected untreated mice (15 of 15) or those treated with an irrelevant Ab (8 of 8) developed severe illness. In contrast, only few infected mice treated with anti-C5 on day 3 developed symptoms (three of eight, p < 0.01 compared with mice treated with the irrelevant Ab on day 3) whereas most mice treated with anti-C5 on day 6 developed symptoms (six of eight). C6-deficient C57BL/6 mice were also inoculated with E. coli O157:H7 and only 1 of 14 developed disease, whereas 10 of 16 wild-type mice developed weight loss and severe disease (p < 0.01). Complement activation via the terminal pathway is thus involved in the development of disease in murine EHEC infection. Early blockade of the terminal complement pathway, before the development of symptoms, was largely protective, whereas late blockade was not. Likewise, lack of C6, and thereby deficient terminal complement complex, was protective in murine E. coli O157:H7 infection.

Haemolytic uraemic syndrome.

Haemolytic uraemic syndrome (HUS) is defined by the simultaneous occurrence of nonimmune haemolytic anaemia, thrombocytopenia and acute renal failure. This leads to the pathological lesion termed thrombotic microangiopathy, which mainly affects the kidney, as well as other organs. HUS is associated with endothelial cell injury and platelet activation, although the underlying cause may differ. Most cases of HUS are associated with gastrointestinal infection with Shiga toxin-producing enterohaemorrhagic Escherichia coli (EHEC) strains. Atypical HUS (aHUS) is associated with complement dysregulation due to mutations or autoantibodies. In this review, we will describe the causes of HUS. In addition, we will review the clinical, pathological, haematological and biochemical features, epidemiology and pathogenetic mechanisms as well as the biochemical, microbiological, immunological and genetic investigations leading to diagnosis. Understanding the underlying mechanisms of the different subtypes of HUS enables tailoring of appropriate treatment and management. To date, there is no specific treatment for EHEC-associated HUS but patients benefit from supportive care, whereas patients with aHUS are effectively treated with anti-C5 antibody to prevent recurrences, both before and after renal transplantation.

A novel mechanism of bacterial toxin transfer within host blood cell-derived microvesicles.

Shiga toxin (Stx) is the main virulence factor of enterohemorrhagic Escherichia coli, which are non-invasive strains that can lead to hemolytic uremic syndrome (HUS), associated with renal failure and death. Although bacteremia does not occur, bacterial virulence factors gain access to the circulation and are thereafter presumed to cause target organ damage. Stx was previously shown to circulate bound to blood cells but the mechanism by which it would potentially transfer to target organ cells has not been elucidated. Here we show that blood cell-derived microvesicles, shed during HUS, contain Stx and are found within patient renal cortical cells. The finding was reproduced in mice infected with Stx-producing Escherichia coli exhibiting Stx-containing blood cell-derived microvesicles in the circulation that reached the kidney where they were transferred into glomerular and peritubular capillary endothelial cells and further through their basement membranes followed by podocytes and tubular epithelial cells, respectively. In vitro studies demonstrated that blood cell-derived microvesicles containing Stx undergo endocytosis in glomerular endothelial cells leading to cell death secondary to inhibited protein synthesis. This study demonstrates a novel virulence mechanism whereby bacterial toxin is transferred within host blood cell-derived microvesicles in which it may evade the host immune system.

Shiga toxin-induced complement-mediated hemolysis and release of complement-coated red blood cell-derived microvesicles in hemolytic uremic syndrome.

Shiga toxin (Stx)-producing Escherichia coli (STEC) cause hemolytic uremic syndrome (HUS). This study investigated whether Stx2 induces hemolysis and whether complement is involved in the hemolytic process. RBCs and/or RBC-derived microvesicles from patients with STEC-HUS (n = 25) were investigated for the presence of C3 and C9 by flow cytometry. Patients exhibited increased C3 deposition on RBCs compared with controls (p < 0.001), as well as high levels of C3- and C9-bearing RBC-derived microvesicles during the acute phase, which decreased after recovery. Stx2 bound to P1 (k) and P2 (k) phenotype RBCs, expressing high levels of the P(k) Ag (globotriaosylceramide), the known Stx receptor. Stx2 induced the release of hemoglobin and lactate dehydrogenase in whole blood, indicating hemolysis. Stx2-induced hemolysis was not demonstrated in the absence of plasma and was inhibited by heat inactivation, as well as by the terminal complement pathway Ab eculizumab, the purinergic P2 receptor antagonist suramin, and EDTA. In the presence of whole blood or plasma/serum, Stx2 induced the release of RBC-derived microvesicles coated with C5b-9, a process that was inhibited by EDTA, in the absence of factor B, and by purinergic P2 receptor antagonists. Thus, complement-coated RBC-derived microvesicles are elevated in HUS patients and induced in vitro by incubation of RBCs with Stx2, which also induced hemolysis. The role of complement in Stx2-mediated hemolysis was demonstrated by its occurrence only in the presence of plasma and its abrogation by heat inactivation, EDTA, and eculizumab. Complement activation on RBCs could play a role in the hemolytic process occurring during STEC-HUS.

Complement Interactions with Blood Cells, Endothelial Cells and Microvesicles in Thrombotic and Inflammatory Conditions.

The complement system is activated in the vasculature during thrombotic and inflammatory conditions. Activation may be associated with chronic inflammation on the endothelial surface leading to complement deposition. Complement mutations allow uninhibited complement activation to occur on platelets, neutrophils, monocytes, and aggregates thereof, as well as on red blood cells and endothelial cells. Furthermore, complement activation on the cells leads to the shedding of cell derived-microvesicles that may express complement and tissue factor thus promoting inflammation and thrombosis. Complement deposition on red blood cells triggers hemolysis and the release of red blood cell-derived microvesicles that are prothrombotic. Microvesicles are small membrane vesicles ranging from 0.1 to 1 µm, shed by cells during activation, injury and/or apoptosis that express components of the parent cell. Microvesicles are released during inflammatory and vascular conditions. The repertoire of inflammatory markers on endothelial cell-derived microvesicles shed during inflammation is large and includes complement. These circulating microvesicles may reflect the ongoing inflammatory process but may also contribute to its propagation. This overview will describe complement activation on blood and endothelial cells and the release of microvesicles from these cells during hemolytic uremic syndrome, thrombotic thrombocytopenic purpura and vasculitis, clinical conditions associated with enhanced thrombosis and inflammation.

Ouabain protects against Shiga toxin-triggered apoptosis by reversing the imbalance between Bax and Bcl-xL.

Hemolytic uremic syndrome, a life-threatening disease often accompanied by acute renal failure, usually occurs after gastrointestinal infection with Shiga toxin 2 (Stx2)-producing Escherichia coli. Stx2 binds to the glycosphingolipid globotriaosylceramide receptor, expressed by renal epithelial cells, and triggers apoptosis by activating the apoptotic factor Bax. Signaling via the ouabain/Na,K-ATPase/IP3R/NF-κB pathway increases expression of Bcl-xL, an inhibitor of Bax, suggesting that ouabain might protect renal cells from Stx2-triggered apoptosis. Here, exposing rat proximal tubular cells to Stx2 in vitro resulted in massive apoptosis, upregulation of the apoptotic factor Bax, increased cleaved caspase-3, and downregulation of the survival factor Bcl-xL; co-incubation with ouabain prevented all of these effects. Ouabain activated the NF-κB antiapoptotic subunit p65, and the inhibition of p65 DNA binding abolished the antiapoptotic effect of ouabain in Stx2-exposed tubular cells. Furthermore, in vivo, administration of ouabain reversed the imbalance between Bax and Bcl-xL in Stx2-treated mice. Taken together, these results suggest that ouabain can protect the kidney from the apoptotic effects of Stx2.

Cerebrospinal fluid reference proteins increase accuracy and interpretability of biomarkers for brain diseases.

Cerebrospinal fluid (CSF) biomarkers reflect brain pathophysiology and are used extensively in translational research as well as in clinical practice for diagnosis of neurological diseases, e.g., Alzheimer's disease (AD). However, CSF biomarker concentrations may be influenced by non-disease related inter-individual variability. Here we use a data-driven approach to demonstrate the existence of inter-individual variability in mean standardized CSF protein levels. We show that these non-disease related differences cause many commonly reported CSF biomarkers to be highly correlated, thereby producing misleading results if not accounted for. To adjust for this inter-individual variability, we identified and evaluated high-performing reference proteins which improved the diagnostic accuracy of key CSF AD biomarkers. Our reference protein method attenuates the risk for false positive findings, and improves the sensitivity and specificity of CSF biomarkers, with broad implications for both research and clinical practice.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

BACKGROUND: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions. METHODS: A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: (1) clinical data only, including demographics, cognitive tests and APOE ε4 status, (2) clinical data plus hippocampal volume, (3) clinical data plus all regional MRI gray matter volumes (N = 68) extracted using FreeSurfer software, (4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. A double cross-validation scheme, with five test folds and for each of those ten validation folds, was used. External evaluation was performed on part of the ADNI dataset, including 108 patients. Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant. RESULTS: In the BioFINDER cohort, 109 patients (33%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC) = 0.85 and four-year cognitive decline was R2 = 0.14. The performance was improved for both outcomes when adding hippocampal volume (AUC = 0.86, R2 = 0.16). Adding FreeSurfer brain regions improved prediction of four-year cognitive decline but not progression to AD (AUC = 0.83, R2 = 0.17), while the DL model worsened the performance for both outcomes (AUC = 0.84, R2 = 0.08). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. In the external evaluation cohort from ADNI, 23 patients (21%) progressed to AD dementia. The results for predicted progression to AD dementia were similar to the results for the BioFINDER test data, while the performance for the cognitive decline was deteriorated. CONCLUSIONS: The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

A machine learning-based prediction of tau load and distribution in Alzheimer's disease using plasma, MRI and clinical variables.

Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, commonly used in Alzheimer's disease (AD) research and clinical trials. However, its routine clinical use is limited by cost and accessibility barriers. Here we explore using machine learning (ML) models to predict clinically useful tau-PET outcomes from low-cost and non-invasive features, e.g., basic clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI). Results demonstrated that models including plasma biomarkers yielded highly accurate predictions of tau-PET burden (best model: R-squared=0.66-0.68), with especially high contribution from plasma P-tau217. In contrast, MRI variables stood out as best predictors (best model: R-squared=0.28-0.42) of asymmetric tau load between the two hemispheres (an example of clinically relevant spatial information). The models showed high generalizability to external test cohorts with data collected at multiple sites. Based on these results, we also propose a proof-of-concept two-step classification workflow, demonstrating how the ML models can be translated to a clinical setting. This study reveals current potential in predicting tau-PET information from scalable cost-effective variables, which could improve diagnosis and prognosis of AD.

Comparing a pre-defined versus deep learning approach for extracting brain atrophy patterns to predict cognitive decline due to Alzheimer's disease in patients with mild cognitive symptoms.

Background: Predicting future Alzheimer's disease (AD)-related cognitive decline among individuals with subjective cognitive decline (SCD) or mild cognitive impairment (MCI) is an important task for healthcare. Structural brain imaging as measured by magnetic resonance imaging (MRI) could potentially contribute when making such predictions. It is unclear if the predictive performance of MRI can be improved using entire brain images in deep learning (DL) models compared to using pre-defined brain regions. Methods: A cohort of 332 individuals with SCD/MCI were included from the Swedish BioFINDER-1 study. The goal was to predict longitudinal SCD/MCI-to-AD dementia progression and change in Mini-Mental State Examination (MMSE) over four years. Four models were evaluated using different predictors: 1) clinical data only, including demographics, cognitive tests and APOE e4 status, 2) clinical data plus hippocampal volume, 3) clinical data plus all regional MRI gray matter volumes (N=68) extracted using FreeSurfer software, 4) a DL model trained using multi-task learning with MRI images, Jacobian determinant images and baseline cognition as input. Models were developed on 80% of subjects (N=267) and tested on the remaining 20% (N=65). Mann-Whitney U-test was used to determine statistically significant differences in performance, with p-values less than 0.05 considered significant. Results: In the test set, 21 patients (32.3%) progressed to AD dementia. The performance of the clinical data model for prediction of progression to AD dementia was area under the curve (AUC)=0.87 and four-year cognitive decline was R2=0.17. The performance was significantly improved for both outcomes when adding hippocampal volume (AUC=0.91, R2=0.26, p-values <0.05) or FreeSurfer brain regions (AUC=0.90, R2=0.27, p-values <0.05). Conversely, the DL model did not show any significant difference from the clinical data model (AUC=0.86, R2=0.13). A sensitivity analysis showed that the Jacobian determinant image was more informative than the MRI image, but that performance was maximized when both were included. Conclusions: The DL model did not significantly improve the prediction of clinical disease progression in AD, compared to regression models with a single pre-defined brain region.

IgG Binds Escherichia coli Serine Protease EspP and Protects Mice From E. coli O157:H7 Infection.

Shiga toxin-producing Escherichia coli O157:H7 is a virulent strain causing severe gastrointestinal infection, hemolytic uremic syndrome and death. To date there are no specific therapies to reduce progression of disease. Here we investigated the effect of pooled immunoglobulins (IgG) on the course of disease in a mouse model of intragastric E. coli O157:H7 inoculation. Intraperitoneal administration of murine IgG on day 3, or both on day 3 and 6, post-inoculation improved survival and decreased intestinal and renal pathology. When given on both day 3 and 6 post-inoculation IgG treatment also improved kidney function in infected mice. Murine and human commercially available IgG preparations bound to proteins in culture filtrates from E. coli O157:H7. Bound proteins were extracted from membranes and peptide sequences were identified by mass spectrometry. The findings showed that murine and human IgG bound to E. coli extracellular serine protease P (EspP) in the culture filtrate, via the IgG Fc domain. These results were confirmed using purified recombinant EspP and comparing culture filtrates from the wild-type E. coli O157:H7 strain to a deletion mutant lacking espP. Culture filtrates from wild-type E. coli O157:H7 exhibited enzymatic activity, specifically associated with the presence of EspP and demonstrated as pepsin cleavage, which was reduced in the presence of murine and human IgG. EspP is a virulence factor previously shown to promote colonic cell injury and the uptake of Shiga toxin by intestinal cells. The results presented here suggest that IgG binds to EspP, blocks its enzymatic activity, and protects the host from E. coli O157:H7 infection, even when given post-inoculation.

Apyrase decreases phage induction and Shiga toxin release from E. coli O157:H7 and has a protective effect during infection.

Shiga toxin (Stx)-producing enterohemorrhagic Escherichia coli (EHEC) cause gastrointestinal infection and, in severe cases, hemolytic uremic syndrome which may lead to death. There is, to-date, no therapy for this infection. Stx induces ATP release from host cells and ATP signaling mediates its cytotoxic effects. Apyrase cleaves and neutralizes ATP and its effect on Stx and EHEC infection was therefore investigated. Apyrase decreased bacterial RecA and dose-dependently decreased toxin release from E. coli O157:H7 in vitro, demonstrated by reduced phage DNA and protein levels. The effect was investigated in a mouse model of E. coli O157:H7 infection. BALB/c mice infected with Stx2-producing E. coli O157:H7 were treated with apyrase intraperitoneally, on days 0 and 2 post-infection, and monitored for 11 days. Apyrase-treated mice developed disease two days later than untreated mice. Untreated infected mice lost significantly more weight than those treated with apyrase. Apyrase-treated mice exhibited less colonic goblet cell depletion and apoptotic cells, as well as lower fecal ATP and Stx2, compared to untreated mice. Apyrase also decreased platelet aggregation induced by co-incubation of human platelet-rich-plasma with Stx2 and E. coli O157 lipopolysaccharide in the presence of collagen. Thus, apyrase had multiple protective effects, reducing RecA levels, stx2 and toxin release from EHEC, reducing fecal Stx2 and protecting mouse intestinal cells, as well as decreasing platelet activation, and could thereby delay the development of disease.

Cross-reactive protection against enterohemorrhagic Escherichia coli infection by enteropathogenic E. coli in a mouse model.

Enteropathogenic Escherichia coli (EPEC) and enterohemorrhagic E. coli (EHEC) are related attaching and effacing (A/E) pathogens. The genes responsible for the A/E pathology are carried on a chromosomal pathogenicity island termed the locus of enterocyte effacement (LEE). Both pathogens share a high degree of homology in the LEE and additional O islands. EHEC prevalence is much lower in areas where EPEC is endemic. This may be due to the development of antibodies against common EPEC and EHEC antigens. This study investigated the hypothesis that EPEC infections may protect against EHEC infections. We used a mouse model to inoculate BALB/c mice intragastrically, first with EPEC and then with EHEC (E. coli O157:H7). Four control groups received either a nonpathogenic E. coli (NPEC) strain followed by EHEC (NPEC/EHEC), phosphate-buffered saline (PBS) followed by EHEC (PBS/EHEC), EPEC/PBS, or PBS/PBS. Mice were monitored for weight loss and symptoms. EPEC colonized the intestine after challenge, and mice developed serum antibodies to intimin and E. coli secreted protein B (encoded in the LEE). Prechallenge with an EPEC strain had a protective effect after EHEC infection, as only a few mice developed mild symptoms, from which they recovered. These mice had an increase in body weight similar to that in control animals, and tissue morphology exhibited mild intestinal changes and normal renal histology. All mice that were not prechallenged with the EPEC strain developed mild to severe symptoms after EHEC infection, with weight loss as well as intestinal and renal histopathological changes. These data suggest that EPEC may protect against EHEC infection in this mouse model.

Annexin Induces Cellular Uptake of Extracellular Vesicles and Delays Disease in Escherichia coli O157:H7 Infection.

Enterohemorrhagic Escherichia coli secrete Shiga toxin and lead to hemolytic uremic syndrome. Patients have high levels of circulating prothrombotic extracellular vesicles (EVs) that expose phosphatidylserine and tissue factor and transfer Shiga toxin from the circulation into the kidney. Annexin A5 (AnxA5) binds to phosphatidylserine, affecting membrane dynamics. This study investigated the effect of anxA5 on EV uptake by human and murine phagocytes and used a mouse model of EHEC infection to study the effect of anxA5 on disease and systemic EV levels. EVs derived from human whole blood or HeLa cells were more readily taken up by THP-1 cells or RAW264.7 cells when the EVs were coated with anxA5. EVs from HeLa cells incubated with RAW264.7 cells induced phosphatidylserine exposure on the cells, suggesting a mechanism by which anxA5-coated EVs can bind to phagocytes before uptake. Mice treated with anxA5 for six days after inoculation with E. coli O157:H7 showed a dose-dependent delay in the development of clinical disease. Treated mice had lower levels of EVs in the circulation. In the presence of anxA5, EVs are taken up by phagocytes and their systemic levels are lower, and, as EVs transfer Shiga toxin to the kidney, this could postpone disease development.

An Artificial Intelligence-based Support Tool for Automation and Standardisation of Gleason Grading in Prostate Biopsies.

BACKGROUND: Gleason grading is the standard diagnostic method for prostate cancer and is essential for determining prognosis and treatment. The dearth of expert pathologists, the inter- and intraobserver variability, as well as the labour intensity of Gleason grading all necessitate the development of a user-friendly tool for robust standardisation. OBJECTIVE: To develop an artificial intelligence (AI) algorithm, based on machine learning and convolutional neural networks, as a tool for improved standardisation in Gleason grading in prostate cancer biopsies. DESIGN, SETTING, AND PARTICIPANTS: A total of 698 prostate biopsy sections from 174 patients were used for training. The training sections were annotated by two senior consultant pathologists. The final algorithm was tested on 37 biopsy sections from 21 patients, with digitised slide images from two different scanners. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: Correlation, sensitivity, and specificity parameters were calculated. RESULTS AND LIMITATIONS: The algorithm shows high accuracy in detecting cancer areas (sensitivity: 100%, specificity: 68%). Compared with the pathologists, the algorithm also performed well in detecting cancer areas (intraclass correlation coefficient [ICC]: 0.99) and assigning the Gleason patterns correctly: Gleason patterns 3 and 4 (ICC: 0.96 and 0.94, respectively), and to a lesser extent, Gleason pattern 5 (ICC: 0.82). Similar results were obtained using two different scanners. CONCLUSIONS: Our AI-based algorithm can reliably detect prostate cancer and quantify the Gleason patterns in core needle biopsies, with similar accuracy as pathologists. The results are reproducible on images from different scanners with a proven low level of intraobserver variability. We believe that this AI tool could be regarded as an efficient and interactive tool for pathologists. PATIENT SUMMARY: We developed a sensitive artificial intelligence tool for prostate biopsies, which detects and grades cancer with similar accuracy to pathologists. This tool holds promise to improve the diagnosis of prostate cancer.

Shiga toxin signals via ATP and its effect is blocked by purinergic receptor antagonism.

Shiga toxin (Stx) is the main virulence factor of enterohemorrhagic Escherichia coli (EHEC), that cause gastrointestinal infection leading to hemolytic uremic syndrome. The aim of this study was to investigate if Stx signals via ATP and if blockade of purinergic receptors could be protective. Stx induced ATP release from HeLa cells and in a mouse model. Toxin induced rapid calcium influx into HeLa cells, as well as platelets, and a P2X1 receptor antagonist, NF449, abolished this effect. Likewise, the P2X antagonist suramin blocked calcium influx in Hela cells. NF449 did not affect toxin intracellular retrograde transport, however, cells pre-treated with NF449 exhibited significantly higher viability after exposure to Stx for 24 hours, compared to untreated cells. NF449 protected HeLa cells from protein synthesis inhibition and from Stx-induced apoptosis, assayed by caspase 3/7 activity. The latter effect was confirmed by P2X1 receptor silencing. Stx induced the release of toxin-positive HeLa cell- and platelet-derived microvesicles, detected by flow cytometry, an effect significantly reduced by NF449 or suramin. Suramin decreased microvesicle levels in mice injected with Stx or inoculated with Stx-producing EHEC. Taken together, we describe a novel mechanism of Stx-mediated cellular injury associated with ATP signaling and inhibited by P2X receptor blockade.

Extracellular vesicles in renal disease.

Extracellular vesicles, such as exosomes and microvesicles, are host cell-derived packages of information that allow cell-cell communication and enable cells to rid themselves of unwanted substances. The release and uptake of extracellular vesicles has important physiological functions and may also contribute to the development and propagation of inflammatory, vascular, malignant, infectious and neurodegenerative diseases. This Review describes the different types of extracellular vesicles, how they are detected and the mechanisms by which they communicate with cells and transfer information. We also describe their physiological functions in cellular interactions, such as in thrombosis, immune modulation, cell proliferation, tissue regeneration and matrix modulation, with an emphasis on renal processes. We discuss how the detection of extracellular vesicles could be utilized as biomarkers of renal disease and how they might contribute to disease processes in the kidney, such as in acute kidney injury, chronic kidney disease, renal transplantation, thrombotic microangiopathies, vasculitides, IgA nephropathy, nephrotic syndrome, urinary tract infection, cystic kidney disease and tubulopathies. Finally, we consider how the release or uptake of extracellular vesicles can be blocked, as well as the associated benefits and risks, and how extracellular vesicles might be used to treat renal diseases by delivering therapeutics to specific cells.