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.


Scientific RepoRtS
| (2020) 10:15109 | https://doi.org/10.1038/s41598-020-71730-3 www.nature.com/scientificreports/ is the most common type of kidney stones and occupies > 77% of 111,196 stones analyzed 12 and assays for investigating calcium oxalate kidney stone formation processes were well-established, this version of the database focuses on modulators of calcium oxalate crystallization, crystal growth, aggregation, and adhesion to renal tubular cells.

Results and discussion
overview of the StoneMod database. The aim of our present work was to build a database that integrates all relevant information of kidney stone modulators with experimental evidence. The StoneMod database provides a collection of modulatory proteins that either promote or inhibit individual steps of kidney stone formation. Using the predefined criteria for inclusion/exclusion ( Fig. 2) (see more details in "Materials and Methods"), the StoneMod database currently contains 10, 16, 13, 8 modulatory proteins that affect calcium oxalate crystallization, crystal growth, crystal aggregation, and crystal adhesion on renal tubular cells, respectively ( Table 1). All of these data were retrieved from 62 published studies, involving urine, serum, cellular secretome, and kidney tissue samples (Supplementary Table S1). Informative details of each modulatory protein and Pub-Med links to the published articles are provided. Additionally, hyperlinks to other protein/gene databases (i.e.,  the tabbed document interface. The StoneMod database website is an open access resource for obtaining detailed information of kidney stone modulatory proteins that has been designed and organized for the ease of use and access. Using the MySQL schema as detailed in "Materials & Methods" (Fig. 3), the website tabbed document interfaces at this initial phase include "home", "about us", "lists", "advanced search", "data submission",  www.nature.com/scientificreports/ "contact", and "help" tabs ( Fig. 4A). The home page provides an overview of the database, brief background of kidney stone formation, and news of the database or related issues (Fig. 4A). This page also shows three most updated modulatory proteins and their modulatory activities.
The lists menu provides two choices, in which modulators are sorted by alphabetical order or by activity involving crystallization, crystal growth, crystal aggregation, or crystal adhesion on renal tubular cells (Figs. 4B,C). For each step of kidney stone formation, individual modulatory proteins are categorized by their modulatory effects (e.g., promotion or inhibition). Quick search can be done through the home page using generalized keyword (e.g. protein common name, protein alternative name, gene name, gene symbol, UniProtKB accession number, etc.) (Fig. 4D). This allows the users to directly access the information of the protein or modulator of interest. In addition to the quick search, the users can perform advanced search by inputting specified and multiple search parameters (Fig. 4E). In either case, the search result will show brief information of the resulting modulator, including its StoneMod ID, protein name, UniProt ID, gene name, and gene symbol (Fig. 4F). Clicking the protein name will lead the users to the detailed information of each modulatory protein (Fig. 5).

Relevant information of each modulatory protein. The detailed information page includes relevant
data of each modulatory protein, including: (i) protein information; (ii) gene information; and (iii) modulatory effects ( Table 2). The protein information (retrieved mainly from the UniProtKB database) includes protein common name, alternative name, UniProt ID, protein isoform (if any), and hyperlinks to the proteomic databases (i.e., Human Protein Atlas and PeptideAtlas) ( Fig. 5; panel (i)). The gene information (retrieved mainly from the NCBI Gene database) provides gene name, gene symbol, and hyperlink to the gene annotation database (i.e., Ensembl) ( Fig. 5; panel (ii)). Details of modulatory effects of each modulator (retrieved mainly from the PubMed search) include all relevant references of its promoting or inhibitory effect on crystallization, crystal growth, crystal aggregation, or crystal adhesion ( Fig. 5; panel (iii)). Number of the references in each category is also summarized and shown on this page. Each reference is further linked to the PubMed literature resource. Finally, the StoneMod database also offers the users to download or export all the detailed information as comma-separated values (csv) file format by clicking "export to csv" icon at the bottom of the detailed information page ( Fig. 5; panel (iv)).
Some of the modulators had contradictory results shown by different studies (mostly due to differential settings/parameters tested). They are then listed within "contradictory" category in the "lists by activity" tab. For example, there are three modulators (albumin, osteopontin, and uromodulin) that are in the "contradictory" www.nature.com/scientificreports/ category for crystal growth (Fig. 4C). The detailed information page of each protein will show all the contradictory data in one place (as in the case for albumin in Fig. 5; panel (iii), in which "modulatory effects" section shows all references for inhibitory and promoting effects of albumin on crystal growth.

Data submission and update. In addition to periodic (monthly) deposition and update by our team, the
StoneMod database also provides a submission form on "data submission" tab ( Fig. 4A) to allow the users to directly deposit or update their own information into the database manually (note that the users must provide the PubMed ID or digital object identifier (DOI) of the published articles). After submission, each filled form will be directly sent to us for review. If the submitted references are relevant and show experimental evidence of modulatory effects of their proteins on kidney stone formation, they will be deposited and updated on the website within a week after submission. Finally, the latest deposited modulator will be highlighted on the home page and the submitter will be credited and notified.

conclusions
StoneMod is the first web-based database that provides relevant information of the kidney stone modulatory proteins with experimental evidence. The database has elements that are easy to use through the user-friendly web interface. Features of the StoneMod database enable the users to freely access to such information in one place. Moreover, the users can also submit their data to be deposited and updated. This database therefore will be a valuable resource of information for kidney stone research community.

Methods
Data collection and curation. Kidney (Fig. 2). Thereafter, the data were manually filtered by including only mammalian proteins with experimental evidence of modulatory effects in kidney stone formation processes. For protein information, UniProtKB (https ://www. unipr ot.org/) was used to retrieve common name, alternative name, isoform, and UniProtKB ID 13 . Human Protein Atlas (https ://www.prote inatl as.org/) 14 and PeptideAtlas (https ://www.pepti deatl as.org/) 15 were also used as the protein annotation database. For gene information, gene name and symbol were retrieved from the NCBI Gene database (https ://www.ncbi.nlm.nih.gov/gene) 16 . Gene name and gene symbol are presented following the HUGO (Human Genome Organization) Gene Nomenclature guideline 17 . Gene annotation was retrieved from Ensembl database (https ://www.ensem bl.org/) 18 . Each modulatory protein was categorized by its effect (either Table 2. Details of the relevant information provided in the StoneMod database. www.nature.com/scientificreports/ promotion or inhibition) on kidney stone formation processes (i.e., crystallization, crystal growth, crystal aggregation, and crystal adhesion on renal tubular cells). Nevertheless, when the references showed inconclusive or contradictory data, the protein was classified into the contradictory category.
Database implementation. StoneMod database website was built by using WampServer (https ://www. wamps erver .com), which is a freely open-source and cross-platform server that supports applications and creation of database using Apache2, PHP and MySQL in Linux subsystem. MySQL workbench (https ://www.mysql .com) was chosen to manage the StoneMod database because of its ease of use. The MySQL schema used for the StoneMod database is illustrated in Fig. 3. The main relational database was structured with twelve tables of parameters, including protein, gene, isoform, crystallization, growth, aggregation, adhesion, effect, summary of crystallization, summary of growth, summary of aggregation, and summary of adhesion. Each table contained information represented in the column and data type, as well as links to the others through the relationship. PHP was also employed in combination with MySQL as the server-side script. In addition, the web framework development Bootstrap (https ://getbo otstr ap.com/), which is the most popular framework for developing responsive website, and the JavaScript framework development JQuery (https ://jquer y.com/) were used for developing the web interface. www.nature.com/scientificreports/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creat iveco mmons .org/licen ses/by/4.0/.