Big science and big data in nephrology

Sáez-Rodríguez, Julio; Rinschen, Markus M.; Floege, Jürgen; Kramann, Rafael

doi:10.1016/j.kint.2018.11.048

Cited by 67 publications

(55 citation statements)

References 134 publications

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“…53 Other data sets, such as (phospho)proteomics 54 and metabolomics, 55 may complement gene expression toward a more complete picture of the CKD entities. 56 Ideally, all these data sets will be collected in a standardized manner to facilitate integration, which was a major hurdle in our study. Such a comprehensive analysis across large cohorts, akin to what has happened for the different tumor types thanks to initiatives such as the International Cancer Genome Consortium, can lead to major improvements in our understanding and treatment of CKD.…”

Section: Resultsmentioning

confidence: 99%

A Functional Landscape of CKD Entities From Public Transcriptomic Data

Tajti

Kuppe

Antoranz

et al. 2020

Kidney International Reports

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Introduction: To develop effective therapies and identify novel early biomarkers for chronic kidney disease, an understanding of the molecular mechanisms orchestrating it is essential. We here set out to understand how differences in chronic kidney disease (CKD) origin are reflected in gene expression. To this end, we integrated publicly available human glomerular microarray gene expression data for 9 kidney disease entities that account for most of CKD worldwide. Our primary goal was to demonstrate the possibilities and potential on data analysis and integration to the nephrology community. Methods: We integrated data from 5 publicly available studies and compared glomerular gene expression profiles of disease with that of controls from nontumor parts of kidney cancer nephrectomy tissues. A major challenge was the integration of the data from different sources, platforms, and conditions that we mitigated with a bespoke stringent procedure. Results: We performed a global transcriptome-based delineation of different kidney disease entities, obtaining a transcriptomic diffusion map of their similarities and differences based on the genes that acquire a consistent differential expression between each kidney disease entity and nephrectomy tissue. We derived functional insights by inferring the activity of signaling pathways and transcription factors from the collected gene expression data and identified potential drug candidates based on expression signature matching. We validated representative findings by immunostaining in human kidney biopsies indicating, for example, that the transcription factor FOXM1 is significantly and specifically expressed in parietal epithelial cells in rapidly progressive glomerulonephritis (RPGN) whereas not expressed in control kidney tissue. Furthermore, we found drug candidates by matching the signature on expression of drugs to that of the CKD entities, in particular, the Food and Drug Administration-approved drug nilotinib. Conclusion: These results provide a foundation to comprehend the specific molecular mechanisms underlying different kidney disease entities that can pave the way to identify biomarkers and potential therapeutic targets. To facilitate further use, we provide our results as a free interactive Web application: https://saezlab.shinyapps.io/ckd_landscape/. However, because of the limitations of the data and the difficulties in its integration, any specific result should be considered with caution. Indeed, we consider this study rather an illustration of the value of functional genomics and integration of existing data.

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Section: Resultsmentioning

confidence: 99%

A Functional Landscape of CKD Entities From Public Transcriptomic Data

Tajti

Kuppe

Antoranz

et al. 2020

Kidney International Reports

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“…AI can assist in shedding light on the specific accuracy of medicines used to treat kidney diseases, for outcome prediction, and also to gain a more precise phenotype. Inspired by the idea of mimicking the biological structure of human brains, deep learning is a subfield of machine learning based on ANN [74]. Deep-learning models can learn many levels of data design with a multiple-processing-layers model structure, attaining more powerful model performance.…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

“…Subsequently, there has been an increasing number of AI studies, with great emphasis on the usage of nephrology and transplantation [85,[87][88][89]. Inspired by the idea of mimicking the biological structure of human brains, deep learning is a subfield of machine learning based on ANN [74]. Deep-learning models can learn many levels of data design with a multiple-processing-layers model structure, attaining more powerful model performance.…”

Section: Using Electronic Health Record Data In Nephrologymentioning

confidence: 99%

“…Despite having numerous imperfections, big data, as well as artificial intelligence have been applied in the field of medication from numerous parts [115,116]. There are numerous possible guidelines of using big data and artificial intelligence in nephrology that requires greater attention, as well as further consideration [74,78,[117][118][119][120][121][122][123][124][125].…”

Section: Potential Directions and Future Scopementioning

confidence: 99%

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Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Thongprayoon

Kaewput

Kovvuru

et al. 2020

JCM

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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.

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“…Extraordinary progress in technologies designed for high-throughput data generation and analysis has had profound repercussions on biomedical research and precision medicine [30]. Unfortunately, nephrology has stayed behind this big-data revolution compared to other medical fields, as evaluation of renal function and clinical decision-making is still carried out by outdated methods [30].…”

Section: Molecular Profiling In Nephrology and Transplantationmentioning

confidence: 99%

Diagnostic, Prognostic, and Therapeutic Value of Non-Coding RNA Expression Profiles in Renal Transplantation

2020

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End-stage renal disease is a public health problem responsible for millions of deaths worldwide each year. Although transplantation is the preferred treatment for patients in need of renal replacement therapy, long-term allograft survival remains challenging. Advances in high-throughput methods for large-scale molecular data generation and computational analysis are promising to overcome the current limitations posed by conventional diagnostic and disease classifications post-transplantation. Non-coding RNAs (ncRNAs) are RNA molecules that, despite lacking protein-coding potential, are essential in the regulation of epigenetic, transcriptional, and post-translational mechanisms involved in both health and disease. A large body of evidence suggests that ncRNAs can act as biomarkers of renal injury and graft loss after transplantation. Hence, the focus of this review is to discuss the existing molecular signatures of non-coding transcripts and their value to improve diagnosis, predict the risk of rejection, and guide therapeutic choices post-transplantation.

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Big science and big data in nephrology

Cited by 67 publications

References 134 publications

A Functional Landscape of CKD Entities From Public Transcriptomic Data

A Functional Landscape of CKD Entities From Public Transcriptomic Data

Promises of Big Data and Artificial Intelligence in Nephrology and Transplantation

Diagnostic, Prognostic, and Therapeutic Value of Non-Coding RNA Expression Profiles in Renal Transplantation

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