Omics studies for comprehensive understanding of immunoglobulin A nephropathy: state-of-the-art and future directions

Serum and urine metabolomic biomarkers for predicting prognosis in patients with immunoglobulin A nephropathy

BACKGROUND

Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification.

METHODS

We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression.

RESULTS

We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667-1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667-1.000) showed the highest discriminatory ability to predict IgAN disease progression.

CONCLUSION

This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

Omics are Getting Us Closer to Understanding IgA Nephropathy

During the last decade, thanks to omics technologies, new light has been shed on the pathogenesis of many diseases. Genomics, epigenomics, transcriptomics, and proteomics have helped to provide a better understanding of the origin and heterogeneity of several diseases. However, the risk factors for most autoimmune diseases remain unknown. The successes and pitfalls of omics have also been observed in nephrology, including immunoglobulin A nephropathy (IgAN), the most common form of glomerulonephritis and a principal cause of end-stage renal disease worldwide. Unfortunately, the immense progress in basic research has not yet been followed by the satisfactory development of a targeted treatment. Although, most omics studies describe changes in the immune system, there is still insufficient data to apply their results in the constantly evolving multi-hit pathogenesis model and thus do to provide a complete picture of the disease. Here, we describe recent findings regarding the pathophysiology of IgAN and link omics studies with immune system dysregulation. This review provides insights into specific IgAN markers, which may lead to the identification of potential targets for personalised treatment in the future.

Downregulation of PPARα mediates FABP1 expression, contributing to IgA nephropathy by stimulating ferroptosis in human mesangial cells

Immunoglobulin A nephropathy (IgAN) is the commonest primary glomerulonephritis, and a major cause of end-stage renal disease; however, its pathogenesis requires elucidation. Here, a hub gene, FABP1, and signaling pathway, PPARα, were selected as key in IgAN pathogenesis by combined weighted gene correlation network analysis of clinical traits and identification of differentially expressed genes from three datasets. FABP1 and PPARα levels were lower in IgAN than control kidney, and linearly positively correlated with one another, while FABP1 levels were negatively correlated with urinary albumin-to-creatinine ratio, and GPX4 levels were significantly decreased in IgAN. In human mesangial cells (HMCs), PPARα and FABP1 levels were significantly decreased after Gd-IgA1 stimulation and mitochondria appeared structurally damaged, while reactive oxygen species (ROS) and malondialdehyde (MDA) were significantly increased, and glutathione and GPX4 decreased, relative to controls. GPX4 levels were decreased, and those of ACSL4 increased on siPPARα and siFABP1 siRNA treatment. In PPARα lentivirus-transfected HMCs stimulated by Gd-IgA1, ROS, MDA, and ACSL4 were decreased; glutathione and GPX4, and immunofluorescence colocalization of PPARα and GPX4, increased; and damaged mitochondria reduced. Hence, PPARα pathway downregulation can reduce FABP1 expression, affecting GPX4 and ACSL4 levels, causing HMC ferroptosis, and contributing to IgAN pathogenesis.

Precision omics data integration and analysis with interoperable ontologies and their application for COVID-19 research

Omics technologies are widely used in biomedical research. Precision medicine focuses on individual-level disease treatment and prevention. Here, we propose the usage of the term 'precision omics' to represent the combinatorial strategy that applies omics to translate large-scale molecular omics data for precision disease understanding and accurate disease diagnosis, treatment and prevention. Given the complexity of both omics and precision medicine, precision omics requires standardized representation and integration of heterogeneous data types. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, we propose a precision omics ontology hypothesis, which hypothesizes that the effectiveness of precision omics is positively correlated with the interoperability of ontologies used for data and knowledge integration. Therefore, to make effective precision omics studies, interoperable ontologies are required to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. Methods for efficient development and application of interoperable ontologies are proposed and illustrated. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying the outcomes of diseases such as COVID-19. Given extensive COVID-19 omics research, we propose the strategy of precision omics supported by interoperable ontologies, accompanied with ontology-based semantic reasoning and machine learning, leading to systematic disease mechanism understanding and rational design of precision treatment and prevention.

SHORT ABSTRACT

Precision medicine focuses on individual-level disease treatment and prevention. Precision omics is a new strategy that applies omics for precision medicine research, which requires standardized representation and integration of individual genetics and phenotypes, experimental conditions, and data analysis settings. Ontology has emerged as an important artificial intelligence component to become critical for standard data and metadata representation, standardization and integration. To support precision omics, interoperable ontologies are required in order to standardize and incorporate heterogeneous data and knowledge in a human- and computer-interpretable manner. With the interoperable omics data and knowledge, omics tools such as OmicsViz can also be evolved to process, integrate, visualize and analyze various omics data, leading to the identification of new knowledge and hypotheses of molecular mechanisms underlying disease outcomes. The precision COVID-19 omics study is provided as the primary use case to illustrate the rationale and implementation of the precision omics strategy.

Periostin Contributes to Immunoglobulin a Nephropathy by Promoting the Proliferation of Mesangial Cells: A Weighted Gene Correlation Network Analysis

Immunoglobulin A nephropathy (IgAN) is a known cause of end-stage kidney disease, but the pathogenesis and factors affecting prognosis are not fully understood. In the present study, we carried out weighted gene correlation network analysis (WGCNA) to identify hub genes related to the occurrence of IgAN and validated candidate genes in experiments using mouse mesangial cells (MMCs) and clinical specimens (kidney tissue from IgAN patients and healthy controls). We screened the GSE37460 and GSE104948 differentially expressed genes common to both datasets and identified periostin (POSTN) as one of the five key genes using the cytoHubba plugin of Cytoscape software and by receiver-operating characteristic curve analysis. The top 25% of genes in the GSE93798 dataset showing variable expression between IgAN and healthy tissue were assessed by WGCNA. The royalblue module in WGCNA was closely related to creatinine and estimated glomerular filtration rate (eGFR) in IgAN patients. POSTN had very high module membership and gene significance values for creatinine (0.82 and 0.66, respectively) and eGFR (0.82 and -0.67, respectively), indicating that it is a co-hub gene. In MMCs, POSTN was upregulated by transforming growth factor β1, and stimulation of MMCs with recombinant POSTN protein resulted in an increase in the level of proliferating cell nuclear antigen (PCNA) and a decrease in that of B cell lymphoma-associated X protein, which were accompanied by enhanced MMC proliferation. POSTN gene knockdown had the opposite effects. Immunohistochemical analysis of kidney tissue specimens showed that POSTN and PCNA levels were elevated, whereas the rate of apoptosis was reduced in IgAN patients relative to healthy controls. POSTN level in the kidney tissue of IgAN patients was positively correlated with creatinine level and negatively correlated with eGFR. Thus, POSTN promotes the proliferation of MCs to promote renal dysfunction in IgAN.

Monitoring Immune Responses in IgA Nephropathy: Biomarkers to Guide Management

IgA nephropathy (IgAN) is the commonest biopsy-reported primary glomerulonephritis worldwide. It has an incidence which peaks among young adults, and 30 to 40% of patients' progress to end stage kidney disease within twenty years of diagnosis. Ten-year kidney survival rates have been reported to be as low as 35% in some parts of the world. The successful management of IgAN is limited by an incomplete understanding of the pathophysiology of IgAN and a poor understanding of how pathophysiology may vary both from patient to patient and between patient groups, particularly across races. This is compounded by a lack of rigorously designed and delivered clinical trials in IgAN. This is slowly changing, with a number of Phase 2 and 3 clinical trials of novel therapies targeting a number of different putative pathogenic pathways in IgAN due to report in the next 5 years. From our current, albeit limited, understanding of the pathophysiology of IgAN it is unlikely a single therapy will be effective in all patients with IgAN. The successful management of IgAN in the future is, therefore, likely to be reliant on targeted therapies, carefully selected based on an individualized understanding of a patient's risk of progression and underlying pathophysiology. The potential role of biomarkers to facilitate personalization of prognostication and treatment of IgAN is immense. Here we review the progress made over the past decade in identifying and validating new biomarkers, with a particular focus on those that reflect immunological responses in IgAN.

Formalin-fixed paraffin-embedded renal biopsy tissues: an underexploited biospecimen resource for gene expression profiling in IgA nephropathy

Primary IgA nephropathy (IgAN) diagnosis is based on IgA-dominant glomerular deposits and histological scoring is done on formalin-fixed paraffin embedded tissue (FFPE) sections using the Oxford classification. Our aim was to use this underexploited resource to extract RNA and identify genes that characterize active (endocapillary–extracapillary proliferations) and chronic (tubulo-interstitial) renal lesions in total renal cortex. RNA was extracted from archival FFPE renal biopsies of 52 IgAN patients, 22 non-IgAN and normal renal tissue of 7 kidney living donors (KLD) as controls. Genome-wide gene expression profiles were obtained and biomarker identification was carried out comparing gene expression signatures a subset of IgAN patients with active (N = 8), and chronic (N = 12) renal lesions versus non-IgAN and KLD. Bioinformatic analysis identified transcripts for active (DEFA4,TNFAIP6,FAR2) and chronic (LTB,CXCL6, ITGAX) renal lesions that were validated by RT-PCR and IHC. Finally, two of them (TNFAIP6 for active and CXCL6 for chronic) were confirmed in the urine of an independent cohort of IgAN patients compared with non-IgAN patients and controls. We have integrated transcriptomics with histomorphological scores, identified specific gene expression changes using the invaluable repository of archival renal biopsies and discovered two urinary biomarkers that may be used for specific clinical decision making.