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

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SUMMARY

Exposure to excessive sound causes noise-induced hearing loss through complex mechanisms and represents a common and unmet neurological condition. We investigate how noise insults affect the cochlea with proteomics and functional assays. Quantitative proteomics reveals that exposure to loud noise causes proteotoxicity. We identify and confirm hundreds of proteins that accumulate, including cytoskeletal proteins, and several nodes of the proteostasis network. Transcriptomic analysis reveals that a subset of the genes encoding these proteins also increases acutely after noise exposure, including numerous proteasome subunits. Global cochlear protein ubiquitylation levels build up after exposure to excess noise, and we map numerous posttranslationally modified lysines residues. Several collagen proteins decrease in abundance, and Col9a1 specifically localizes to pillar cell heads. After two weeks of recovery, the cochlea selectively elevates the abundance of the protein synthesis machinery. We report that overstimulation of the auditory system drives a robust cochlear proteotoxic stress response.

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In Brief

Jongkamonwiwat et al. perform quantitative proteomic analysis of mouse cochlear extracts immediately after noise exposure. They discover that many proteins have elevated levels and the proteostasis network is activated. During recovery, ribosomal proteins are upregulated. These results show that loud noise causing hearing loss results in cochlear proteotoxicity.

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RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

Bo Li, Colin Dewey (2011)

Background RNA-Seq is revolutionizing the way transcript abundances are measured. A key challenge in transcript quantification from RNA-Seq data is the handling of reads that map to multiple genes or isoforms. This issue is particularly important for quantification with de novo transcriptome assemblies in the absence of sequenced genomes, as it is difficult to determine which transcripts are isoforms of the same gene. A second significant issue is the design of RNA-Seq experiments, in terms of the number of reads, read length, and whether reads come from one or both ends of cDNA fragments. Results We present RSEM, an user-friendly software package for quantifying gene and isoform abundances from single-end or paired-end RNA-Seq data. RSEM outputs abundance estimates, 95% credibility intervals, and visualization files and can also simulate RNA-Seq data. In contrast to other existing tools, the software does not require a reference genome. Thus, in combination with a de novo transcriptome assembler, RSEM enables accurate transcript quantification for species without sequenced genomes. On simulated and real data sets, RSEM has superior or comparable performance to quantification methods that rely on a reference genome. Taking advantage of RSEM's ability to effectively use ambiguously-mapping reads, we show that accurate gene-level abundance estimates are best obtained with large numbers of short single-end reads. On the other hand, estimates of the relative frequencies of isoforms within single genes may be improved through the use of paired-end reads, depending on the number of possible splice forms for each gene. Conclusions RSEM is an accurate and user-friendly software tool for quantifying transcript abundances from RNA-Seq data. As it does not rely on the existence of a reference genome, it is particularly useful for quantification with de novo transcriptome assemblies. In addition, RSEM has enabled valuable guidance for cost-efficient design of quantification experiments with RNA-Seq, which is currently relatively expensive.

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The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

Damian Szklarczyk, John Morris, Helen Cook … (2016)

A system-wide understanding of cellular function requires knowledge of all functional interactions between the expressed proteins. The STRING database aims to collect and integrate this information, by consolidating known and predicted protein–protein association data for a large number of organisms. The associations in STRING include direct (physical) interactions, as well as indirect (functional) interactions, as long as both are specific and biologically meaningful. Apart from collecting and reassessing available experimental data on protein–protein interactions, and importing known pathways and protein complexes from curated databases, interaction predictions are derived from the following sources: (i) systematic co-expression analysis, (ii) detection of shared selective signals across genomes, (iii) automated text-mining of the scientific literature and (iv) computational transfer of interaction knowledge between organisms based on gene orthology. In the latest version 10.5 of STRING, the biggest changes are concerned with data dissemination: the web frontend has been completely redesigned to reduce dependency on outdated browser technologies, and the database can now also be queried from inside the popular Cytoscape software framework. Further improvements include automated background analysis of user inputs for functional enrichments, and streamlined download options. The STRING resource is available online, at http://string-db.org/.

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Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

Michael Washburn, Dirk Wolters, John R R Yates (2001)

We describe a largely unbiased method for rapid and large-scale proteome analysis by multidimensional liquid chromatography, tandem mass spectrometry, and database searching by the SEQUEST algorithm, named multidimensional protein identification technology (MudPIT). MudPIT was applied to the proteome of the Saccharomyces cerevisiae strain BJ5460 grown to mid-log phase and yielded the largest proteome analysis to date. A total of 1,484 proteins were detected and identified. Categorization of these hits demonstrated the ability of this technology to detect and identify proteins rarely seen in proteome analysis, including low-abundance proteins like transcription factors and protein kinases. Furthermore, we identified 131 proteins with three or more predicted transmembrane domains, which allowed us to map the soluble domains of many of the integral membrane proteins. MudPIT is useful for proteome analysis and may be specifically applied to integral membrane proteins to obtain detailed biochemical information on this unwieldy class of proteins.

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Author and article information

Journal

Journal ID (nlm-journal-id): 101573691

Journal ID (pubmed-jr-id): 39703

Journal ID (nlm-ta): Cell Rep

Journal ID (iso-abbrev): Cell Rep

Title: Cell reports

ISSN (Electronic): 2211-1247

Publication date Nihms-submitted: 28 November 2020

Publication date (Print): 24 November 2020

Publication date PMC-release: 08 December 2020

Volume: 33

Issue: 8

Page: 108431

Affiliations

[1 ]Department of Neurology, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA

[2 ]Departments of Surgery and Neuroscience, University of California San Diego and Veterans Administration Medical Center, La Jolla, CA 92093, USA

[3 ]Translational Neuroscience Facility, Department of Physiology, NSW Australia, Sydney, NSW 2052, Australia

[4 ]Northwestern University Atomic and Nanoscale Characterization Experimental (NUANCE) Center, Northwestern University, Evanston, IL 60208, USA

[5 ]Present address: Department of Anatomy, Center for Neuroscience, Faculty of Science, Mahidol University, Bangkok 10400, Thailand

[6 ]These authors contributed equally

[7 ]Lead Contact

Author notes

AUTHOR CONTRIBUTIONS

J.N.S. and A.F.R. designed experiments. N.J., A.C.Y.W., and J.Y. performed ABR and DPOAE experiments. N.J., M.A.R., and J.N.S. performed MS analysis. S.E., M.A.R., T.A., and J.Y. performed WB, SEM, and IF. N.J., M.A.R., and J.N.S. analyzed the data. N.J., M.A.R., and J.N.S. wrote the manuscript.

[* ]Correspondence: jeffrey.savas@ 123456northwestern.edu

Article

Manuscript ID: NIHMS1649703

DOI: 10.1016/j.celrep.2020.108431

PMC ID: 7722268

PubMed ID: 33238128

SO-VID: 4e395468-843f-49b5-ac71-2386dafbb2a6

License:

This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/).

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

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Stress signalling in stem cells

Most cited references 78

RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome

The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible

Large-scale analysis of the yeast proteome by multidimensional protein identification technology.

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