Systems biology of kidney diseases

He, John Cijiang; Chuang, Peter Y.; Ma’ayan, Avi; Iyengar, Ravi

doi:10.1038/ki.2011.314

Cited by 73 publications

(40 citation statements)

References 128 publications

(136 reference statements)

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“…Our increasing understanding of molecular (genetic, epigenetic, transcriptomic, proteomic, metabolomics) [36], clinical, environmental, socioeconomic data, and of the renal disease pathophysiology [37] demand that novel quantitative approaches, suitable for dataset integration, are developed. Three different tissue interrogation approaches exploit digital pathology in clinical research: visual morphologic assessment; visual morphometric assessment; and computer-aided assessment (Table 1).…”

Section: Digital Pathology In Clinical Researchmentioning

confidence: 99%

Digital pathology in nephrology clinical trials, research, and pathology practice

Barisoni

Hodgin

2017

Current Opinion in Nephrology and Hypertension

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Purpose of review In this review, we will discuss (i) how the recent advancements in digital technology and computational engineering are currently applied to nephropathology in the setting of clinical research, trials, and practice; (ii) the benefits of the new digital environment; (iii) how recognizing its challenges provides opportunities for transformation; and (iv) nephropathology in the upcoming era of kidney precision and predictive medicine. Recent findings Recent studies highlighted how new standardized protocols facilitate the harmonization of digital pathology database infrastructure and morphologic, morphometric, and computer-aided quantitative analyses. Digital pathology enables robust protocols for clinical trials and research, with the potential to identify previously underused or unrecognized clinically useful parameters. The integration of digital pathology with molecular signatures is leading the way to establishing clinically relevant morphoomic taxonomies of renal diseases. Summary The introduction of digital pathology in clinical research and trials, and the progressive implementation of the modern software ecosystem, opens opportunities for the development of new predictive diagnostic paradigms and computer-aided algorithms, transforming the practice of renal disease into a modern computational science.

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Section: Digital Pathology In Clinical Researchmentioning

confidence: 99%

Digital pathology in nephrology clinical trials, research, and pathology practice

Barisoni

Hodgin

2017

Current Opinion in Nephrology and Hypertension

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“…We can consider how to relate these NS-associated intermediates to underlying genetic variation. Similar approaches have contributed to our integrated, systems genomics approach to NS [18,19,45,46]. …”

Section: Analytic Challenges Of Genome-scale Data In Glomerular Diseasementioning

confidence: 99%

Defining nephrotic syndrome from an integrative genomics perspective

2014

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Nephrotic syndrome (NS) is a clinical condition with a high degree of morbidity and mortality, caused by failure of the glomerular filtration barrier, resulting in massive proteinuria. Our current diagnostic, prognostic and therapeutic decisions in NS are largely based upon clinical or histological patterns such as “focal segmental glomerulosclerosis” or “steroid sensitive”. Yet these descriptive classifications lack the precision to explain the physiologic origins and clinical heterogeneity observed in this syndrome. A more precise definition of NS is required to identify mechanisms of disease and capture various clinical trajectories. An integrative genomics approach to NS applies bioinformatics and computational methods to comprehensive experimental, molecular and clinical data for holistic disease definition. A unique aspect is analysis of data together to discover NS-associated molecules, pathways and networks. Integrating multidimensional datasets from the outset highlights how molecular lesions impact the entire individual. Data sets integrated range from mutation to gene expression, to histologic changes, to progression of chronic kidney disease (CKD). This review will introduce the tenets of integrative genomics and suggest how it can increase our understanding of NS from molecular and pathophysiological perspectives. A diverse group of genome-scale experiments are presented that have sought to define molecular signatures of NS. Finally, the Nephrotic Syndrome Study Network (NEPTUNE) will be introduced as an international, prospective cohort study of patients with NS that utilizes an integrated systems genomics approach from the outset. A major NEPTUNE goal is to achieve comprehensive disease definition from a genomics perspective and identify shared molecular drivers of disease.

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“…Several previous reviews are excellent resources for the specifics of the techniques used [11] [12] [13] [14] [15] [16] [17]. We focus on rationale of these “omics” approaches, and highlight pertinent examples of early progress integrating different layers within the genome-phenome continuum.…”

Section: Introductionmentioning

confidence: 99%

Integrative Biology of Diabetic Kidney Disease

Harder¹,

Hodgin²,

Kretzler³

2015

Kidney Dis

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Background: The leading cause of end-stage renal disease in the US is diabetic kidney disease (DKD). Despite significant efforts to improve outcomes in DKD, the impact on disease progression has been disappointing. This has prompted clinicians and researchers to search for alternative approaches to identify persons at risk, and to search for more effective therapies to halt progression of DKD. The identification of novel therapies is critically dependent on a more comprehensive understanding of the pathophysiology of DKD, specifically at the molecular level. A more expansive and exploratory view of DKD is needed to complement more traditional research approaches that have focused on single molecules. Summary: In recent years, sophisticated research methodologies have emerged within systems biology that should allow for a more comprehensive disease definition of DKD. Systems biology provides an interdisciplinary approach to describe complex interactions within biological systems, including how these interactions influence systems' functions and behaviors. Computational modeling of large, system-wide, quantitative data sets is used to generate molecular interaction pathways, such as metabolic and cell signaling networks. Key Messages: Importantly, the interpretation of data generated by systems biology tools requires integration with enhanced clinical research data and validation using model systems. Such an integrative biological approach has already generated novel insights into pathways and molecules involved in DKD. In this review, we highlight recent examples of how combining systems biology with traditional clinical and model research efforts results in an integrative biology approach that significantly adds to the understanding of the complex pathophysiology of DKD.

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Systems biology of kidney diseases

Cited by 73 publications

References 128 publications

Digital pathology in nephrology clinical trials, research, and pathology practice

Digital pathology in nephrology clinical trials, research, and pathology practice

Defining nephrotic syndrome from an integrative genomics perspective

Integrative Biology of Diabetic Kidney Disease

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