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

Metabolome evidence of CKDu risks after chronic exposure to simulated Sri Lanka drinking water in zebrafish

It is still a serious public health issue that chronic kidney disease of uncertain etiology (CKDu) in Sri Lanka poses challenges in identification, prevention, and treatment. What environmental factors in drinking water cause kidney damage remains unclear. This study aimed to investigate the risks of various environmental factors that may induce CKDu, including water hardness, fluoride (HF), heavy metals (HM), microcystin-LR (MC-LR), and their combined exposure (HFMM). The research focused on comprehensive metabolome analysis, and correlation with transcriptomic and gut microbiota changes. Results revealed that chronic exposure led to kidney damage and pancreatic toxicity in adult zebrafish. Metabolomics profiling showed significant alterations in biochemical processes, with enriched metabolic pathways of oxidative phosphorylation, folate biosynthesis, arachidonic acid metabolism, FoxO signaling pathway, lysosome, pyruvate metabolism, and purine metabolism. The network analysis revealed significant changes in metabolites associated with renal function and diseases, including 20-Hydroxy-LTE4, PS(18:0/22:2(13Z,16Z)), Neuromedin N, 20-Oxo-Leukotriene E4, and phenol sulfate, which are involved in the fatty acyls and glycerophospholipids class. These metabolites were closely associated with the disrupted gut bacteria of g_ZOR0006, g_Pseudomonas, g_Tsukamurella, g_Cetobacterium, g_Flavobacterium, which belonged to dominant phyla of Firmicutes and Proteobacteria, etc., and differentially expressed genes (DEGs) such as egln3, ca2, jun, slc2a1b, and gls2b in zebrafish. Exploratory omics analyses revealed the shared significantly changed pathways in transcriptome and metabolome like calcium signaling and necroptosis, suggesting potential biomarkers for assessing kidney disease.

Elucidating environmental factors and their combined effects on CKDu in Sri Lanka using zebrafish

Chronic kidney disease with uncertain etiology (CKDu) in Sri Lanka has attracted much attention as a global health issue. However, how environmental factors in local drinking water induce kidney damage in organisms is still elusive. We investigated multiple environmental factors including water hardness and fluoride (HF), heavy metals (HM), microcystin-LR (MC-LR), and their combined exposure (HFMM) to elucidate their toxic effects on CKDu risk in zebrafish. Acute exposure affected renal development and inhibited the fluorescence of Na, K-ATPase alpha1A4:GFP zebrafish kidney. Chronic exposure influenced the body weight of both genders of adult fish and induced kidney damage by histopathological analyses. Furthermore, the exposure significantly disturbed differential expression genes (DEGs), diversity and richness of gut microbiota, and critical metabolites related to renal functions. The transcriptomic analysis revealed that kidney-related DEGs were linked with renal cell carcinoma, proximal tubule bicarbonate reclamation, calcium signaling pathway, and HIF-1 signaling pathway. The significantly disrupted intestinal microbiota was closely related to the environmental factors and H&E score, which demonstrated the mechanisms of kidney risks. Notably, the Spearman correlation analysis indicated that the changed bacteria such as Pseudomonas, Paracoccus, and ZOR0006, etc were significantly connected to the DEGs and metabolites. Therefore, the assessment of multiple environmental factors provided new insights on "bio-markers" as potential therapies of the target signaling pathways, metabolites, and gut bacteria to monitor or protect residents from CKDu.

Allograft tissue under the microscope: only the beginning

PURPOSE OF REVIEW

To review novel modalities for interrogating a kidney allograft biopsy to complement the current Banff schema.

RECENT FINDINGS

Newer approaches of Artificial Intelligence (AI), Machine Learning (ML), digital pathology including Ex Vivo Microscopy, evaluation of the biopsy gene expression using bulk, single cell, and spatial transcriptomics and spatial proteomics are now available for tissue interrogation.

SUMMARY

Banff Schema of classification of allograft histology has standardized reporting of tissue pathology internationally greatly impacting clinical care and research. Inherent sampling error of biopsies, and lack of automated morphometric analysis with ordinal outputs limit its performance in prognostication of allograft health. Over the last decade, there has been an explosion of newer methods of evaluation of allograft tissue under the microscope. Digital pathology along with the application of AI and ML algorithms could revolutionize histopathological analyses. Novel molecular diagnostics such as spatially resolved single cell transcriptomics are identifying newer mechanisms underlying the pathologic diagnosis to delineate pathways of immunological activation, tissue injury, repair, and regeneration in allograft tissues. While these techniques are the future of tissue analysis, costs and complex logistics currently limit their clinical use.

Omics and Artificial Intelligence in Kidney Diseases

Omics applications in nephrology may have relevance in the future to improve clinical care of kidney disease patients. In a short term, patients will benefit from specific measurement and computational analyses around biomarkers identified at various omics-levels. In mid term and long term, these approaches will need to be integrated into a holistic representation of the kidney and all its influencing factors for individualized patient care. Research demonstrates robust data to justify the application of omics for better understanding, risk stratification, and individualized treatment of kidney disease patients. Despite these advances in the research setting, there is still a lack of evidence showing the combination of omics technologies with artificial intelligence and its application in clinical diagnostics and care of patients with kidney disease.

Modeling development of genitourinary birth defects to understand disruption due to changes in gene dosage

Genitourinary development is a delicately orchestrated process that begins in the embryo. Once complete, the genitourinary system is a collection of functionally disparate organs spread throughout the abdominal and pelvic regions. These distinct organs are interconnected through an elaborate duct system which aggregates the organs' products to a common exit point. The complicated nature of the genitourinary system makes it highly susceptible to developmental disruptions that produce anomalies. In fact, genitourinary anomalies are among the most common class of human birth defects. Aside from congenital anomalies of the kidney and urinary tract (CAKUT), for males, these birth defects can also occur in the penis (hypospadias) and testis (cryptorchism), which impact male fertility and male mental health. As genetic technology has advanced, it has become clear that a subset of cases of genitourinary birth defects are due to gene variation causing dosage changes in critical regulatory genes. Here we first review the parallels between human and mouse genitourinary development. We then demonstrate how translational research leverages mouse models of human gene variation cases to advance mechanistic understanding of causation in genitourinary birth defects. We close with a view to the future highlighting upcoming technologies that will provide a deeper understanding of gene variation affecting regulation of genitourinary development, which should ultimately advance treatment options for patients.

Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments

The functional interpretation of genome-wide association studies (GWAS) is challenging due to the cell-type-dependent influences of genetic variants. Here, we generated comprehensive maps of expression quantitative trait loci (eQTLs) for 659 microdissected human kidney samples and identified cell-type-eQTLs by mapping interactions between cell type abundances and genotypes. By partitioning heritability using stratified linkage disequilibrium score regression to integrate GWAS with single-cell RNA sequencing and single-nucleus assay for transposase-accessible chromatin with high-throughput sequencing data, we prioritized proximal tubules for kidney function and endothelial cells and distal tubule segments for blood pressure pathogenesis. Bayesian colocalization analysis nominated more than 200 genes for kidney function and hypertension. Our study clarifies the mechanism of commonly used antihypertensive and renal-protective drugs and identifies drug repurposing opportunities for kidney disease.

Single-cell mapper (scMappR): using scRNA-seq to infer the cell-type specificities of differentially expressed genes

RNA sequencing (RNA-seq) is widely used to identify differentially expressed genes (DEGs) and reveal biological mechanisms underlying complex biological processes. RNA-seq is often performed on heterogeneous samples and the resulting DEGs do not necessarily indicate the cell-types where the differential expression occurred. While single-cell RNA-seq (scRNA-seq) methods solve this problem, technical and cost constraints currently limit its widespread use. Here we present single cell Mapper (scMappR), a method that assigns cell-type specificity scores to DEGs obtained from bulk RNA-seq by leveraging cell-type expression data generated by scRNA-seq and existing deconvolution methods. After evaluating scMappR with simulated RNA-seq data and benchmarking scMappR using RNA-seq data obtained from sorted blood cells, we asked if scMappR could reveal known cell-type specific changes that occur during kidney regeneration. scMappR appropriately assigned DEGs to cell-types involved in kidney regeneration, including a relatively small population of immune cells. While scMappR can work with user-supplied scRNA-seq data, we curated scRNA-seq expression matrices for ∼100 human and mouse tissues to facilitate its stand-alone use with bulk RNA-seq data from these species. Overall, scMappR is a user-friendly R package that complements traditional differential gene expression analysis of bulk RNA-seq data.

The study of single cells in diabetic kidney disease

In the past few years there has been a rapid expansion of interest in the study of single cells, especially through the new techniques that involve single-cell RNA sequencing (scRNA-seq). Recently, these techniques have provided new insights into kidney health and disease, including insights into diabetic kidney disease (DKD). However, despite the interest and the technological advances, the study of individual cells in DKD is not a new concept. Many clinicians and researchers who work within the DKD space may be familiar with experimental techniques that actually involve the study of individual cells, but may be unfamiliar with newer scRNA-seq technology. Here, with the goal of improving accessibility to the single-cell field, we provide a primer on single-cell studies with a focus on DKD. We situate the technology in its historical context and provide a brief explanation of the common aspects of the different technologies available. Then we review some of the most important recent studies of kidney (patho)biology that have taken advantage of scRNA-seq techniques, before emphasizing the new insights into the molecular pathogenesis of DKD gleaned with these techniques. Finally, we highlight common pitfalls and limitations of scRNA-seq methods and we look toward the future to how single-cell experiments may be incorporated into the study of DKD and how to interpret the findings of these experiments.

Kidney Histopathology and Prediction of Kidney Failure: A Retrospective Cohort Study

RATIONALE & OBJECTIVE

The use of kidney histopathology for predicting kidney failure is not established. We hypothesized that the use of histopathologic features of kidney biopsy specimens would improve prediction of clinical outcomes made using demographic and clinical variables alone.

STUDY DESIGN

Retrospective cohort study and development of a clinical prediction model.

SETTING & PARTICIPANTS

All 2,720 individuals from the Biopsy Biobank Cohort of Indiana who underwent kidney biopsy between 2002 and 2015 and had at least 2 years of follow-up.

NEW PREDICTORS & ESTABLISHED PREDICTORS

Demographic variables, comorbid conditions, baseline clinical characteristics, and histopathologic features.

OUTCOMES

Time to kidney failure, defined as sustained estimated glomerular filtration rate ≤ 10mL/min/1.73m².

ANALYTICAL APPROACH

Multivariable Cox regression model with internal validation by bootstrapping. Models including clinical and demographic variables were fit with the addition of histopathologic features. To assess the impact of adding a histopathology variable, the amount of variance explained (r²) and the C index were calculated. The impact on prediction was assessed by calculating the net reclassification index for each histopathologic variable and for all combined.

RESULTS

Median follow-up was 3.1 years. Within 5 years of biopsy, 411 (15.1%) patients developed kidney failure. Multivariable analyses including demographic and clinical variables revealed that severe glomerular obsolescence (adjusted HR, 2.03; 95% CI, 1.51-2.03), severe interstitial fibrosis and tubular atrophy (adjusted HR, 1.99; 95% CI, 1.52-2.59), and severe arteriolar hyalinosis (adjusted HR, 1.53; 95% CI, 1.14-2.05) were independently associated with the primary outcome. The addition of all histopathologic variables to the clinical model yielded a net reclassification index for kidney failure of 5.1% (P < 0.001) with a full model C statistic of 0.915. Analyses addressing the competing risk for death, optimism, or shrinkage did not significantly change the results.

LIMITATIONS

Selection bias from the use of clinically indicated biopsies and exclusion of patients with less than 2 years of follow-up, as well as reliance on surrogate indicators of kidney failure onset.

CONCLUSIONS

A model incorporating histopathologic features from kidney biopsy specimens improved prediction of kidney failure and may be valuable clinically. Future studies will be needed to understand whether even more detailed characterization of kidney tissue may further improve prognostication about the future trajectory of estimated glomerular filtration rate.

Contemporary Renal Tumor Categorization With Biomarker and Translational Updates: A Practical Review

CONTEXT.—

Renal tumor classification has evolved in recent decades, as evidenced by the comparable complexity of the 2016 revision to the World Health Organization Classification of Tumours of the Urinary System and Male Genital Organs. A recent expansion of the knowledge base surrounding the cells of origin and evolutionary genomic characteristics of renal tumors has led to molecular characterization of novel entities and enriched understanding of established entities. This pace of research and its implementation into clinical practice has again begun to surpass that of our own classification schemata, with significant discoveries having been made since the introduction of the 2016 revision to the World Health Organization classification. In particular, biomarkers for renal tumor diagnosis and prognosis are in translation for future clinical application.

OBJECTIVES.—

To provide a brief framework for clinical characterization of renal tumors rooted in morphologic assessment, to briefly review the current and future status of renal tumor biomarkers with an emphasis on practical use of these ancillary tools for accurate diagnosis, and to discuss the impact of emerging technologies and clinical trials relevant to renal cell carcinoma classification and biomarker development.

DATA SOURCES.—

We review recent literature relevant to renal tumor classification (including established and proposed entities), focusing on molecular characterization and biomarker assessment.

CONCLUSIONS.—

Accurate renal tumor diagnosis requires an up-to-date understanding of renal tumor classification, including an awareness of morphologic clues that should stimulate consideration of molecularly defined entities, as well as the ancillary biomarker testing required to confirm diagnoses.