Molecular determinants of acute kidney injury

BACKGROUND: Acute kidney injury (AKI) is a condition that leads to a rapid deterioration of renal function associated with impairment to maintain electrolyte and acid balance, and, if left untreated, ultimately irreversible kidney damage and renal necrosis. There are a number of causes that can trigger AKI, ranging from underlying conditions as well as trauma and surgery. Specifically, the global rise in surgical procedures led to a substantial increase of AKI incidence rates, which in turn impacts on mortality rates, quality of life and economic costs to the healthcare system. However, no effective therapy for AKI exists. Current approaches, such as pharmacological intervention, help in alleviating symptoms in slowing down the progression, but do not prevent or reverse AKI-induced organ damage. METHODS: An in-depth understanding of the molecular machinery involved in and modulated by AKI induction and progression is necessary to specifically pharmacologically target key molecules. A major hurdle to devise a successful strategy is the multifactorial and complex nature of the disorder itself, whereby the activation of a number of seemingly independent molecular pathways in the kidney leads to apoptotic and necrotic events. RESULTS: The renin-angiotensin-aldosterone-system (RAAS) axis appears to be a common element, leading to downstream events such as triggers of immune responses via the NFB pathway. Other pathways intricately linked with AKI-induction and progression are the tumor necrosis factor alpha (TNF) and transforming growth factor beta (TGF) signaling cascades, as well as a number of other modulators. Surprisingly, it has been shown that the involvement of the glutamatergic axis, believed to be mainly a component of the neurological system, is also a major contributor. CONCLUSIONS: Here we address the current understanding of the molecular pathways evoked in AKI, their interplay, and the potential to pharmacologically intervene in the effective prevention and/or progression of AKI

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

Current advances in systems and integrative biology

Systems biology has gained a tremendous amount of interest in the last few years. This is partly due to the realization that traditional approaches focusing only on a few molecules at a time cannot describe the impact of aberrant or modulated molecular environments across a whole system. Furthermore, a hypothesis-driven study aims to prove or disprove its postulations, whereas a hypothesis-free systems approach can yield an unbiased and novel testable hypothesis as an end-result. This latter approach foregoes assumptions which predict how a biological system should react to an altered microenvironment within a cellular context, across a tissue or impacting on distant organs. Additionally, re-use of existing data by systematic data mining and re-stratification, one of the cornerstones of integrative systems biology, is also gaining attention. While tremendous efforts using a systems methodology have already yielded excellent results, it is apparent that a lack of suitable analytic tools and purpose-built databases poses a major bottleneck in applying a systematic workflow. This review addresses the current approaches used in systems analysis and obstacles often encountered in large-scale data analysis and integration which tend to go unnoticed, but have a direct impact on the final outcome of a systems approach. Its wide applicability, ranging from basic research, disease descriptors, pharmacological studies, to personalized medicine, makes this emerging approach well suited to address biological and medical questions where conventional methods are not ideal

Proteomics for rejection diagnosis in renal transplant patients: where are we now?

Rejection is one of the key factors that determine the long-term allograft function and survival in renal transplant patients. Reliable and timely diagnosis is important to treat rejection as early as possible. Allograft biopsies are not suitable for continuous monitoring of rejection. Thus, there is an unmet need for non-invasive methods to diagnose acute and chronic rejection. Proteomics in urine and blood samples has been explored for this purpose in 29 studies conducted since 2003. This review describes the different proteomic approaches and summarizes the results from the studies that examined proteomics for the rejection diagnoses. The potential limitations and open questions in establishing proteomic markers for rejection are discussed, including ongoing trials and future challenges to this topic

Chapter Integrative Systems Biology Resources and Approaches in Disease Analytics

Currently, our analytical competences are struggling to keep-up the pace of in-deep analysis of all generated large-scale data resultant of high-throughput omics platforms. While, a substantial effort was spent on methods enhancement regarding technical aspects across many detection omics platforms, the development of integrative down-stream approaches is still challenging. Systems biology has an immense applicability in the biomedical and pharmacological areas since the main goal of those focuses in the translation of measured outputs into potential markers of a Human ailment and/or to provide new compound leads for drug discovery. This approach would become more straightforward and realistic to use in standard analysis workflows if the collation of all available information of every component of a biological system was ensured into a single database framework, instead of search and fetch a single component at time across a scatter of databases resources. Here, we will describe several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms

Integrative analysis of Multiple Sclerosis using a systems biology approach

Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammatory-demyelinating events in the central nervous system. Despite more than 40 years of MS research its aetiology remains unknown. This study aims to identify the most frequently reported and consistently regulated molecules in MS in order to generate molecular interaction networks and thereby leading to the identification of deregulated processes and pathways which could give an insight of the underlying molecular mechanisms of MS. Driven by an integrative systems biology approach, gene-expression profiling datasets were combined and stratified into "Non-treated" and "Treated" groups and additionally compared to other disease patterns. Molecular identifiers from dataset comparisons were matched to our Multiple Sclerosis database (MuScle; www.padb.org/muscle ). From 5079 statistically significant molecules, correlation analysis within groups identified a panel of 16 high-confidence genes unique to the naïve MS phenotype, whereas the "Treated" group reflected a common pattern associated with autoimmune disease. Pathway and gene-ontology clustering identified the Interferon gamma signalling pathway as the most relevant amongst all significant molecules, and viral infections as the most likely cause of all down-stream events observed. This hypothesis-free approach revealed the most significant molecular events amongst different MS phenotypes which can be used for further detailed studies

Integrative OMICS Data-Driven Procedure Using a Derivatized Meta-Analysis Approach

The wealth of high-throughput data has opened up new opportunities to analyze and describe biological processes at higher resolution, ultimately leading to a significant acceleration of scientific output using high-throughput data from the different omics layers and the generation of databases to store and report raw datasets. The great variability among the techniques and the heterogeneous methodologies used to produce this data have placed meta-analysis methods as one of the approaches of choice to correlate the resultant large-scale datasets from different research groups. Through multi-study meta-analyses, it is possible to generate results with greater statistical power compared to individual analyses. Gene signatures, biomarkers and pathways that provide new insights of a phenotype of interest have been identified by the analysis of large-scale datasets in several fields of science. However, despite all the efforts, a standardized regulation to report large-scale data and to identify the molecular targets and signaling networks is still lacking. Integrative analyses have also been introduced as complementation and augmentation for meta-analysis methodologies to generate novel hypotheses. Currently, there is no universal method established and the different methods available follow different purposes. Herein we describe a new unifying, scalable and straightforward methodology to meta-analyze different omics outputs, but also to integrate the significant outcomes into novel pathways describing biological processes of interest. The significance of using proper molecular identifiers is highlighted as well as the potential to further correlate molecules from different regulatory levels. To show the methodology's potential, a set of transcriptomic datasets are meta-analyzed as an example

Integrative Systems Biology Resources and Approaches in Disease Analytics

Currently, our analytical competences are struggling to keep-up the pace of in-deep analysis of all generated large-scale data resultant of high-throughput omics platforms. While, a substantial effort was spent on methods enhancement regarding technical aspects across many detection omics platforms, the development of integrative down-stream approaches is still challenging. Systems biology has an immense applicability in the biomedical and pharmacological areas since the main goal of those focuses in the translation of measured outputs into potential markers of a Human ailment and/or to provide new compound leads for drug discovery. This approach would become more straightforward and realistic to use in standard analysis workflows if the collation of all available information of every component of a biological system was ensured into a single database framework, instead of search and fetch a single component at time across a scatter of databases resources. Here, we will describe several database resources, standalone and web-based tools applied in disease analytics workflows based in data-driven integration of outputs of multi-omic detection platforms

CVD and oxidative stress

Nowadays, it is known that oxidative stress plays at least two roles within the cell, the generation of cellular damage and the involvement in several signaling pathways in its balanced normal state. So far, a substantial amount of time and effort has been expended in the search for a clear link between cardiovascular disease (CVD) and the effects of oxidative stress. Here, we present an overview of the different sources and types of reactive oxygen species in CVD, highlight the relationship between CVD and oxidative stress and discuss the most prominent molecules that play an important role in CVD pathophysiology. Details are given regarding common pharmacological treatments used for cardiovascular distress and how some of them are acting upon ROS-related pathways and molecules. Novel therapies, recently proposed ROS biomarkers, as well as future challenges in the field are addressed. It is apparent that the search for a better understanding of how ROS are contributing to the pathophysiology of CVD is far from over, and new approaches and more suitable biomarkers are needed for the latter to be accomplished

Acute kidney injury prediction in cardiac surgery patients by a urinary peptide pattern: a case-control validation study

Background Acute kidney injury (AKI) is a prominent problem in hospitalized patients and associated with increased morbidity and mortality. Clinical medicine is currently hampered by the lack of accurate and early biomarkers for diagnosis of AKI and the evaluation of the severity of the disease. In 2010, we established a multivariate peptide marker pattern consisting of 20 naturally occurring urinary peptides to screen patients for early signs of renal failure. The current study now aims to evaluate if, in a different study population and potentially various AKI causes, AKI can be detected early and accurately by proteome analysis. Methods Urine samples from 60 patients who developed AKI after cardiac surgery were analyzed by capillary electrophoresis-mass spectrometry (CE-MS). The obtained peptide profiles were screened by the AKI peptide marker panel for early signs of AKI. Accuracy of the proteomic model in this patient collective was compared to that based on urinary neutrophil gelatinase-associated lipocalin (NGAL) and kidney injury molecule-1 (KIM-1) ELISA levels. Sixty patients who did not develop AKI served as negative controls. Results From the 120 patients, 110 were successfully analyzed by CE-MS (59 with AKI, 51 controls). Application of the AKI panel demonstrated an AUC in receiver operating characteristics (ROC) analysis of 0.81 (95 % confidence interval: 0.72–0.88). Compared to the proteomic model, ROC analysis revealed poorer classification accuracy of NGAL and KIM-1 with the respective AUC values being outside the statistical significant range (0.63 for NGAL and 0.57 for KIM-1)