Perspectives on systems biology applications in diabetic kidney disease

Single-cell RNA sequencing in diabetic kidney disease: a literature review

Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease (ESRD), and its pathogenesis has not been clarified. Current research suggests that DKD involves multiple cell types and extra-renal factors, and it is particularly important to clarify the pathogenesis and identify new therapeutic targets. Single-cell RNA sequencing (scRNA-seq) technology is high-throughput sequencing of the transcriptomes of individual cells at the single-cell level, which is an effective technology for exploring the development of diseases by comparing genetic information, reflecting the differences in genetic information between cells, and identifying different cell subpopulations. Accumulating evidence supports the role of scRNA-seq in revealing the pathogenesis of diabetes and strengthening our understanding of the molecular mechanisms of DKD. We reviewed the scRNA-seq data this time. Then, we analyzed and discussed the applications of scRNA-seq technology in DKD research, including annotation of cell types, identification of novel cell types (or subtypes), identification of intercellular communication, analysis of cell differentiation trajectories, gene expression detection, and analysis of gene regulatory networks, and lastly, we explored the future perspectives of scRNA-seq technology in DKD research.

Association between gut microbiota and diabetic microvascular complications: a two-sample Mendelian randomization study

Background

Gut microbiota (GM) homeostasis in the human body is closely associated with health, which can be used as a regulator for preventing the onset and progression of disease. Diabetic microvascular complications bring about not only a huge economic burden to society, but also miserable mental and physical pain. Thus, alteration of the GM may be a method to delay diabetic microvascular complications.

Objective

A two-sample Mendelian randomization (MR) analysis was conducted to reveal the causal inference between GM and three core diabetic microvascular complications, namely, diabetic kidney disease (DKD), diabetic retinopathy (DR), and diabetic neuropathy (DNP).

Methods

First, genome-wide association study (GWAS) summary statistics for GM from the MiBioGen consortium and three main diabetic microvascular complications acquired from the FinnGen research project were assessed. Second, a forward MR analysis was conducted to assess the causality of GM on the risk of DKD, DR, and DNP. Third, a series of sensitivity studies, such as heterogeneity tests, pleiotropy evaluations, and leave-one-out analyses, were further conducted to assess the accuracy of MR analysis. Finally, Steiger tests and reverse MR analyses were performed to appraise the possibility of reverse causation.

Results

A total of 2,092 single-nucleotide polymorphisms related to 196 bacterial traits were selected as instrumental variables. This two-sample MR analysis provided strongly reasonable evidence that 28 genetically predicted abundance of specific GM that played non-negligible roles in the occurrence of DKD, DR, and DNP complications were causally associated with 23 GM, the odds ratio of which generally ranged from 0.9 to 1.1. Further sensitivity analysis indicated low heterogeneity, low pleiotropy, and high reliability of the causal estimates.

Conclusion

The study raised the possibility that GM may be a potential target to prevent and delay the progression of diabetic microvascular complications. Further experiments of GM therapy on diabetic microvascular complications are warranted to clarify their effects and specific mechanisms.

Association of Thallium with Diabetes Risk among Patients with Hearing Loss: Result from NHANES 2013 to 2018

To evaluate the correlation between thallium and diabetes risk among participants with hearing loss. This retrospective cohort study extracted related data such as demographic characteristics, lifestyle factors, and laboratory findings from the National Health and Nutrition Examination Survey (NHANES) database (2013-2018). Logistic regression analysis and interaction analysis were adopted to analyze the correlation between thallium and diabetes risk among patients with hearing loss. Then, the restricted cubic spline was employed to assess the nonlinear relationship between thallium and diabetes risk. The receiver operating characteristic curve and decision curve analysis were used to assess the predictive values of 3 multivariate models with or without thallium for diabetes risk. The Delong test was adopted to assess the significant change of the area under the curves (AUCs) upon thallium addition. A total of 425 participants with hearing loss were enrolled in the study: without diabetes group (n = 316) and diabetes group (n = 109). Patients with hearing loss in the diabetes group had significantly lower thallium (P < .05). The thallium was an independent predictor for diabetes risk after adjusting various covariates (P < .05). The restricted cubic spline (RCS) result showed that there was a linear correlation between thallium and diabetes risk (P nonlinear > .05). Finally, the receiver operating characteristic and decision curve analysis results revealed that adding thallium to the models slightly increased the performance in predicting diabetes risk but without significance in AUC change. Thallium was an independent predictor of diabetes risk among patients with hearing loss. The addition of thallium might help improve the predictive ability of models for risk reclassification. However, the conclusions should be verified in our cohort in the future due to the limitations inherent in the NHANES database.

Myricetin induces M2 macrophage polarization to alleviate renal tubulointerstitial fibrosis in diabetic nephropathy via PI3K/Akt pathway

BACKGROUND

Development of end-stage renal disease is predominantly attributed to diabetic nephropathy (DN). Previous studies have indicated that myricetin possesses the potential to mitigate the pathological alterations observed in renal tissue. Nevertheless, the precise molecular mechanism through which myricetin influences the progression of DN remains uncertain.

AIM

To investigate the effects of myricetin on DN and explore its potential therapeutic mechanism.

METHODS

Db/db mice were administered myricetin intragastrically on a daily basis at doses of 50 mg/kg or 100 mg/kg for a duration of 12 wk. Subsequently, blood and urine indexes were assessed, along with examination of renal tissue pathology. Kidney morphology and fibrosis were evaluated using various staining techniques including hematoxylin and eosin, periodic acid-Schiff, Masson's trichrome, and Sirius-red. Additionally, high-glucose culturing was conducted on the RAW 264.7 cell line, treated with 25 mM myricetin or co-administered with the PI3K/Akt inhibitor LY294002 for a period of 24 h. In both in vivo and in vitro settings, quantification of inflammation factor levels was conducted using western blotting, real-time qPCR and ELISA.

RESULTS

In db/db mice, administration of myricetin led to a mitigating effect on DN-induced renal dysfunction and fibrosis. Notably, we observed a significant reduction in expressions of the kidney injury markers kidney injury molecule-1 and neutrophil gelatinase associated lipocalin, along with a decrease in expressions of inflammatory cytokine-related factors. Furthermore, myricetin treatment effectively inhibited the up-regulation of tumor necrosis factor-alpha, interleukin-6, and interluekin-1β induced by high glucose in RAW 264.7 cells. Additionally, myricetin modulated the M1-type polarization of the RAW 264.7 cells. Molecular docking and bioinformatic analyses revealed Akt as the target of myricetin. The protective effect of myricetin was nullified upon blocking the polarization of RAW 264.7 via inhibition of PI3K/Akt activation using LY294002.

CONCLUSION

This study demonstrated that myricetin effectively mitigates kidney injury in DN mice through the regulation of macrophage polarization via the PI3K/Akt signaling pathway.

Insulin and the kidneys: a contemporary view on the molecular basis

Insulin is a hormone that is composed of 51 amino acids and structurally organized as a hexamer comprising three heterodimers. Insulin is the central hormone involved in the control of glucose and lipid metabolism, aiding in processes such as body homeostasis and cell growth. Insulin is synthesized as a large preprohormone and has a leader sequence or signal peptide that appears to be responsible for transport to the endoplasmic reticulum membranes. The interaction of insulin with the kidneys is a dynamic and multicenter process, as it acts in multiple sites throughout the nephron. Insulin acts on a range of tissues, from the glomerulus to the renal tubule, by modulating different functions such as glomerular filtration, gluconeogenesis, natriuresis, glucose uptake, regulation of ion transport, and the prevention of apoptosis. On the other hand, there is sufficient evidence showing the insulin receptor's involvement in renal functions and its responsibility for the regulation of glucose homeostasis, which enables us to understand its contribution to the insulin resistance phenomenon and its association with the progression of diabetic kidney disease.

Diabetes Mellitus and Hearing Loss: A Complex Relationship

Background and Objectives: Discussion is open about the relationship between diabetes (DM) and hearing loss (HL). There is a lot of evidence in the literature suggesting a causal link between these conditions, beyond being considered simple comorbidities. The difficulty in identifying populations free from confounding factors makes it difficult to reach definitive conclusions on the pathophysiological mechanisms at play. Nonetheless, there is numerous evidence that demonstrates how the population affected by DM is more affected by sensorineural HL (SNHL) and exhibit a higher prevalence of idiopathic sudden sensorineural HL (ISSNHL). Materials and Methods: Articles reporting potentially relevant information were reviewed, and the most significant results are discussed in this article. Starting from the possible mechanisms relating to auditory impairment in the diabetic condition, this article summarizes the studies on auditory evaluation in subjects with DM1 and DM2 and addresses the relationship between DM and ISSNHL. Results: DM is considered a risk factor for SNHL, although some studies have reported no relationship when the associations were adjusted for age, gender, and hypertension. Macro and microvascular insults that cause decreased blood flow, oxygen exchange, and ion transport are major complications of hypertension and DM and can have a direct effect on the sensory and support cells of the cochlea. Conclusions: Given the difficulty of carrying out studies on populations without confounding factors, new laboratory studies are strongly required to clarify which specific physiopathological mechanisms underlie the diabetic damage caused to the hearing organs and how pharmacological management may contribute to counteracting the pathophysiological effects of the diabetic condition on the auditory system.

Serum integrative omics reveals the landscape of human diabetic kidney disease

Objective

Diabetic kidney disease (DKD) is the most common microvascular complication of type 2 diabetes mellitus (2-DM). Currently, urine and kidney biopsy specimens are the major clinical resources for DKD diagnosis. Our study proposes to evaluate the diagnostic value of blood in monitoring the onset of DKD and distinguishing its status in the clinic.

Methods

This study recruited 1,513 participants including healthy adults and patients diagnosed with 2-DM, early-stage DKD (DKD-E), and advanced-stage DKD (DKD-A) from 4 independent medical centers. One discovery and four testing cohorts were established. Sera were collected and subjected to training proteomics and large-scale metabolomics.

Results

Deep profiling of serum proteomes and metabolomes revealed several insights. First, the training proteomics revealed that the combination of α₂-macroglobulin, cathepsin D, and CD324 could serve as a surrogate protein biomarker for monitoring DKD progression. Second, metabolomics demonstrated that galactose metabolism and glycerolipid metabolism are the major disturbed metabolic pathways in DKD, and serum metabolite glycerol-3-galactoside could be used as an independent marker to predict DKD. Third, integrating proteomics and metabolomics increased the diagnostic and predictive stability and accuracy for distinguishing DKD status.

Conclusions

Serum integrative omics provide stable and accurate biomarkers for early warning and diagnosis of DKD. Our study provides a rich and open-access data resource for optimizing DKD management.

•

Serum proteomics and metabolomics are novel, noninvasive approaches to detect DKD.

•

Integrated serum omics enhances the diagnostic stability and accuracy of DKD diagnoses.

•

Galactose/glycerolipid metabolism is the major disturbed metabolic pathway in DKD.

•

Serum metabolite glycerol-3-galactoside is an independent predictive marker of DKD.

Gene expression profiles of diabetic kidney disease and neuropathy in eNOS knockout mice: Predictors of pathology and RAS blockade effects

Diabetic kidney disease (DKD) and diabetic peripheral neuropathy (DPN) are two common diabetic complications. However, their pathogenesis remains elusive and current therapies are only modestly effective. We evaluated genome-wide expression to identify pathways involved in DKD and DPN progression in db/db eNOS-/- mice receiving renin-angiotensin-aldosterone system (RAS)-blocking drugs to mimic the current standard of care for DKD patients. Diabetes and eNOS deletion worsened DKD, which improved with RAS treatment. Diabetes also induced DPN, which was not affected by eNOS deletion or RAS blockade. Given the multiple factors affecting DKD and the graded differences in disease severity across mouse groups, an automatic data analysis method, SOM, or self-organizing map was used to elucidate glomerular transcriptional changes associated with DKD, whereas pairwise bioinformatic analysis was used for DPN. These analyses revealed that enhanced gene expression in several pro-inflammatory networks and reduced expression of development genes correlated with worsening DKD. Although RAS treatment ameliorated the nephropathy phenotype, it did not alter the more abnormal gene expression changes in kidney. Moreover, RAS exacerbated expression of genes related to inflammation and oxidant generation in peripheral nerves. The graded increase in inflammatory gene expression and decrease in development gene expression with DKD progression underline the potentially important role of these pathways in DKD pathogenesis. Since RAS blockers worsened this gene expression pattern in both DKD and DPN, it may partly explain the inadequate therapeutic efficacy of such blockers.

Integrative network analyses of transcriptomics data reveal potential drug targets for acute radiation syndrome

Recent political unrest has highlighted the importance of understanding the short- and long-term effects of gamma-radiation exposure on human health and survivability. In this regard, effective treatment for acute radiation syndrome (ARS) is a necessity in cases of nuclear disasters. Here, we propose 20 therapeutic targets for ARS identified using a systematic approach that integrates gene coexpression networks obtained under radiation treatment in humans and mice, drug databases, disease-gene association, radiation-induced differential gene expression, and literature mining. By selecting gene targets with existing drugs, we identified potential candidates for drug repurposing. Eight of these genes (BRD4, NFKBIA, CDKN1A, TFPI, MMP9, CBR1, ZAP70, IDH3B) were confirmed through literature to have shown radioprotective effect upon perturbation. This study provided a new perspective for the treatment of ARS using systems-level gene associations integrated with multiple biological information. The identified genes might provide high confidence drug target candidates for potential drug repurposing for ARS.

Epigenetics and epigenomics in diabetic kidney disease and metabolic memory

The development and progression of diabetic kidney disease (DKD), a highly prevalent complication of diabetes mellitus, are influenced by both genetic and environmental factors. DKD is an important contributor to the morbidity of patients with diabetes mellitus, indicating a clear need for an improved understanding of disease aetiology to inform the development of more efficacious treatments. DKD is characterized by an accumulation of extracellular matrix, hypertrophy and fibrosis in kidney glomerular and tubular cells. Increasing evidence shows that genes associated with these features of DKD are regulated not only by classical signalling pathways but also by epigenetic mechanisms involving chromatin histone modifications, DNA methylation and non-coding RNAs. These mechanisms can respond to changes in the environment and, importantly, might mediate the persistent long-term expression of DKD-related genes and phenotypes induced by prior glycaemic exposure despite subsequent glycaemic control, a phenomenon called metabolic memory. Detection of epigenetic events during the early stages of DKD could be valuable for timely diagnosis and prompt treatment to prevent progression to end-stage renal disease. Identification of epigenetic signatures of DKD via epigenome-wide association studies might also inform precision medicine approaches. Here, we highlight the emerging role of epigenetics and epigenomics in DKD and the translational potential of candidate epigenetic factors and non-coding RNAs as biomarkers and drug targets for DKD.

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.

Losartan Alleviates Renal Fibrosis and Inhibits Endothelial-to-Mesenchymal Transition (EMT) Under High-Fat Diet-Induced Hyperglycemia

The endothelial-to-mesenchymal transition (EMT) of glomerular vascular endothelial cells is considered to be pivotal in diabetic nephropathy (DN). The risk of DN can be decreased by losartan, but the potential molecular mechanism(s) are not fully understood. Extensive data show that the EMT occurs in proximal tubular endothelial cells resulting in an endothelial phenotype switch (fibrotic matrix accumulation), consequently enhancing the development of renal interstitial fibrosis. Here, we found that losartan significantly ameliorated DN-induced renal fibrosis progression via inhibition of the EMT in mice. In vivo experiments suggested that losartan significantly alleviated microalbuminuria and pathologic changes under high-fat diet-induced hyperglycemia. Immunohistochemistry indicated that losartan suppressed the EMT in glomeruli. In addition, losartan decreased oxidative stress damage and inhibited the transforming growth factor (TGF)-β1/Smad pathway. Furthermore, consistent changes were detected in vitro where losartan markedly inhibited the EMT and TGF-β1/Smad pathway induced by high glucose in glomerular endothelial cells. Together, these results suggested that losartan could alleviate the EMT in glomeruli via inhibition of oxidative stress damage and the TGF-β1/Smad signaling pathway under hyperglycemia.

Prognosis and treatment of diabetic nephropathy: Recent advances and perspectives

Approximately 20 to 40% of patients with type 1 or type 2 diabetes develop diabetic kidney disease. It is a clinical syndrome characterized by persistent albuminuria (>300mg/24h, or 300mg/g creatinine), a relentless decline in glomerular filtration rate, raised arterial blood pressure and enhanced cardiovascular morbidity and mortality. The natural course of classical diabetic nephropathy is initially microalbuminuria or moderately increased urine albumin excretion (30-300mg/g creatinine). Untreated microalbuminuria may then rise gradually, reaching severely increased albuminuric (macroalbuminuria) over 5 to 15 years. Glomerular filtration rate then begins to decline and end-stage renal failure is reached without treatment in 5 to 7 years. Regular, systematic screening for diabetic kidney disease is needed to identify patients at risk for, or with presymptomatic stages of diabetic kidney disease. Multifactorial intervention targeting glucose, lipids and blood pressure including blockade of renin angiotensin system and lifestyle, has improved renal and cardiovascular prognosis and reduced mortality with 50%. Recent data suggest beneficial pleiotropic effects on renal endpoint with new glucose lowering agents. It is also being investigated if blocking aldosterone could be an option as a potential new treatment. Thus, although diabetic nephropathy remains a major burden, prognosis has improved and new options for further improvements are currently tested in phase 3 clinical renal outcome studies.

Diagnosis of diabetic kidney disease: state of the art and future perspective

Approximately 20% to 40% of patients with type 1 or type 2 diabetes mellitus develop diabetic kidney disease. This is a clinical syndrome characterized by persistent albuminuria (> 300 mg/24 h, or > 300 mg/g creatinine), a relentless decline in glomerular filtration rate (GFR), raised arterial blood pressure, and enhanced cardiovascular morbidity and mortality. There is a characteristic histopathology. In classical diabetic nephropathy, the first clinical sign is moderately increased urine albumin excretion (microalbuminuria: 30-300 mg/24 h, or 30-300 mg/g creatinine; albuminuria grade A2). Untreated microalbuminuria will gradually worsen, reaching clinical proteinuria or severely increased albuminuria (albuminuria grade A3) over 5 to 15 years. The GFR then begins to decline, and without treatment, end-stage renal failure is likely to result in 5 to 7 years. Although albuminuria is the first sign of diabetic nephropathy, the first symptom is usually peripheral edema, which occurs at a very late stage. Regular, systematic screening for diabetic kidney disease is needed in order to identify patients at risk of or with presymptomatic diabetic kidney disease. Annual monitoring of urinary albumin-to-creatinine ratio, estimated GFR, and blood pressure is recommended. Several new biomarkers or profiles of biomarkers have been investigated to improve prognostic and diagnostic precision, but none have yet been implemented in routine clinical care. In the future such techniques may pave the way for personalized treatment.

Fast renal decline to end-stage renal disease: an unrecognized feature of nephropathy in diabetes

A new model of diabetic nephropathy in type 1 diabetes emerged from our studies of Joslin Clinic patients. The dominant feature is progressive renal decline, not albuminuria. This decline is a unidirectional process commencing while patients have normal renal function and, in the majority, progressing steadily (linearly) to end-stage renal disease (ESRD). While an individual's rate of renal decline is constant, the estimated glomerular filtration rate (eGFR) slope varies widely among individuals from -72 to -3.0 ml/min/year. Kidney Disease: Improving Global Outcomes guidelines define rapid progression as rate of eGFR declines > 5 ml/min/year, a value exceeded by 80% of patients in Joslin's type 1 diabetes ESRD cohort. The extraordinary range of slopes within the rapid progression category prompted us to partition it into "very fast," "fast" and "moderate" decline. We showed, for the first time, that very fast and fast decline from normal eGFR to ESRD within 2 to 10 years constitutes 50% of the Joslin cohort. In this review we present data about frequency of fast decliners in both diabetes types, survey some mechanisms underlying fast renal decline, discuss methods of identifying patients at risk and comment on the need for effective therapeutic interventions. Whether the initiating mechanism of fast renal decline affects glomerulus, tubule, interstitium or vasculature is unknown. Since no animal model mimics progressive renal decline, studies in humans are needed. Prospective studies searching for markers predictive of the rate of renal decline yield findings that may make detection of fast decliners feasible. Identifying such patients will be the foundation for developing effective individualized methods to prevent or delay onset of ESRD in diabetes.

Prognostic clinical and molecular biomarkers of renal disease in type 2 diabetes

Diabetic kidney disease occurs in ∼ 25-40% of patients with type 2 diabetes. Given the high risk of progressive renal function loss and end-stage renal disease, early identification of patients with a renal risk is important. Novel biomarkers may aid in improving renal risk stratification. In this review, we first focus on the classical panel of albuminuria and estimated glomerular filtration rate as the primary clinical predictors of renal disease and then move our attention to novel biomarkers, primarily concentrating on assay-based multiple/panel biomarkers, proteomics biomarkers and metabolomics biomarkers. We focus on multiple biomarker panels since the molecular processes of renal disease progression in type 2 diabetes are heterogeneous, rendering it unlikely that a single biomarker significantly adds to clinical risk prediction. A limited number of prospective studies of multiple biomarkers address the predictive performance of novel biomarker panels in addition to the classical panel in type 2 diabetes. However, the prospective studies conducted so far have small sample sizes, are insufficiently powered and lack external validation. Adequately sized validation studies of multiple biomarker panels are thus required. There is also a paucity of studies that assess the effect of treatments on novel biomarker panels and determine whether initial treatment-induced changes in novel biomarkers predict changes in long-term renal outcomes. Such studies can not only improve our healthcare but also our understanding of the mechanisms of actions of existing and novel drugs and may yield biomarkers that can be used to monitor drug response. We conclude that this will be an area to focus research on in the future.

Drugs meeting the molecular basis of diabetic kidney disease: bridging from molecular mechanism to personalized medicine

Diabetic kidney disease (DKD) is a complex, multifactorial disease and is associated with a high risk of renal and cardiovascular morbidity and mortality. Clinical practice guidelines for diabetes recommend essentially identical treatments for all patients without taking into account how the individual responds to the instituted therapy. Yet, individuals vary widely in how they respond to medications and therefore optimal therapy differs between individuals. Understanding the underlying molecular mechanisms of variability in drug response will help tailor optimal therapy. Polymorphisms in genes related to drug pharmacokinetics have been used to explore mechanisms of response variability in DKD, but with limited success. The complex interaction between genetic make-up and environmental factors on the abundance of proteins and metabolites renders pharmacogenomics alone insufficient to fully capture response variability. A complementary approach is to attribute drug response variability to individual variability in underlying molecular mechanisms involved in the progression of disease. The interplay of different processes (e.g. inflammation, fibrosis, angiogenesis, oxidative stress) appears to drive disease progression, but the individual contribution of each process varies. Drugs at the other hand address specific targets and thereby interfere in certain disease-associated processes. At this level, biomarkers may help to gain insight into which specific pathophysiological processes are involved in an individual followed by a rational assessment whether a specific drug's mode of action indeed targets the relevant process at hand. This article describes the conceptual background and data-driven workflow developed by the SysKid consortium aimed at improving characterization of the molecular mechanisms underlying DKD at the interference of the molecular impact of individual drugs in order to tailor optimal therapy to individual patients.

Management of anemia in patients with diabetic kidney disease: A consensus statement

This consensus statement focuses on the window of opportunity, which exists while treating patients with diabetic kidney disease and anemia.

Advances in urinary proteome analysis and applications in systems biology

The urinary proteome is the focus of many studies due to the ease of urine collection and the relative proteome stability. Systems biology allows the combination of multiple omics studies, forming a link between proteomics, metabolomics, genomics and transcriptomics. In-depth data interpretation is achieved by bioinformatics analysis of -omics data sets. It is expected that the contribution of systems biology to the study of the urinary proteome will offer novel insights. The main focus of this review is on technical aspects of proteomics studies, available tools for systems biology analysis and the application of urinary proteomics in clinical studies and systems biology.

Genetics and Epigenetics of Diabetic Nephropathy

BACKGROUND

Diabetic nephropathy (DN) is the most common cause of end-stage renal disease (ESRD) in the USA and worldwide, contributing to significant morbidity and mortality in diabetic patients. A genetic factor for the development of DN is strongly implicated, as only one third of diabetic patients eventually develop kidney disease. Growing evidence also supports an important role of epigenetic modifications in DN.

SUMMARY

Multiple studies have been performed to identify risk genes and loci associated with DN. So far, only several genes and loci have been identified, none of which showed a strong association with DN. Therefore, a better study design with a larger sample size to identify rare variants and a clinically defined patient population to identify genes and loci associated with progressive DN are still needed. In addition to genetic factors, epigenetic modifications, such as DNA methylation, histone modifications and microRNAs, also play a major role in the pathogenesis of DN through a second layer of gene regulation. Although a major progress has been made in this field, epigenetic studies in DN are still in the early phase and have been limited mostly due to the heterogeneity of kidney tissue samples with multiple cells. However, rapid development of high-throughput genome-wide techniques will help us to better identify genetic variants and epigenetic changes in DN.

KEY MESSAGE

Understanding of genetic and epigenetic changes in DN is needed for the development of new biomarkers and better drug targets against DN. Summarized in this review are important recent findings on genetic and epigenetic studies in the field of DN.

Low-Level Fluoride Exposure Increases Insulin Sensitivity in Experimental Diabetes

The effect of chronic fluoride (F) exposure from the drinking water on parameters related to glucose homeostasis was investigated. Wistar rats were randomly distributed into 2 groups (diabetic [D] and nondiabetic [ND]; n = 54 each). In D, diabetes was induced with streptozotocin. Each group was further divided into 3 subgroups (0, 10, or 50 mgF/L in drinking water). After 22 days of treatment, plasma and liver samples were collected. No alterations in glycemia, insulinemia, KITT, and HOMA2-IR (homeostasis model assessment 2 of insulin resistance) were seen for ND. F-exposure of D rats led to significantly lower insulinemia, without alterations in glycemia (increased %S). Proteomic analysis detected 19, 39, and 16 proteins differentially expressed for the comparisons D0 vs. D10, D0 vs. D50, and D10 vs. D50, respectively. Gene Ontology with the most significant terms in the comparisons D0 vs. D10, D0 vs. D50, and D50 vs. D10 were organic acid metabolic process and carboxylic acid metabolic process, organic acid metabolic process, and cellular ketone metabolic process. Analysis of subnetworks revealed that proteins with fold changes interacted with GLUT4 in comparison D0 vs. D10. Among these proteins, ERj3p was present in D10. Upregulation of this protein in the presence of F might help to explain the higher %S found in these animals. These data suggest that fluoride might enhance glucose homeostasis in diabetes and identify specific biological mechanisms that merit future studies.

From molecular signatures to predictive biomarkers: modeling disease pathophysiology and drug mechanism of action

Omics profiling significantly expanded the molecular landscape describing clinical phenotypes. Association analysis resulted in first diagnostic and prognostic biomarker signatures entering clinical utility. However, utilizing Omics for deepening our understanding of disease pathophysiology, and further including specific interference with drug mechanism of action on a molecular process level still sees limited added value in the clinical setting. We exemplify a computational workflow for expanding from statistics-based association analysis toward deriving molecular pathway and process models for characterizing phenotypes and drug mechanism of action. Interference analysis on the molecular model level allows identification of predictive biomarker candidates for testing drug response. We discuss this strategy on diabetic nephropathy (DN), a complex clinical phenotype triggered by diabetes and presenting with renal as well as cardiovascular endpoints. A molecular pathway map indicates involvement of multiple molecular mechanisms, and selected biomarker candidates reported as associated with disease progression are identified for specific molecular processes. Selective interference of drug mechanism of action and disease-associated processes is identified for drug classes in clinical use, in turn providing precision medicine hypotheses utilizing predictive biomarkers.

Diabetic nephropathy--emerging epigenetic mechanisms

Diabetic nephropathy (DN), a severe microvascular complication frequently associated with both type 1 and type 2 diabetes mellitus, is a leading cause of renal failure. The condition can also lead to accelerated cardiovascular disease and macrovascular complications. Currently available therapies have not been fully efficacious in the treatment of DN, suggesting that further understanding of the molecular mechanisms underlying the pathogenesis of DN is necessary for the improved management of this disease. Although key signal transduction and gene regulation mechanisms have been identified, especially those related to the effects of hyperglycaemia, transforming growth factor β1 and angiotensin II, progress in functional genomics, high-throughput sequencing technology, epigenetics and systems biology approaches have greatly expanded our knowledge and uncovered new molecular mechanisms and factors involved in DN. These mechanisms include DNA methylation, chromatin histone modifications, novel transcripts and functional noncoding RNAs, such as microRNAs and long noncoding RNAs. In this Review, we discuss the significance of these emerging mechanisms, how they mediate the actions of growth factors to augment the expression of extracellular matrix and inflammatory genes associated with DN and their potential usefulness as diagnostic biomarkers or novel therapeutic targets for DN.

Dioxins, furans and dioxin-like PCBs in human blood: causes or consequences of diabetic nephropathy?

Nephropathy, or kidney disease, is a major, potential complication of diabetes. We assessed the association of 6 chlorinated dibenzo-p-dioxins, 9 chlorinated dibenzofurans and 8 polychlorinated biphenyls (PCBs) in blood with diabetic nephropathy in the 1999-2004 National Health and Nutrition Examination Survey (unweighted N=2588, population estimate=117,658,357). Diabetes was defined as diagnosed or undiagnosed (glycohemoglobin ≥ 6.5%) and nephropathy defined as urinary albumin to creatinine ratio >30 mg/g, representing microalbuminuria or macroalbuminuria. For the 8 chemicals analyzed separately, values above the 75th percentile were considered elevated, whereas for the other 15 compounds values above the maximum limit of detection were considered elevated. Seven of 8 dioxins and dioxin-like compounds, analyzed separately, were found to be associated with diabetic nephropathy. The chemicals associated with diabetic nephropathy were: 1,2,3,6,7,8-Hexachlorodibenzo-p-dioxin; 1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin; 2,3,4,7,8-Pentachlorodibenzofuran; PCB 126; PCB 169; PCB 118; and PCB 156. Three of the 8 dioxins and dioxin-like compounds; 1,2,3,4,6,7,8,9-Octachlorodibenzo-p-dioxin; 2,3,4,7,8-Pentachlorodibenzofuran and PCB 118; expressed as log-transformed continuous variables; were associated with diabetes without nephropathy. When 4 or more of the 23 chemicals were elevated the odds ratios were 7.00 (95% CI=1.80-27.20) for diabetic nephropathy and 2.13 (95% CI=0.95-4.78) for diabetes without nephropathy. Log-transformed toxic equivalency (TEQ) was associated with both diabetic nephropathy, and diabetes without nephropathy, the odds ratios were 2.35 (95% CI=1.57-3.52) for diabetic nephropathy, and 1.44 (95% CI=1.11-1.87) for diabetes without nephropathy. As the kidneys function to remove waste products from the blood, diabetic nephropathy could be either the cause or the consequence (or both) of exposure to dioxins, furans and dioxin-like PCBs.

Network analysis: a new approach to study endocrine disorders

Systems biology is the study of the interactions that occur between the components of individual cells - including genes, proteins, transcription factors, small molecules, and metabolites, and their relationships to complex physiological and pathological processes. The application of systems biology to medicine promises rapid advances in both our understanding of disease and the development of novel treatment options. Network biology has emerged as the primary tool for studying systems biology as it utilises the mathematical analysis of the relationships between connected objects in a biological system and allows the integration of varied 'omic' datasets (including genomics, metabolomics, proteomics, etc.). Analysis of network biology generates interactome models to infer and assess function; to understand mechanisms, and to prioritise candidates for further investigation. This review provides an overview of network methods used to support this research and an insight into current applications of network analysis applied to endocrinology. A wide spectrum of endocrine disorders are included ranging from congenital hyperinsulinism in infancy, through childhood developmental and growth disorders, to the development of metabolic diseases in early and late adulthood, such as obesity and obesity-related pathologies. In addition to providing a deeper understanding of diseases processes, network biology is also central to the development of personalised treatment strategies which will integrate pharmacogenomics with systems biology of the individual.

The primary glomerulonephritides: a systems biology approach

Our understanding of the pathogenesis of most primary glomerular diseases, including IgA nephropathy, membranous nephropathy and focal segmental glomerulosclerosis, is limited. Advances in molecular technology now permit genome-wide, high-throughput characterization of genes and gene products from biological samples. Comprehensive examinations of the genome, transcriptome, proteome and metabolome (collectively known as omics analyses), have been applied to the study of IgA nephropathy, membranous nephropathy and focal segmental glomerulosclerosis in both animal models and human patients. However, most omics studies of primary glomerular diseases, with the exception of large genomic studies, have been limited by inadequate sample sizes and the lack of kidney-specific data sets derived from kidney biopsy samples. Collaborative efforts to develop a standardized approach for prospective recruitment of patients, scheduled monitoring of clinical outcomes, and protocols for sampling of kidney tissues will be instrumental in uncovering the mechanisms that drive these diseases. Integration of molecular data sets with the results of clinical and histopathological studies will ultimately enable these diseases to be characterized in a comprehensive and systematic manner, and is expected to improve the diagnosis and treatment of these diseases.