How will artificial intelligence and bioinformatics change our understanding of IgA Nephropathy in the next decade?

Abstract

IgA nephropathy (IgAN) is the most common glomerulonephritis. It is characterized by the deposition of immune complexes containing immunoglobulin A (IgA) in the kidney's glomeruli, triggering an inflammatory process. In many patients, the disease has a progressive course, eventually leading to end-stage kidney disease. The current understanding of IgAN's pathophysiology is incomplete, with the involvement of several potential players, including the mucosal immune system, the complement system, and the microbiome. Dissecting this complex pathophysiology requires an integrated analysis across molecular, cellular, and organ scales. Such data can be obtained by employing emerging technologies, including single-cell sequencing, next-generation sequencing, proteomics, and complex imaging approaches. These techniques generate complex "big data," requiring advanced computational methods for their analyses and interpretation. Here, we introduce such methods, focusing on the broad areas of bioinformatics and artificial intelligence and discuss how they can advance our understanding of IgAN and ultimately improve patient care. The close integration of advanced experimental and computational technologies with medical and clinical expertise is essential to improve our understanding of human diseases. We argue that IgAN is a paradigmatic disease to demonstrate the value of such a multidisciplinary approach.

Keywords: Artificial intelligence; Bioinformatics; IgA nephropathy; Imaging; Omics.

Fig. 1

Overview of big-data experimental technologies and how they can improve our understanding of the pathophysiology of IgA nephropathy

Fig. 2

Examples of AI-based applications for nephrology and nephropathology. AI-based methods have been primarily applied to nephrology to group patients based on outcome, perform real-time monitoring of acute kidney injury or to establish a prognosis. In nephropathology, main applications include classification (mostly of glomeruli) and semantic segmentation, often combined with quantification of the segmented compartments