Profiling proteome-level amino acid substitutions in Alzheimer's disease brain tissue
- 1The Hong Kong University of Science and Technology
Abstract
As the most prevalent neurodegenerative disorder worldwide, Alzheimer's disease (AD) remains incompletely understood at the proteome level. In particular, current studies on amino acid (AA) substitutions have predominantly relied on genomic and transcriptomic profiling. Proteome-scale AA substitutions remain largely uncharacterized. Nevertheless, alterations at the DNA and RNA levels cannot fully recapitulate the spectrum of AA substitutions observed at the proteome level. This critical research gap persists largely due to the inherent analytical challenges posed by large-scale proteomic datasets. In this study, we address this limitation by analyzing two independent AD proteomic datasets, AMP-AD and PXD013753, using PIPI-C, an open-search mass spectrometry engine capable of resolving multiple co-occurring modifications per peptide. We introduce a pipeline that enables the characterization of AA substitutions at the proteome level and the dissection of regulatory functions of key proteins with such substitutions. In both datasets, we observe that, after controlling for ambiguous post-translational modification mass shifts, the N>M substitution is the most frequent variant among N>X substitutions in the AD data. Furthermore, among the 701 overlapping proteins shared by the two datasets, we identified 6 literature-reported substitution sites, including residues 242 and 352 in glial fibrillary acidic protein, site 370 in actin gamma 1, as well as sites 111, 115, and 116 in hemoglobin subunit beta. Since most reported AA substitutions rely on genomic or transcriptomic evidence, and our pipeline adopts rigorous false-positive controls, those unreported substitutions are presumably detectable only via proteomic methods, emphasizing the unique value of our proteome-based substitutomics workflow for AD research.
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BibTeX
@misc{zhao2026profiling,
title = {{Profiling proteome-level amino acid substitutions in Alzheimer's disease brain tissue}},
author = {P. Zhao},
howpublished = {bioRxiv},
year = {2026},
month = sep,
doi = {10.64898/2026.09.08.750259},
url = {https://www.biorxiv.org/content/10.64898/2026.09.08.750259v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Profiling proteome-level amino acid substitutions in Alzheimer's disease brain tissue
AU - P. Zhao
PY - 2026
DA - 2026/09/08
PB - bioRxiv
DO - 10.64898/2026.09.08.750259
UR - https://www.biorxiv.org/content/10.64898/2026.09.08.750259v1
AB - As the most prevalent neurodegenerative disorder worldwide, Alzheimer's disease (AD) remains incompletely understood at the proteome level. In particular, current studies on amino acid (AA) substitutions have predominantly relied on genomic and transcriptomic profiling. Proteome-scale AA substitutions remain largely uncharacterized. Nevertheless, alterations at the DNA and RNA levels cannot fully recapitulate the spectrum of AA substitutions observed at the proteome level. This critical research gap persists largely due to the inherent analytical challenges posed by large-scale proteomic datasets. In this study, we address this limitation by analyzing two independent AD proteomic datasets, AMP-AD and PXD013753, using PIPI-C, an open-search mass spectrometry engine capable of resolving multiple co-occurring modifications per peptide. We introduce a pipeline that enables the characterization of AA substitutions at the proteome level and the dissection of regulatory functions of key proteins with such substitutions. In both datasets, we observe that, after controlling for ambiguous post-translational modification mass shifts, the N>M substitution is the most frequent variant among N>X substitutions in the AD data. Furthermore, among the 701 overlapping proteins shared by the two datasets, we identified 6 literature-reported substitution sites, including residues 242 and 352 in glial fibrillary acidic protein, site 370 in actin gamma 1, as well as sites 111, 115, and 116 in hemoglobin subunit beta. Since most reported AA substitutions rely on genomic or transcriptomic evidence, and our pipeline adopts rigorous false-positive controls, those unreported substitutions are presumably detectable only via proteomic methods, emphasizing the unique value of our proteome-based substitutomics workflow for AD research.
ER -