Omics databases on kidney disease: where they can be found and how to benefit from them

Abstract

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

The systemic nature of CKD

“…Proteomics has proven to be an effective tool to identify drug targets in cancer, a chronic condition that involves extensive protein networks 266 , much like CKD 267 . In the specific context of CKD, rich databases that combine findings at various biological levels have already been created 268 . These databases, which will potentially be fuelled by future discoveries, will soon provide critical information for our understanding of the complexity of CKD and its systemic complications, thereby enabling the discovery of novel biomarkers and therapeutic targets 268 .…”

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

“…In this article, we discuss the big data concepts in nephrology, describe the potential use of AI in nephrology and transplantation, and also encourage researchers and clinicians to submit their invaluable research, including original clinical research studies [26][27][28][29][30], database studies from registries [31][32][33], meta-analyses [34][35][36][37][38][39][40][41][42][43][44], and artificial intelligence research [25,[45][46][47][48] in nephrology and transplantation. Table 1 demonstrates known and commonly used databases that have provided big data in nephrology and transplantation [49][50][51]. For example, the United States Renal Data System (USRDS) is recognized as a state reconnaissance system that has the responsibility of collecting, analyzing, and subsequently distributing information regarding CKD and ESKD, all based on numerous big datasets.…”

Gene, cell, and organ multiplication drives inner ear evolution

“…Interestingly enough, several other transcription factors that are essential for vertebrate ear development, such as Eya1/Six1 (Xu et al, 1999; Zou et al, 2008), Gata3 (Duncan and Fritzsch, 2013; Karis et al, 2001) are also essential for both kidney and ear development. In addition, several other transcription factors are needed for ear development (Foxi3, Fgf3/10, Fgfr2, Sox9, Tfap2a (Alsina and Streit, 2016; Birol et al, 2016; Khatri et al, 2014; McMahon, 2016; Papadopoulos et al, 2016; Singh and Groves, 2016). Based on the evolution of statocysts in diploblasts, one could argue that evolution of the proneural gene regulatory network (GRN), involving possibly Eya1/Six1, Foxi3, Pax2/8 and Gata3 network of interacting factors and its dependence on Fgfr2 signaling (Pauley et al, 2003; Pirvola et al, 2000), evolved in ectodermal sensory placodes and was co-opted for mesodermal kidney development in triploblasts.…”