Advancing Our Understanding of Glomerular Disease Through Omics

The word omics or multiomics refers to high-throughput agnostic methods commonly used in systems biology and systems genetics. Systems genetics is a relatively new approach that combines a range of experimental and computational methods to quantify and integrate genetic effects across intermediate phenotypes, such as transcript, protein, or metabolite levels, in order to better understand the flow of genetic information through cellular regulatory networks. This approach studies the effects of DNA variants (genome) on RNA transcription (transcriptome), protein synthesis (proteome), metabolic functions (metabolome), and ultimately, disease phenotype (phenome). These approaches are ideally suited to study multifactorial traits with complex genetic architecture. Notably, most types of kidney and glomerular disorders fall in this category.

In light of recent progress in the genomics of complex traits, where do we stand with glomerular disease?

The progress in the genetics of complex traits has been remarkable due to the declining cost of genotyping, sequencing, and computation combined with unprecedented multicenter collaborations and open data sharing models widely adopted in genomic sciences. These advances allowed for discovery of thousands of susceptibility alleles for complex traits in large-scale population-based genetic studies. From the time of the first genome-wide association study (GWAS), there have been over 2500 GWASs published, reporting over 20,000 unique single-nucleotide polymorphism trait associations.

In addition to GWAS, next generation sequencing technology, such as whole-exome sequencing (WES) and whole-genome sequencing (WGS), is now being used to investigate the role of rare genetic variants in complex traits. Unfortunately, glomerular disease has not been at the forefront of this genomic revolution. Nevertheless, several notable success stories in our field are worth mentioning. One of the landmark successes was the identification of APOL1 risk alleles with large effect on the risk of focal segmental glomerulosclerosis (FSGS) in African Americans (1). Another success involved membranous nephropathy, where a small GWAS identified impressively strong associations of the class II major histocompatibility region and at the M-type phospholipase A2 receptor (PLA2R) gene locus (2).

In the field of IgA nephropathy, several large GWASs have been conducted and identified nearly 20 independent risk alleles, shedding new light on the underlying pathogenic pathways and refocusing mechanistic research toward better understanding of the role of intestinal immunity in this disease (3). Although these success stories are extremely encouraging, the GWAS approach has not been systematically applied to the entire spectrum of glomerular diseases. Adequately powered genetic studies are still missing for many common disorders, such as minimal change disease, mesangioproliferative glomerulonephritis (MPGN), lupus nephritis, or Henoch–Schönlein purpura nephritis, to name a few. The lack of systematic genetic work in this area is particularly alarming, because many glomerular disorders are in dire need of effective treatments, but potential drug targets remain largely undefined owing to poorly understood pathogenesis.

What are the main challenges in functional genomics of glomerular disease?

Presently, the key challenges in the field are to elucidate dysregulated pathways downstream of known genetic susceptibility loci, to understand the nature of their pleiotropic effects and interactions, and to place their functional consequences within a coherent biologic network. Such insights may then be translated into clinical benefits, including reliable biomarkers, effective strategies for screening and prevention, and rational selection of new therapeutic targets.

For follow-up of GWAS findings, the key challenges are that many of the causal alleles reside in the noncoding regions of the genome and that the target genes for these regions are frequently unknown. Because many regulatory regions are tissue specific, another challenge is to correctly identify the causal cell type for each disease. The structural complexity of kidney tissue and the relative inaccessibility of relevant cell types represent major challenges for functional genomics in nephrology. The National Institute of Diabetes and Digestive and Kidney Diseases recognized this problem and announced the new Kidney Precision Medicine Project (KPMP) that aims to build a kidney tissue resource for the purpose of such studies. This new initiative, although not specifically targeting glomerular disease, offers prospects to enhance our ability to implement disease- and cell type–specific functional genomics.

Can proteomics help with identification of pathogenic targets in glomerular disease?

Proteomics, the large-scale study of proteins, their structures, and their functions, has been successfully applied to kidney tissue and body fluids, including serum and urine. One of many examples in the field of glomerular diseases is the discovery of the cause of primary membranous nephropathy. Targeted proteomic analyses identified a major antigen recognized by circulatory autoantibodies as PLA2R (4) and a minor antigen as thrombospondin type 1 domain-containing 7A protein (5). Both antigens are membrane glycoproteins present in normal podocytes and immune deposits in idiopathic membranous nephropathy. Independently, GWAS for membranous nephropathy discovered risk alleles in the region encoding the PLA2R gene. The convergence of proteomic and genetic results solidified the evidence for the pathogenic role of antibodies against PLA2R in membranous nephropathy and exemplified the power of these approaches in the field of glomerular disease.

Can urine peptidomics enhance discovery of disease-specific biomarkers?

Urine is thought to contain molecules reflecting the health status of the kidney. Consequently, urine from patients with glomerular diseases may contain disease-specific molecules, including naturally occurring peptide fragments of protein originating from the circulation and/or the kidney. Analyses of peptides—peptidomics—in the urine have identified >5000 different peptide fragments that can be used for disease stratification. A recent article outlined approaches that characterized association of urinary peptides with kidney disease, with the goal to further our understanding of the pathophysiology of kidney disease and the related extracellular matrix remodeling (6).

What is glycomics, and what can it do for studies of glomerular disease?

Most human proteins are glycosylated by N- and/or O-linked glycans. Glycomic workflows are being developed to characterize glycosylation of proteins and better understand changes of glycosylation in organ development and disease pathogenesis (7, 8).

The kidney filtration system depends on proper glycosylation of proteins produced by the resident glomerular cells; several genetic studies revealed key roles of specific glycoproteins in normal kidney function. Moreover, changes in glycosylation of immunoglobulins (Igs) are related to glomerular diseases. For example, in IgA nephropathy, an elevated proportion of IgA1 has some of the clustered O-glycans without their normal complement of galactose; these galactose-deficient glycoforms are recognized by autoantibodies, resulting in the formation of nephritogenic complexes (9). Interestingly, IgA1 glycosylation profiles in IgA nephropathy have a strong genetic determination and recent GWAS demonstrated that O-glycosylation defects were influenced by functional genetic variants in key glycosylation enzymes (10).

As another application of glycomics to kidney disease, a recent study of monozygotic twin pairs discordant for renal function revealed that galactosylation, sialylation, and level of bisecting N-acetylglucosamine of the IgG glycans associate with GFR (11). Thus, glycomics can provide new information that is highly relevant to pathogenesis of kidney disease, and specifically glomerular disorders.

What is the role of the microbiome in the pathogenesis of glomerular disease?

At this time, we do not know. Next generation sequencing technology has the ability to accurately quantify commensal microbial communities, including their transcriptional activity and diversity across multiple body sites, such as skin, intestine, urine, and mucosal surfaces. Our recent genetic data indicate that host–pathogen interactions might have shaped the genetic susceptibility to IgA nephropathy (3), raising questions about the role of the microbiome in the pathogenesis of this disease. However, well-designed and adequately powered microbiome studies are presently missing for IgA nephropathy and other forms of glomerular disorders. The interaction between human genome, microbiome, and disease susceptibility remains one of the most exciting areas of research, and we are certain to see more glomerular nephritis-related microbiome studies in the near future.

How are Electronic Health Record data being used to enhance omics approaches?

As the number of patients undergoing GWAS, WES, and WGS continues to increase, DNA sequence information will inevitably become part of our Electronic Health Record (EHR). There are already thousands of patients with genetic data linked to EHR information for research studies. EHRs represent a rich source of phenotypic data for genetic studies, allowing us to define an electronic phenome or disease-trait signature of an individual. This allows for a completely new class of genetic studies, such as phenome-wide association studies, in which individual genetic variants are tested for associations with thousands of disease-related traits. This specific approach may be particularly helpful to establish new pleiotropic effects of genetic susceptibility variants. Other active research in this area involves the development of novel computational algorithms to refine electronic kidney phenotypes using natural language processing of clinical notes or to predict disease course and prognosis in real time on the basis of longitudinal information contained in health records. This relatively young field is evolving rapidly as our electronic health systems continue to improve in terms of accuracy and interoperability. Without a doubt, these developments will have a large effect on the future studies of glomerular diseases.

Krzysztof Kiryluk, MD, is affiliated with the College of Physicians and Surgeons, Columbia University, Department of Medicine, Division of Nephrology, and Jan Novak, PhD, is affiliated with the University of Alabama at Birmingham, Department of Microbiology.

Suggested Reading

1. Genovese G, et al. Association of trypanolytic ApoL1 variants with kidney disease in African Americans. Science 2010; 329:841–845.

2. Stanescu HC, et al. Risk HLA-DQA1 and PLA(2)R1 alleles in idiopathic membranous nephropathy. N Engl J Med 2011; 364:616–626.

3. Kiryluk K, et al. Discovery of new risk loci for IgA nephropathy implicates genes involved in immunity against intestinal pathogens. Nat Genet 2014; 46:1187–1196.

4. Beck LH Jr., et al. M-type phospholipase A2 receptor as target antigen in idiopathic membranous nephropathy. N Engl J Med 2009; 361:11–21.

5. Tomas NM, et al. Thrombospondin type-1 domain-containing 7A in idiopathic membranous nephropathy. N Engl J Med 2014; 371:2277–2287.

6. Klein J, et al. The role of urinary peptidomics in kidney disease research. Kidney Int 2016; 89:539–545.

7. Wada Y, et al. Comparison of methods for profiling O-glycosylation: Human Proteome Organisation Human Disease Glycomics/Proteome Initiative multi-institutional study of IgA1. Mol Cell Proteomics 2010; 9:719–727.

8. Takahashi K, et al. Naturally occurring structural isomers in serum IgA1 O-glycosylation. J Proteome Res 2012; 11:692–702.

9. Kiryluk K, Novak J. The genetics and immunobiology of IgA nephropathy. J Clin Invest 2014; 124:2325–2332.

10. Kiryluk K, et al. GWAS for serum galactose-deficient IgA1 implicates critical genes of the O-glycosylation pathway. PLoS Genet 2017; 13(2):e1006609.

11. Barrios C, et al. Glycosylation profile of IgG in moderate kidney dysfunction. J Am Soc Nephrol 2016; 27:933–941.