Precision Medicine Approaches to Diabetic Kidney Disease: Tissue as an Issue

Unbiased kidney-centric molecular categorization of chronic kidney disease as a step towards precision medicine

Current classification of chronic kidney disease (CKD) into stages using indirect systemic measures (estimated glomerular filtration rate (eGFR) and albuminuria) is agnostic to the heterogeneity of underlying molecular processes in the kidney thereby limiting precision medicine approaches. To generate a novel CKD categorization that directly reflects within kidney disease drivers we analyzed publicly available transcriptomic data from kidney biopsy tissue. A Self-Organizing Maps unsupervised artificial neural network machine-learning algorithm was used to stratify a total of 369 patients with CKD and 46 living kidney donors as healthy controls. Unbiased stratification of the discovery cohort resulted in identification of four novel molecular categories of disease termed CKD-Blue, CKD-Gold, CKD-Olive, CKD-Plum that were replicated in independent CKD and diabetic kidney disease datasets and can be further tested on any external data at kidneyclass.org. Each molecular category spanned across CKD stages and histopathological diagnoses and represented transcriptional activation of distinct biological pathways. Disease progression rates were highly significantly different between the molecular categories. CKD-Gold displayed rapid progression, with significant eGFR-adjusted Cox regression hazard ratio of 5.6 [1.01-31.3] for kidney failure and hazard ratio of 4.7 [1.3-16.5] for composite of kidney failure or a 40% or more eGFR decline. Urine proteomics revealed distinct patterns between the molecular categories, and a 25-protein signature was identified to distinguish CKD-Gold from other molecular categories. Thus, patient stratification based on kidney tissue omics offers a gateway to non-invasive biomarker-driven categorization and the potential for future clinical implementation, as a key step towards precision medicine in CKD.

Precision Medicine in Nephrology: An Integrative Framework of Multidimensional Data in the Kidney Precision Medicine Project

Chronic kidney disease (CKD) and acute kidney injury (AKI) are heterogeneous syndromes defined clinically by serial measures of kidney function. Each condition possesses strong histopathologic associations, including glomerular obsolescence or acute tubular necrosis, respectively. Despite such characterization, there remains wide variation in patient outcomes and treatment responses. Precision medicine efforts, as exemplified by the Kidney Precision Medicine Project (KPMP), have begun to establish evolving, spatially anchored, cellular and molecular atlases of the cell types, states, and niches of the kidney in health and disease. The KPMP atlas provides molecular context for CKD and AKI disease drivers and will help define subtypes of disease that are not readily apparent from canonical functional or histopathologic characterization but instead are appreciable through advanced clinical phenotyping, pathomic, transcriptomic, proteomic, epigenomic, and metabolomic interrogation of kidney biopsy samples. This perspective outlines the structure of the KPMP, its approach to the integration of these diverse datasets, and its major outputs relevant to future patient care.

Design and methodology of the PRIMETIME 1 cohort study: PRecIsion MEdicine based on kidney TIssue Molecular interrogation in diabetic nEphropathy

Background

Clinical features of diabetic kidney disease alone cannot differentiate between the histopathology that defines diabetic nephropathy (DN) and non-diabetic nephropathy (NDN). A kidney biopsy is necessary to make the definitive diagnosis of DN. However, there is no consensus on when to perform a kidney biopsy in individuals with diabetes and kidney disease. Furthermore, the implications of NDN versus DN for management, morbidity and kidney prognosis are unclear. To address the gap in knowledge, we aimed to create a national retrospective cohort of people with diabetes and a performed kidney biopsy.

Methods

Adults diagnosed with diabetes in Denmark between 1996 and 2020 who had a kidney biopsy performed were included. The cohort was established by linking a nationwide diabetes registry with the Danish Pathology Registry. Data from 11 national registries and databases were compiled. The type of kidney disease was classified using a three-step analysis of Systematized Nomenclature of Medicine codes reported in relation to the histopathological examinations of kidney tissue. The final cohort and classification of kidney disease was as follows: out of 485 989 individuals with diabetes 2586 were included, 2259 of whom had type 2 diabetes. We were able to classify 599 (26.5%) with DN, 703 (31.1%) with NDN and 165 (7.3%) with mixed disease in individuals with type 2 diabetes. In individuals with type 1 diabetes, 132 (40.4%) had DN, 73 (22.3%) NDN and 39 (11.9%) mixed disease. The remaining could not be classified or had normal histology. The overall median (Q1-Q3) follow-up time was 3.8 (1.6-7.2) years.

Conclusions

This cohort is a novel platform based on high-quality registry data for important longitudinal studies of the impact of kidney disease diagnosis on prognosis. With regular updates of data from the Danish registries, the presented follow-up will increase over time and is only limited by emigration or death.

Precision Medicine Approaches to Diabetic Kidney Disease: Personalized Interventions on the Horizon

Diabetic kidney disease (DKD) is a significant complication of diabetes that requires innovative interventions to address its increasing impact. Precision medicine is a rapidly emerging paradigm that shows excellent promise in tailoring therapeutic strategies to the unique profiles of individual patients. This abstract examines the potential of precision medicine in managing DKD. It explores the genetic and molecular foundations, identifies biomarkers for risk assessment, provides insights into pharmacogenomics, and discusses targeted therapies. Integrating omics data and data analytics provides a comprehensive landscape for making informed decisions. The abstract highlights the difficulties encountered during the clinical implementation process, the ethical factors to be considered, and the importance of involving patients. In addition, it showcases case studies that demonstrate the effectiveness of precision-based interventions. As the field progresses, the abstract anticipates a future characterized by the integration of artificial intelligence in diagnostics and treatment. It highlights the significant impact that precision medicine can have in revolutionizing the provision of care for DKD.

Rationale and design of a prospective, clinical study of kidney biopsies in people with type 2 diabetes and severely increased albuminuria (the PRIMETIME 2 study)

INTRODUCTION

Diabetic kidney disease is a severe complication of diabetes. The diagnosis is based on clinical characteristics such as persistently elevated albuminuria, hypertension and decline in kidney function, although this definition is not specific to kidney disease caused by diabetes. The only way to establish an accurate diagnosis-diabetic nephropathy-is by performing a kidney biopsy. The histological presentation of diabetic nephropathy can be associated with a heterogeneous range of histological features with many pathophysiological factors involved demonstrating the complexity of the condition. Current treatment strategies aim to slow disease progression and are not specific to the underlying pathological processes.This study will investigate the prevalence of diabetic nephropathy in individuals with type 2 diabetes (T2D) and severely elevated albuminuria. The deep molecular characterisation of the kidney biopsy and biological specimens may pave the way for improved diagnostic accuracy and a better understanding of the pathological processes involved and may also reveal new targets for individualised treatment.

METHODS AND ANALYSIS

In the PRecIsion MEdicine based on kidney TIssue Molecular interrogation in diabetic nEphropathy 2 study, research kidney biopsies will be performed in 300 participants with T2D, urine albumin/creatinine ratio ≥700 mg/g and estimated glomerular filtration ratio >30 mL/min/1.73 m2. Cutting-edge molecular technologies will be applied to the kidney, blood, urine, faeces and saliva samples for comprehensive multi-omics profiling. The associated disease course and clinical outcomes will be assessed by annual follow-up for 20 years.

ETHICS AND DISSEMINATION

The Danish Regional Committee on Health Research Ethics and the Knowledge Center on Data Protection (in the Capital Region of Denmark) have granted approval for the study. The results will be published in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04916132.

Osteopontin as a Biomarker in Chronic Kidney Disease

Osteopontin (OPN) is a ubiquitously expressed protein with a wide range of physiological functions, including roles in bone mineralization, immune regulation, and wound healing. OPN has been implicated in the pathogenesis of several forms of chronic kidney disease (CKD) where it promotes inflammation and fibrosis and regulates calcium and phosphate metabolism. OPN expression is increased in the kidneys, blood, and urine of patients with CKD, particularly in those with diabetic kidney disease and glomerulonephritis. The full-length OPN protein is cleaved by various proteases, including thrombin, matrix metalloproteinase (MMP)-3, MMP-7, cathepsin-D, and plasmin, producing N-terminal OPN (ntOPN), which may have more detrimental effects in CKD. Studies suggest that OPN may serve as a biomarker in CKD, and while more research is needed to fully evaluate and validate OPN and ntOPN as CKD biomarkers, the available evidence suggests that they are promising candidates for further investigation. Targeting OPN may be a potential treatment strategy. Several studies show that inhibition of OPN expression or activity can attenuate kidney injury and improve kidney function. In addition to its effects on kidney function, OPN has been linked to cardiovascular disease, which is a major cause of morbidity and mortality in patients with CKD.

Growth hormone induces transforming growth factor-β1 in podocytes: Implications in podocytopathy and proteinuria

Pituitary growth hormone (GH) is essential for growth, metabolism, and renal function. Overactive GH signaling is associated with impaired kidney function. Glomerular podocytes, a key kidney cell type, play an indispensable role in the renal filtration and express GH receptors (GHR), suggesting the direct action of GH on these cells. However, the precise mechanism and the downstream signaling events by which GH leads to diabetic nephropathy remain to be elucidated. Here we performed proteome analysis of the condition media from human podocytes and confirmed that GH-induces TGF-β1. Inhibition of GH/GHR stimulated-JAK2 signaling abrogates GH-induced TGF-β1 secretion. Mice administered with GH showed glomerular manifestations concomitant with proteinuria. Pharmacological inhibition of TGF-βR1 in mice prevented GH-induced TGF-β dependent SMAD signaling and proteinuria. Conditional deletion of GHR in podocytes protected mice from streptozotocin-induced diabetic nephropathy. GH and TGF-β1 signaling components expression was elevated in the kidneys of human diabetic nephropathy patients. Our study identifies that GH induces TGF-β1 in podocytes, contributing to diabetic nephropathy.

Emerging Role of Clinical Genetics in CKD

Chronic kidney disease (CKD) afflicts 15% of adults in the United States, of whom 25% have a family history. Genetic testing is supportive in identifying and possibly confirming diagnoses of CKD, thereby guiding care. Advances in the clinical genetic evaluation include next-generation sequencing with targeted gene panels, whole exome sequencing, and whole genome sequencing. These platforms provide DNA sequence reads with excellent coverage throughout the genome and have identified novel genetic causes of CKD. New pathologic genetic variants identified in previously unrecognized biological pathways have elucidated disease mechanisms underlying CKD etiologies, potentially establishing prognosis and guiding treatment selection. Molecular diagnoses using genetic sequencing can detect rare, potentially treatable mutations, avoid misdiagnoses, guide selection of optimal therapy, and decrease the risk of unnecessary and potentially harmful interventions. Genetic testing has been widely adopted in pediatric nephrology; however, it is less frequently used to date in adult nephrology. Extension of clinical genetic approaches to adult patients may achieve similar benefits in diagnostic refinement and treatment selection. This review aimed to identify clinical CKD phenotypes that may benefit the most from genetic testing, outline the commonly available platforms, and provide examples of successful deployment of these approaches in CKD.

Development of Biomarkers and Molecular Therapy Based on Inflammatory Genes in Diabetic Nephropathy

Diabetic Nephropathy (DN) is a debilitating consequence of both Type 1 and Type 2 diabetes affecting the kidney and renal tubules leading to End Stage Renal Disease (ESRD). As diabetes is a world epidemic and almost half of diabetic patients develop DN in their lifetime, a large group of people is affected. Due to the complex nature of the disease, current diagnosis and treatment are not adequate to halt disease progression or provide an effective cure. DN is now considered a manifestation of inflammation where inflammatory molecules regulate most of the renal physiology. Recent advances in genetics and genomic technology have identified numerous susceptibility genes that are associated with DN, many of which have inflammatory functions. Based on their role in DN, we will discuss the current aspects of developing biomarkers and molecular therapy for advancing precision medicine.

The Role of Glomerular Epithelial Injury in Kidney Function Decline in Patients With Diabetic Kidney Disease in the TRIDENT Cohort

Introduction

Although diabetic kidney disease (DKD) is responsible for more than half of all chronic and end-stage kidney disease (ESKD), the association of light (LM) and electron microscopic (EM) structural changes with clinical parameters and prognosis in DKD is incompletely understood.

Methods

This is an interim analysis of 62 patients diagnosed with biopsy-confirmed DKD from the multicenter TRIDENT (Transformative Research in Diabetic Nephropathy) study. Twelve LM and 8 EM descriptors, representing changes in glomeruli, tubulointerstitium, and vasculature were analyzed for their relationship with clinical measures of renal function. Patients were followed every 6 months.

Results

Multivariable linear regression analysis revealed that estimated glomerular filtration rate (eGFR) upon enrollment correlated the best with interstitial fibrosis. On the other hand, the rate of kidney function decline (eGFR slope) correlated the most with glomerular lesions including global glomerulosclerosis and mesangiolysis. Unbiased clustering analysis based on histopathologic data identified 3 subgroups. The first cluster, encompassing subjects with the mildest histologic lesions, had the most preserved kidney function. The second and third clusters had similar degrees of kidney dysfunction and structural damage, but differed in the degree of glomerular epithelial cell and podocyte injury (podocytopathy DKD subtype). Cox proportional hazard analysis showed that subjects in cluster 2 had the highest risk to reach ESKD (hazard ratio: 17.89; 95% confidence interval: 2.13–149.79). Glomerular epithelial hyperplasia and interstitial fibrosis were significant predictors of ESKD in the multivariate model.

Conclusion

The study highlights the association between fibrosis and kidney function and identifies the role of glomerular epithelial changes and kidney function decline.

Identifying genetic variants associated with ritodrine-induced pulmonary edema

Introduction

Ritodrine is one of the most commonly used tocolytics in preterm labor, acting as a ß2-adrenergic agonist that reduces intracellular calcium levels and prevents myometrial activation. Ritodrine infusion can result in serious maternal complications, and pulmonary edema is a particular concern among these. The cause of pulmonary edema following ritodrine treatment is multifactorial; however, the contributing genetic factors remain poorly understood. This study investigates the genetic variants associated with ritodrine-induced pulmonary edema.

Methods

In this case-control study, 16 patients who developed pulmonary edema during ritodrine infusion [case], and 16 pregnant women who were treated with ritodrine and did not develop pulmonary edema [control] were included. The control pregnant women were selected after matching for plurality and gestational age at the time of tocolytic use. Maternal blood was collected during admission for tocolytic treatment, and whole exome sequencing was performed with the stored blood samples.

Results

Gene-wise variant burden (GVB) analysis resulted in a total of 71 candidate genes by comparing the cumulative effects of multiple coding variants for 19729 protein-coding genes between the patients with pulmonary edema and the matched controls. Subsequent data analysis selected only the statistically significant and deleterious variants compatible with ritodrine-induced pulmonary edema. Two final candidate variants in CPT2 and ADRA1A were confirmed by Sanger sequencing.

Conclusions

We identified new potential variants in genes that play a role in cyclic adenosine monophosphate (cAMP)/protein kinase A (PKA) regulation, which supports their putative involvement in the predisposition to ritodrine-induced pulmonary edema in pregnant women.

Single cell transcriptomics identifies focal segmental glomerulosclerosis remission endothelial biomarker

To define cellular mechanisms underlying kidney function and failure, the KPMP analyzes biopsy tissue in a multicenter research network to build cell-level process maps of the kidney. This study aimed to establish a single cell RNA sequencing strategy to use cell-level transcriptional profiles from kidney biopsies in KPMP to define molecular subtypes in glomerular diseases. Using multiple sources of adult human kidney reference tissue samples, 22,268 single cell profiles passed KPMP quality control parameters. Unbiased clustering resulted in 31 distinct cell clusters that were linked to kidney and immune cell types using specific cell markers. Focusing on endothelial cell phenotypes, in silico and in situ hybridization methods assigned 3 discrete endothelial cell clusters to distinct renal vascular beds. Transcripts defining glomerular endothelial cells (GEC) were evaluated in biopsies from patients with 10 different glomerular diseases in the NEPTUNE and European Renal cDNA Bank (ERCB) cohort studies. Highest GEC scores were observed in patients with focal segmental glomerulosclerosis (FSGS). Molecular endothelial signatures suggested 2 distinct FSGS patient subgroups with α-2 macroglobulin (A2M) as a key downstream mediator of the endothelial cell phenotype. Finally, glomerular A2M transcript levels associated with lower proteinuria remission rates, linking endothelial function with long-term outcome in FSGS.

Diabetic kidney diseases revisited: A new perspective for a new era

BACKGROUND

Globally, diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. As the most common microvascular complication of diabetes, DKD is a thorny, clinical problem in terms of its diagnosis and management. Intensive glucose control in DKD could slow down but not significantly halt disease progression. Revisiting the tremendous advances that have occurred in the field would enhance recognition of DKD pathogenesis as well as improve our understanding of translational science in DKD in this new era.

SCOPE OF REVIEW

In this review, we summarize advances in the understanding of the local microenvironmental changes in diabetic kidneys and discuss the involvement of genetic and epigenetic factors in the pathogenesis of DKD. We also review DKD prevalence changes and analyze the challenges in optimizing the diagnostic approaches and management strategies for DKD in the clinic. As we enter the era of 'big data', we also explore the possibility of linking systems biology with translational medicine in DKD in the current healthcare system.

MAJOR CONCLUSION

Newer understanding of the structural changes of diabetic kidneys and mechanisms of DKD pathogenesis, as well as emergent research technologies will shed light on new methods of dealing with the existing clinical challenges of DKD.

Histological Examination of the Diabetic Kidney

The increasing prevalence of diabetes worldwide has led to a concomitant rise in diabetic kidney disease (DKD) as a major cause of end-stage renal disease. Glomerular lesions constitute the most striking and consistent features identified in biopsies from patients with DKD, although tubulointerstitial injury has an important and often under-recognized role in the progression to overt nephropathy. In advanced stages of the disease, podocyte detachment is a pivotal event in the loss of glomerular filtration barrier integrity and may explain, at least in part, the inability of current therapies to halt renal function decline. This chapter details the systematic method that can be used to study renal tissue samples from diabetic patients, and the specific role of different imaging techniques, such as light microscopy, immunofluorescence microscopy, and transmission and scanning electron microscopy in detecting histologic lesions specific to DKD.

Understanding the kidney one cell at a time

A revolution in cellular measurement technology is underway. Whereas prior studies have been able to analyze only the averaged outputs from renal tissue, we now can accurately monitor genome-wide gene expression, regulation, function, cellular history, and cellular interactions in thousands of individual cells in a single experiment. These methods are key drivers in changing our previous morphotype-based organ and disease descriptions to unbiased genomic definitions and therefore improving our understanding of kidney development, homeostasis, and disease.

Cytosine Methylation Studies in Patients with Diabetic Kidney Disease

PURPOSE OF THE REVIEW

Kidney disease is the major cause of morbidity and mortality in patients with diabetes. Poor glycemic control shows the strongest correlation with diabetic kidney disease (DKD) development. A period of poor glycemia increases kidney disease risk even after an extended period of improved glucose control-a phenomenon called metabolic memory. Changes in the epigenome have been proposed to mediate the metabolic memory effect, as epigenome editing enzymes are regulated by substrates of intermediate metabolism and changes in the epigenome can be maintained after cell division.

RECENT FINDINGS

Epigenome-wide association studies (EWAS) have reported differentially methylated cytosines in blood and kidney samples of DKD subjects when compared with controls. Differentially methylated cytosines were enriched on regulatory regions and some correlated with gene expression. Methylation changes predicted the speed of kidney function decline. Site-specific methylome editing tools now can be used to interrogate the functional role of differentially methylated regions. Methylome changes can be detected in blood and kidneys of patients with DKD. Methylation changes can predict future kidney function changes. Future studies shall determine their role in disease development.

Development of a 2-dimensional atlas of the human kidney with imaging mass cytometry

An incomplete understanding of the biology of the human kidney, including the relative abundances of and interactions between intrinsic and immune cells, has long constrained the development of therapies for kidney disease. The small amount of tissue obtained by renal biopsy has previously limited the ability to use patient samples for discovery purposes. Imaging mass cytometry (IMC) is an ideal technology for quantitative interrogation of scarce samples, permitting concurrent analysis of more than 40 markers on a single tissue section. Using a validated panel of metal-conjugated antibodies designed to confer unique signatures on the structural and infiltrating cells comprising the human kidney, we performed simultaneous multiplexed imaging with IMC in 23 channels on 16 histopathologically normal human samples. We devised a machine-learning pipeline (Kidney-MAPPS) to perform single-cell segmentation, phenotyping, and quantification, thus creating a spatially preserved quantitative atlas of the normal human kidney. These data define selected baseline renal cell types, respective numbers, organization, and variability. We demonstrate the utility of IMC coupled to Kidney-MAPPS to qualitatively and quantitatively distinguish individual cell types and reveal expected as well as potentially novel abnormalities in diseased versus normal tissue. Our studies define a critical baseline data set for future quantitative analysis of human kidney disease.

Kidney cytosine methylation changes improve renal function decline estimation in patients with diabetic kidney disease

Epigenetic changes might provide the biological explanation for the long-lasting impact of metabolic alterations of diabetic kidney disease development. Here we examined cytosine methylation of human kidney tubules using Illumina Infinium 450 K arrays from 91 subjects with and without diabetes and varying degrees of kidney disease using a cross-sectional design. We identify cytosine methylation changes associated with kidney structural damage and build a model for kidney function decline. We find that the methylation levels of 65 probes are associated with the degree of kidney fibrosis at genome wide significance. In total 471 probes improve the model for kidney function decline. Methylation probes associated with kidney damage and functional decline enrich on kidney regulatory regions and associate with gene expression changes, including epidermal growth factor (EGF). Altogether, our work shows that kidney methylation differences can be detected in patients with diabetic kidney disease and improve kidney function decline models indicating that they are potentially functionally important.

Patients with diabetes commonly develop diabetic kidney disease (DKD). Here Gluck et al. identify a set of probes differentially methylated in renal samples from patients with DKD, and find that inclusion of these methylation probes improves current prediction models of renal function decline.

The kidney transcriptome, from single cells to whole organs and back

PURPOSE OF REVIEW

Transcriptome analysis of human kidney samples provides an integrated output of genetic, physiological, or environmental inputs. This review summarizes recent findings including gene expression and genetic variation integration, bulk and single cell gene expression analysis, and describes how such studies have improved our understanding of kidney disease development.

RECENT FINDINGS

Bulk or whole tissue analysis of patient kidney samples identified a large number of genes, whose levels correlate with kidney function and/or structural damage. These genes were enriched for metabolic and immune functions. Using expression quantitative trait analysis, genetic variations-driven gene expression can be identified. Recent developments in single cell sequencing defined cell-type-specific gene expression changes and highlighted specific cell types for disease development.

SUMMARY

Recent advancement in whole tissue transcriptomics, specifically incorporating genotype information and single cell data have been powerful to identify kidney disease-associated genes, pathways, and cell types.

Identification of key lncRNAs contributing to diabetic nephropathy by gene co-expression network analysis

LncRNA is reported to have important role in diabetic nephropathy (DN). Here, we aim to identify key lncRNAs of DN using bioinformatics and systems biological methods. Method: Five microarray data sets from Gene Expression Omnibus (GEO) database were included. Probe sets were re-annotated. In the training set, differential expressed genes (DEGs) were identified. Weighted gene co-expression network analysis (WGCNA) was constructed to screen diabetic-related hub genes and reveal their potential biological function. Two more human data sets and mouse data sets were used as validation sets. Results: A total of 424 DEGs, including 10 lncRNAs, were filtered in the training data set. WGCNA and enrichment analysis of hub genes showed that inflammation and metabolic disorders are prominent in DN. Three key lncRNAs (NR_130134.1, NR_029395.1 and NR_038335.1) were identified. These lncRNAs are also differently expressed in another two human data sets. Functional enrichment of the mouse data sets showed consistent changes with that in human, indicating similar changes in gene expression pattern of DN and confirmed confidence of our analysis. Human podocytes and mesangial cells were culture in vitro. QPCR and fluorescence in situ hybridization were taken out to validate the expression and relationship of key lncRNAs and their related mRNAs. Results were also consistent with our analysis. Conclusions: Inflammation and metabolic disorders are prominent in DN. We identify three lncRNAs that are involved in these processes possibly by interacting with co-expressed mRNAs.

Quantitative Large-Scale Three-Dimensional Imaging of Human Kidney Biopsies: A Bridge to Precision Medicine in Kidney Disease

Kidney biopsy remains the gold standard for uncovering the pathogenesis of acute and chronic kidney diseases. However, the ability to perform high resolution, quantitative, molecular and cellular interrogation of this precious tissue is still at a developing stage compared to other fields such as oncology. Here, we discuss recent advances in performing large-scale, three-dimensional (3D), multi-fluorescence imaging of kidney biopsies and quantitative analysis referred to as 3D tissue cytometry. This approach allows the accurate measurement of specific cell types and their spatial distribution in a thick section spanning the entire length of the biopsy. By uncovering specific disease signatures, including rare occurrences, and linking them to the biology in situ, this approach will enhance our understanding of disease pathogenesis. Furthermore, by providing accurate quantitation of cellular events, 3D cytometry may improve the accuracy of prognosticating the clinical course and response to therapy. Therefore, large-scale 3D imaging and cytometry of kidney biopsy is poised to become a bridge towards personalized medicine for patients with kidney disease.

Human Kidney Tubule-Specific Gene Expression Based Dissection of Chronic Kidney Disease Traits

Chronic kidney disease (CKD) has diverse phenotypic manifestations including structural (such as fibrosis) and functional (such as glomerular filtration rate and albuminuria) alterations. Gene expression profiling has recently gained popularity as an important new tool for precision medicine approaches. Here we used unbiased and directed approaches to understand how gene expression captures different CKD manifestations in patients with diabetic and hypertensive CKD.

Transcriptome data from ninety-five microdissected human kidney samples with a range of demographics, functional and structural changes were used for the primary analysis. Data obtained from 41 samples were available for validation. Using the unbiased Weighted Gene Co-Expression Network Analysis (WGCNA) we identified 16 co-expressed gene modules.

We found that modules that strongly correlated with eGFR primarily encoded genes with metabolic functions. Gene groups that mainly encoded T-cell receptor and collagen pathways, showed the strongest correlation with fibrosis level, suggesting that these two phenotypic manifestations might have different underlying mechanisms. Linear regression models were then used to identify genes whose expression showed significant correlation with either structural (fibrosis) or functional (eGFR) manifestation and mostly corroborated the WGCNA findings.

We concluded that gene expression is a very sensitive sensor of fibrosis, as the expression of 1654 genes correlated with fibrosis even after adjusting to eGFR and other clinical parameters. The association between GFR and gene expression was mostly mediated by fibrosis. In conclusion, our transcriptome-based CKD trait dissection analysis suggests that the association between gene expression and renal function is mediated by structural changes and that there may be differences in pathways that lead to decline in kidney function and the development of fibrosis, respectively.

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Gene expression analysis of kidney samples shows the relationship between gene expression and eGFR is mediated by fibrosis

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Immune related pathways show the strongest correlation with fibrosis development

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Metabolic pathways show a strong correlation with eGFR

Chronic kidney disease is characterized by functional changes (glomerular filtration rate, eGFR) and structural changes (mainly renal fibrosis). Gene expression profiles of human kidney samples were analyzed to understand the relationship between these two manifestations. We found that the association between gene expression and eGFR is mediated by fibrosis, suggesting that fibrosis is a crucial determinant of functional kidney decline, and a potential therapeutic target. Gene expression analysis also indicates that fibrosis strongly correlates with immune pathways, and eGFR with metabolic pathways, highlighting potential mechanistic differences between structural and functional manifestations of kidney disease.