Connectivity mapping of a chronic kidney disease progression signature identified lysine deacetylases as novel therapeutic targets

Screening of potential drugs for the treatment of diabetic kidney disease using single-cell transcriptome sequencing and connectivity map data

OBJECTIVE

To explore the feasibility of screening potential drugs for the treatment of diabetic kidney disease (DKD) using a single-cell transcriptome sequencing dataset and Connectivity Map (CMap) database screening.

METHODS

A DKD single-nucleus transcriptome sequencing dataset was analyzed using Seurat 4.0 to obtain specific podocyte subclusters and differentially expressed genes (DEGs) related to DKD. These DEGs were subsequently subjected to a search against the CMap database to screen for drug candidates. Cell and animal experiments were conducted to evaluate the efficacy of the top 3 drug candidates.

RESULTS

Initially, we analyzed the DKD single-nucleus transcriptome sequencing dataset to obtain intrinsic renal cells such as podocytes, endothelial cells, mesangial cells, proximal tubular cells, collecting duct cells and immune cells. Podocytes were further divided into four subclusters, among which the proportion of POD_1 podcytes was significantly greater in DKD kidneys than in control kidneys (34.0 % vs. 3.4 %). The CMap database was searched using the identified DEGs in the POD_1 subcluster, and the drugs, including tozasertib, paroxetine, and xylazine, were obtained. Cell-based experiments showed that tozasertib, paroxetine and xylazine had no significant podocyte toxicity in the concentration range of 0.01-50 μM. Tozasertib, paroxetine, and xylazine all reversed the advanced glycation end products (AGEs)-induced decrease in podocyte marker levels, but the effect of paroxetine was more prominent. Animal experiments showed that paroxetine decreased urine ALB/Cr levels in DKD model mice by approximately 51.5 % (115.7 mg/g vs. 238.8 mg/g, P < 0.05). Histopathological assessment revealed that paroxetine attenuated basement membrane thickening, restored the number of foot processes of podocytes, and reduced foot process fusion. In addition, paroxetine also attenuated renal tubular-interstitial fibrosis. Mechanistically, paroxetine inhibited the expression of GRK2 and NLRP3, decreased the phosphorylation level of p65, restored NRF2 expression, and relieved inflammation and oxidative stress.

CONCLUSION

This strategy based on single-cell transcriptome sequencing and CMap data can facilitate the identification and aid the rapid development of clinical DKD drugs. Paroxetine, screened by this strategy, has excellent renoprotective effects.

A Systems Nephrology Approach to Diabetic Kidney Disease Research and Practice

BACKGROUND

Diagnosis and staging of diabetic kidney disease (DKD) via the serial assessment of routine laboratory indices lacks the granularity required to resolve the heterogeneous disease mechanisms driving progression in the individual patient. A systems nephrology approach may help resolve mechanisms underlying this clinically apparent heterogeneity, paving a way for targeted treatment of DKD.

SUMMARY

Given the limited access to kidney tissue in routine clinical care of patients with DKD, data derived from renal tissue in preclinical model systems, including animal and in vitro models, can play a central role in the development of a targeted systems-based approach to DKD. Multi-centre prospective cohort studies, including the Kidney Precision Medicine Project (KPMP) and the European Nephrectomy Biobank (ENBiBA) project, will improve access to human diabetic kidney tissue for research purposes. Integration of diverse data domains from such initiatives including clinical phenotypic data, renal and retinal imaging biomarkers, histopathological and ultrastructural data, and an array of molecular omics (transcriptomics, proteomics, etc.) alongside multi-dimensional data from preclinical modelling offers exciting opportunities to unravel individual-level mechanisms underlying progressive DKD. The application of machine and deep learning approaches may particularly enhance insights derived from imaging and histopathological/ultrastructural data domains.

KEY MESSAGES

Integration of data from multiple model systems (in vitro, animal models, and patients) and from diverse domains (clinical phenotypic, imaging, histopathological/ultrastructural, and molecular omics) offers potential to create a precision medicine approach to DKD care wherein the right treatments are offered to the right patients at the right time.

Network medicine: an approach to complex kidney disease phenotypes

Scientific reductionism has been the basis of disease classification and understanding for more than a century. However, the reductionist approach of characterizing diseases from a limited set of clinical observations and laboratory evaluations has proven insufficient in the face of an exponential growth in data generated from transcriptomics, proteomics, metabolomics and deep phenotyping. A new systematic method is necessary to organize these datasets and build new definitions of what constitutes a disease that incorporates both biological and environmental factors to more precisely describe the ever-growing complexity of phenotypes and their underlying molecular determinants. Network medicine provides such a conceptual framework to bridge these vast quantities of data while providing an individualized understanding of disease. The modern application of network medicine principles is yielding new insights into the pathobiology of chronic kidney diseases and renovascular disorders by expanding the understanding of pathogenic mediators, novel biomarkers and new options for renal therapeutics. These efforts affirm network medicine as a robust paradigm for elucidating new advances in the diagnosis and treatment of kidney disorders.

Inhibition of HDAC6 With CAY10603 Ameliorates Diabetic Kidney Disease by Suppressing NLRP3 Inflammasome

Background: Diabetic nephropathy (DN) is one of the leading causes of chronic kidney disease (CKD) worldwide, tubular injury is the driving force during the pathogenesis and progression of DN. Thus, we aim to utilize the connectivity map (CMap) with renal tubulointerstitial transcriptomic profiles of biopsy-proven DN to identify novel drugs for treating DN.

Methods: We interrogated the CMap profile with tubulointerstitial transcriptomic data from renal biopsy-proven early- and late-stage DN patients to screen potential drugs for DN. Therapeutic effects of candidate drug were assessed in Murine model of diabetic kidney disease (STZ-induced CD-1 mice), and HK-2 cells and immortalized bone marrow-derived macrophages (iBMDMs).

Results: We identified CAY10603, a specific inhibitor of histone deacetylase 6 (HDAC6), as a potential drug that could significantly reverse the altered genes in the tubulointerstitial component. In DN patients and mice, upregulation of HDAC6 was mainly observed in renal tubular cells and infiltrated macrophages surrounding the diluted tubules. In both early- and late-onset diabetic mice, daily CAY10603 administration effectively alleviated renal dysfunction and reduced macrophage infiltration, tubular injury and tubulointerstitial fibrosis. Mechanistically, CAY10603 suppressed NLRP3 activation in both HK-2 cells and iBMDMs.

Conclusion: CAY10603 exhibited therapeutic potential for DN by suppressing NLRP3 inflammasome activation in both tubular cells and macrophages.

Identification of Hub Genes and Therapeutic Agents for IgA Nephropathy Through Bioinformatics Analysis and Experimental Validation

Background

IgA nephropathy (IgAN) is the most common primary glomerular disease and the leading cause of the end-stage renal disease in the world. The pathogenesis of IgAN has not been well elucidated, and yet treatment is limited. High-throughput microarray has been applied for elucidating molecular biomarkers and potential mechanisms involved in IgAN. This study aimed to identify the potential key genes and therapeutics associated with IgAN using integrative bioinformatics and transcriptome-based computational drug repurposing approach.

Methods

Three datasets of mRNA expression profile were obtained from the gene expression omnibus database and differentially expressed genes (DEGs) between IgAN glomeruli and normal tissue were identified by integrated analysis. Gene ontology and pathway enrichment analyses of the DEGs were performed by R software, and protein-protein interaction networks were constructed using the STRING online search tool. External dataset and immunohistochemical assessment of kidney biopsy specimens were used for hub gene validation. Potential compounds for IgAN therapy were obtained by Connectivity Map (CMap) analysis and preliminarily verified in vitro. Stimulated human mesangial cells were collected for cell proliferation and cell cycle analysis using cell counting kit 8 and flow cytometry, respectively.

Results

134 DEGs genes were differentially expressed across kidney transcriptomic data from IgAN patients and healthy living donors. Enrichment analysis showed that the glomerular compartments underwent a wide range of interesting pathological changes during kidney injury, focused on anion transmembrane transporter activity and protein digestion and absorption mostly. Hub genes (ITGB2, FCER1G, CSF1R) were identified and verified to be significantly upregulated in IgAN patients, and associated with severity of renal lesions. Computational drug repurposing with the CMap identified tetrandrine as a candidate treatment to reverse IgAN hub gene expression. Tetrandrine administration significantly reversed mesangial cell proliferation and cell cycle transition.

Conclusion

The identification of DEGs and related therapeutic strategies of IgAN through this integrated bioinformatics analysis provides a valuable resource of therapeutic targets and agents of IgAN. Especially, our findings suggest that tetrandrine might be beneficial for IgAN, which deserves future research.

Epithelial-mesenchymal transition in organ fibrosis development: current understanding and treatment strategies

Organ fibrosis is a process in which cellular homeostasis is disrupted and extracellular matrix is excessively deposited. Fibrosis can lead to vital organ failure and there are no effective treatments yet. Although epithelial–mesenchymal transition (EMT) may be one of the key cellular mechanisms, the underlying mechanisms of fibrosis remain largely unknown. EMT is a cell phenotypic process in which epithelial cells lose their cell-to-cell adhesion and polarization, after which they acquire mesenchymal features such as infiltration and migration ability. Upon injurious stimulation in different organs, EMT can be triggered by multiple signaling pathways and is also regulated by epigenetic mechanisms. This narrative review summarizes the current understanding of the underlying mechanisms of EMT in fibrogenesis and discusses potential strategies for attenuating EMT to prevent and/or inhibit fibrosis. Despite better understanding the role of EMT in fibrosis development, targeting EMT and beyond in developing therapeutics to tackle fibrosis is challenging but likely feasible.

Identification of small molecules as novel anti-adipogenic compounds based on Connectivity Map

Several physiological and pathological conditions such as aging, obesity, diabetes, anorexia nervosa are associated with increased adipogenesis in the bone marrow. A lack of effective drugs hinder the improved treatment for aberrant accumulation of bone marrow adipocytes. Given the higher costs, longer duration and sometimes lack of efficacy in drug discovery, computational and experimental strategies have been used to identify previously approved drugs for the treatment of diseases, also known as drug repurposing. Here, we describe the method of small molecule-prioritization by employing adipocyte-specific genes using the connectivity map (CMap). We then generated transcriptomic profiles using human mesenchymal stromal cells under adipogenic differentiation with the treatment of prioritized compounds, and identified emetine and kinetin-riboside to have a potent inhibitory effect on adipogenesis. Overall, we demonstrated a proof-of-concept method to identify repurposable drugs capable of inhibiting adipogenesis, using the Connectivity Map.

Follistatin-Like-1 (FSTL1) Is a Fibroblast-Derived Growth Factor That Contributes to Progression of Chronic Kidney Disease

Our understanding of the mechanisms responsible for the progression of chronic kidney disease (CKD) is incomplete. Microarray analysis of kidneys at 4 and 7 weeks of age in Col4a3^-/- mice, a model of progressive nephropathy characterized by proteinuria, interstitial fibrosis, and inflammation, revealed that Follistatin-like-1 (Fstl1) was one of only four genes significantly overexpressed at 4 weeks of age. mRNA levels for the Fstl1 receptors, Tlr4 and Dip2a, increased in both Col4a^-/- mice and mice subjected to unilateral ureteral obstruction (UUO). RNAscope^® (Advanced Cell Diagnostics, Newark CA, USA) localized Fstl1 to interstitial cells, and in silico analysis of single cell transcriptomic data from human kidneys showed Fstl1 confined to interstitial fibroblasts/myofibroblasts. In vitro, FSTL1 activated AP1 and NFκB, increased collagen I (COL1A1) and interleukin-6 (IL6) expression, and induced apoptosis in cultured kidney cells. FSTL1 expression in the NEPTUNE cohort of humans with focal segmental glomerulosclerosis (FSGS), membranous nephropathy (MN), and IgA nephropathy (IgAN) was positively associated with age, eGFR, and proteinuria by multiple linear regression, as well as with interstitial fibrosis and tubular atrophy. Clinical disease progression, defined as dialysis or a 40 percent reduction in eGFR, was greater in patients with high baseline FSTL1 mRNA levels. FSTL1 is a fibroblast-derived cytokine linked to the progression of experimental and clinical CKD.

Deconvolution of Focal Segmental Glomerulosclerosis Pathophysiology Using Transcriptomics Techniques

Background

Focal segmental glomerulosclerosis is a histopathological pattern of renal injury and comprises a heterogeneous group of clinical conditions with different pathophysiology, clinical course, prognosis, and treatment. Nevertheless, subtype differentiation in clinical practice often remains challenging, and we currently lack reliable diagnostic, prognostic, and therapeutic biomarkers. The advent of new transcriptomics techniques in kidney research poses great potential in the identification of gene expression biomarkers that can be applied in clinical practice.

Summary

Transcriptomics techniques have been completely revolutionized in the last 2 decades, with the evolution from low-throughput reverse-transcription polymerase chain reaction and in situ hybridization techniques to microarrays and next-generation sequencing techniques, including RNA-sequencing and single-cell transcriptomics. The integration of human gene expression profiles with functional in vitro and in vivo experiments provides a deeper mechanistic insight into the candidate genes, which enable the development of novel-targeted therapies. The correlation of gene expression profiles with clinical outcomes of large patient cohorts allows for the development of clinically applicable biomarkers that can aid in diagnosis and predict prognosis and therapy response. Finally, the integration of transcriptomics with other "omics" modalities creates a holistic view on disease pathophysiology.

Key Messages

New transcriptomics techniques allow high-throughput gene expression profiling of patients with focal segmental glomerulosclerosis (FSGS). The integration with clinical outcomes and fundamental mechanistic studies enables the discovery of new clinically useful biomarkers that will finally improve the clinical outcome of patients with FSGS.

Natural killer T cell/IL-4 signaling promotes bone marrow-derived fibroblast activation and M2 macrophage-to-myofibroblast transition in renal fibrosis

Renal fibrosis is a histological manifestation of chronic kidney disease. Natural killer T (NKT) cells have a critical role in the pathogenesis of fibrotic disorder. However, the role of NKT cells in regulating kidney fibrosis remains largely unknown. In the current study, we showed that the percentages of NKT⁺ cells and NKT⁺-IL-4⁺ cells were notably increased in folic acid (FA) and obstructive nephropathy. CD1d deficiency protected mice from renal fibrosis induced by FA and obstructive injury. Specifically, Loss of CD1d reduced bone marrow-derived myofibroblasts and CD206⁺/α-smooth muscle actin⁺ cells in the kidneys of injured mice. But mice treated with α-galactosylceramide (α-GC, a specific activator of NKT cells) developed more severe fibrosis, accumulated more myeloid myofibroblasts and M2 macrophages-myofibroblasts transition (M2MMT) cells in FA injured kidneys. Furthermore, IL-4 expression was markedly reduced in CD1d deficiency mice but increased in α-GC-treated mice. Administration of IL-4 abrogates the inhibiting effect of CD1d deficiency on renal fibrosis, bone marrow-derived fibroblasts activation, and M2MMT in FA injured kidneys. Conversely, pharmacological inhibition of IL-4 attenuated the development of renal fibrosis, decreased bone marrow-derived myofibroblasts, and suppressed M2MMT. Thus, this study revealed a novel role of NKT cells in the bone marrow-derived fibroblasts activation and M2MMT during renal fibrosis. Targeting NKT cell/IL-4 signaling may be an effective treatment for renal fibrosis.

Perspectives in systems nephrology

Chronic kidney diseases (CKD) are a major health problem affecting approximately 10% of the world’s population and posing increasing challenges to the healthcare system. While CKD encompasses a broad spectrum of pathological processes and diverse etiologies, the classification of kidney disease is currently based on clinical findings or histopathological categorizations. This descriptive classification is agnostic towards the underlying disease mechanisms and has limited progress towards the ability to predict disease prognosis and treatment responses. To gain better insight into the complex and heterogeneous disease pathophysiology of CKD, a systems biology approach can be transformative. Rather than examining one factor or pathway at a time, as in the reductionist approach, with this strategy a broad spectrum of information is integrated, including comprehensive multi-omics data, clinical phenotypic information, and clinicopathological parameters. In recent years, rapid advances in mathematical, statistical, computational, and artificial intelligence methods enable the mapping of diverse big data sets. This holistic approach aims to identify the molecular basis of CKD subtypes as well as individual determinants of disease manifestation in a given patient. The emerging mechanism-based patient stratification and disease classification will lead to improved prognostic and predictive diagnostics and the discovery of novel molecular disease-specific therapies.

Deep convolutional neural network for chronic kidney disease prediction using ultrasound imaging

Objectives

Chronic kidney disease (CKD) is a common disease and it is related to a higher risk of cardiovascular disease and end-stage renal disease that can be prevented by the earlier recognition and diagnosis of individuals at risk. Even though risk factors for CKD have been recognized, the effectiveness of CKD risk classification via prediction models remains uncertain. This paper intends to introduce a new predictive model for CKD using US image.

Methods

The proposed model includes three main phases “(1) preprocessing, (2) feature extraction, (3) and classification.” In the first phase, the input image is subjected to preprocessing, which deploys image inpainting and median filtering processes. After preprocessing, feature extraction takes place under four cases; (a) texture analysis to detect the characteristics of texture, (b) proposed high-level feature enabled local binary pattern (LBP) extraction, (c) area based feature extraction, and (d) mean intensity based feature extraction. These extracted features are then subjected for classification, where “optimized deep convolutional neural network (DCNN)” is used. In order to make the prediction more accurate, the weight and the activation function of DCNN are optimally chosen by a new hybrid model termed as diversity maintained hybrid whale moth flame optimization (DM-HWM) model.

Results

The accuracy of adopted model at 40th training percentage was 44.72, 11.02, 5.59, 3.92, 3.92, 3.57, 2.59, 1.71, 1.68, and 0.42% superior to traditional artificial neural networks (ANN), support vector machine (SVM), NB, J48, NB-tree, LR, composite hypercube on iterated random projection (CHIRP), CNN, moth flame optimization (MFO), and whale optimization algorithm (WOA) models.

Conclusions

Finally, the superiority of the adopted scheme is validated over other conventional models in terms of various measures.

Acute Kidney Injury Results in Long-Term Diastolic Dysfunction That Is Prevented by Histone Deacetylase Inhibition

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This is the first long-term (1-year) study to evaluate both the kidney and systemic sequelae of acute kidney injury in mice.

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Serial kidney function was measured via transcutaneous glomerular filtration rate.

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AKI resulted in diastolic dysfunction, followed by hypertension. Ejection fraction was preserved. One year after AKI, cardiac ATP levels were reduced compared with sham controls.

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Mice treated with the histone deacetylase inhibitor, ITF2357, maintained normal diastolic function normal blood pressure, and normal cardiac ATP after AKI.

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Metabolomics data suggest that treatment with ITF2357 preserves pathways related to energy metabolism.

Growing epidemiological data demonstrate that acute kidney injury (AKI) is associated with long-term cardiovascular morbidity and mortality. Here, the authors present a 1-year study of cardiorenal outcomes following bilateral ischemia-reperfusion injury in male mice. These data suggest that AKI causes long-term dysfunction in the cardiac metabolome, which is associated with diastolic dysfunction and hypertension. Mice treated with the histone deacetylase inhibitor, ITF2357, had preservation of cardiac function and remained normotensive throughout the study. ITF2357 did not protect against the development of kidney fibrosis after AKI.

Inhibition of BRD4 Reduces Neutrophil Activation and Adhesion to the Vascular Endothelium Following Ischemia Reperfusion Injury

Renal ischemia reperfusion injury (IRI) is associated with inflammation, including neutrophil infiltration that exacerbates the initial ischemic insult. The molecular pathways involved are poorly characterized and there is currently no treatment. We performed an in silico analysis demonstrating changes in NFκB-mediated gene expression in early renal IRI. We then evaluated NFκB-blockade with a BRD4 inhibitor on neutrophil adhesion to endothelial cells in vitro, and tested BRD4 inhibition in an in vivo IRI model. BRD4 inhibition attenuated neutrophil adhesion to activated endothelial cells. In vivo, IRI led to increased expression of cytokines and adhesion molecules at 6 h post-IRI with sustained up-regulated expression to 48 h post-IRI. These effects were attenuated, in part, with BRD4 inhibition. Absolute neutrophil counts increased significantly in the bone marrow, blood, and kidney 24 h post-IRI. Activated neutrophils increased in the blood and kidney at 6 h post-IRI and remained elevated in the kidney until 48 h post-IRI. BRD4 inhibition reduced both total and activated neutrophil counts in the kidney. IRI-induced tubular injury correlated with neutrophil accumulation and was reduced by BRD4 inhibition. In summary, BRD4 inhibition has important systemic and renal effects on neutrophils, and these effects are associated with reduced renal injury.

TGF-β1 is a regulator of the pyruvate dehydrogenase complex in fibroblasts

TGF-β1 reprograms metabolism in renal fibroblasts, inducing a switch from oxidative phosphorylation to aerobic glycolysis. However, molecular events underpinning this are unknown. Here we identify that TGF-β1 downregulates acetyl-CoA biosynthesis via regulation of the pyruvate dehydrogenase complex (PDC). Flow cytometry showed that TGF-β1 reduced the PDC subunit PDH-E1α in fibroblasts derived from injured, but not normal kidneys. An increase in expression of PDH kinase 1 (PDK1), and reduction in the phosphatase PDP1, were commensurate with net phosphorylation and inactivation of PDC. Over-expression of mutant PDH-E1α, resistant to phosphorylation, ameliorated effects of TGF-β1, while inhibition of PDC activity with CPI-613 was sufficient to induce αSMA and pro-collagen I expression, markers of myofibroblast differentiation and fibroblast activation. The effect of TGF-β1 on PDC activity, acetyl-CoA, αSMA and pro-collagen I was also ameliorated by sodium dichloroacetate, a small molecule inhibitor of PDK. A reduction in acetyl-CoA, and therefore acetylation substrate, also resulted in a generalised loss of protein acetylation with TGF-β1. In conclusion, TGF-β1 in part regulates fibroblast activation via effects on PDC activity.

The tissue proteome in the multi-omic landscape of kidney disease

Kidney research is entering an era of 'big data' and molecular omics data can provide comprehensive insights into the molecular footprints of cells. In contrast to transcriptomics, proteomics and metabolomics generate data that relate more directly to the pathological symptoms and clinical parameters observed in patients. Owing to its complexity, the proteome still holds many secrets, but has great potential for the identification of drug targets. Proteomics can provide information about protein synthesis, modification and degradation, as well as insight into the physical interactions between proteins, and between proteins and other biomolecules. Thus far, proteomics in nephrology has largely focused on the discovery and validation of biomarkers, but the systematic analysis of the nephroproteome can offer substantial additional insights, including the discovery of mechanisms that trigger and propagate kidney disease. Moreover, proteome acquisition might provide a diagnostic tool that complements the assessment of a kidney biopsy sample by a pathologist. Such applications are becoming increasingly feasible with the development of high-throughput and high-coverage technologies, such as versatile mass spectrometry-based techniques and protein arrays, and encourage further proteomics research in nephrology.