AI Article Synopsis
- Two methods for extracting proteins were used alongside an automated system to examine the specific proteins found in sweetpotato tissues, discovering 74,255 peptides that relate to 4,321 distinct proteins.
- Different extraction methods yielded varying numbers and types of unique proteins, with leaves showing more proteins related to primary metabolism and protein translation, while roots had proteins connected to folding and carbohydrate metabolism.
- The analysis revealed 90.4% of peptides matched the sweetpotato genome, led to the prediction of 741 new protein-coding genes, and highlighted 2,056 areas for possible gene annotation improvements; data from this study can be found on ProteomeXchange using the identifier PXD012999.
Article Abstract
Two complementary protein extraction methodologies coupled with an automated proteomic platform were employed to analyze tissue-specific proteomes and characterize biological and metabolic processes in sweetpotato. A total of 74 255 peptides corresponding to 4321 nonredundant proteins were successfully identified. Data were compared to predicted protein accessions for Ipomoea species and mapped on the sweetpotato transcriptome and haplotype-resolved genome. The two methodologies exhibited differences in the number and class of the unique proteins extracted. Overall, 39 916 peptides mapped to 3143 unique proteins in leaves, and 34 339 peptides mapped to 2928 unique proteins in roots. Primary metabolism and protein translation processes were enriched in leaves, whereas genetic pathways associated with protein folding, transport, sorting, as well as pathways in the primary carbohydrate metabolism were enriched in storage roots. A proteogenomics analysis successfully mapped 90.4% of the total uniquely identified peptides against the sweetpotato transcriptome and genome, predicted 741 new protein-coding genes, and specified 2056 loci where gene annotations can be further improved. The proteogenomics results provide evidence for the translation of new open reading frames (ORFs), alternative ORFs, exon extensions, and intronic ORF sequences. Data are available via ProteomeXchange with identifier PXD012999.
Download full-text PDF |
Source |
|---|---|
| http://dx.doi.org/10.1021/acs.jproteome.8b00943 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!