Integrating Next-Generation Genomic Sequencing and Mass Spectrometry To Estimate Allele-Specific Protein Abundance in Human Brain
Open accessAbstract
Gene expression contributes to phenotypic traits and human disease. To date, comparatively less is known about regulators of protein abundance, which is also under genetic control and likely influences clinical phenotypes. However, identifying and quantifying allele-specific protein abundance by bottom-up proteomics is challenging since single nucleotide variants (SNVs) that alter protein sequence are not considered in standard human protein databases. To address this, we developed the GenPro software and used it to create personalized protein databases (PPDs) to identify single amino acid variants (SAAVs) at the protein level from whole exome sequencing. In silico assessment of PPDs generated by GenPro revealed only a 1% increase in tryptic search space compared to a direct translation of all human transcripts and an equivalent search space compared to the UniProtKB reference database. To identify a large unbiased number of SAAV peptides, we performed high-resolution mass spectrometry-based proteomics for two human post-mortem brain samples and searched the collected MS/MS spectra against their respective PPD. We found an average of ∼117 000 unique peptides mapping to ∼9300 protein groups for each sample, and of these, 977 were unique variant peptides. We found that over 400 reference and SAAV peptide pairs were, on average, equally abundant in human brain by label-free ion intensity measurements and confirmed the absolute levels of three reference and SAAV peptide pairs using heavy labeled peptides standards coupled with parallel reaction monitoring (PRM). Our results highlight the utility of integrating genomic and proteomic sequencing data to identify sample-specific SAAV peptides and support the hypothesis that most alleles are equally expressed in human brain.
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Our group previously performed a detailed co-expression network analysis for the BLSA cohort and identified 16 networks of co-expressed proteins.
“Of note, our group previously performed a detailed co-expression network analysis for the BLSA cohort and identified 16 networks of co-expressed proteins23.”
From Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age (2019) · cites this paper as [23]
BLSA module M3 was not significantly associated with clinical diagnosis or AD pathologies after multiple testing correction in our previous publication.
“This module was not significantly associated with clinical diagnosis or AD pathologies after multiple testing correction in our previous publication23”
From Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age (2019) · cites this paper as [23]
The same approach (Combat and bootstrap regression) successfully removed the effects of batch, sex, age at death, and PMI from the BLSA proteome.
“The same approach was used to successfully remove the effects of these same covariates from the BLSA proteome23.”
From Large-scale proteomic analysis of human brain identifies proteins associated with cognitive trajectory in advanced age (2019) · cites this paper as [23]
LysC generates peptides that are, on average, of greater length than peptides generated after trypsin digestion.
“LysC therefore generates peptides that are, on average, of greater length than peptides generated after trypsin digestion [13].”
From Deep proteomic network analysis of Alzheimer’s disease brain reveals alterations in RNA binding proteins and RNA splicing associated with disease (2018) · cites this paper as [13]
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- Authors
- Wingo, T. S., Duong, D. M., Zhou, M., Dammer, E. B., Wu, H., Cutler, D. J., Lah, J. J., Levey, A. I., & Seyfried, N. T.
- Affiliations
- United States Department of Veterans Affairs (US)
- Corresponding author
- Wingo, Thomas S.
- Seyfried, Nicholas T.
- ORCID
- Venue
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Published
- 2017-08-09
- Volume
- 16
- Issue
- 9
- Pages
- 3336-3347
- ISSN
- 1535-3893 (ISSN-L)
- 1535-3907
- PMC
- PMC5698003
- OpenAlex
- W2735378853
- Cited by
- 51
- References
- 48
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 4 · 2023: 12 · 2022: 8 · 2021: 4 · 2020: 5 · 2019: 6 · 2018: 7
- FWCI
- 2.43
- Type
- journal article
- Language
- en
- Open access
- green
- europepmc.org
- Locations
- Journal of Proteome Research (publishedVersion)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- U.S. Department of Veterans Affairs (BX001820)
- Alzheimer's Association (NIRG12-242297)
- Alzheimer’s Research UK
- Michael J. Fox Foundation for Parkinson's Research
- National Institute on Aging (AG025688, AG046161)
- Weston Brain Institute (11060)
- School of Medicine, Emory University
- Topics
- Physical Sciences › Chemistry › Spectroscopy › Advanced Proteomics Techniques and Applications
- Physical Sciences › Chemistry › Spectroscopy › Mass Spectrometry Techniques and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
- Keywords
- single amino acid variants, single nucleotide variants, postmortem brain, peptide quantification, parallel reaction monitoring, label-free quantification, exome sequencing
- MeSH terms
- Proteogenomics, Exome Sequencing, Alleles, Amino Acid Sequence, Autopsy, Brain Chemistry, Chromatography, Humans, Peptides, Software, Prefrontal Cortex, Amino Acid Substitution, Tandem Mass Spectrometry, Hydrophobic and Hydrophilic Interactions
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