Metabolic deregulation associated with aging modulates protein aggregation in the yeast model of Huntington’s disease

Abstract

Huntington’s disease is associated with increased CAG repeat resulting in an expanded polyglutamine tract in the protein Huntingtin (HTT) leading to its aggregation resulting in neurodegeneration. Previous studies have shown that N-terminal HTT with 46Q aggregated in the stationary phase but not the logarithmic phase in the yeast model of HD. We carried out a metabolomic analysis of logarithmic and stationary phase yeast model of HD expressing different polyQ lengths attached to N-terminal HTT tagged with enhanced green fluorescent protein (EGFP). The results show significant changes in the metabolic profile and deregulated pathways in stationary phase cells compared to logarithmic phase cells. Comparison of metabolic pathways obtained from logarithmic phase 46Q versus 25Q with those obtained for presymptomatic HD patients from our previous study and drosophila model of HD showed considerable overlap. The arginine biosynthesis pathway emerged as one of the key pathways that is common in stationary phase yeast compared to logarithmic phase and HD patients. Treatment of yeast with arginine led to a significant decrease, while transfer to arginine drop-out media led to a significant increase in the size of protein aggregates in both logarithmic and stationary phase yeast model of HD. Knockout of arginine transporters in the endoplasmic reticulum and vacuole led to a significant decrease in mutant HTT aggregation. Overall our results highlight arginine as a critical metabolite that modulates the aggregation of mutant HTT and disease progression in HD.

Communicated by Ramaswamy H. Sarma

Keywords:

• Huntingtin
• Huntington’s disease
• metabolomics
• neurodegeneration
• aging
• protein aggregation
• chaperon

Acknowledgments

The authors acknowledge that they are highly indebted to Sri Sathya Sai Institute of Higher Learning, Central Research Instruments Facility (CRIF), Prasanthi Nilayam for generous support for this study.

Author contributions

SSP carried out all the experiments and analysis and assisted in manuscript writing and figure generation. RSS helped with metabolomics data analysis. KSP helped with yeast experiments. DDVM helped with the metabolomics experiment and manuscript preparation. AP and RBD helped in the analysis and interpretation of metabolomic experiments. SV conceptualization of the idea, guidance with experiments and interpretation, and manuscript writing.

Disclosure statement

The authors declare no conflict of interest or financial interest.

Additional information

Funding

The authors acknowledge the grant support from the Science and Engineering Research Board, India (grant number: DST-SERB-EMR: EMR/2017/005381), the Department of Biotechnology, Ministry of Science and Technology, India-funded Bioinformatics Infrastructure facility DBT-BIF (grant number: BT/BI/25/063/2012), the Department of Science and Technology, India-funded Infrastructure in Higher Educational Institutions (DST-FIST; grant number: SR/FST/LSI-616/2014 to Sri Sathya Sai Institute of Higher Learning) and the University Grants Commission, India-funded Special Assistance Program (UGC-SAP III; grant number: F.3-19/2018/DRS-III(SAP-II)) to Sri Sathya Sai Institute of Higher Learning.