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.

IgA nephropathy: a perspective for 2021

“…We conclude this edition with a final review by Peter Boor and Julio Saez-Rodriguez, who review the future of evaluation of the kidney biopsy, in particular the use of artificial intelligence, and of systems biology approaches to gain further develop histopathologic insights and a deeper understanding of the pathogenesis of IgAN [11].…”

Immunoglobulin A nephropathy in paediatrics: An up‐to‐date

“…However, this process requires advanced computational methods in the generation, process, and analysis of big data. 62 This integrated information is essential to map the full spectrum of common and rare IgAN risk genes, the transcriptomic pattern, and its modulation by epigenetic factors (e.g., microRNAs). 63 This approach could guide medicine towards a more individualized treatment.…”

“…The understanding of the complex pathophysiology of the disease through the OMICS provides more information about novel promising biomarkers. However, this process requires advanced computational methods in the generation, process, and analysis of big data 62 . This integrated information is essential to map the full spectrum of common and rare IgAN risk genes, the transcriptomic pattern, and its modulation by epigenetic factors (e.g., microRNAs) 63 .…”

Precision medicine for the treatment of glomerulonephritis: a bold goal but not yet a transformative achievement

“…While prognostic scoring systems using clinical features and pathology (Oxford MEST scores) are well established, their role in the selection of patients for specific treatment regimens is highly uncertain [ 42 ]. AI methods may even further refine the predictive accuracy for outcomes, such as kidney failure [ 43 ]; however, applying these tools to examine the differential efficacy of therapeutic regimens to alter the predicted trajectory is still needed. Perhaps one or more of the trials in progress will fill this gap.…”