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Fine-Tuning of PGC1α Expression Regulates Cardiac Function and Longevity (RNA-seq: WT vs PKI)

ABSTRACT: PGC1alpha plays an important role in energy metabolism and mitochondrial function in the heart, multiple attempts have failed to upregulate PGC1alpha expression as a therapy, with the underlying mechanism to be answered. We used RNA sequencing to detail the global programme of gene expression in the hearts of wild-type mice with or without cardiomyocyte-restricted PGC1alpha overexpression.

ORGANISM(S): Mus musculus

PROVIDER: GSE136770 | GEO | 2022/08/01

REPOSITORIES: GEO

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Project description:RATIONALE:PGC1? (peroxisome proliferator-activated receptor gamma coactivator 1?) represents an attractive target interfering bioenergetics and mitochondrial homeostasis, yet multiple attempts have failed to upregulate PGC1? expression as a therapy, for instance, causing cardiomyopathy. OBJECTIVE:To determine whether a fine-tuning of PGC1? expression is essential for cardiac homeostasis in a context-dependent manner. METHODS AND RESULTS:Moderate cardiac-specific PGC1? overexpression through a ROSA26 locus knock-in strategy was utilized in WT (wild type) mice and in G3Terc-/- (third generation of telomerase deficient; hereafter as G3) mouse model, respectively. Ultrastructure, mitochondrial stress, echocardiographic, and a variety of biological approaches were applied to assess mitochondrial physiology and cardiac function. While WT mice showed a relatively consistent PGC1? expression from 3 to 12 months old, age-matched G3 mice exhibited declined PGC1? expression and compromised mitochondrial function. Cardiac-specific overexpression of PGC1? (PGC1?OE) promoted mitochondrial and cardiac function in 3-month-old WT mice but accelerated cardiac aging and significantly shortened life span in 12-month-old WT mice because of increased mitochondrial damage and reactive oxygen species insult. In contrast, cardiac-specific PGC1? knock in in G3 (G3 PGC1?OE) mice restored mitochondrial homeostasis and attenuated senescence-associated secretory phenotypes, thereby preserving cardiac performance with age and extending health span. Mechanistically, age-dependent defect in mitophagy is associated with accumulation of damaged mitochondria that leads to cardiac impairment and premature death in 12-month-old WT PGC1?OE mice. In the context of telomere dysfunction, PGC1? induction replenished energy supply through restoring the compromised mitochondrial biogenesis and thus is beneficial to old G3 heart. CONCLUSIONS:Fine-tuning the expression of PGC1? is crucial for the cardiac homeostasis because the balance between mitochondrial biogenesis and clearance is vital for regulating mitochondrial function and homeostasis. These results reinforce the importance of carefully evaluating the PGC1?-boosting strategies in a context-dependent manner to facilitate clinical translation of novel cardioprotective therapies.

Project description:To create a dataset of PSMs that does not exhibit tryptic bias, we selected PSMs with a uniform distribution of amino acids at the C-terminal peptide positions from two datasets: MassIVE-KB [Wang2018] and PROSPECT [Shouman2022]. The MassIVE-KB dataset contains 30 million PSMs and consists entirely of data generated using trypsin; hence, only a small proportion of the MassIVE-KB peptides do not end in K or R, corresponding to those that appear at the C-terminus of the corresponding protein. The PROSPECT dataset is a collection of 61 million PSMs generated from synthetic peptides. To avoid overrepresentation of some peptides in this dataset, we randomly selected at most 100 PSMs per unique peptide, similar to the processing that was done during the creation of the MassIVE-KB dataset. This pre-selection step reduced the size of the PROSPECT dataset to 12.6 million PSMs. Finally, to create a non-enzymatic dataset containing 1 million peptides, we selected 50,000 PSMs for each canonical amino acid. These PSMs were selected at random from MassIVE-KB, then supplemented as necessary from PROSPECT to obtain the desired count (see Yilmaz et al. [Yilmaz2023] Supplementary Table S1). This dataset contained PSMs from 247,859 unique peptides, which were randomly split into training, validation and test sets in the ratio 80/10/10. FTP directory contains the mgf files corresponding to train, validation and test splits. [Wang2018] M. Wang, J. Wang, J. Carver, B. Pullman, S. Won Cha, N. Bandeira, "Assembling the Community-Scale Discoverable Human Proteome", Cell Systems, Volume 7, Issue 4, 2018. [Shouman2022] O. Shouman, W. Gabriel, V. Giurcoiu, V. Sternlicht, M. Wilhelm, "PROSPECT: Labeled Tandem Mass Spectrometry Dataset for Machine Learning in Proteomics", NeurIPS Datasets and Benchmarks, 2022 [Yilmaz2023] M. Yilmaz*, W. Fondrie*, W. Bittremieux, R. Nelson, V. Ananth, S. Oh, and W. Noble,"Sequence-to-sequence translation from mass spectra to peptides with a transformer model", bioRxiv, 2023

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Fine-Tuning of PGC1α Expression Regulates Cardiac Function and Longevity (RNA-seq: WT vs PKI)

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