Noise Exposures Causing Hearing Loss Generate Proteotoxic Stress and Activate the Proteostasis Network

Jongkamonwiwat, Nopporn; Ramírez, Miguel Ángel; Edassery, Seby L.; Wong, Ann Chi Yan; Yu, Jintao; Abbott, Tirzah; Pak, Kwang; Ryan, Allen F.; Savas, Jeffrey N.

doi:10.1016/j.celrep.2020.108431

Cited by 40 publications

(45 citation statements)

References 77 publications

(86 reference statements)

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“…We hypothesize that mistranslating tRNAs are genetic modifiers of disease by increasing the level of proteotoxic stress. This increased stress could lead to earlier onset and/or increased severity of diseases that have proteotoxic stress as their hallmark, including many neurodegenerative diseases, cardiomyopathies and hearing loss (Willis and Patterson 2013; Labbadia and Morimoto 2015; McLendon and Robbins 2015; Jongkamonwiwat et al 2020). In this framework, our results suggest that even for tRNA variants with a similar mistranslation frequency, their impact on disease would differ based on the specific substitution.…”

Section: Discussionmentioning

confidence: 99%

The amino acid substitution affects cellular response to mistranslation

Berg

Zhu

Ruiz

et al. 2021

Preprint

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Mistranslation, the mis-incorporation of an amino acid not specified by the standard genetic code, occurs in all organisms. tRNA variants that increase mistranslation arise spontaneously and engineered tRNAs can achieve mistranslation frequencies approaching 10% in yeast and bacteria. Interestingly, human genomes contain tRNA variants with the potential to mistranslate. Cells cope with increased mistranslation through multiple mechanisms, though high levels cause proteotoxic stress. The goal of this study was to compare the genetic interactions and the impact on transcriptome and cellular growth of two tRNA variants that mistranslate at a similar frequency but create different amino acid substitutions in Saccharomyces cerevisiae. One tRNA variant inserts alanine at proline codons whereas the other inserts serine for arginine. Both tRNAs decreased growth rate, with the effect being greater for arginine to serine than for proline to alanine. The tRNA that substituted serine for arginine resulted in a heat shock response. In contrast, heat shock response was minimal for proline to alanine substitution. Further demonstrating the significance of the amino acid substitution, transcriptome analysis identified unique up- and downregulated genes in response to each mistranslating tRNA. Number and extent of negative synthetic genetic interactions also differed depending upon type of mistranslation. Based on the unique responses observed for these mistranslating tRNAs, we predict that the potential of mistranslation to exacerbate diseases caused by proteotoxic stress depends on the tRNA variant. Furthermore, based on their unique transcriptomes and genetic interactions, different naturally occurring mistranslating tRNAs have the potential to negatively influence specific diseases.

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Section: Discussionmentioning

confidence: 99%

The amino acid substitution affects cellular response to mistranslation

Berg

Zhu

Ruiz

et al. 2021

Preprint

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“…TMT-MS sample preparation was performed as previously described [Jongkamonwiwat et al, 2020]. In brief, homogenized cortical brain extracts were prepared and 200µg of protein was used for TMT-MS sample preparation.…”

Section: Methodsmentioning

confidence: 99%

“…TMT-MS analysis was performed as previously described [Jongkamonwiwat et al, 2020]. In short, samples were resuspended in 20µl Buffer A (5% acetonitrile, 0.125% formic acid) and micro BCA was performed.…”

Section: Methodsmentioning

confidence: 99%

“…Buffer A contained 5% ACN and 0.125% formic acid in H20, and Buffer B contained 99.875 ACN with 0.125% formic acid. Multinotch MS3 method was programmed as the following parameter: Ion transfer tube temp = 300 °C, Easy-IC internal mass calibration, default charge state = 2 and cycle time = 3 s. MS1 detector set to orbitrap with 60 K resolution, wide quad isolation, mass range = normal, scan range = 300–1800 m/z, max injection time = 50 ms, AGC target = 6 × 10 5 , microscans = 1, RF lens = 60%, without source fragmentation, and datatype = positive and centroid [Jongkamonwiwat et al, 2020]. Monoisotopic precursor selection was set to included charge states 2–7 and reject unassigned.…”

Section: Methodsmentioning

confidence: 99%

“…TMT-MS data analysis was performed as previously described in [Jongkamonwiwat et al, 2020]. In short, protein identification, TMT quantification, and analysis were performed with The Integrated Proteomics Pipeline-IP2 (Integrated Proteomics Applications, Inc., http://www.integratedproteomics.com/).…”

Section: Methodsmentioning

confidence: 99%

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Levetiracetam treatment normalizes levels of the presynaptic endocytosis machinery and restores non-amyloidogenic APP processing inAppknock-in mice

Rao

Savas

2021

Preprint

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Increasing evidence indicates that toxic amyloid-beta (Abeta) peptides, produced by sequential proteolytic cleavage of the Amyloid Precursor Protein (APP), induce neuronal circuit hyperexcitability in the early stages of Alzheimer's disease (AD). As a result, treatments that modulate this early excitatory/inhibitory imbalance could act as potential AD therapies. Levetiracetam, an atypical antiepileptic drug, has garnered recent interest, despite the mechanism(s) of action remaining elusive. In this study, we set out to identify the pathways and mechanisms primarily affected by levetiracetam in diseased brains of amyloid pathology. Using the App knock-in mouse models and multiplexed TMT-quantitative mass spectrometry-based proteomic analysis to determine how levetiracetam affects the proteome, our findings demonstrate that levetiracetam treatment selectively normalizes levels of presynaptic endocytosis proteins and is capable of lowering Abeta42 levels by altering APP processing. These novel findings demonstrate a mechanism of action for how levetiracetam lowers Abeta42 production.

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