Omics databases on kidney disease: where they can be found and how to benefit from them

Loss of NLRP6 increases the severity of kidney fibrosis

While NLRP3 contributes to kidney fibrosis, the function of most NOD-like receptors (NLRs) in chronic kidney disease (CKD) remains unexplored. To identify further NLR members involved in the pathogenesis of CKD, we searched for NLR genes expressed by normal kidneys and differentially expressed in human CKD transcriptomics databases. For NLRP6, lower kidney expression correlated with decreasing glomerular filtration rate. The role and molecular mechanisms of Nlrp6 in kidney fibrosis were explored in wild-type and Nlrp6-deficient mice and cell cultures. Data mining of single-cell transcriptomics databases identified proximal tubular cells as the main site of Nlrp6 expression in normal human kidneys and tubular cell Nlrp6 was lost in CKD. We confirmed kidney Nlrp6 downregulation following murine unilateral ureteral obstruction. Nlrp6-deficient mice had higher kidney p38 MAPK activation and more severe kidney inflammation and fibrosis. Similar results were obtained in adenine-induced kidney fibrosis. Mechanistically, profibrotic cytokines transforming growth factor beta 1 (TGF-β1) and TWEAK decreased Nlrp6 expression in cultured tubular cells, and Nlrp6 downregulation resulted in increased TGF-β1 and CTGF expression through p38 MAPK activation, as well as in downregulation of the antifibrotic factor Klotho, suggesting that loss of Nlrp6 promotes maladaptive tubular cell responses. The pattern of gene expression following Nlrp6 targeting in cultured proximal tubular cells was consistent with maladaptive transitions for proximal tubular cells described in single-cell transcriptomics datasets. In conclusion, endogenous constitutive Nlrp6 dampens sterile kidney inflammation and fibrosis. Loss of Nlrp6 expression by tubular cells may contribute to CKD progression.

Omics and Artificial Intelligence in Kidney Diseases

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

Pan-Src kinase inhibitor treatment attenuates diabetic kidney injury via inhibition of Fyn kinase-mediated endoplasmic reticulum stress

Src family kinases (SFKs) have been implicated in the pathogenesis of kidney fibrosis. However, the specific mechanism by which SFKs contribute to the progression of diabetic kidney disease (DKD) remains unclear. Our preliminary transcriptome analysis suggested that SFK expression was increased in diabetic kidneys and that the expression of Fyn (a member of the SFKs), along with genes related to unfolded protein responses from the endoplasmic reticulum (ER) stress signaling pathway, was upregulated in the tubules of human diabetic kidneys. Thus, we examined whether SFK-induced ER stress is associated with DKD progression. Mouse proximal tubular (mProx24) cells were transfected with Fyn or Lyn siRNA and exposed to high glucose and palmitate (HG-Pal). Streptozotocin-induced diabetic rats were treated with KF-1607, a novel pan-Src kinase inhibitor (SKI) with low toxicity. The effect of KF-1607 was compared to that of losartan, a standard treatment for patients with DKD. Among the SFK family members, the Fyn and Lyn kinases were upregulated under diabetic stress. HG-Pal induced p70S6 kinase and JNK/CHOP signaling and promoted tubular injury. Fyn knockdown but not Lyn knockdown inhibited this detrimental signaling pathway. In addition, diabetic rats treated with KF-1607 showed improved kidney function and decreased ER stress, inflammation, and fibrosis compared with those treated with losartan. Collectively, these findings indicate that Fyn kinase is a specific member of the SFKs implicated in ER stress activation leading to proximal tubular injury in the diabetic milieu and that pan-SKI treatment attenuates kidney injury in diabetic rats. These data highlight Fyn kinase as a viable target for the development of therapeutic agents for DKD.

Insights into a signaling pathway that promotes diabetic kidney disease could lead to new therapies that protect against this major cause of kidney failure. Past studies have suggested that the various Src family kinase (SFK) signaling proteins play a part in the cell death and scar tissue formation associated with diabetic kidney disease. Hunjoo Ha of Ewha Womans University, Seoul, South Korea, and colleagues have now focused on one particular SFK, Fyn, as a direct driver of the kidney damage seen in mouse models of diabetes. Genetic interventions that selectively inhibit Fyn suppressed this damage, as did treatment with an oral drug that broadly inactivates SFKs. This experimental drug proved as effective as controlling inflammation and oxidative damage in the kidney as an already clinically approved treatment, confirming the significance of SFK signaling in this condition.

Multi-Omics Techniques Make it Possible to Analyze Sepsis-Associated Acute Kidney Injury Comprehensively

Sepsis-associated acute kidney injury (SA-AKI) is a common complication in critically ill patients with high morbidity and mortality. SA-AKI varies considerably in disease presentation, progression, and response to treatment, highlighting the heterogeneity of the underlying biological mechanisms. In this review, we briefly describe the pathophysiology of SA-AKI, biomarkers, reference databases, and available omics techniques. Advances in omics technology allow for comprehensive analysis of SA-AKI, and the integration of multiple omics provides an opportunity to understand the information flow behind the disease. These approaches will drive a shift in current paradigms for the prevention, diagnosis, and staging and provide the renal community with significant advances in precision medicine in SA-AKI analysis.

Implications of Sphingolipid Metabolites in Kidney Diseases

Sphingolipids, which act as a bioactive signaling molecules, are involved in several cellular processes such as cell survival, proliferation, migration and apoptosis. An imbalance in the levels of sphingolipids can be lethal to cells. Abnormalities in the levels of sphingolipids are associated with several human diseases including kidney diseases. Several studies demonstrate that sphingolipids play an important role in maintaining proper renal function. Sphingolipids can alter the glomerular filtration barrier by affecting the functioning of podocytes, which are key cellular components of the glomerular filtration barrier. This review summarizes the studies in our understanding of the regulation of sphingolipid signaling in kidney diseases, especially in glomerular and tubulointerstitial diseases, and the potential to target sphingolipid pathways in developing therapeutics for the treatment of renal diseases.

Emerging Biomarkers for Early Detection of Chronic Kidney Disease

Chronic kidney disease (CKD) is a major and serious global health problem that leads to kidney damage as well as multiple systemic diseases. Early diagnosis and treatment are two major measures to prevent further deterioration of kidney function and to delay adverse outcomes. However, the paucity of early, predictive and noninvasive biomarkers has undermined our ability to promptly detect and treat this common clinical condition which affects more than 10% of the population worldwide. Despite all limitations, kidney function is still measured by serum creatinine, cystatin C, and albuminuria, as well as estimating glomerular filtration rate using different equations. This review aims to provide comprehensive insight into diagnostic methods available for early detection of CKD. In the review, we discuss the following topics: (i) markers of glomerular injury; (ii) markers of tubulointerstitial injury; (iii) the role of omics; (iv) the role of microbiota; (v) and finally, the role of microRNA in the early detection of CKD. Despite all novel findings, none of these biomarkers have met the criteria of an ideal early marker. Since the central role in CKD progression is the proximal tubule (PT), most data from the literature have analyzed biomarkers of PT injury, such as KIM-1 (kidney injury molecule-1), NGAL (neutrophil gelatinase-associated lipocalin), and L-FABP (liver fatty acid-binding protein).

The Use of 'Omics for Diagnosing and Predicting Progression of Chronic Kidney Disease: A Scoping Review

Globally, chronic kidney disease (CKD) contributes substantial morbidity and mortality. Recently, various 'omics platforms have provided insight into the molecular basis of kidney dysfunction. This scoping review is a synthesis of the current literature on the use of different 'omics platforms to identify biomarkers that could be used to detect early-stage CKD, predict disease progression, and identify pathways leading to CKD. This review includes 123 articles published from January 2007 to May 2021, following a structured selection process. The most common type of 'omic platform was proteomics, appearing in 55 of the studies and two of these included a metabolomics component. Most studies (n = 91) reported on CKD associated with diabetes mellitus. Thirteen studies that provided information on the biomarkers associated with CKD and explored potential pathways involved in CKD are discussed. The biomarkers that are associated with risk or early detection of CKD are SNPs in the MYH9/APOL1 and UMOD genes, the proteomic CKD273 biomarker panel and metabolite pantothenic acid. Pantothenic acid and the CKD273 biomarker panel were also involved in predicting CKD progression. Retinoic acid pathway genes, UMOD, and pantothenic acid provided insight into potential pathways leading to CKD. The biomarkers were mainly used to detect CKD and predict progression in high-income, European ancestry populations, highlighting the need for representative 'omics research in other populations with disparate socio-economic strata, including Africans, since disease etiologies may differ across ethnic groups. To assess the transferability of findings, it is essential to do research in diverse populations.

Big Data in Nephrology

A huge array of data in nephrology is collected through patient registries, large epidemiological studies, electronic health records, administrative claims, clinical trial repositories, mobile health devices and molecular databases. Application of these big data, particularly using machine-learning algorithms, provides a unique opportunity to obtain novel insights into kidney diseases, facilitate personalized medicine and improve patient care. Efforts to make large volumes of data freely accessible to the scientific community, increased awareness of the importance of data sharing and the availability of advanced computing algorithms will facilitate the use of big data in nephrology. However, challenges exist in accessing, harmonizing and integrating datasets in different formats from disparate sources, improving data quality and ensuring that data are secure and the rights and privacy of patients and research participants are protected. In addition, the optimism for data-driven breakthroughs in medicine is tempered by scepticism about the accuracy of calibration and prediction from in silico techniques. Machine-learning algorithms designed to study kidney health and diseases must be able to handle the nuances of this specialty, must adapt as medical practice continually evolves, and must have global and prospective applicability for external and future datasets.

Integrative Analysis of Prognostic Biomarkers for Acute Rejection in Kidney Transplant Recipients

BACKGROUND

Noninvasive biomarkers may predict adverse events such as acute rejection after kidney transplantation and may be preferable to existing methods because of superior accuracy and convenience. It is uncertain how these biomarkers, often derived from a single study, perform across different cohorts of recipients.

METHODS

Using a cross-validation framework that evaluates the performance of biomarkers, the aim of this study was to devise an integrated gene signature set that predicts acute rejection in kidney transplant recipients. Inclusion criteria were publicly available datasets of gene signatures that reported acute rejection episodes after kidney transplantation. We tested the predictive probability for acute rejection using gene signatures within individual datasets and validated the set using other datasets. Eight eligible studies of 1454 participants, with a total of 512 acute rejections episodes were included.

RESULTS

All sets of gene signatures had good positive and negative predictive values (79%-96%) for acute rejection within their own cohorts, but the predictability reduced to <50% when tested in other independent datasets. By integrating signature sets with high specificity scores across all studies, a set of 150 genes (included CXCL6, CXCL11, OLFM4, and PSG9) which are known to be associated with immune responses, had reasonable predictive values (varied between 69% and 90%).

CONCLUSIONS

A set of gene signatures for acute rejection derived from a specific cohort of kidney transplant recipients do not appear to provide adequate prediction in an independent cohort of transplant recipients. However, the integration of gene signature sets with high specificity scores may improve the prediction performance of these markers.

Chronic Kidney Disease Cohort Studies: A Guide to Metabolome Analyses

Kidney diseases still pose one of the biggest challenges for global health, and their heterogeneity and often high comorbidity load seriously hinders the unraveling of their underlying pathomechanisms and the delivery of optimal patient care. Metabolomics, the quantitative study of small organic compounds, called metabolites, in a biological specimen, is gaining more and more importance in nephrology research. Conducting a metabolomics study in human kidney disease cohorts, however, requires thorough knowledge about the key workflow steps: study planning, sample collection, metabolomics data acquisition and preprocessing, statistical/bioinformatics data analysis, and results interpretation within a biomedical context. This review provides a guide for future metabolomics studies in human kidney disease cohorts. We will offer an overview of important a priori considerations for metabolomics cohort studies, available analytical as well as statistical/bioinformatics data analysis techniques, and subsequent interpretation of metabolic findings. We will further point out potential research questions for metabolomics studies in the context of kidney diseases and summarize the main results and data availability of important studies already conducted in this field.

Identification of a foetal epigenetic compartment in adult human kidney

The mammalian kidney has extensive repair capacity; however, identifying adult renal stem cells has proven elusive. We applied an epigenetic marker of foetal cell origin (FCO) in diverse human tissues as a probe for developmental cell persistence, finding a 5.4-fold greater FCO proportion in kidney. Normal kidney FCO proportions averaged 49% with extensive interindividual variation. FCO proportions were significantly negatively correlated with immune-related gene expression and positively correlated with genes expressed in the renal medulla, including those involved in renal organogenesis (e.g., FGF2, PAX8, and HOXB7). FCO associated genes also mapped to medullary nephron segments in mouse and rat, suggesting evolutionary conservation of this cellular compartment. Renal cancer patients whose tumours contained non-zero FCO scores survived longer. The kidney appears unique in possessing substantial foetal epigenetic features. Further study of FCO-related gene methylation may elucidate regenerative regulatory programmes in tissues without apparent discrete stem cell compartments.

KF-1607, a Novel Pan Src Kinase Inhibitor, Attenuates Obstruction-Induced Tubulointerstitial Fibrosis in Mice

Src family kinases (SFKs), an important group of non-receptor tyrosine kinases, are suggested to be excessively activated during various types of tissue fibrosis. The present study investigated the effect of KF-1607, an orally active and a newly synthesized Src kinase inhibitor (SKI) with proposed low toxicity, in preventing the progression of renal interstitial fibrosis. Unilateral ureteral obstruction (UUO) surgery was performed in 6-week-old male C57BL/6 mice to induce renal interstitial fibrosis. Either KF-1607 (30 mg/kg, oral gavage) or PP2 (2 mg/kg, intraperitoneal injection), a common experimental SKI, was administered to mice for seven days, started one day prior to surgery. UUO injury-induced SFK expression, including Src, Fyn, and Lyn kinase. SFK inhibition by KF-1607 prevented the progression of tubular injury in UUO mice, as indicated by decreases in albuminuria, urinary KIM-1 excretion, and kidney NGAL protein expression. Renal tubulointerstitial fibrosis was attenuated in response to KF-1607, as shown by decreases in α-SMA, collagen I and IV protein expression, along with reduced Masson’s trichrome and collagen-I staining in kidneys. KF-1607 also inhibited inflammation in the UUO kidney, as exhibited by reductions in F4/80 positive-staining and protein expression of p-NFκB and ICAM. Importantly, the observed effects of KF-1607 were similar to those of PP2. A new pan Src kinase inhibitor, KF-1607, is a potential pharmaceutical agent to prevent the progression of renal interstitial fibrosis.

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Kidney diseases form part of the major health burdens experienced all over the world. Kidney diseases are linked to high economic burden, deaths, and morbidity rates. The great importance of collecting a large quantity of health-related data among human cohorts, what scholars refer to as “big data”, has increasingly been identified, with the establishment of a large group of cohorts and the usage of electronic health records (EHRs) in nephrology and transplantation. These data are valuable, and can potentially be utilized by researchers to advance knowledge in the field. Furthermore, progress in big data is stimulating the flourishing of artificial intelligence (AI), which is an excellent tool for handling, and subsequently processing, a great amount of data and may be applied to highlight more information on the effectiveness of medicine in kidney-related complications for the purpose of more precise phenotype and outcome prediction. In this article, we discuss the advances and challenges in big data, the use of EHRs and AI, with great emphasis on the usage of nephrology and transplantation.

Multi omics analysis of fibrotic kidneys in two mouse models

Kidney fibrosis represents an urgent unmet clinical need due to the lack of effective therapies and an inadequate understanding of the molecular pathogenesis. We have generated a comprehensive and combined multi-omics dataset (proteomics, mRNA and small RNA transcriptomics) of fibrotic kidneys that is searchable through a user-friendly web application: http://hbcreports.med.harvard.edu/fmm/ . Two commonly used mouse models were utilized: a reversible chemical-induced injury model (folic acid (FA) induced nephropathy) and an irreversible surgically-induced fibrosis model (unilateral ureteral obstruction (UUO)). mRNA and small RNA sequencing, as well as 10-plex tandem mass tag (TMT) proteomics were performed with kidney samples from different time points over the course of fibrosis development. The bioinformatics workflow used to process, technically validate, and combine the single omics data will be described. In summary, we present temporal multi-omics data from fibrotic mouse kidneys that are accessible through an interrogation tool (Mouse Kidney Fibromics browser) to provide a searchable transcriptome and proteome for kidney fibrosis researchers.

In Silico Toxicology Data Resources to Support Read-Across and (Q)SAR

A plethora of databases exist online that can assist in in silico chemical or drug safety assessment. However, a systematic review and grouping of databases, based on purpose and information content, consolidated in a single source, has been lacking. To resolve this issue, this review provides a comprehensive listing of the key in silico data resources relevant to: chemical identity and properties, drug action, toxicology (including nano-material toxicity), exposure, omics, pathways, Absorption, Distribution, Metabolism and Elimination (ADME) properties, clinical trials, pharmacovigilance, patents-related databases, biological (genes, enzymes, proteins, other macromolecules etc.) databases, protein-protein interactions (PPIs), environmental exposure related, and finally databases relating to animal alternatives in support of 3Rs policies. More than nine hundred databases were identified and reviewed against criteria relating to accessibility, data coverage, interoperability or application programming interface (API), appropriate identifiers, types of in vitro, in vivo,-clinical or other data recorded and suitability for modelling, read-across, or similarity searching. This review also specifically addresses the need for solutions for mapping and integration of databases into a common platform for better translatability of preclinical data to clinical data.

Big science and big data in nephrology

There have been tremendous advances during the last decade in methods for large-scale, high-throughput data generation and in novel computational approaches to analyze these datasets. These advances have had a profound impact on biomedical research and clinical medicine. The field of genomics is rapidly developing toward single-cell analysis, and major advances in proteomics and metabolomics have been made in recent years. The developments on wearables and electronic health records are poised to change clinical trial design. This rise of 'big data' holds the promise to transform not only research progress, but also clinical decision making towards precision medicine. To have a true impact, it requires integrative and multi-disciplinary approaches that blend experimental, clinical and computational expertise across multiple institutions. Cancer research has been at the forefront of the progress in such large-scale initiatives, so-called 'big science,' with an emphasis on precision medicine, and various other areas are quickly catching up. Nephrology is arguably lagging behind, and hence these are exciting times to start (or redirect) a research career to leverage these developments in nephrology. In this review, we summarize advances in big data generation, computational analysis, and big science initiatives, with a special focus on applications to nephrology.

Urinary Biomarkers of Renal Fibrosis

Renal fibrosis is the common pathological pathway of progressive CKD. The commonly used biomarkers in clinical practice are not optimal to detect injury or predict prognosis. Therefore, it is crucial to develop novel biomarkers to allow prompt intervention. Urine serves as a valuable resource of biomarker discovery for kidney diseases. Owing to the rapid development of omics platforms and bioinformatics, research on novel urinary biomarkers for renal fibrosis has proliferated in recent years. In this chapter, we discuss the current status and provide basic knowledge in this field. We present novel promising biomarkers including tubular injury markers, proteins related to activated inflammation/fibrosis pathways, CKD273, transcriptomic biomarkers, as well as metabolomic biomarkers. Furthermore, considering the complex nature of the pathogenesis of renal fibrosis, we also highlight the combination of biomarkers to further improve the diagnostic and prognostic performance.

Investigation into the underlying molecular mechanisms of hypertensive nephrosclerosis using bioinformatics analyses

Hypertensive nephrosclerosis (HNS) is a major risk factor for end-stage renal disease. However, the underlying pathogenesis of HNS remains to be fully determined. The gene expression profile of GSE20602, which consists of 14 glomeruli samples from patients with HNS and 4 normal glomeruli control samples, was obtained from the Gene Expression Omnibus database. Gene ontology (GO) and pathway enrichment analyses were performed in order to investigate the functions and pathways of differentially expressed genes (DEGs). Pathway relation and co‑expression networks were constructed in order to identify key genes and signaling pathways involved in HNS. In total, 483 DEGs were identified to be associated with HNS, including 302 upregulated genes and 181 downregulated genes. Furthermore, GO analysis revealed that DEGs were significantly enriched in the small molecule metabolic process. In addition, pathway analysis also revealed that DEGs were predominantly involved in metabolic pathways. The tricarboxylic acid (TCA) cycle was identified as the hub pathway in the pathway relation network, whereas the sorbitol dehydrogenase (SORD) and cubulin (CUBN) genes were revealed to be the hub genes in the co‑expression network. The present study revealed that the SORD, CUBN and albumin genes as well as the TCA cycle and metabolic pathways are involved in the pathogenesis of HNS. The results of the present study may contribute to the determination of the molecular mechanisms underlying HNS, and provide insight into the exploration of novel targets for the diagnosis and treatment of HNS.

Gene, cell, and organ multiplication drives inner ear evolution

We review the development and evolution of the ear neurosensory cells, the aggregation of neurosensory cells into an otic placode, the evolution of novel neurosensory structures dedicated to hearing and the evolution of novel nuclei in the brain and their input dedicated to processing those novel auditory stimuli. The evolution of the apparently novel auditory system lies in duplication and diversification of cell fate transcription regulation that allows variation at the cellular level [transforming a single neurosensory cell into a sensory cell connected to its targets by a sensory neuron as well as diversifying hair cells], organ level [duplication of organ development followed by diversification and novel stimulus acquisition] and brain nuclear level [multiplication of transcription factors to regulate various neuron and neuron aggregate fate to transform the spinal cord into the unique hindbrain organization]. Tying cell fate changes driven by bHLH and other transcription factors into cell and organ changes is at the moment tentative as not all relevant factors are known and their gene regulatory network is only rudimentary understood. Future research can use the blueprint proposed here to provide both the deeper molecular evolutionary understanding as well as a more detailed appreciation of developmental networks. This understanding can reveal how an auditory system evolved through transformation of existing cell fate determining networks and thus how neurosensory evolution occurred through molecular changes affecting cell fate decision processes. Appreciating the evolutionary cascade of developmental program changes could allow identifying essential steps needed to restore cells and organs in the future.

Analysis of Urine Composition in Type II Diabetic Mice after Intervention Therapy Using Holothurian Polypeptides

Hydrolysates and peptide fractions (PF) obtained from sea cucumber with commercial enzyme were studied on the hyperglycemic and renal protective effects on db/db rats using urine metabolomics. Compared with the control group the polypeptides from the two species could significantly reduce the urine glucose and urea. We also tried to address the compositions of highly expressed urinary proteins using a proteomics approach. They were serum albumins, AMBP proteins, negative trypsin, elastase, and urinary protein, GAPDH, a receptor of urokinase-type plasminogen activator (uPAR), and Ig kappa chain C region. We used the electronic nose to quickly detect changes in the volatile substances in mice urine after holothurian polypeptides (HPP) fed, and the results show it can identify the difference between treatment groups with the control group without overlapping. The protein express mechanism of HPP treating diabetes was discussed, and we suggested these two peptides with the hypoglycemic and renal protective activity might be utilized as nutraceuticals.

Translational science in chronic kidney disease

The KDIGO definition of chronic kidney disease (CKD) allowed a more detailed characterization of CKD causes, epidemiology and consequences. The picture that has emerged is worrisome from the point of view of translation. CKD was among the fastest growing causes of death in the past 20 years in age-adjusted terms. The gap between recent advances and the growing worldwide mortality appears to result from sequential roadblocks that limit the flow from basic research to clinical development (translational research type 1, T1), from clinical development to clinical practice (translational research T2) and result in deficient widespread worldwide implementation of already available medical advances (translational research T3). We now review recent advances and novel concepts that have the potential to change the practice of nephrology in order to improve the outcomes of the maximal number of individuals in the shortest possible interval. These include: (i) updating the CKD concept, shifting the emphasis to the identification, risk stratification and care of early CKD and redefining the concept of aging-associated 'physiological' decline of renal function; (ii) advances in the characterization of aetiological factors, including challenging the concept of hypertensive nephropathy, the better definition of the genetic contribution to CKD progression, assessing the role of the liquid biopsy in aetiological diagnosis and characterizing the role of drugs that may be applied to the earliest stages of injury, such as SGLT2 inhibitors in diabetic kidney disease (DKD); (iii) embracing the complexity of CKD as a network disease and (iv) exploring ways to optimize implementation of existing knowledge.

The systemic nature of CKD

The accurate definition and staging of chronic kidney disease (CKD) is one of the major achievements of modern nephrology. Intensive research is now being undertaken to unravel the risk factors and pathophysiologic underpinnings of this disease. In particular, the relationships between the kidney and other organs have been comprehensively investigated in experimental and clinical studies in the last two decades. Owing to technological and analytical limitations, these links have been studied with a reductionist approach focusing on two organs at a time, such as the heart and the kidney or the bone and the kidney. Here, we discuss studies that highlight the complex and systemic nature of CKD. Energy balance, innate immunity and neuroendocrine signalling are highly integrated biological phenomena. The diseased kidney disrupts such integration and generates a high-risk phenotype with a clinical profile encompassing inflammation, protein-energy wasting, altered function of the autonomic and central nervous systems and cardiopulmonary, vascular and bone diseases. A systems biology approach to CKD using omics techniques will hopefully enable in-depth study of the pathophysiology of this systemic disease, and has the potential to unravel critical pathways that can be targeted for CKD prevention and therapy.

Clinical proteomics in kidney disease as an exponential technology: heading towards the disruptive phase

Exponential technologies double in power or processing speed every year, whereas their cost halves. Deception and disruption are two key stages in the development of exponential technologies. Deception occurs when, after initial introduction, technologies are dismissed as irrelevant, while they continue to progress, perhaps not as fast or with so many immediate practical applications as initially thought. Twenty years after the first publications, clinical proteomics is still not available in most hospitals and some clinicians have felt deception at unfulfilled promises. However, there are indications that clinical proteomics may be entering the disruptive phase, where, once refined, technologies disrupt established industries or procedures. In this regard, recent manuscripts in CKJ illustrate how proteomics is entering the clinical realm, with applications ranging from the identification of amyloid proteins in the pathology lab, to a new generation of urinary biomarkers for chronic kidney disease (CKD) assessment and outcome prediction. Indeed, one such panel of urinary peptidomics biomarkers, CKD273, recently received a Food and Drug Administration letter of support, the first ever in the CKD field. In addition, a must-read resource providing information on kidney disease-related proteomics and systems biology databases and how to access and use them in clinical decision-making was also recently published in CKJ.

Establishment of a integrative multi-omics expression database CKDdb in the context of chronic kidney disease (CKD)

Complex human traits such as chronic kidney disease (CKD) are a major health and financial burden in modern societies. Currently, the description of the CKD onset and progression at the molecular level is still not fully understood. Meanwhile, the prolific use of high-throughput omic technologies in disease biomarker discovery studies yielded a vast amount of disjointed data that cannot be easily collated. Therefore, we aimed to develop a molecule-centric database featuring CKD-related experiments from available literature publications. We established the Chronic Kidney Disease database CKDdb, an integrated and clustered information resource that covers multi-omic studies (microRNAs, genomics, peptidomics, proteomics and metabolomics) of CKD and related disorders by performing literature data mining and manual curation. The CKDdb database contains differential expression data from 49395 molecule entries (redundant), of which 16885 are unique molecules (non-redundant) from 377 manually curated studies of 230 publications. This database was intentionally built to allow disease pathway analysis through a systems approach in order to yield biological meaning by integrating all existing information and therefore has the potential to unravel and gain an in-depth understanding of the key molecular events that modulate CKD pathogenesis.

Integrative Systems Biology Investigation of Fabry Disease

Fabry disease (FD) is a rare X-linked recessive genetic disorder caused by a deficient activity of the lysosomal enzyme alpha-galactosidase A (GLA) and is characterised by intra-lysosomal accumulation of globotriaosylceramide (Gb3). We performed a meta-analysis of peer-reviewed publications including high-throughput omics technologies including naïve patients and those undergoing enzyme replacement therapy (ERT). This study describes FD on a systems level using a systems biology approach, in which molecular data sourced from multi-omics studies is extracted from the literature and integrated as a whole in order to reveal the biochemical processes and molecular pathways potentially affected by the dysregulation of differentially expressed molecules. In this way new insights are provided that describe the pathophysiology of this rare disease. Using gene ontology and pathway term clustering, FD displays the involvement of major biological processes such as the acute inflammatory response, regulation of wound healing, extracellular matrix (ECM) remodelling, regulation of peptidase activity, and cellular response to reactive oxygen species (ROS). Differential expression of acute-phase response proteins in the groups of naïve (up-regulation of ORM1, ORM2, ITIH4, SERPINA3 and FGA) and ERT (down-regulation of FGA, ORM1 and ORM2) patients could be potential hallmarks for distinction of these two patient groups.

PeptiCKDdb-peptide- and protein-centric database for the investigation of genesis and progression of chronic kidney disease

The peptiCKDdb is a publicly available database platform dedicated to support research in the field of chronic kidney disease (CKD) through identification of novel biomarkers and molecular features of this complex pathology. PeptiCKDdb collects peptidomics and proteomics datasets manually extracted from published studies related to CKD. Datasets from peptidomics or proteomics, human case/control studies on CKD and kidney or urine profiling were included. Data from 114 publications (studies of body fluids and kidney tissue: 26 peptidomics and 76 proteomics manuscripts on human CKD, and 12 focusing on healthy proteome profiling) are currently deposited and the content is quarterly updated. Extracted datasets include information about the experimental setup, clinical study design, discovery-validation sample sizes and list of differentially expressed proteins (P-value < 0.05). A dedicated interactive web interface, equipped with multiparametric search engine, data export and visualization tools, enables easy browsing of the data and comprehensive analysis. In conclusion, this repository might serve as a source of data for integrative analysis or a knowledgebase for scientists seeking confirmation of their findings and as such, is expected to facilitate the modeling of molecular mechanisms underlying CKD and identification of biologically relevant biomarkers.

Database URL:www.peptickddb.com