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Abstract: PO1602

Metabolic State Modeling of Kidney Single Nuclei Data Reveals Cell-Specific Signatures at Baseline and in Disease

Session Information

Category: Genetic Diseases of the Kidneys

  • 1002 Genetic Diseases of the Kidneys: Non-Cystic

Authors

  • Sidhom, Eriene-Heidi, Harvard Medical School, Boston, Massachusetts, United States
  • Greka, Anna, Harvard Medical School, Boston, Massachusetts, United States
Background

The kidney is a metabolically active and cellularly diverse organ. Perturbations in metabolic pathways, such as lipid metabolism, is a well-established sequelae of chronic kidney diseases, such as diabetic nephropathy. Single cell RNA sequencing has allowed for an unprecedented understanding of the kidney’s transcriptomic complexity. However, until now, understanding the diverse metabolic states of the kidney has been limited to either expression analysis of single metabolic enzymes or bulk metabolomics experiments. Given the highly interconnected nature of metabolic networks and the kidney’s cellular complexity, integrating a systems-level understanding of metabolic perturbations with single cell sequencing has the potential to reveal previously unappreciated metabolic cell states and disease perturbations.

Methods

We have applied the newly developed Flux Balance Analysis (FBA) algorithm for single cell sequencing data, Compass (doi: 10.1101/2020.01.23.912717), to a dataset of 36,560 single nucleus transcriptomes from mouse kidney comprised of three healthy mice and three mice with CoQ-deficiency proteinuric kidney disease.

Results

First, Compass correctly predicted well-established cell-specific kidney transport processes. Next, when comparing proximal tubule clusters, corresponding to S1, S2 and S3 segments, the S3 segment was found to have both high activity of branched chain amino acid (BCAA) metabolism and high activity of fatty acid oxidation (FAO). This previously unknown link between BCAAs and FAO in the kidney is of particular interest, given the known relationship between BCAA metabolism, FAO and metabolic disease. Finally, when comparing transcriptomes between disease and healthy mice, podocyte-specific changes in FAO and steroid metabolism were observed which correlated with podocyte cytoskeletal regulation, a hallmark of podocyte injury.

Conclusion

In summary, the combination of an enhanced resolution of single nucleus transcriptomics with a systems-level analysis of metabolic networks in the kidney have revealed cell-specific metabolic states at baseline and in disease. Future application of this analysis to human data will provide important validation for the generalizability of these findings and further insight into metabolic perturbations in human disease.

Funding

  • NIDDK Support