Proteomic Analysis of Urinary Extracellular Vesicles Reveals a Role for the Complement System in Medullary Sponge Kidney Disease

Significant Changes in Low-Abundance Protein Content Detected by Proteomic Analysis of Urine from Patients with Renal Stones After Extracorporeal Shock Wave Lithotripsy

Extracorporeal shock wave lithotripsy (ESWL), although a highly effective method for the treatment of kidney stones, can cause significant kidney damage. Since urinary protein composition directly reflects kidney function, proteomic analysis of this fluid may be useful to identify changes in protein levels induced by patient exposure to ESWL as a sign of kidney damage. To this end, we collected urine samples from 80 patients with nephrolithiasis 2 h before and 24 h after exposure to ESWL, which were concentrated and subsequently processed with a commercially available enrichment method to extract low-abundance urinary proteins. These were then separated by 2D electrophoresis and subsequently analyzed by a proteomic approach. A large number of proteins were identified as being related to inflammatory, fibrotic, and antioxidant processes and changes in the levels of some of them were confirmed by Western blot analysis. Therefore, although further experimental confirmation is needed, our results demonstrate that ESWL significantly influences the low urinary protein profile of patients with nephrolithiasis. Notably, among the identified proteins, matrix metalloproteinase 7, alpha1-antitrypsin, and clusterin, as well as dimethyl arginine dimethyl amino hydrolase 2 and ab-hydrolase, may play an important role as putative biomarkers in the monitoring and management of ESWL-induced renal damage.

Porous perspectives: a comprehensive review of medullary sponge kidney

BACKGROUND AND AIM

Medullary sponge kidney (MSK), a congenital abnormality characterized by dilated collecting ducts in the kidneys, presents with a variable clinical spectrum. This narrative review summarizes the current knowledge on MSK, encompassing its clinical presentation, pathogenesis, recent developments in imaging and laboratory techniques for diagnosis, and the growing understanding of its genetic basis.

RESULTS

Some individuals with MSK may be asymptomatic, others may experience hematuria, renal colic due to kidney stones, recurrent urinary tract infections, and metabolic imbalances. The precise cause of MSK remains unclear, but genetic factors are believed to play a role, with genetic variants identified in genes like GDNF (Glial cell line-derived neurotrophic factor), RET (Rearranged during transfection), and PKHD1 (Polycystic kidney and hepatic disease 1). The diagnosis is based on imaging findings and MSK has no specific treatment.

CONCLUSION

Further research is warranted to improve our understanding of MSK and develop targeted therapies.

Machine learning for the advancement of genome-scale metabolic modeling

Constraint-based modeling (CBM) has evolved as the core systems biology tool to map the interrelations between genotype, phenotype, and external environment. The recent advancement of high-throughput experimental approaches and multi-omics strategies has generated a plethora of new and precise information from wide-ranging biological domains. On the other hand, the continuously growing field of machine learning (ML) and its specialized branch of deep learning (DL) provide essential computational architectures for decoding complex and heterogeneous biological data. In recent years, both multi-omics and ML have assisted in the escalation of CBM. Condition-specific omics data, such as transcriptomics and proteomics, helped contextualize the model prediction while analyzing a particular phenotypic signature. At the same time, the advanced ML tools have eased the model reconstruction and analysis to increase the accuracy and prediction power. However, the development of these multi-disciplinary methodological frameworks mainly occurs independently, which limits the concatenation of biological knowledge from different domains. Hence, we have reviewed the potential of integrating multi-disciplinary tools and strategies from various fields, such as synthetic biology, CBM, omics, and ML, to explore the biochemical phenomenon beyond the conventional biological dogma. How the integrative knowledge of these intersected domains has improved bioengineering and biomedical applications has also been highlighted. We categorically explained the conventional genome-scale metabolic model (GEM) reconstruction tools and their improvement strategies through ML paradigms. Further, the crucial role of ML and DL in omics data restructuring for GEM development has also been briefly discussed. Finally, the case-study-based assessment of the state-of-the-art method for improving biomedical and metabolic engineering strategies has been elaborated. Therefore, this review demonstrates how integrating experimental and in silico strategies can help map the ever-expanding knowledge of biological systems driven by condition-specific cellular information. This multiview approach will elevate the application of ML-based CBM in the biomedical and bioengineering fields for the betterment of society and the environment.

Urinary extracellular vesicles in childhood kidney diseases

Most biological fluids contain extracellular vesicles (EVs). EVs are surrounded by a lipid bilayer and contain biological macromolecules such as proteins, lipids, RNA, and DNA. They lack a functioning nucleus and are incapable of replicating. The physiological characteristics and molecular composition of EVs in body fluids provide valuable information about the status of originating cells. Consequently, they could be effectively utilized for diagnostic and prognostic applications. Urine contains a heterogeneous population of EVs. To date, these urinary extracellular vesicles (uEVs) have been ignored in the standard urinalysis. In recent years, knowledge has accumulated on how uEVs should be separated and analyzed. It has become clear how uEVs reflect the expression of each molecule in cells in nephron segments and how they are altered in disease states such as glomerular/tubular disorders, rare congenital diseases, acute kidney injury (AKI), and chronic kidney disease (CKD). Significant promise exists for the molecular expression signature of uEVs detected by simple techniques such as enzyme-linked immunosorbent assay (ELISA), making them more applicable in clinical settings. This review presents the current understanding regarding uEVs, emphasizing the potential for non-invasive diagnostics, especially for childhood kidney diseases.

Effect of urine alkalization on urinary inflammatory markers in cystinuric patients

Background

Cystinuria is associated with a high prevalence of chronic kidney disease (CKD). We previously described a urinary inflammatory-protein signature (UIS), including 38 upregulated proteins, in cystinuric patients (Cys-patients), compared with healthy controls (HC). This UIS was higher in Cys-patients with CKD. In the present observational study, we aimed to investigate the UIS in Cys-patients without CKD and patients with calcium nephrolithiasis (Lith-patients), versus HC and the effect of urine alkalization on the UIS of Cys-patients.

Methods

UIS was evaluated by nano-liquid chromatography coupled to high-resolution mass spectrometry in adult HC, Lith-patients and non-treated Cys-patients with an estimated glomerular filtration rate >60 mL/min/1.73 m2, and after a 3-month conventional alkalizing treatment in Cys-patients.

Results

Twenty-one Cys-patients [12 men, median age (interquartile range) 30.0 (25.0-44.0) years], 12 Lith-patients [8 men, 46.2 (39.5-54.2) years] and 7 HC [2 men, 43.1 (31.0-53.9) years] were included. Among the 38 proteins upregulated in our previous work, 11 proteins were also upregulated in Cys-patients compared with HC in this study (5 circulating inflammatory proteins and 6 neutrophil-derived proteins). This UIS was also found in some Lith-patients. Using this UIS, we identified two subclusters of Cys-patients (5 with a very high/high UIS and 16 with a moderate/low UIS). In the Cys-patients with very high/high UIS, urine alkalization induced a significant decrease in urinary neutrophil-derived proteins.

Conclusion

A high UIS is present in some Cys-patients without CKD and decreases under alkalizing treatment. This UIS could be a prognostic marker to predict the evolution towards CKD in cystinuria.

Extracellular Vesicles as Source of Biomarkers in Glomerulonephritis

Kidney disease is a global health and healthcare burden. Glomerulonephritis (Gn), both primary and secondary, is generally characterized by an inflammatory glomerular injury and may lead to end-stage renal disease. Kidney biopsy is fundamental to the diagnosis; however, kidney biopsy presents some concerns that may partly hamper the clinical process. Therefore, more accurate diagnostic tools are needed. Extracellular vesicles (EVs) are membranous vesicles released by cells and found in bodily fluids, including urine. EVs mediate intercellular signaling both in health and disease. EVs can have both harmful and cytoprotective effects in kidney diseases, especially Gn. Previous findings reported that the specific cargo of urinary EV contains an aerobic metabolic ability that may either restore the recipient cell metabolism or cause oxidative stress production. Here, we provide an overview of the most recent proteomic findings on the role of EVs in several aspects of glomerulopathies, with a focus on this metabolic and redox potential. Future studies may elucidate how the ability of EVs to interfere with aerobic metabolism and redox status can shed light on aspects of Gn etiology which have remained elusive so far.

Proteomic Approaches and Potential Applications in Autosomal Dominant Polycystic Kidney Disease and Fabry Disease

Although rare, hereditary diseases, such as autosomal dominant polycystic kidney disease (ADPKD) and Fabry disease (FD) may significantly progress towards severe nephropathy. It is crucial to characterize it accurately, predict the course of the illness and estimate treatment effectiveness. A huge effort has been undertaken to find reliable biomarkers that might be useful for an early prevention of the disease progression and/or any invasive diagnostic procedures. The study of proteomics, or the small peptide composition of a sample, is a field of study under continuous development. Over the past years, several strategies have been created to study and define the proteome of samples from widely varying origins. However, urinary proteomics has become essential for discovering novel biomarkers in kidney disease. Here, the extracellular vesicles in human urine that contain cell-specific marker proteins from every segment of the nephron, offer a source of potentially valuable urinary biomarkers, and may play an essential role in kidney development and kidney disease. This review summarizes the relevant literature investigating the proteomic approaches and potential applications in the regular studies of ADPKD and FD.

Urinary proteome of dogs with renal disease secondary to leishmaniosis

Canine leishmaniosis is frequently associated with the development of renal disease. Its pathogenesis is complex and not fully understood. For this reason, this study aimed to describe the urinary proteome, and identify possible new biomarkers in dogs with kidney disease secondary to leishmaniosis. Urine samples were collected from 20 dogs, 5 from healthy dogs, and 15 from stages Leishvet III and IV. Urine samples were analyzed by UHPLC-MS/MS. The data are available via ProteomeXchange with identifier PXD029165. A total of 951 proteins were obtained. After bioinformatic analysis, 93 urinary proteins were altered in the study group. Enrichment analysis performed on these proteins showed an overrepresentation of the complement activation pathway, among others. Finally, 12 discriminant variables were found in dogs with renal disease secondary to leishmaniosis, highlighting C4a anaphylatoxin, apolipoprotein A-I, haptoglobin, leucine-rich alpha-2-glycoprotein 1, and beta-2-microglobulin. This study is the first to describe the urinary proteomics of dogs with renal disease caused by leishmaniosis, and it provides new possible biomarkers for the diagnosis and monitoring of this disease.

Exosomal proteomics in kidney disease: From technical approaches to clinical applications

Exosomes are small extracellular vesicles (sEVs) secreted from cells and have a general diameter ranging from 30-150 nm. It was reported that exosomes have essential roles in intercellular communication and can be targeted as biomarkers of disease or as therapeutic agents. Among the different techniques used for exosome investigation, the mass spectrometry-based proteomics approach has accelerated the unraveling of the molecular composition of exosomes and has contributed to improved knowledge of molecular processes in various diseases. In this review, we focused on proteomics-based studies of exosomes and clinical applications in kidney diseases. A general introduction of exosomes, isolation and characterization techniques, and proteomics-based study workflows are included in this article. We also categorized applications in acute kidney injury, chronic kidney disease, renal transplantation, congenital kidney disease, and malignant kidney disorder to show the important findings from proteomics-based exosomal investigations.

Urinary proteomics for kidney dysfunction: insights and trends

Introduction: Kidney dysfunction poses a high burden on patients and health care systems. Early detection and accurate prediction of kidney disease progression remains a major challenge. Compared to existing clinical parameters, urinary proteomics has the potential to reveal molecular alterations within the kidney that may alter its function before the onset of clinical symptoms. Thus, urinary proteomics has greater prognostic potential for assessment of kidney dysfunction progression.Areas covered: Advances in urinary proteomics for major causes of kidney dysfunction are discussed. The application of urinary extracellular vesicles for studying kidney dysfunction are discussed. Technological advances in urinary proteomics are discussed. The literature was identified using a database search for titles containing 'proteom*' and 'urin*' and published within the past 5 years. Retrieved literature was manually filtered to retain kidney dysfunctions-related studies.Expert opinion: Despite major advances, diagnosis by urinary proteomics has not been fully applied in any clinical settings. This could be attributed to the complex nature of kidney diseases, in addition to the constraints on study power and feasibility of incorporating mass spectrometry techniques in daily routine analysis. Nevertheless, we are confident that advances in urinary proteomics will soon provide superior insights into kidney disease beyond existing clinical parameters.

Sphingomyelin and Medullary Sponge Kidney Disease: A Biological Link Identified by Omics Approach

Background: Molecular biology has recently added new insights into the comprehension of the physiopathology of the medullary sponge kidney disease (MSK), a rare kidney malformation featuring nephrocalcinosis and recurrent renal stones. Pathogenesis and metabolic alterations associated to this disorder have been only partially elucidated.

Methods: Plasma and urine samples were collected from 15 MSK patients and 15 controls affected by idiopathic calcium nephrolithiasis (ICN). Plasma metabolomic profile of 7 MSK and 8 ICN patients was performed by liquid chromatography combined with electrospray ionization tandem mass spectrometry (UHPLC–ESI-MS/MS). Subsequently, we reinterrogated proteomic raw data previously obtained from urinary microvesicles of MSK and ICN focusing on proteins associated with sphingomyelin metabolism. Omics results were validated by ELISA in the entire patients' cohort.

Results: Thirteen metabolites were able to discriminate MSK from ICN (7 increased and 6 decreased in MSK vs. ICN). Sphingomyelin reached the top level of discrimination between the two study groups (FC: −1.8, p < 0.001). Ectonucleotide pyrophophatase phosphodiesterase 6 (ENPP6) and osteopontin (SPP1) resulted the most significant deregulated urinary proteins in MSK vs. ICN (p < 0.001). ENPP6 resulted up-regulated also in plasma of MSK by ELISA.

Conclusion: Our data revealed a specific high-throughput metabolomics signature of MSK and indicated a pivotal biological role of sphingomyelin in this disease.

Analysis of urinary exosomes applications for rare kidney disorders

Introduction: Exosomes are nanovesicles that play important functions in a variety of physiological and pathological conditions. They are powerful cell-to-cell communication tool thanks to the protein, mRNA, miRNA, and lipid cargoes they carry. They are also emerging as valuable diagnostic and prognostic biomarker sources. Urinary exosomes carry information from all the cells of the urinary tract, downstream of the podocyte. Rare kidney diseases are a subset of an inherited diseases whose genetic diagnosis can be unclear, and presentation can vary due to genetic, epigenetic, and environmental factors. Areas covered: In this review, we focus on a group of rare and often neglected kidney diseases, for which we have sufficient available literature data on urinary exosomes. The analysis of their content can help to comprehend pathological mechanisms and to identify biomarkers for diagnosis, prognosis, and therapeutic targets. Expert opinion: The foreseeable large-scale application of system biology approach to the profiling of exosomal proteins as a source of renal disease biomarkers will be also useful to stratify patients with rare kidney diseases whose penetrance, phenotypic presentation, and age of onset vary sensibly. This can ameliorate the clinical management.

Comparison of Conventional Statistical Methods with Machine Learning in Medicine: Diagnosis, Drug Development, and Treatment

Futurists have anticipated that novel autonomous technologies, embedded with machine learning (ML), will substantially influence healthcare. ML is focused on making predictions as accurate as possible, while traditional statistical models are aimed at inferring relationships between variables. The benefits of ML comprise flexibility and scalability compared with conventional statistical approaches, which makes it deployable for several tasks, such as diagnosis and classification, and survival predictions. However, much of ML-based analysis remains scattered, lacking a cohesive structure. There is a need to evaluate and compare the performance of well-developed conventional statistical methods and ML on patient outcomes, such as survival, response to treatment, and patient-reported outcomes (PROs). In this article, we compare the usefulness and limitations of traditional statistical methods and ML, when applied to the medical field. Traditional statistical methods seem to be more useful when the number of cases largely exceeds the number of variables under study and a priori knowledge on the topic under study is substantial such as in public health. ML could be more suited in highly innovative fields with a huge bulk of data, such as omics, radiodiagnostics, drug development, and personalized treatment. Integration of the two approaches should be preferred over a unidirectional choice of either approach.

Exosomes in Nephropathies: A Rich Source of Novel Biomarkers

The biomarkers commonly utilized in diagnostic evaluations of kidney disease suffer from low sensitivity, especially in the early stages of renal damage. On the other hand, obtaining a renal biopsy to augment clinical decision making can lead to potentially serious complications. In order to overcome the shortcomings of currently available diagnostic tools, recent studies suggest that exosomes, cell-secreted extracellular vesicles containing a large array of active molecules to facilitate cell-to-cell communication, may represent a rich source of novel disease biomarkers. Because of their endocytic origin, exosomes carry markers typical for their parent cells, which could permit the localization of biochemical cellular alterations in specific kidney compartments. Different types of exosomes can be isolated from noninvasively obtained biofluids; however, in the context of kidney disease, evidence has emerged on the role of urinary exosomes in the diagnostic and predictive modeling of renal pathology. The current review summarizes the potential application of exosomes in the detection of acute and chronic inflammatory, metabolic, degenerative, and genetic renal diseases.

Computational advances of tumor marker selection and sample classification in cancer proteomics

Cancer proteomics has become a powerful technique for characterizing the protein markers driving transformation of malignancy, tracing proteome variation triggered by therapeutics, and discovering the novel targets and drugs for the treatment of oncologic diseases. To facilitate cancer diagnosis/prognosis and accelerate drug target discovery, a variety of methods for tumor marker identification and sample classification have been developed and successfully applied to cancer proteomic studies. This review article describes the most recent advances in those various approaches together with their current applications in cancer-related studies. Firstly, a number of popular feature selection methods are overviewed with objective evaluation on their advantages and disadvantages. Secondly, these methods are grouped into three major classes based on their underlying algorithms. Finally, a variety of sample separation algorithms are discussed. This review provides a comprehensive overview of the advances on tumor maker identification and patients/samples/tissues separations, which could be guidance to the researches in cancer proteomics.