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A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis

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Abstract

Focal Segmental Glomerulosclerosis (FSGS) is a type of nephrotic syndrome which accounts for 20 and 40 % of such cases in children and adults, respectively. The high prevalence of FSGS makes it the most common primary glomerular disorder causing end-stage renal disease. Although the pathogenesis of this disorder has been widely investigated, the exact mechanism underlying this disease is still to be discovered. Current therapies seek to stop the progression of FSGS and often fail to cure the patients since progression to end-stage renal failure is usually inevitable. In the present work, we use a kidney-specific metabolic network model to study FSGS. The model was obtained by merging two previously published kidney-specific metabolic network models. The validity of the new model was checked by comparing the inactivating reaction genes identified in silico to the list of kidney disease implicated genes. To model the disease state, we used a complete list of FSGS metabolic biomarkers extracted from transcriptome and proteome profiling of patients as well as genetic deficiencies known to cause FSGS. We observed that some specific pathways including chondroitin sulfate degradation, eicosanoid metabolism, keratan sulfate biosynthesis, vitamin B6 metabolism, and amino acid metabolism tend to show variations in FSGS model compared to healthy kidney. Furthermore, we computationally searched for the potential drug targets that can revert the diseased metabolic state to the healthy state. Interestingly, only one drug target, N-acetylgalactosaminidase, was found whose inhibition could alter cellular metabolism towards healthy state.

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Acknowledgements

S.-A. Marashi acknowledges the financial support of the University of Tehran for this research under Grant Number 28791/1/2. S. Kalantari thanks the Chronic Kidney Disease Research Center at Shahid Beheshti University of Medical Sciences, Tehran, Iran, for their support (Grant Number 426/30).

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Authors and Affiliations

  1. Department of Biotechnology, College of Science, University of Tehran, Tehran, Iran

    Salma Sohrabi-Jahromi & Sayed-Amir Marashi

  2. Chronic Kidney Disease Research Center (CKDRC), Shahid Beheshti University of Medical Sciences, Tehran, Iran

    Shiva Kalantari

Authors
  1. Salma Sohrabi-Jahromi
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  2. Sayed-Amir Marashi
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  3. Shiva Kalantari
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Corresponding authors

Correspondence to Sayed-Amir Marashi or Shiva Kalantari.

Electronic supplementary material

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335_2016_9622_MOESM1_ESM.xml

Supplementary material 1: The "merged" metabolic network. This kidney-specific genome-scale metabolic network model is presented in SBML format (XML 5098 kb)

Supplementary File S2: Some technical details on metabolic network analysis (PDF 437 kb)

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Sohrabi-Jahromi, S., Marashi, SA. & Kalantari, S. A kidney-specific genome-scale metabolic network model for analyzing focal segmental glomerulosclerosis. Mamm Genome 27, 158–167 (2016). https://doi.org/10.1007/s00335-016-9622-2

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  • DOI: https://doi.org/10.1007/s00335-016-9622-2

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