Conformational ensembles of the human intrinsically disordered proteome.

Intrinsically disordered proteins and regions (collectively, IDRs) are pervasive across proteomes in all kingdoms of life, help to shape biological functions and are involved in numerous diseases. IDRs populate a diverse set of transiently formed structures and defy conventional sequence-structure-function relationships1. Developments in protein science have made it possible to predict the three-dimensional structures of folded proteins at the proteome scale2. By contrast, there is a lack of knowledge about the conformational properties of IDRs, partly because the sequences of disordered proteins are poorly conserved and also because only a few of these proteins have been characterized experimentally. The inability to predict structural properties of IDRs across the proteome has limited our understanding of the functional roles of IDRs and how evolution shapes them. As a supplement to previous structural studies of individual IDRs3, we developed an efficient molecular model to generate conformational ensembles of IDRs and thereby to predict their conformational properties from sequences4,5. Here we use this model to simulate nearly all of the IDRs in the human proteome. Examining conformational ensembles of 28,058 IDRs, we show how chain compaction is correlated with cellular function and localization. We provide insights into how sequence features relate to chain compaction and, using a machine-learning model trained on our simulation data, show the conservation of conformational properties across orthologues. Our results recapitulate observations from previous studies of individual protein systems and exemplify how to link-at the proteome scale-conformational ensembles with cellular function and localization, amino acid sequence, evolutionary conservation and disease variants. Our freely available database of conformational properties will encourage further experimental investigation and enable the generation of hypotheses about the biological roles and evolution of IDRs.

A Bayesian nonparametric approach to correct for underreporting in count data.

We propose a nonparametric compound Poisson model for underreported count data that introduces a latent clustering structure for the reporting probabilities. The latter are estimated with the model's parameters based on experts' opinion and exploiting a proxy for the reporting process. The proposed model is used to estimate the prevalence of chronic kidney disease in Apulia, Italy, based on a unique statistical database covering information on m = 258 municipalities obtained by integrating multisource register information. Accurate prevalence estimates are needed for monitoring, surveillance, and management purposes; yet, counts are deemed to be considerably underreported, especially in some areas of Apulia, one of the most deprived and heterogeneous regions in Italy. Our results agree with previous findings and highlight interesting geographical patterns of the disease. We compare our model to existing approaches in the literature using simulated as well as real data on early neonatal mortality risk in Brazil, described in previous research: the proposed approach proves to be accurate and particularly suitable when partial information about data quality is available.

Assessment and management of heart failure in patients with chronic kidney disease.

Heart failure (HF) and chronic kidney disease (CKD) are two pathological conditions with a high prevalence in the general population. When they coexist in the same patient, a strict interplay between them is observed, such that patients affected require a clinical multidisciplinary and personalized management. The diagnosis of HF and CKD relies on signs and symptoms of the patient but several additional tools, such as blood-based biomarkers and imaging techniques, are needed to clarify and discriminate the main characteristics of these diseases. Improved survival due to new recommended drugs in HF has increasingly challenged physicians to manage patients with multiple diseases, especially in case of CKD. However, the safe administration of these drugs in patients with HF and CKD is often challenging. Knowing up to which values ​​of creatinine or renal clearance each drug can be administered is fundamental. With this review we sought to give an insight on this sizable and complex topic, in order to get clearer ideas and a more precise reference about the diagnostic assessment and therapeutic management of HF and CKD.

Updates on C3 Glomerulopathy in Kidney Transplantation: Pathogenesis and Treatment Options.

C3 glomerulopathy is a rare disease, characterized by an abnormal activation of the complement's alternative pathway that leads to the accumulation of the C3 component in the kidney. The disease recurs in more than half of kidney transplant recipients, with a significant impact on graft survival. Recurrence of the primary disease represents the second cause of graft loss after organ rejection. In C3 glomerulopathy, there are several risk factors which can promote a recurrence during transplantation, such as delayed graft function, infection and monoclonal gammopathy. All these events can trigger the alternative complement pathway. In this review, we summarize the impact of C3 glomerulopathy on kidney grafts and present the latest treatment options. The most widely used treatments for the disease include corticosteroids and mycophenolate mofetil, which are already used chronically by kidney transplant recipients; thus, additional treatments for C3 glomerulopathy are required. Currently, several studies using anti-complement drugs (i.e., eculizumab, Ravalizumab, avacopan) for C3 glomerulopathy in kidney transplant patients are ongoing with encouraging results.

Assessment of models for calculating the hydrodynamic radius of intrinsically disordered proteins.

Diffusion measurements by pulsed-field gradient NMR and fluorescence correlation spectroscopy can be used to probe the hydrodynamic radius of proteins, which contains information about the overall dimension of a protein in solution. The comparison of this value with structural models of intrinsically disordered proteins is nonetheless impaired by the uncertainty of the accuracy of the methods for computing the hydrodynamic radius from atomic coordinates. To tackle this issue, we here build conformational ensembles of 11 intrinsically disordered proteins that we ensure are in agreement with measurements of compaction by small-angle x-ray scattering. We then use these ensembles to identify the forward model that more closely fits the radii derived from pulsed-field gradient NMR diffusion experiments. Of the models we examined, we find that the Kirkwood-Riseman equation provides the best description of the hydrodynamic radius probed by pulsed-field gradient NMR experiments. While some minor discrepancies remain, our results enable better use of measurements of the hydrodynamic radius in integrative modeling and for force field benchmarking and parameterization.

Gut-Derived Uremic Toxins in CKD: An Improved Approach for the Evaluation of Serum Indoxyl Sulfate in Clinical Practice.

In the past years, indoxyl sulfate has been strongly implicated in kidney disease progression and contributed to cardiovascular morbidity. Moreover, as a result of its elevated albumin affinity rate, indoxyl sulfate is not adequately cleared by extracorporeal therapies. Within this scenario, although LC-MS/MS represents the conventional approach for IS quantification, it requires dedicated equipment and expert skills and does not allow real-time analysis. In this pilot study, we implemented a fast and simple technology designed to determine serum indoxyl sulfate levels that can be integrated into clinical practice. Indoxyl sulfate was detected at the time of enrollment by Tandem MS from 25 HD patients and 20 healthy volunteers. Next, we used a derivatization reaction to transform the serum indoxyl sulfate into Indigo blue. Thanks to the spectral shift to blue, its quantity was measured by the colorimetric assay at a wavelength of 420-450 nm. The spectrophotometric analysis was able to discriminate the levels of IS between healthy subjects and HD patients corresponding to the LC-MS/MS. In addition, we found a strong linear relationship between indoxyl sulfate levels and Indigo levels between the two methods (Tandem MS and spectrophotometry). This innovative method in the assessment of gut-derived indoxyl sulfate could represent a valid tool for clinicians to monitor CKD progression and dialysis efficacy.

Underlying Genetics of aHUS: Which Connection with Outcome and Treatment Discontinuation?

Atypical hemolytic uremic syndrome (aHUS) is a rare disease caused by a genetic dysregulation of the alternative complement pathway, characterized by thrombocytopenia, hemolytic anemia, and acute kidney injury, and included in the group of thrombotic microangiopathies. With the introduction of humanized monoclonal antibodies that inhibit C5 activation, the natural history of aHUS completely changed, with a better prognosis, a quick recovery of renal function, and a significant reduction of end-stage renal disease incidence. Nowadays, there is an increasing interest in the molecular and genetic bases of this severe disease. The aim of this narrative review is to provide readers with a practical guide about different possible involved genes, elucidating the specific role of each transcribed protein in the pathogenesis of aHUS. Moreover, we analyzed the main current evidence about the relationship among genetic mutations, outcomes, and the risk of recurrence of this manifold disease.

Use of official municipal demographics for the estimation of mortality in cities suffering from heavy environmental pollution: Results of the first study on all the neighborhoods of Taranto from 2011 to 2020.

BACKGROUND: In cities suffering from heavy environmental pressure or pollution, it is extremely important to rapidly access municipal demographics that can be used as indicators of population health status. Among those, mortality rates represent the most reliable data as they are officially retained and available to municipality with high level of details, thus allowing epidemiological comparison between different neighborhoods of the city across several years. Our study was aimed at validating and propose as universally applicable approach the use of municipal demographics as first-line tool to rapidly assess population health and drive health policies or urban planning in cities characterized by heavy environmental pressure. The case study of Taranto has been chosen due to the presence of the biggest European steel plant since 1960s resulting in heavy burden on environment and population health. METHODS: We have performed an ecological study on general mortality data due to all causes, specific by gender, age groups and disaggregated at sub-municipal level (highest data granularity) into neighborhoods from 2011 to 2020 by using official demographics related to all people living in Taranto available at General Registry Office of the municipality. A preliminary analysis comparing data available at Municipality and those provided by the Italian National Institute of Statistics (ISTAT) was performed and confirmed the high level of reliability of the municipal source of data. For comparative analyses, we used Regional demographics and mortality from ISTAT. Indirect age-standardized mortality ratios (SMR; CI 90% and 95%), specific for gender and neighborhoods, were calculated in reference to the city of Taranto and Apulia Region; direct age-standardized and neighborhoods mortality rates were computed on city population. RESULTS: The city of Taranto shows relevant inequalities in terms of mortality between the northern neighborhoods, closest to the industrial area (Paolo VI, Tamburi and Città Vecchia-Borgo), with excess mortality highlighted across 10 years described by SMRs always higher than those of the entire Apulia region, with peaks exceeding 50% between 2015 and 2017 both in women and men. The significant excesses of mortality have increased from 2011 to 2020 and progressively extended across several neighborhoods of Taranto city. Compared to the Apulia region, in the 3 Northern neighborhoods of the city (Paolo VI, Tamburi and Città Vecchia-Borgo) a total of 1020 excess deaths were recorded from 2011 to 2019 in both males and females (showing statistical significance), with a peak of 68% mortality excess in 2019 for men living in Paolo VI district. CONCLUSION: The use of official mortality data allows a timely, reliable and costless assessment of population health in cities heavily impacted by environmental pollution like Taranto.

Refining conformational ensembles of flexible proteins against small-angle x-ray scattering data.

Intrinsically disordered proteins and flexible regions in multidomain proteins display substantial conformational heterogeneity. Characterizing the conformational ensembles of these proteins in solution typically requires combining one or more biophysical techniques with computational modeling or simulations. Experimental data can either be used to assess the accuracy of a computational model or to refine the computational model to get a better agreement with the experimental data. In both cases, one generally needs a so-called forward model (i.e., an algorithm to calculate experimental observables from individual conformations or ensembles). In many cases, this involves one or more parameters that need to be set, and it is not always trivial to determine the optimal values or to understand the impact on the choice of parameters. For example, in the case of small-angle x-ray scattering (SAXS) experiments, many forward models include parameters that describe the contribution of the hydration layer and displaced solvent to the background-subtracted experimental data. Often, one also needs to fit a scale factor and a constant background for the SAXS data but across the entire ensemble. Here, we present a protocol to dissect the effect of the free parameters on the calculated SAXS intensities and to identify a reliable set of values. We have implemented this procedure in our Bayesian/maximum entropy framework for ensemble refinement and demonstrate the results on four intrinsically disordered proteins and a protein with three domains connected by flexible linkers. Our results show that the resulting ensembles can depend on the parameters used for solvent effects and suggest that these should be chosen carefully. We also find a set of parameters that work robustly across all proteins.

DelCFHR3-1 influences graft survival in transplant patients with IgA nephropathy via complement-mediated cellular senescence.

IgA nephropathy (IgAN) is a frequent cause of chronic kidney disease (CKD) and progressive renal impairment. A native renal biopsy diagnosis of IgAN is a predictor of graft loss, with a relative risk of 47% but it is difficult to predict graft survival and progressive allograft dysfunction in these patients. Deletion of complement factor H-related genes 1 and 3 (delCFHR3-1) has been associated with a decreased risk of developing IgAN on native kidneys, but the impact on the graft in IgAN-transplanted patients is unknown. We hypothesized that delCFHR3-1 is also associated with the processes that influence graft survival in transplant recipients with IgAN and tested whether cellular senescence is involved in mediating graft damage. We found that patients carrying two copies of CFHR1-3 had a worse outcome (P = .000321) and presented increased FHR1 deposits at glomerular and tubulointerstitial level associated with higher expression of the senescence marker p16INK4a (P = .001) and tubulointerstitial fibrosis (P = .005). Interestingly, FHR1 deposits were associated with increased complement activation as demonstrated by C5b-9 deposits. These data support both the role of FHR1 in mediating complement activation and tubular senescence, and suggest the possibility of genotyping delCFHR3-1 to predict graft survival in IgAN-transplanted patients.

High levels of gut-homing immunoglobulin A+ B lymphocytes support the pathogenic role of intestinal mucosal hyperresponsiveness in immunoglobulin A nephropathy patients.

BACKGROUND: Immunoglobulin A nephropathy (IgAN) is the most frequent primary glomerulonephritis. The role of the microbiota and mucosal immunity in the pathogenesis of IgAN remains a key element. To date, the hypothetical relationship between commensal bacteria, elevated tumour necrosis factor (TNF) superfamily member 13 [also known as B-cell activating factor (BAFF)] levels, perturbed homoeostasis of intestinal-activated B cells and intestinal IgA class switch has not been clearly shown in IgAN patients. METHODS: We studied the intestinal-renal axis connections, analysing levels of BAFF, TNF ligand superfamily member 13 (APRIL) and intestinal-activated B cells in IgAN patients, healthy subjects (HSs) and patients with non-IgA glomerulonephritides. RESULTS: IgAN patients had increased serum levels of BAFF cytokine, correlating with higher amounts of five specific microbiota metabolites, and high APRIL cytokine serum levels. We also found that subjects with IgAN have a higher level of circulating gut-homing (CCR9+ ß7 integrin+) regultory B cells, memory B cells and IgA+ memory B cells compared with HSs. Finally, we found that IgAN patients had high levels of both total plasmablasts (PBs) and intestinal-homing PBs. Interestingly, PBs significantly increased in IgAN but not in patients with other glomerulonephritides. CONCLUSIONS: Our results demonstrate a significant difference in the amount of intestinal-activated B lymphocytes between IgAN patients and HSs, confirming the hypothesis of the pathogenic role of intestinal mucosal hyperresponsiveness in IgAN. The intestinal-renal axis plays a crucial role in IgAN and several factors may contribute to its complex pathogenesis and provide an important area of research for novel targeted therapies to modulate progression of the disease.

A neural network for glomerulus classification based on histological images of kidney biopsy.

BACKGROUND: Computer-aided diagnosis (CAD) systems based on medical images could support physicians in the decision-making process. During the last decades, researchers have proposed CAD systems in several medical domains achieving promising results. CAD systems play an important role in digital pathology supporting pathologists in analyzing biopsy slides by means of standardized and objective workflows. In the proposed work, we designed and tested a novel CAD system module based on image processing techniques and machine learning, whose objective was to classify the condition affecting renal corpuscles (glomeruli) between sclerotic and non-sclerotic. Such discrimination is useful for the biopsy slides evaluation performed by pathologists. RESULTS: We collected 26 digital slides taken from the kidneys of 19 donors with Periodic Acid-Schiff staining. Expert pathologists have conducted the slides preparation, digital acquisition and glomeruli annotations. Before setting the classifiers, we evaluated several feature extraction techniques from the annotated regions. Then, a feature reduction procedure followed by a shallow artificial neural network allowed discriminating between the glomeruli classes. We evaluated the workflow considering an independent dataset (i.e., processing images not used in the training procedure). Ten independent runs of the training algorithm, and evaluation, allowed achieving MCC and Accuracy of 0.95 (± 0.01) and 0.99 (standard deviation < 0.00), respectively. We also obtained good precision (0.9844 ± 0.0111) and recall (0.9310 ± 0.0153). CONCLUSIONS: Results on the test set confirm that the proposed workflow is consistent and reliable for the investigated domain, and it can support the clinical practice of discriminating the two classes of glomeruli. Analyses on misclassifications show that the involved images are usually affected by staining artefacts or present partial sections due to slice preparation and staining processes. In clinical practice, however, pathologists discard images showing such artefacts.

Predictive Machine Learning Models and Survival Analysis for COVID-19 Prognosis Based on Hematochemical Parameters.

The coronavirus disease 2019 (COVID-19) pandemic has affected hundreds of millions of individuals and caused millions of deaths worldwide. Predicting the clinical course of the disease is of pivotal importance to manage patients. Several studies have found hematochemical alterations in COVID-19 patients, such as inflammatory markers. We retrospectively analyzed the anamnestic data and laboratory parameters of 303 patients diagnosed with COVID-19 who were admitted to the Polyclinic Hospital of Bari during the first phase of the COVID-19 global pandemic. After the pre-processing phase, we performed a survival analysis with Kaplan-Meier curves and Cox Regression, with the aim to discover the most unfavorable predictors. The target outcomes were mortality or admission to the intensive care unit (ICU). Different machine learning models were also compared to realize a robust classifier relying on a low number of strongly significant factors to estimate the risk of death or admission to ICU. From the survival analysis, it emerged that the most significant laboratory parameters for both outcomes was C-reactive protein min; HR=17.963 (95% CI 6.548-49.277, p < 0.001) for death, HR=1.789 (95% CI 1.000-3.200, p = 0.050) for admission to ICU. The second most important parameter was Erythrocytes max; HR=1.765 (95% CI 1.141-2.729, p < 0.05) for death, HR=1.481 (95% CI 0.895-2.452, p = 0.127) for admission to ICU. The best model for predicting the risk of death was the decision tree, which resulted in ROC-AUC of 89.66%, whereas the best model for predicting the admission to ICU was support vector machine, which had ROC-AUC of 95.07%. The hematochemical predictors identified in this study can be utilized as a strong prognostic signature to characterize the severity of the disease in COVID-19 patients.

Inhibition of Lysine 63 Ubiquitination Prevents the Progression of Renal Fibrosis in Diabetic DBA/2J Mice.

Diabetic nephropathy (DN) is the most frequent cause of end-stage renal disease. Tubulointerstitial accumulation of lysine 63 (K63)-ubiquitinated (Ub) proteins is involved in the progression of DN fibrosis and correlates with urinary miR-27b-3p downregulation. We explored the renoprotective effect of an inhibitor of K63-Ub (NSC697923), alone or in combination with the ACE-inhibitor ramipril, in vitro and in vivo. Proximal tubular epithelial cells and diabetic DBA/2J mice were treated with NSC697923 and/or ramipril. K63-Ub protein accumulation along with α-SMA, collagen I and III, FSP-1, vimentin, p16INK4A expression, SA-α Gal staining, Sirius Red, and PAS staining were measured. Finally, we measured the urinary albumin to creatinine ratio (uACR), and urinary miR-27b-3p expression in mice. NSC697923, both alone and in association with ramipril, in vitro and in vivo inhibited hyperglycemia-induced epithelial to mesenchymal transition by significantly reducing K63-Ub proteins, α-SMA, collagen I, vimentin, FSP-1 expression, and collagen III along with tubulointerstitial and glomerular fibrosis. Treated mice also showed recovery of urinary miR-27b-3p and restored expression of p16INK4A. Moreover, NSC697923 in combination with ramipril demonstrated a trend in the reduction of uACR. In conclusion, we suggest that selective inhibition of K63-Ub, when combined with the conventional treatment with ACE inhibitors, might represent a novel treatment strategy to prevent the progression of fibrosis and proteinuria in diabetic nephropathy and we propose miR-27b-3p as a biomarker of treatment efficacy.

A comparison between two semantic deep learning frameworks for the autosomal dominant polycystic kidney disease segmentation based on magnetic resonance images.

BACKGROUND: The automatic segmentation of kidneys in medical images is not a trivial task when the subjects undergoing the medical examination are affected by Autosomal Dominant Polycystic Kidney Disease (ADPKD). Several works dealing with the segmentation of Computed Tomography images from pathological subjects were proposed, showing high invasiveness of the examination or requiring interaction by the user for performing the segmentation of the images. In this work, we propose a fully-automated approach for the segmentation of Magnetic Resonance images, both reducing the invasiveness of the acquisition device and not requiring any interaction by the users for the segmentation of the images. METHODS: Two different approaches are proposed based on Deep Learning architectures using Convolutional Neural Networks (CNN) for the semantic segmentation of images, without needing to extract any hand-crafted features. In details, the first approach performs the automatic segmentation of images without any procedure for pre-processing the input. Conversely, the second approach performs a two-steps classification strategy: a first CNN automatically detects Regions Of Interest (ROIs); a subsequent classifier performs the semantic segmentation on the ROIs previously extracted. RESULTS: Results show that even though the detection of ROIs shows an overall high number of false positives, the subsequent semantic segmentation on the extracted ROIs allows achieving high performance in terms of mean Accuracy. However, the segmentation of the entire images input to the network remains the most accurate and reliable approach showing better performance than the previous approach. CONCLUSION: The obtained results show that both the investigated approaches are reliable for the semantic segmentation of polycystic kidneys since both the strategies reach an Accuracy higher than 85%. Also, both the investigated methodologies show performances comparable and consistent with other approaches found in literature working on images from different sources, reducing both the invasiveness of the analyses and the interaction needed by the users for performing the segmentation task.

A New Vision of IgA Nephropathy: The Missing Link.

IgA Nephropathy (IgAN) is a primary glomerulonephritis problem worldwide that develops mainly in the 2nd and 3rd decade of life and reaches end-stage kidney disease after 20 years from the biopsy-proven diagnosis, implying a great socio-economic burden. IgAN may occur in a sporadic or familial form. Studies on familial IgAN have shown that 66% of asymptomatic relatives carry immunological defects such as high IgA serum levels, abnormal spontaneous in vitro production of IgA from peripheral blood mononuclear cells (PBMCs), high serum levels of aberrantly glycosylated IgA1, and an altered PBMC cytokine production profile. Recent findings led us to focus our attention on a new perspective to study the pathogenesis of this disease, and new studies showed the involvement of factors driven by environment, lifestyle or diet that could affect the disease. In this review, we describe the results of studies carried out in IgAN patients derived from genomic and epigenomic studies. Moreover, we discuss the role of the microbiome in the disease. Finally, we suggest a new vision to consider IgA Nephropathy as a disease that is not disconnected from the environment in which we live but influenced, in addition to the genetic background, also by other environmental and behavioral factors that could be useful for developing precision nephrology and personalized therapy.

The nephrologist of tomorrow: towards a kidney-omic future.

Omics refers to the collective technologies used to explore the roles and relationships of the various types of molecules that make up the phenotype of an organism. Systems biology is a scientific discipline that endeavours to quantify all of the molecular elements of a biological system. Therefore, it reflects the knowledge acquired by omics in a meaningful manner by providing insights into functional pathways and regulatory networks underlying different diseases. The recent advances in biotechnological platforms and statistical tools to analyse such complex data have enabled scientists to connect the experimentally observed correlations to the underlying biochemical and pathological processes. We discuss in this review the current knowledge of different omics technologies in kidney diseases, specifically in the field of pediatric nephrology, including biomarker discovery, defining as yet unrecognized biologic therapeutic targets and linking omics to relevant standard indices and clinical outcomes. We also provide here a unique perspective on the field, taking advantage of the experience gained by the large-scale European research initiative called "Systems Biology towards Novel Chronic Kidney Disease Diagnosis and Treatment" (SysKid). Based on the integrative framework of Systems biology, SysKid demonstrated how omics are powerful yet complex tools to unravel the consequences of diabetes and hypertension on kidney function.

In a retrospective international study, circulating miR-148b and let-7b were found to be serum markers for detecting primary IgA nephropathy.

Immunoglobulin A nephropathy (IgAN) is a worldwide disease characterized by the presence of galactose-deficient IgA1 deposits in the glomerular mesangium. A kidney biopsy for diagnosis is required. Here, we measured two miRNAs (let-7b and miR-148b), previously identified as regulators of the O-glycosylation process of IgA1, in serum samples from patients with IgAN and healthy blood donors (controls) recruited in an international multicenter study. Two predictive models, based on these miRNAs, were developed and the diagnostic accuracy of the combined biomarkers was assessed by the area under the receiver operating characteristic (ROC) curve (AUC) carried out in three steps. In a training study, the combined miRNAs were able to discriminate between 100 patients with IgAN and 119 controls (AUC, 0.82). A validation study confirmed the model in an independent cohort of 145 patients with IgAN and 64 controls (AUC, 0.78). Finally, in a test study, the combined biomarkers were able to discriminate patients with IgAN from 105 patients affected by other forms of primary glomerulonephritis, supporting the specificity (AUC, 0.76). Using the same study design, we also performed two subgroup analyses (one for Caucasians and one for East Asians) and found that race-specific models were the best fit to distinguish IgAN patients from controls. Thus, serum levels of the combined miRNA biomarker, let-7b and miR-148b, appears to be a novel, reliable, and noninvasive test to predict the probability of having IgAN.