Genomic approaches in the search for molecular biomarkers in chronic kidney disease

Abstract Background Chronic kidney disease (CKD) is recognised as a global public health problem, more prevalent in older persons and associated with multiple co-morbidities. Diabetes mellitus and hypertension are common aetiologies for CKD, but IgA glomerulonephritis, membranous glomerulonephritis, lupus nephritis and autosomal dominant polycystic kidney disease are also common causes of CKD. Main body Conventional biomarkers for CKD involving the use of estimated glomerular filtration rate (eGFR) derived from four variables (serum creatinine, age, gender and ethnicity) are recommended by clinical guidelines for the evaluation, classification, and stratification of CKD. However, these clinical biomarkers present some limitations, especially for early stages of CKD, elderly individuals, extreme body mass index values (serum creatinine), or are influenced by inflammation, steroid treatment and thyroid dysfunction (serum cystatin C). There is therefore a need to identify additional non-invasive biomarkers that are useful in clinical practice to help improve CKD diagnosis, inform prognosis and guide therapeutic management. Conclusion CKD is a multifactorial disease with associated genetic and environmental risk factors. Hence, many studies have employed genetic, epigenetic and transcriptomic approaches to identify biomarkers for kidney disease. In this review, we have summarised the most important studies in humans investigating genomic biomarkers for CKD in the last decade. Several genes, including UMOD, SHROOM3 and ELMO1 have been strongly associated with renal diseases, and some of their traits, such as eGFR and serum creatinine. The role of epigenetic and transcriptomic biomarkers in CKD and related diseases is still unclear. The combination of multiple biomarkers into classifiers, including genomic, and/or epigenomic, may give a more complete picture of kidney diseases

Genetics of Type 2 Diabetes - Pitfalls and Possibilities

Type 2 diabetes (T2D) is a complex disease that is caused by a complex interplay between genetic, epigenetic and environmental factors. While the major environmental factors, diet and activity level, are well known, identification of the genetic factors has been a challenge. However, recent years have seen an explosion of genetic variants in risk and protection of T2D due to the technical development that has allowed genome-wide association studies and next-generation sequencing. Today, more than 120 variants have been convincingly replicated for association with T2D and many more with diabetes-related traits. Still, these variants only explain a small proportion of the total heritability of T2D. In this review, we address the possibilities to elucidate the genetic landscape of T2D as well as discuss pitfalls with current strategies to identify the elusive unknown heritability including the possibility that our definition of diabetes and its subgroups is imprecise and thereby makes the identification of genetic causes difficult.Peer reviewe

Defining Glomerular Disease in Mechanistic Terms: Implementing an Integrative Biology Approach in Nephrology

Advances in biomedical research allow for the capture of an unprecedented level of genetic, molecular, and clinical information from large patient cohorts, where the quest for precision medicine can be pursued. An overarching goal of precision medicine is to integrate the large–scale genetic and molecular data with deep phenotypic information to identify a new mechanistic disease classification. This classification can ideally be used to meet the clinical goal of the right medication for the right patient at the right time. Glomerular disease presents a formidable challenge for precision medicine. Patients present with similar signs and symptoms, which cross the current disease categories. The diseases are grouped by shared histopathologic features, but individual patients have dramatic variability in presentation, progression, and response to therapy, reflecting the underlying biologic heterogeneity within each glomerular disease category. Despite the clinical challenge, glomerular disease has several unique advantages to building multilayered datasets connecting genetic, molecular, and structural information needed to address the goals of precision medicine in this population. Kidney biopsy tissue, obtained during routine clinical care, provides a direct window into the molecular mechanisms active in the affected organ. In addition, urine is a biofluid ideally suited for repeated measurement from the diseased organ as a liquid biopsy with potential to reflect the dynamic state of renal tissue. In our review, current approaches for large–scale data generation and integration along the genotype-phenotype continuum in glomerular disease will be summarized. Several successful examples of this integrative biology approach within glomerular disease will be highlighted along with an outlook on how achieving a mechanistic disease classification could help to shape glomerular disease research and care in the future

The Omics-Driven Machine Learning Path to Cost-Effective Precision Medicine in Chronic Kidney Disease.

Chronic kidney disease (CKD) poses a significant and growing global health challenge, making early detection and slowing disease progression essential for improving patient outcomes. Traditional diagnostic methods such as glomerular filtration rate and proteinuria are insufficient to capture the complexity of CKD. In contrast, omics technologies have shed light on the molecular mechanisms of CKD, helping to identify biomarkers for disease assessment and management. Artificial intelligence (AI) and machine learning (ML) could transform CKD care, enabling biomarker discovery for early diagnosis and risk prediction, and personalized treatment. By integrating multi-omics datasets, AI can provide real-time, patient-specific insights, improve decision support, and optimize cost efficiency by early detection and avoidance of unnecessary treatments. Multidisciplinary collaborations and sophisticated ML methods are essential to advance diagnostic and therapeutic strategies in CKD. This review presents a comprehensive overview of the pipeline for translating CKD omics data into personalized treatment, covering recent advances in omics research, the role of ML in CKD, and the critical need for clinical validation of AI-driven discoveries to ensure their efficacy, relevance, and cost-effectiveness in patient care

An in vitro approach to understand contribution of kidney cells to human urinary extracellular vesicles

Extracellular vesicles (EV) are membranous particles secreted by all cells and found in body fluids. Established EV contents include a variety of RNA species, proteins, lipids and metabolites that are considered to reflect the physiological status of their parental cells. However, to date, little is known about cell-type enriched EV cargo in complex EV mixtures, especially in urine. To test whether EV secretion from distinct human kidney cells in culture differ and can recapitulate findings in normal urine, we comprehensively analysed EV components, (particularly miRNAs, long RNAs and protein) from conditionally immortalised human kidney cell lines (podocyte, glomerular endothelial, mesangial and proximal tubular cells) and compared to EV secreted in human urine. EV from cell culture media derived from immortalised kidney cells were isolated by hydrostatic filtration dialysis (HFD) and characterised by electron microscopy (EM), nanoparticle tracking analysis (NTA) and Western blotting (WB). RNA was isolated from EV and subjected to miRNA and RNA sequencing and proteins were profiled by tandem mass tag proteomics. Representative sets of EV miRNAs, RNAs and proteins were detected in each cell type and compared to human urinary EV isolates (uEV), EV cargo database, kidney biopsy bulk RNA sequencing and proteomics, and single-cell transcriptomics. This revealed that a high proportion of the in vitro EV signatures were also found in in vivo datasets. Thus, highlighting the robustness of our in vitro model and showing that this approach enables the dissection of cell type specific EV cargo in biofluids and the potential identification of cell-type specific EV biomarkers of kidney disease.Peer reviewe

Cáncer y diabetes: influencia del estado pro-inflamatorio diabético en las características del cáncer de colon

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina. Fecha de lectura: 26-06-2017La diabetes mellitus tipo 2 y el cáncer son enfermedades de epidémicas proporciones a nivel mundial. Las muertes atribuidas a estas dos enfermedades se han incrementado entre un 90% y un 57% respectivamente a lo largo de los últimos 20 años. El riesgo de cáncer colorrectal se estima un 27% más alto en pacientes con diabetes tipo 2 que en controles no diabéticos, aunque existen muchos factores de confusión en los estudios publicados. A nivel mundial, no existe una correlación clara entre la prevalencia de la diabetes mellitus tipo 2 y la incidencia del cáncer colorrectal. Las estimaciones de esta asociación se han modificado a lo largo de los años, sugiriendo el impacto de factores ambientales coexistentes. El cáncer colorrectal comparte algunas vías celulares y moleculares implicadas en el daño producido en los órganos diana de la diabetes. Estas vías incluyen daño a las células epiteliales, activación de la inflamación y vías con implicación del factor de crecimiento epidérmico o de Wnt/ß-catenina, entre otros. Además, el tratamiento para la diabetes puede impactar en la aparición o evolución del cáncer colorrectal: la insulina podría estar asociada con un aumento de la incidencia de cáncer colorrectal mientras que a la metformina se le asocia un efecto protector. Revisada esta evidencia, existen suficientes estudios epidemiológicos que analizan el posible mayor riesgo de padecer cáncer colorrectal en pacientes diabéticos pero no hay estudios que indiquen si existen diferencias en las características de estos tumores, una vez que la enfermedad está establecida. Esta tesis estudia las posibles diferencias en el cáncer colorrectal atribuibles al microambiente que condiciona la diabetes mellitus. Con este objetivo, se ha analizado una base de datos de pacientes con cáncer colorrectal, diabéticos y no diabéticos, y se ha desarrollado un modelo animal en el que se indujeron las dos enfermedades, estudiando las diferencias del cáncer en ratones con y sin diabetes, tanto a nivel histológico como molecular. Según la hipótesis de trabajo de esta tesis, conocido el ambiente pro-inflamatorio que rodea a la diabetes y que es responsable de diversas alteraciones en los órganos diana, los individuos que sufren diabetes deberían de ser más proclives a sufrir tumores con características diferentes, presuponiéndose mayor agresividad a nivel clínico, histológico o molecular, así como diferente respuesta a los tratamientos estándar

Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are responsible for RNA silencing and post-transcriptional regulation of gene expression. They can mediate a fine-tuned crosstalk among coding and non-coding RNA molecules sharing miRNA response elements (MREs). In a suitable environment, both coding and non-coding RNA molecules can be targeted by the same miRNAs and can indirectly regulate each other by competing for them. These RNAs, otherwise known as competing endogenous RNAs (ceRNAs), lead to an additional post-transcriptional regulatory layer, where non-coding RNAs can find new significance. The miRNA-mediated interplay among different types of RNA molecules has been observed in many different contexts. The analyses of ceRNA networks in cancer and other pathologies, as well as in other physiological conditions, provide new opportunities for interpreting omics data for the field of personalized medicine. The development of novel computational tools, providing putative predictions of ceRNA interactions, is a rapidly growing field of interest. In this review, I discuss and present the current knowledge of the ceRNA mechanism and its implications in a broad spectrum of different pathologies, such as cardiovascular or autoimmune diseases, cancers and neurodegenerative disorders

The future of "omics" in hypertension

Despite decades of research and clinical practice the pathogenesis of hypertension remains incompletely understood and blood pressure is often suboptimally controlled. Omics technologies allow the description of a large number of molecular features and have the potential to identify new factors that contribute to blood pressure regulation and how they interact. In this review we will focus on the potential of genomics, transcriptomics, proteomics and metabolomics and explore their role in unravelling the pathophysiology and diagnosis of hypertension; prediction of organ damage and treatment response; and monitoring of treatment effect. Substantial progress has been made in the area of genomics where genome-wide association studies have identified more than 50 blood pressure-related single nucleotide polymorphisms and sequencing studies especially in secondary forms of hypertension have discovered novel regulatory pathways. In contrast, other omics technologies, despite their ability to provide detailed insights into the physiological state of an organism, have only more recently demonstrated their impact on hypertension research and clinical practice. The majority of current proteomic studies focuses on organ damage due to hypertension and may have the potential to understand the link between blood pressure and organ failure but also serve as biomarker for early detection of cerebrovascular or coronary disease. Examples include signatures for early detection of left ventricular dysfunction or albuminuria. Metabolomic studies have potential to integrate environmental and intrinsic factors and are particularly suited to monitor the response to treatment. We will discuss examples of omics studies in hypertension and explore the challenges related to these novel technologies

Therapeutic Landscape of Diabetic Nephropathy: Insights from Long Noncoding RNAs

Objective: Diabetic nephropathy (DN) is a major complication of diabetes mellitus and a leading cause of endstage renal disease. Long noncoding RNAs (lncRNAs) have emerged as critical regulators in various biological processes, including those implicated in DN pathogenesis. This manuscript provides a comprehensive review of the therapeutic potential of lncRNAs in the context of DN, elucidating their roles as diagnostic markers, prognostic indicators, and therapeutic targets. Materials and methods: A systematic review of current literature was conducted, focusing on studies investigating the involvement of lncRNAs in DN pathophysiology and therapeutic interventions. The literature search was performed in Medline, Scopus, WOS, and PubMed databases. Key findings related to the regulatory mechanisms of lncRNAs in DN progression and their modulation by pharmacological agents or gene therapy approaches were synthesized. Results: This extensive analysis examines the many functions of lncRNAs in DN, including their participation in crucial physiological mechanisms. The analysis systematically examines the abnormal functioning of certain lncRNAs in the progression of DN, with a focus on their possible use as indicators for diagnosis and prognosis. Furthermore, we examine the molecular mechanisms by which lncRNAs regulate the course of DN. Conclusions: Understanding the intricate roles of lncRNAs in DN pathogenesis opens avenues for the development of novel diagnostic tools and therapeutic interventions. Targeting dysregulated lncRNAs holds considerable promise in mitigating DN progression and improving clinical outcomes for patients with diabetic kidney disease. Further research efforts are warranted to validate the clinical utility of lncRNA-based therapeutics in DN management