Proteomic-based research strategy identified laminin subunit alpha 2 as a potential urinary-specific biomarker for the medullary sponge kidney disease

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.

Recent progress in mass spectrometry-based urinary proteomics

Serum or plasma is frequently utilized in biomedical research; however, its application is impeded by the requirement for invasive sample collection. The non-invasive nature of urine collection makes it an attractive alternative for disease characterization and biomarker discovery. Mass spectrometry-based protein profiling of urine has led to the discovery of several disease-associated biomarkers. Proteomic analysis of urine has not only been applied to disorders of the kidney and urinary bladder but also to conditions affecting distant organs because proteins excreted in the urine originate from multiple organs. This review provides a progress update on urinary proteomics carried out over the past decade. Studies summarized in this review have expanded the catalog of proteins detected in the urine in a variety of clinical conditions. The wide range of applications of urine analysis-from characterizing diseases to discovering predictive, diagnostic and prognostic markers-continues to drive investigations of the urinary proteome.

Ultrasound Patterns and Disease Progression in Medullary Sponge Kidney in Adults

Our paper presents the ultrasound (US) patterns of a rare kidney disease-medullary sponge kidney (MSK)-that have not been described before in comparison with other causes of medullary hyperechogenicity and correlates them with the severity of the disease and prognosis. This is a clinical observational study of all US examinations in the Nephrology Department over a period of 6 years. The abdominal US focused on the kidneys was recorded. US characteristics of the medulla and cortex were analyzed. We found 10 patients with characteristic daisy flower (DF) kidneys. Positive diagnosis in association with other renal risk factors, prognosis, and evolution were evaluated. Two patterns of medullary hyperechogenicity were found and were correlated with disease severity and kidney function. The first pattern is a homogenous echogenicity of the medulla described as a "daisy-like" appearance. The second pattern: calcifications associated with medullar echogenicity, stone production, nephrocalcinosis, and impaired kidney function: "atypical daisy-like." Medullary hyperechogenicity can have more US patterns. In MSK, if the medullary echogenicity is homogenous the evolution is benign, whereas the second, inhomogeneous pattern, has a variable clinical presentation with nephrocalcinosis and the outcome is more severe, leading to chronic kidney disease and impairing the quality of life.

The Need for Artificial Intelligence Based Risk Factor Analysis for Age-Related Macular Degeneration: A Review

In epidemiology, a risk factor is a variable associated with increased disease risk. Understanding the role of risk factors is significant for developing a strategy to improve global health. There is strong evidence that risk factors like smoking, alcohol consumption, previous cataract surgery, age, high-density lipoprotein (HDL) cholesterol, BMI, female gender, and focal hyper-pigmentation are independently associated with age-related macular degeneration (AMD). Currently, in the literature, statistical techniques like logistic regression, multivariable logistic regression, etc., are being used to identify AMD risk factors by employing numerical/categorical data. However, artificial intelligence (AI) techniques have not been used so far in the literature for identifying risk factors for AMD. On the other hand, artificial intelligence (AI) based tools can anticipate when a person is at risk of developing chronic diseases like cancer, dementia, asthma, etc., in providing personalized care. AI-based techniques can employ numerical/categorical and/or image data thus resulting in multimodal data analysis, which provides the need for AI-based tools to be used for risk factor analysis in ophthalmology. This review summarizes the statistical techniques used to identify various risk factors and the higher benefits that AI techniques provide for AMD-related disease prediction. Additional studies are required to review different techniques for risk factor identification for other ophthalmic diseases like glaucoma, diabetic macular edema, retinopathy of prematurity, cataract, and diabetic retinopathy.

Machine learning for morbid glomerular hypertrophy

A practical research method integrating data-driven machine learning with conventional model-driven statistics is sought after in medicine. Although glomerular hypertrophy (or a large renal corpuscle) on renal biopsy has pathophysiological implications, it is often misdiagnosed as adaptive/compensatory hypertrophy. Using a generative machine learning method, we aimed to explore the factors associated with a maximal glomerular diameter of ≥ 242.3 μm. Using the frequency-of-usage variable ranking in generative models, we defined the machine learning scores with symbolic regression via genetic programming (SR via GP). We compared important variables selected by SR with those selected by a point-biserial correlation coefficient using multivariable logistic and linear regressions to validate discriminatory ability, goodness-of-fit, and collinearity. Body mass index, complement component C3, serum total protein, arteriolosclerosis, C-reactive protein, and the Oxford E1 score were ranked among the top 10 variables with high machine learning scores using SR via GP, while the estimated glomerular filtration rate was ranked 46 among the 60 variables. In multivariable analyses, the R² value was higher (0.61 vs. 0.45), and the corrected Akaike Information Criterion value was lower (402.7 vs. 417.2) with variables selected with SR than those selected with point-biserial r. There were two variables with variance inflation factors higher than 5 in those using point-biserial r and none in SR. Data-driven machine learning models may be useful in identifying significant and insignificant correlated factors. Our method may be generalized to other medical research due to the procedural simplicity of using top-ranked variables selected by machine learning.

Single-Cell Gene Expression Analysis in Patients with Medullary Sponge Kidney and a Retrospective Study

OBJECTIVE

To establish better diagnosis thinking and provide advanced understanding of MSK, the CT imaging features, clinical characteristics, and the expression of suspected genes in the kidney spatiotemporal immune zonation and fetal renal development were investigated.

METHODS

17 patients with MSK hospitalized in our hospital were selected as our research subjects. Human Phenotype Ontology, MalaCards: The Human Disease Database, GeneCards: The Human Gene Database, Human Protein Atlas, and Single Cell Expression Atlas were used to analyze this disease.

RESULTS

In our 17 patients, the incidence of MSK tended to be the same in male and female, and the onset age of MSK was probably 31-50 years old. The top one related disease of MSK was nephrocalcinosis and the most frequent phenotype related to MSK was nephrolithiasis. In addition, the expression of HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 has been implicated in both human kidney immune zonation and fetal kidney development.

CONCLUSIONS

HNF1B, CLCN5, GDNF, ATP6V0A4, ATP6V1B1, LAMA2, RET, ACAN, and ABCC8 could be independent indicators for the diagnosis and preventive intervention of MSK patients, and abnormal kidney development due to mutations in key genes was the underlying cause of MSK.

Progression-Free Survival Prediction in Patients with Nasopharyngeal Carcinoma after Intensity-Modulated Radiotherapy: Machine Learning vs. Traditional Statistics

Background: The Cox proportional hazards (CPH) model is the most commonly used statistical method for nasopharyngeal carcinoma (NPC) prognostication. Recently, machine learning (ML) models are increasingly adopted for this purpose. However, only a few studies have compared the performances between CPH and ML models. This study aimed at comparing CPH with two state-of-the-art ML algorithms, namely, conditional survival forest (CSF) and DeepSurv for disease progression prediction in NPC. Methods: From January 2010 to March 2013, 412 eligible NPC patients were reviewed. The entire dataset was split into training cohort and testing cohort in a ratio of 90%:10%. Ten features from patient-related, disease-related, and treatment-related data were used to train the models for progression-free survival (PFS) prediction. The model performance was compared using the concordance index (c-index), Brier score, and log-rank test based on the risk stratification results. Results: DeepSurv (c-index = 0.68, Brier score = 0.13, log-rank test p = 0.02) achieved the best performance compared to CSF (c-index = 0.63, Brier score = 0.14, log-rank test p = 0.38) and CPH (c-index = 0.57, Brier score = 0.15, log-rank test p = 0.81). Conclusions: Both CSF and DeepSurv outperformed CPH in our relatively small dataset. ML-based survival prediction may guide physicians in choosing the most suitable treatment strategy for NPC patients.

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.

Differential expression of the five redox complexes in the retinal mitochondria or rod outer segment disks is consistent with their different functionality

PURPOSE

The retinal rod outer segment (OS) disk membranes, devoid of mitochondria, conducts oxidative phosphorylation (OxPhos). This study aimed at identifying which proteins expressed in the retinal rod OS disks determined the considerable adenosine-5'-triphosphate production and oxygen consumption observed in comparison with retinal mitochondria.

PROCEDURES

Characterization was conducted by immunogold transmission electron microscopy on retinal sections. OxPhos was studied by oximetry and luminometry. The proteomes of OS disks and mitochondria purified from bovine retinas were studied by mass spectrometry. Statistical and bioinformatic analyses were conducted by univariate, multivariate, and machine learning methods.

RESULTS

Weighted gene coexpression network analysis identified two protein expression profile modules functionally associated with either retinal mitochondria or disk samples, in function of a strikingly different ability of each sample to utilized diverse substrate for F1Fo-ATP synthase. The OS disk proteins correlated better than mitochondria with the tricarboxylic acids cycle and OxPhos proteins.

CONCLUSIONS

The differential enrichment of the expression profile of the OxPhos proteins in the disks versus mitochondria suggests that these proteins may represent a true proteome component of the former, with different functionality. These findings may shed new light on the pathogenesis of rod-driven retinal degenerative diseases.

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

Medullary sponge kidney (MSK) disease is a rare and neglected kidney condition often associated with nephrocalcinosis/nephrolithiasis and cystic anomalies in the precalyceal ducts. Little is known about the pathogenesis of this disease, so we addressed the knowledge gap using a proteomics approach. The protein content of microvesicles/exosomes isolated from urine of 15 MSK and 15 idiopathic calcium nephrolithiasis (ICN) patients was investigated by mass spectrometry, followed by weighted gene co-expression network analysis, support vector machine (SVM) learning, and partial least squares discriminant analysis (PLS-DA) to select the most discriminative proteins. Proteomic data were verified by ELISA. We identified 2998 proteins in total, 1764 (58.9%) of which were present in both vesicle types in both diseases. Among the MSK samples, only 65 (2.2%) and 137 (4.6%) proteins were exclusively found in the microvesicles and exosomes, respectively. Similarly, among the ICN samples, only 75 (2.5%) and 94 (3.1%) proteins were exclusively found in the microvesicles and exosomes, respectively. SVM learning and PLS-DA revealed a core panel of 20 proteins that distinguished extracellular vesicles representing each clinical condition with an accuracy of 100%. Among them, three exosome proteins involved in the lectin complement pathway maximized the discrimination between MSK and ICN: Ficolin 1, Mannan-binding lectin serine protease 2, and Complement component 4-binding protein β. ELISA confirmed the proteomic results. Our data show that the complement pathway is involved in the MSK, revealing a new range of potential therapeutic targets and early diagnostic biomarkers.

Proteomic Analysis of Urinary Microvesicles and Exosomes in Medullary Sponge Kidney Disease and Autosomal Dominant Polycystic Kidney Disease

BACKGROUND AND OBJECTIVES

Microvesicles and exosomes are involved in the pathogenesis of autosomal dominant polycystic kidney disease. However, it is unclear whether they also contribute to medullary sponge kidney, a sporadic kidney malformation featuring cysts, nephrocalcinosis, and recurrent kidney stones. We addressed this knowledge gap by comparative proteomic analysis.

DESIGN, SETTING, PARTICIPANTS, & MEASUREMENTS

The protein content of microvesicles and exosomes isolated from the urine of 15 patients with medullary sponge kidney and 15 patients with autosomal dominant polycystic kidney disease was determined by mass spectrometry followed by weighted gene coexpression network analysis, support vector machine learning, and partial least squares discriminant analysis to compare the profiles and select the most discriminative proteins. The proteomic data were verified by ELISA.

RESULTS

A total of 2950 proteins were isolated from microvesicles and exosomes, including 1579 (54%) identified in all samples but only 178 (6%) and 88 (3%) specific for medullary sponge kidney microvesicles and exosomes, and 183 (6%) and 98 (3%) specific for autosomal dominant polycystic kidney disease microvesicles and exosomes, respectively. The weighted gene coexpression network analysis revealed ten modules comprising proteins with similar expression profiles. Support vector machine learning and partial least squares discriminant analysis identified 34 proteins that were highly discriminative between the diseases. Among these, CD133 was upregulated in exosomes from autosomal dominant polycystic kidney disease and validated by ELISA.

CONCLUSIONS

Our data indicate a different proteomic profile of urinary microvesicles and exosomes in patients with medullary sponge kidney compared with patients with autosomal dominant polycystic kidney disease. The urine proteomic profile of patients with autosomal dominant polycystic kidney disease was enriched of proteins involved in cell proliferation and matrix remodeling. Instead, proteins identified in patients with medullary sponge kidney were associated with parenchymal calcium deposition/nephrolithiasis and systemic metabolic derangements associated with stones formation and bone mineralization defects.

PODCAST

This article contains a podcast at https://www.asn-online.org/media/podcast/CJASN/2019_04_24_CJASNPodcast_19_06_.mp3.

Breaking the ice: urine proteomics of medullary sponge kidney disease

Urinary proteomics is a promising tool for biomarker investigation, particularly in complex kidney diseases. Fabris and colleagues report that urinary laminin subunit alpha-2 is a potential diagnostic marker of medullary sponge kidney (MSK) disease by using a label-free quantitative proteomics platform and a clinically compatible enzyme-linked immunosorbent assay. The neglected issue of stone pathogenesis was also evidenced. This commentary discusses several considerations in biomarker validation, and how urinary proteomics breaks new ground in MSK research.

Proteome of Bovine Mitochondria and Rod Outer Segment Disks: Commonalities and Differences

The retinal rod outer segment (OS) is a stack of disks surrounded by the plasma membrane, housing proteins related to phototransduction, as well as mitochondrial proteins involved in oxidative phosphorylation (OxPhos). This prompted us to compare the proteome of bovine OS disks and mitochondria to assess the significant top gene signatures of each sample. The two proteomes, obtained by LTQ-Orbitrap Velos mass spectrometry, were compared by statistical analyses. In total, 4139 proteins were identified, 2045 of which overlapping in the two sets. Nonhierarchical Spearman's correlogram revealed that the groups were clearly discriminated. Partial least square discriminant plus support vector machine analysis identified the major discriminative proteins, implied in phototransduction and lipid metabolism, respectively. Gene Ontology analysis identified top gene signatures of the disk proteome, enriched in vesiculation, glycolysis, and OxPhos proteins. The tricarboxylic acid cycle and the electron transport proteins were similarly enriched in the two samples, but the latter was up regulated in disks. Data suggest that the mitochondrial OxPhos proteins may represent a true OS proteome component, outside the mitochondrion. This knowledge may help the scientific community in the further studies of retinal physiology and pathology.