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• W2789736974 abstract "Diabetic kidney disease (DKD) has a complex and prolonged pathogenesis involving many cell types in the kidney as well as extrarenal factors. It is clinically silent for many years after the onset of diabetes and usually progresses over decades. Given this complexity, a comprehensive and unbiased molecular approach is best suited to help identify the most critical mechanisms responsible for progression of DKD and those most suited for targeted intervention. Systems biological investigations provide such an approach since they examine the entire network of molecular changes that occur in a disease process in a comprehensive way instead of focusing on a single abnormal molecule or pathway. Systems biological studies can also start with analysis of the disease in humans, not in animal or cell culture models that often poorly reproduce the changes in human DKD. Indeed, in the last decade, systems biological approaches have led to the identification of critical molecular abnormalities in DKD and have directly led to development of new biomarkers and potential treatments for DKD. Diabetic kidney disease (DKD) has a complex and prolonged pathogenesis involving many cell types in the kidney as well as extrarenal factors. It is clinically silent for many years after the onset of diabetes and usually progresses over decades. Given this complexity, a comprehensive and unbiased molecular approach is best suited to help identify the most critical mechanisms responsible for progression of DKD and those most suited for targeted intervention. Systems biological investigations provide such an approach since they examine the entire network of molecular changes that occur in a disease process in a comprehensive way instead of focusing on a single abnormal molecule or pathway. Systems biological studies can also start with analysis of the disease in humans, not in animal or cell culture models that often poorly reproduce the changes in human DKD. Indeed, in the last decade, systems biological approaches have led to the identification of critical molecular abnormalities in DKD and have directly led to development of new biomarkers and potential treatments for DKD. Clinical Summary•Systems biological approaches examine the network of all molecular changes that occur in a disease process instead of focusing on a single abnormal molecule (eg, gene or protein) or pathway.•Diseases such as diabetic kidney disease (DKD) that involve multiple cell types in the kidney; result from multiple metabolic, inflammatory, and signaling changes; and evolve over years or decades are well suited for systems biological investigation.•A major advantage of systems biological investigation is that it can start with investigation of DKD in humans and does not initially rely on models that generally fail to fully represent the human disease process.•New biomarkers of progressive DKD and molecular targets for the treatment of DKD have been identified by systems biological approaches. •Systems biological approaches examine the network of all molecular changes that occur in a disease process instead of focusing on a single abnormal molecule (eg, gene or protein) or pathway.•Diseases such as diabetic kidney disease (DKD) that involve multiple cell types in the kidney; result from multiple metabolic, inflammatory, and signaling changes; and evolve over years or decades are well suited for systems biological investigation.•A major advantage of systems biological investigation is that it can start with investigation of DKD in humans and does not initially rely on models that generally fail to fully represent the human disease process.•New biomarkers of progressive DKD and molecular targets for the treatment of DKD have been identified by systems biological approaches. Diabetic kidney disease (DKD) is the major cause of CKD and ESRD in the United States and probably in the world.12016 USRDS annual data report: Epidemiology of kidney disease in the United States National Institutes of Health. National Institute of Diabetes and Digestive and Kidney Disease, Bethesda, MD2016https://www.usrds.org/adr.aspxGoogle Scholar Despite some progress in reducing mortality and delaying kidney disease in the last few decades due to improved glycemic control, blood pressure lowering, and the use of renin-angiotensin system blockade, the percentage of diabetic patients who develop kidney failure has not materially declined.2Afkarian M. Zelnick L.R. Hall Y.N. et al.Clinical manifestations of kidney disease among us adults with diabetes, 1988-2014.JAMA. 2016; 316: 602-610Crossref PubMed Scopus (505) Google Scholar Thus, millions of individual patients worldwide are in urgent need of new approaches to treatment that will actually prevent progression to ESRD. One reason for the slow progress in finding adequate therapies for DKD is the lack of comprehensive understanding of the underlying pathogenic mechanisms. Unfortunately, targeting single pathways and molecules based on hypothesis-driven research has resulted in no significant advances in treatment in the last 25 years. Unraveling the underlying mechanisms for DKD is complicated by the likelihood that a number of molecular processes interrelate over a number of years to cause the tissue damage that is the ultimate manifestation of the disease. A comprehensive and unbiased molecular approach is best suited to reveal this complex pathogenesis. Systems biological methods provide such an approach and have the additional important advantage that they can start with human disease samples to help ensure that the processes being studied are relevant to human DKD. This is important since animal and cell culture models fail to recapitulate many aspects of DKD found in humans.3Brosius 3rd, F.C. Alpers C.E. Bottinger E.P. et al.Mouse models of diabetic nephropathy.J Am Soc Nephrol. 2009; 20: 2503-2512Crossref PubMed Scopus (430) Google Scholar In this brief review, we will discuss how a systems biological approach to understand and treat DKD has advanced the field over the last decade or so. There are many definitions to Systems Biology and different definitions apply to different areas of research. For those interested in disease pathogenesis, Systems Biology may be defined as an information-rich discovery process that analyzes biological systems and their behavior as regulatory networks. Studying these networks in multiple relevant tissues and associating them to clinically relevant data can identify the complex biological patterns that underlie disease onset and progression. To restate this in terms of DKD research, Systems Biology uses a set of experimental tools and computational approaches that comprehensively identify networks of molecular changes that occur in patients with DKD compared to normal individuals and focuses when possible on changes in individual kidney cells or complexes of interrelated kidney cells. The analysis of these altered networks is used to identify associations among the many molecular changes that best predict and are likely to enhance disease progression or amelioration. Pragmatically, these studies are often restricted to one “scale” of molecular analysis (eg, transcriptomics focusing on gene expression phenotypes) although they may combine several scales, so called “multiscalar” or “multiomics” approaches (Fig 1), which often give the most powerful results. By their nature, systems biological studies are often hypothesis generating, not hypothesis testing. While not an issue for biomarker discovery, potential mechanisms of disease and therapeutic targets identified by systems biological approaches need to be validated by more conventional experimental methodologies. Examples of how systems biological approaches can be interwoven with more conventional experimental studies are noted in the following sections on DKD biomarkers and targets for therapy. A number of systems biological reports on human DKD have been published over the past decade or more. Many of these report transcriptomic analyses examine the changes in gene expression in diabetic kidney tissues. A number of epigenomic, proteomic, and metabolomic analyses have also been reported. In the following sections, examples of each of these approaches will be described. This review is restricted to studies that have used human tissues and that demonstrate how systems biological approaches can move the field forward. It is not a systematic review. Moreover, we have not reviewed DKD genetic studies, as these have been extensively reviewed relatively and recently,4Regele F. Jelencsics K. Shiffman D. et al.Genome-wide studies to identify risk factors for kidney disease with a focus on patients with diabetes.Nephrol Dial Transplant. 2015; 30 Suppl 4: iv26-iv34Crossref PubMed Scopus (40) Google Scholar and no major advances have occurred in this area since that publication. The following examples have been chosen for illustrative purposes only, not because we believe they are definitive. As noted previously, Systems Biology is inherently computational. The associations and connections in genome-wide data sets can only be made with computational tools, and the better and more sophisticated the computational tools, the better and more sophisticated the systems biological analysis. As will be noted in the following sections, many of the initial systems-wide studies utilized quite simple analytic tools and therefore provided little new insight into disease processes, though were important as proof-of-principle studies. Later studies have utilized a panoply of computational tools and have often generated specific tools to lead to the most meaningful observations and discoveries. We have emphasized these computational aspects in the following sections. Because animal and cell culture models have only partly replicated the molecular phenotype found in humans with DKD and because diabetic animals do not develop progressive DKD, it has become imperative for meaningful DKD research to utilize human phenotypic data as a benchmark. This is especially true for systems biological studies which synthesize data from all molecular changes in the examined tissue or cell type and reflect species-specific pathways which, if taken from models, do not always overlap with those in humans.5Hodgin J.B. Nair V. Zhang H. et al.Identification of cross-species shared transcriptional networks of diabetic nephropathy in human and mouse glomeruli.Diabetes. 2013; 62: 299-308Crossref PubMed Scopus (133) Google Scholar Thus, a critical underpinning for systems biological studies of DKD has been the establishment and availability of long-standing cohorts of patients with CKDs including DKD. Especially important for this work has been the availability of carefully obtained and preserved kidney biopsy, blood and urine samples paired with detailed longitudinal clinical information acquired after kidney biopsies from participants in the cohorts. These have allowed identification of molecular pathways or molecules that best predict progression of DKD or are likely to be therapeutic targets without having to follow patients for years in the future. There now exist multiple CKD registries accompanied by large well-curated biobanks with multiple types of samples (including plasma, urine, and kidney tissue) that can be used for molecular analysis. Noninvasive molecular biomarkers with better sensitivity and specificity are urgently needed for the early diagnosis of DKD as well as identification of patients who are most at risk of disease progression. An ideal DKD biomarker would be easily detectable in body fluids such as plasma or urine, would represent diverse molecular mechanisms that underlie DKD pathogenesis, and would be kidney specific in order to eliminate effects of extrarenal sources. Most useful biomarkers are either proteins or metabolites, and unbiased proteomic and metabolomic approaches have helped discover a number of new potential biomarkers. Although none of these DKD biomarkers have been fully validated, several promising candidates have been discovered by these methods. A study from our group illustrated the feasibility of using transcriptomic data from human kidney biopsies to identify critical kidney-specific pathophysiologic molecules based on alterations in gene expression and then to test whether the protein products of these mRNAs could serve as urinary biomarkers to predict CKD progression.6Ju W. Nair V. Smith S. et al.Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker.Sci Transl Med. 2015; 7 (316ra193)Crossref PubMed Scopus (217) Google Scholar Following this strategy, urinary epidermal growth factor (uEGF) was identified as an independent predictor of estimated glomerular filtration rate (eGFR) slope and of the composite CKD progression end point of ESRD or 40% reduction of baseline eGFR after adjusting for age, sex, baseline eGFR, and albumin-to-creatinine ratio. Moreover, uEGF added improved prognostic accuracy to eGFR and ACR in predicting progression in 3 separate CKD cohorts from different parts of the world. Although there was no specific DKD cohort included in this study, DKD patients were highly represented in one of the cohorts, and 135 CKD patients with diabetes, 70 of whom had biopsy-proven DKD contributed to the final results. A subgroup analysis of these diabetic patients also revealed a strong correlation between uEGF and eGFR or eGFR slope,6Ju W. Nair V. Smith S. et al.Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker.Sci Transl Med. 2015; 7 (316ra193)Crossref PubMed Scopus (217) Google Scholar supporting the prognostic value of uEGF in DKD. More recently, a urinary proteomics study in a mouse model by Betz and colleagues7Betz B.B. Jenks S.J. Cronshaw A.D. et al.Urinary peptidomics in a rodent model of diabetic nephropathy highlights epidermal growth factor as a biomarker for renal deterioration in patients with type 2 diabetes.Kidney Int. 2016; 89: 1125-1135Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar was used as a basis for human studies to also show that uEGF predicts CKD in diabetic patients without albuminuria. Thus, the combination of transcriptomics and targeted proteomics has identified a new kidney-specific urinary biomarker that has been validated in multiple cohorts in 2 studies. Proteomic studies have produced less rapid success in this area than was initially predicted a decade ago,8Klein J.B. Applying proteomics to detect early signs of chronic kidney disease: where has the magic gone?.Expert Rev Proteomics. 2017; 14: 387-390Crossref PubMed Scopus (5) Google Scholar due to multiple challenges such as the complexity of proteomics technology; strict requirements for standardized specimen collection, processing, and storage; difficulties in quantification of urinary markers with low abundances; and the large variation in urine protein excretion between individuals. Despite these difficulties, a urinary biomarker panel was derived from capillary electrophoresis-mass spectrometry analysis of urine samples of patients with CKD. This panel of 273 urinary peptides, the CKD273 classifier,9Good D.M. Zurbig P. Argiles A. et al.Naturally occurring human urinary peptides for use in diagnosis of chronic kidney disease.Mol Cell Proteomics. 2010; 9: 2424-2437Crossref PubMed Scopus (360) Google Scholar has subsequently been validated in several CKD cohorts, including those with DKD. The CD273 classifier has been shown to predict onset of DKD in type 2 diabetic patients.10Lindhardt M. Persson F. Zurbig P. et al.Urinary proteomics predict onset of microalbuminuria in normoalbuminuric type 2 diabetic patients, a sub-study of the DIRECT-Protect 2 study.Nephrol Dial Transplant. 2017; 32: 1866-1873PubMed Google Scholar, 11Nkuipou-Kenfack E. Zurbig P. Mischak H. The long path towards implementation of clinical proteomics: Exemplified based on CKD273.Proteomics Clin Appl. 2017; 11PubMed Google Scholar In a meta-analysis of CKD patient data from the Human Urinary Proteome database in which over 75% of the patients had diabetes, the CKD273 classifier also better predicted a sustained decline in eGFR in patients with early DKD (eGFR 70-80 mL/min/1.73 m2) than did baseline albuminuria.2Afkarian M. Zelnick L.R. Hall Y.N. et al.Clinical manifestations of kidney disease among us adults with diabetes, 1988-2014.JAMA. 2016; 316: 602-610Crossref PubMed Scopus (505) Google Scholar, 12Pontillo C. Jacobs L. Staessen J.A. et al.A urinary proteome-based classifier for the early detection of decline in glomerular filtration.Nephrol Dial Transplant. 2017; 32: 1510-1516PubMed Google Scholar Finally, the classifier has now been used to help stratify patients for a randomized controlled trial of spironolactone in the treatment of early DKD.13Lindhardt M. Persson F. Currie G. et al.Proteomic prediction and Renin angiotensin aldosterone system Inhibition prevention of early diabetic nephRopathy in TYpe 2 diabetic patients with normoalbuminuria (PRIORITY): essential study design and rationale of a randomised clinical multicentre trial.BMJ Open. 2016; 6: e010310Crossref PubMed Scopus (93) Google Scholar Results with the CKD273 classifier have been consistent enough that the US Food and Drug Administration has encouraged “the further development of CKD273…to be used in combination with current measures (ie, albuminuria, serum creatinine) in early phase clinical trials in …DKD to identify patients with early stage disease who may be more likely to progress.”11Nkuipou-Kenfack E. Zurbig P. Mischak H. The long path towards implementation of clinical proteomics: Exemplified based on CKD273.Proteomics Clin Appl. 2017; 11PubMed Google Scholar Metabolomic analyses can also identify useful biomarker panels.14Hocher B. Adamski J. Metabolomics for clinical use and research in chronic kidney disease.Nat Rev Nephrol. 2017; 13: 269-284Crossref PubMed Scopus (188) Google Scholar Similar to proteomics, sample processing and handling are critical to the success of metabolomic studies. Additional confounding factors, in addition to those that beset proteomic biomarker approaches, may impact metabolomics studies. These confounding factors were recently and comprehensively reviewed by Hocher and Adamski,14Hocher B. Adamski J. Metabolomics for clinical use and research in chronic kidney disease.Nat Rev Nephrol. 2017; 13: 269-284Crossref PubMed Scopus (188) Google Scholar including genetic background, sex, age, body mass index, medication, lifestyle, circadian rhythms, hormonal status, and nutrition and fasting. These confounding factors, together with a lack of large prospective study cohorts and a dependency on sophisticated bioinformatics techniques for data interpretation, resulted in the slow progress of DKD metabolomic biomarker identification. However there have been promising examples such as the study carried out by Niewczas and colleagues.15Niewczas M.A. Sirich T.L. Mathew A.V. et al.Uremic solutes and risk of end-stage renal disease in type 2 diabetes: metabolomic study.Kidney Int. 2014; 85: 1214-1224Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar This nested case-control study used plasma samples of type 2 diabetic patients with normal or mildly impaired baseline kidney function from the Joslin Kidney Study cohort and aimed to identify metabolites that were associated with progression to ESRD. Metabolomic profiles of 40 patients who developed ESRD during 8-12 years of follow-up were compared to those of 40 control patients who were alive but did not progress to ESRD. Seventy-eight metabolites previously reported to be elevated in ESRD (uremic solutes) were identified in this study, and 16 of them were elevated in the baseline plasma of cases years before ESRD developed. Essential amino acids and their derivatives were significantly depleted in the cases. These findings remained statistically significant after adjustment for albumin excretion rate, eGFR, or HbA1c. Uremic solute differences were then confirmed by targeted quantitative metabolite measurements. Abnormal plasma concentrations of uremic solutes and essential amino acids were associated with progression to ESRD. The findings from this exploratory study will need to be replicated in independent and prospective study cohorts. One concern about biomarker candidates derived from unbiased proteomic and metabolomics approach, especially if they are expressed in other tissues, is whether a biomarker candidate truly represents processes in the kidneys and whether it may have reduced specificity due to its production by extrarenal tissues. These concerns are obviated by selecting candidate biomarkers that appear to be derived solely from kidney cells. Multiomic approaches to biomarker discovery that combine transcriptomics with either metabolomics or proteomics have recently become easier as investigators can now take advantage of published DKD transcriptomic data sets. To facilitate access to such data sets, a web-based search and analytical platform, Nephroseq (www.nephroseq.org), was established. This user-friendly tool includes all published human DKD (and other CKD) transcriptomic data sets to allow for easy data mining and in-depth data analysis. As an example of the utility of such a tool, we identified from published literature a nonexhaustive list of putative protein biomarkers for kidney diseases and determined the association of the expression levels for the genes encoding each biomarker with kidney function in DKD patients from two Nephroseq data sets.6Ju W. Nair V. Smith S. et al.Tissue transcriptome-driven identification of epidermal growth factor as a chronic kidney disease biomarker.Sci Transl Med. 2015; 7 (316ra193)Crossref PubMed Scopus (217) Google Scholar, 16Ju W. Greene C.S. Eichinger F. et al.Defining cell-type specificity at the transcriptional level in human disease.Genome Res. 2013; 23: 1862-1873Crossref PubMed Scopus (155) Google Scholar, 17Woroniecka K.I. Park A.S. Mohtat D. Thomas D.B. Pullman J.M. Susztak K. Transcriptome analysis of human diabetic kidney disease.Diabetes. 2011; 60: 2354-2369Crossref PubMed Scopus (364) Google Scholar In Table 1, we have displayed the association of gene expression with eGFR as well as the gene expression differences between DKD patients and normal controls for each biomarker. Using these data, investigators can assess whether the mRNA expression for the gene encoding a putative protein biomarker in kidney tissue correlates with disease pathophysiology. 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• W2789736974 created "2018-03-29" @default.
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• W2789736974 date "2018-03-01" @default.
• W2789736974 modified "2023-10-17" @default.
• W2789736974 title "The Promise of Systems Biology for Diabetic Kidney Disease" @default.
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