A native chemical chaperone in the human eye lens
Abstract
Cataract is one of the most prevalent protein aggregation disorders and still the most common cause of vision loss worldwide. The metabolically quiescent core region of the human lens lacks cellular or protein turnover; it has therefore evolved remarkable mechanisms to resist light-scattering protein aggregation for a lifetime. We now report that one such mechanism involves an unusually abundant lens metabolite, myo-inositol, suppressing aggregation of lens crystallins. We quantified aggregation suppression using our previously well-characterized in vitro aggregation assays of oxidation-mimicking human γD-crystallin variants and investigated myo-inositol's molecular mechanism of action using solution NMR, negative-stain TEM, differential scanning fluorometry, thermal scanning Raman spectroscopy, turbidimetry in redox buffers, and free thiol quantitation. Unlike many known chemical chaperones, myo-inositol's primary target was not the native, unfolded, or final aggregated states of the protein; rather, we propose that it was the rate-limiting bimolecular step on the aggregation pathway. Given recent metabolomic evidence that it is severely depleted in human cataractous lenses compared to age-matched controls, we suggest that maintaining or restoring healthy levels of myo-inositol in the lens may be a simple, safe, and globally accessible strategy to prevent or delay lens opacification due to age-onset cataract.
Data availability
Numerical data and fits used to generate the plots and fits of Figure 1B,C,D,E have been uploaded as source data files. Raw numerical measurements behind the graphs presented in Figure 4 are included as a supplementary table (Figure 4-source data 1). All raw TEM images and instrument files have been uploaded to the Dataverse repository and are available at: https://doi.org/10.7910/DVN/BVRS9M. Full unedited gels used to generate Figure 6 - figure supplement 1 were uploaded as source data. The numerical data and fits used to generate Figure 6D were uploaded as a source data file. All other data are contained in the manuscript.
Article and author information
Author details
Funding
National Institutes of Health (R01EY030444)
- Sourav Chowdhury
- David C Thorn
- Eugene I Shakhnovich
National Institutes of Health (F32GM126651)
- Eugene Serebryany
National Institutes of Health (K99GM141459)
- Eugene Serebryany
National Institutes of Health (R01EY017370)
- Christopher N Woods
- Rachel E Klevit
National Science Foundation (National Nanoscience Infrastructure Network)
- Nicki E Watson
- Arthur A McClelland
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2022, Serebryany et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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