The NIH National Center for Integrative Biomedical Informatics (NCIBI)

The kidney matrisome in health, aging, and disease

Dysregulated extracellular matrix is the hallmark of fibrosis, and it has a profound impact on kidney function in disease. Furthermore, perturbation of matrix homeostasis is a feature of aging and is associated with declining kidney function. Understanding these dynamic processes, in the hope of developing therapies to combat matrix dysregulation, requires the integration of data acquired by both well-established and novel technologies. Owing to its complexity, the extracellular proteome, or matrisome, still holds many secrets and has great potential for the identification of clinical biomarkers and drug targets. The molecular resolution of matrix composition during aging and disease has been illuminated by cutting-edge mass spectrometry-based proteomics in recent years, but there remain key questions about the mechanisms that drive altered matrix composition. Basement membrane components are particularly important in the context of kidney function; and data from proteomic studies suggest that switches between basement membrane and interstitial matrix proteins are likely to contribute to organ dysfunction during aging and disease. Understanding the impact of such changes on physical properties of the matrix, and the subsequent cellular response to altered stiffness and viscoelasticity, is of critical importance. Likewise, the comparison of proteomic data sets from multiple organs is required to identify common matrix biomarkers and shared pathways for therapeutic intervention. Coupled with single-cell transcriptomics, there is the potential to identify the cellular origin of matrix changes, which could enable cell-targeted therapy. This review provides a contemporary perspective of the complex kidney matrisome and draws comparison to altered matrix in heart and liver disease.

Using hierarchical similarity to examine the genetics of Behçet's disease

OBJECTIVE

Behçet's disease (BD) is a multisystem inflammatory disease that affects patients along the historic silk road. Thus far, the pathogenesis of the disease has proved elusive due to the complex genetic interactions of the disease. In this paper, we seek to clarify the genetic factors of the disease while also uncovering other diseases of interest that present with a similar genotype as BD.

RESULTS

To do this, we employ a convergent functional genomics approach by leveraging the hierarchical similarity tool available in Geneweaver. Through our analysis, we were able to ascertain 7 BD consensus genes and 16 autoimmune diseases with genetic overlap with BD. The results of our study will inform further research into the pathogenesis of Behçet's disease.

A Functional Landscape of CKD Entities From Public Transcriptomic Data

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.

Metabolomics and Gene Expression Analysis Reveal Down-regulation of the Citric Acid (TCA) Cycle in Non-diabetic CKD Patients

Chronic kidney disease (CKD) is a public health problem with very high prevalence and mortality. Yet, there is a paucity of effective treatment options, partly due to insufficient knowledge of underlying pathophysiology. We combined metabolomics (GCMS) with kidney gene expression studies to identify metabolic pathways that are altered in adults with non-diabetic stage 3-4 CKD versus healthy adults. Urinary excretion rate of 27 metabolites and plasma concentration of 33 metabolites differed significantly in CKD patients versus controls (estimate range-68% to +113%). Pathway analysis revealed that the citric acid cycle was the most significantly affected, with urinary excretion of citrate, cis-aconitate, isocitrate, 2-oxoglutarate and succinate reduced by 40-68%. Reduction of the citric acid cycle metabolites in urine was replicated in an independent cohort. Expression of genes regulating aconitate, isocitrate, 2-oxoglutarate and succinate were significantly reduced in kidney biopsies. We observed increased urine citrate excretion (+74%, p=0.00009) and plasma 2-oxoglutarate concentrations (+12%, p=0.002) in CKD patients during treatment with a vitamin-D receptor agonist in a randomized trial. In conclusion, urinary excretion of citric acid cycle metabolites and renal expression of genes regulating these metabolites were reduced in non-diabetic CKD. This supports the emerging view of CKD as a state of mitochondrial dysfunction.

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

In the recent decades, the evolution of omics technologies has led to advances in all biological fields, creating a demand for effective storage, management and exchange of rapidly generated data and research discoveries. To address this need, the development of databases of experimental outputs has become a common part of scientific practice in order to serve as knowledge sources and data-sharing platforms, providing information about genes, transcripts, proteins or metabolites. In this review, we present omics databases available currently, with a special focus on their application in kidney research and possibly in clinical practice. Databases are divided into two categories: general databases with a broad information scope and kidney-specific databases distinctively concentrated on kidney pathologies. In research, databases can be used as a rich source of information about pathophysiological mechanisms and molecular targets. In the future, databases will support clinicians with their decisions, providing better and faster diagnoses and setting the direction towards more preventive, personalized medicine. We also provide a test case demonstrating the potential of biological databases in comparing multi-omics datasets and generating new hypotheses to answer a critical and common diagnostic problem in nephrology practice. In the future, employment of databases combined with data integration and data mining should provide powerful insights into unlocking the mysteries of kidney disease, leading to a potential impact on pharmacological intervention and therapeutic disease management.

Medical student awareness of and interest in clinical informatics

OBJECTIVE

We aimed to investigate medical students' attitudes about Clinical Informatics (CI) training and careers.

MATERIALS AND METHODS

We distributed a web-based survey to students at four US allopathic medical schools.

RESULTS

Five hundred and fifty-seven medical students responded. Interest in CI training opportunities (medical school electives, residency electives, or academic fellowships) surpassed respondents' prior awareness of these opportunities. Thirty percent of student respondents expressed at least some interest in a CI-related career, but they were no more aware of training opportunities than their peers who did not express such an interest.

DISCUSSION

Almost one third of medical students who responded to our survey expressed an interest in a CI-related career, but they were generally unaware of CI training and mentoring opportunities available to them. Early outreach to such medical students, through elective classes, professional society incentives, or expert partnerships, may positively influence the size and skill set of the future CI workforce.

CONCLUSION

We should work as a field to increase the quantity, quality, and publicity of CI learning opportunities for interested medical students.

Recent trends in biomedical informatics: a study based on JAMIA articles

In a growing interdisciplinary field like biomedical informatics, information dissemination and citation trends are changing rapidly due to many factors. To understand these factors better, we analyzed the evolution of the number of articles per major biomedical informatics topic, download/online view frequencies, and citation patterns (using Web of Science) for articles published from 2009 to 2012 in JAMIA. The number of articles published in JAMIA increased significantly from 2009 to 2012, and there were some topic differences in the last 4 years. Medical Record Systems, Algorithms, and Methods are topic categories that are growing fast in several publications. We observed a significant correlation between download frequencies and the number of citations per month since publication for a given article. Earlier free availability of articles to non-subscribers was associated with a higher number of downloads and showed a trend towards a higher number of citations. This trend will need to be verified as more data accumulate in coming years.

Exposome informatics: considerations for the design of future biomedical research information systems

The environment's contribution to health has been conceptualized as the exposome. Biomedical research interest in environmental exposures as a determinant of physiopathological processes is rising as such data increasingly become available. The panoply of miniaturized sensing devices now accessible and affordable for individuals to use to monitor a widening range of parameters opens up a new world of research data. Biomedical informatics (BMI) must provide a coherent framework for dealing with multi-scale population data including the phenome, the genome, the exposome, and their interconnections. The combination of these more continuous, comprehensive, and personalized data sources requires new research and development approaches to data management, analysis, and visualization. This article analyzes the implications of a new paradigm for the discipline of BMI, one that recognizes genome, phenome, and exposome data and their intricate interactions as the basis for biomedical research now and for clinical care in the near future.

The utility of modeling and simulation in drug development and regulatory review

US Food and Drug Administration (FDA) has identified innovation in clinical evaluations as a major scientific priority area. This paper provides case studies and updates to describe the efforts by the FDA's Office of Clinical Pharmacology in its development and application of regulatory science, focusing on modeling and simulation. Key issues and challenges are identified that need to be addressed to promote the uptake of modeling and simulation approaches in drug regulation. Published 2013. This article is a U.S. Government work and is in the public domain in the USA. 102:2912-2923, 2013.

RCM: a novel association approach to search for coronary artery disease genetic related metabolites based on SNPs and metabolic network

Integration of genetic and metabolic network holds promise for providing insight into human disease. Coronary artery disease (CAD) is strongly heritable, but the heritability of metabolic compounds has not been evaluated in human metabolic context. Here we performed a genetic-based computational approach within eight sub-cellular networks from Edinburgh Human Metabolic Network to identify significant genetic risk compounds (SGRCs) of CAD. Our results provide the evidence that the high heritabilities of SGRCs played an important role in CAD pathogenesis. Besides, SGRCs were discovered to be strongly associated with lipid metabolism. We also established a possible disease-causing reference table to decipher genetic associations of SGRCs with CAD. Comparing with traditional method, RCM experienced better performance in CAD genetic risk compounds' identification. These findings provided novel insights into CAD pathogenesis from a genetic perspective.