StoneMod 2.0: Database and prediction of kidney stone modulatory proteins.

Stone modulators are various kinds of molecules that play crucial roles in promoting/inhibiting kidney stone formation. Several recent studies have extensively characterized the stone modulatory proteins with the ultimate goal of preventing kidney stone formation. Herein, we introduce the StoneMod 2.0 database (https://www.stonemod.org), which has been dramatically improved from the previous version by expanding the number of the modulatory proteins in the list (from 32 in the initial version to 17,130 in this updated version). The stone modulatory proteins were recruited from solid experimental evidence (via PubMed) and/or predicted evidence (via UniProtKB, QuickGO, ProRule, STITCH and OxaBIND to retrieve calcium-binding and oxalate-binding proteins). Additionally, StoneMod 2.0 has implemented a scoring system that can be used to determine the likelihood and to classify the potential stone modulatory proteins as either "solid" (modulator score ≥ 50) or "weak" (modulator score < 50) modulators. Furthermore, the updated version has been designed with more user-friendly interfaces and advanced visualization tools. In addition to the monthly scheduled update, the users can directly submit their experimental evidence online anytime. Therefore, StoneMod 2.0 is a powerful database with prediction scores that will be very useful for many future studies on the stone modulatory proteins.

Proteomic and computational analyses followed by functional validation of protective effects of trigonelline against calcium oxalate-induced renal cell deteriorations.

Trigonelline is a phytoalkaloid commonly found in green and roasted coffee beans. It is also found in decaffeinated coffee. Previous report has shown that extract from trigonelline-rich plant exhibits anti-lithiatic effects in a nephrolithiatic rat model. Nevertheless, cellular mechanisms underlying the anti-lithiatic properties of trigonelline remain hazy. Herein, we used nanoLC-ESI-Qq-TOF MS/MS and MaxQuant-based quantitative proteomics to identify trigonelline-induced changes in protein expression in MDCK renal cells. From a total of 1006 and 1011 proteins identified from control and trigonelline-treated cells, respectively, levels of 62 (23 upregulated and 39 downregulated) proteins were significantly changed by trigonelline. Functional enrichment and reactome pathway analyses suggested that these 62 altered proteins were related to stress response, cell cycle and cell polarity. Functional validation by corresponding experimental assays revealed that trigonelline prevented calcium oxalate monohydrate crystal-induced renal cell deteriorations by inhibiting crystal-induced overproduction of intracellular reactive oxygen species, G0/G1 to G2/M cell cycle shift, tight junction disruption, and epithelial-mesenchymal transition. These findings provide cellular mechanisms and convincing evidence for the renoprotective effects of trigonelline, particularly in kidney stone prevention.

OxaBIND: A tool for identifying oxalate-binding domain(s)/motif(s) in protein(s).

High oxalate level in blood and urine may cause oxalate-related disorders, particularly kidney stone disease. To unravel disease mechanisms, investigations of oxalate level and its binding proteins are required. However, the information on oxalate-binding proteins is limited due to a lack of appropriate tool for their investigations. Therefore, we have developed a freely accessible web-based tool, namely OxaBIND (https://www.stonemod.org/oxabind.php), to identify oxalate-binding site(s) in any proteins of interest. The prediction model was generated by recruiting all of the known oxalate-binding proteins with solid experimental evidence (from PubMed and RCSB Protein Data Bank). The potential oxalate-binding domains/motifs were predicted from these oxalate-binding proteins using PRATT tool and used to discriminate these known oxalate-binding proteins from the known non-oxalate-binding proteins. The best one, which provided highest fitness score, sensitivity and specificity, was then implemented to create the OxaBIND tool. After inputting protein identifier or sequence (which can be single or multiple), details of all the identified oxalate-binding site(s), if any, are presented in both textual and graphical formats. OxaBIND also provides theoretical three-dimensional (3D) structure of the protein with oxalate-binding site(s) being highlighted. This tool will be beneficial for future research on the oxalate-binding proteins, which play important roles in the oxalate-related disorders.

Theranostic roles of machine learning in clinical management of kidney stone disease.

Kidney stone disease (KSD) is a common illness caused by deposition of solid minerals formed inside the kidney. The disease prevalence varies, based on sociodemographic, lifestyle, dietary, genetic, gender, age, environmental and climatic factors, but has been continuously increasing worldwide. KSD is a highly recurrent disease, and the recurrence rate is about 11% within two years after the stone removal. Recently, machine learning has been widely used for KSD detection, stone type prediction, determination of appropriate treatment modality and prediction of therapeutic outcome. This review provides a brief overview of KSD and discusses how machine learning can be applied to diagnostics, therapeutics and prognostics in clinical management of KSD for better therapeutic outcome.

StoneMod: a database for kidney stone modulatory proteins with experimental evidence.

Better understanding of molecular mechanisms for kidney stone formation is required to improve management of kidney stone disease with better therapeutic outcome. Recent kidney stone research has indicated critical roles of a group of proteins, namely 'stone modulators', in promotion or inhibition of the stone formation. Nevertheless, such information is currently dispersed and difficult to obtain. Herein, we present the kidney stone modulator database (StoneMod), which is a curated resource by obtaining necessary information of such stone modulatory proteins, which can act as stone promoters or inhibitors, with experimental evidence from previously published studies. Currently, the StoneMod database contains 10, 16, 13, 8 modulatory proteins that affect calcium oxalate crystallization, crystal growth, crystal aggregation, and crystal adhesion on renal tubular cells, respectively. Informative details of each modulatory protein and PubMed links to the published articles are provided. Additionally, hyperlinks to other protein/gene databases (e.g., UniProtKB, Swiss-Prot, Human Protein Atlas, PeptideAtlas, and Ensembl) are made available for the users to obtain additional in-depth information of each protein. Moreover, this database provides a user-friendly web interface, in which the users can freely access to the information and/or submit their data to deposit or update. Database URL: https://www.stonemod.org .