UAB Childhood Cystic Kidney Disease Center (UAB-CCKDC) - Informatic and Data Analytics Resource

University Of Alabama At Birmingham, Birmingham AL

Investigators

Linked publications, trials & patents

Abstract

SPECIFIC AIMS (INFORMATIC AND DATA ANALYTICS RESOURCE, IDAR): Recent advances in genomics technologies hold the potential to fundamentally transform the field of polycystic kidney disease (PKD) by providing unprecedented insights into biological processes at single-cell, subcellular, and spatial resolution. However, a significant bottleneck hindering the broader application of this disruptive technology in PKD research is the shortage of skilled computational biologists who grasp the disease’s complex pathobiology. Traditional computational, statistical, or bioinformatics cores frequently lack the specialized knowledge necessary to navigate the unique challenges specific to PKD, including i) variability introduced by different PKD gene variants, ii) phenotypic differences in animal models, and their design limitations that prevent them from meaningfully replicating human PKD, and iii) known PKD-modulating factors (e.g., genetic background, animal species, sex, environment) that further complicate experimental design and data interpretation. Therefore, there is an urgent need for bioinformatics teams that combine deep expertise in PKD biology with the analytical skills required to interpret large omics datasets. The mission of the Informatic and Data Analytics Resource (IDAR) is to collaborate with the PKD RRC Steering Committee, NIDDK, and the other PKD U54 Centers to prioritize, develop, and deploy innovative PKD- focused training opportunities and web-based data and analytical apps to advance studies into the pathogenic mechanisms and treatments for PKD. The proposed IDAR team leaders are experienced in managing complex multi-institution and international projects and consortia: Dr. Lasseigne, an internationally- recognized bioinformatics expert who uncovered and published a comprehensive list of druggable pathways based on Pkd2 transcriptomic analyses1, and her close collaborator Dr. Mrug, a physician-scientist who was among the first to use transcriptomic analyses in the PKD field2, highlighting paradigm-changing roles of injury and inflammation in animal models and validating their relevance to human ARPKD and ADPKD. To help PKD researchers address their bioinformatic needs, we propose to establish three fundamental resources through the UAB PKD RRC Informatic and Data Analytics Resource (IDAR): Aim 1: To establish consultation clinics and bioinformatics training opportunities. The IDAR team has the rare sought-after expertise in PKD pathobiology and PKD informatics to help PKD and non-PKD scientists formulate the optimal genomics approaches (e.g., single-cell/nucleus, long-read, transcriptomics, ATAC, spatial, multiomics) for their given biological questions and to ensure proper study design (e.g., number of samples, read depth, quality controls) to maximize accuracy and efficiency of data interpretation. In addition, IDAR will offer training on using our forthcoming data hub and web-based tools, as well as for topics of interest in the PKD research community. Aim 2: To deploy a harmonized transcriptomics PKD data hub. To expedite PKD transcriptomics research, we will establish a robust hub of public domain data – harmonized via best practices – to facilitate existing and future pivotal experiments focused on unmasking early events of renal cystogenesis versus disease progression. This hub will feature a user-friendly web portal with metadata and quality control metrics and straightforward harmonized data retrieval from a third-party data host. Aim 3: To develop a suite of web-based analytical tools. We will develop and validate intuitive, point-and- click tools to empower PKD researchers to leverage the disruptive potential of transcriptomics for both hypothesis generation and initial concept validation. For example, these tools will enable users to: i. Identify pathways specific to particular PKD perturbations vs. those common across conditions and their activation signatures using existing (e.g., ATLAS-D2K, other data) and newly generated transcript expression datasets (e.g., lrscRNA-seq); ii. unmask, at the single-cell level, events that immediately follow polycystin loss of function in animal models as the foundation for conclusive studies of cystogenesis initiating events (e.g., time- resolved studies of conditional models); iii. map isoforms and exon usage for polycystin and fibrocystin transcripts in renal cell populations using lrscRNA-seq; iv. assess sex and species effects (or their lack of) on the expression of genes in the presence or absence of polycystin function-disrupting gene variants; and v. identify and prioritize drug targets and repurposing candidates for therapeutic PKD pathways (e.g., those conserved across sex and species). To ensure the highest standards of service to the research community, IDAR will rigorously assess the quality of its resources and ensure their timely release to the public in transparent formats, specifically by depositing in standard community repositories and using digital object identifiers (DOIs; e.g., processed expression data, gene variant assessment, analyses and software code via Zenodo; map embeddings in Kipoi; consensus signatures in mSigDB; raw sequencing data in GEO). To support the entire PKD research community, IDAR will seek advice and will closely coordinate its activity with all components of the PKD RRC Steering Committee (including any other omics- or informatics-focused resources), fostering collaboration and maximizing the impact of our resources.