The “Missing” Proteome: Undetected Proteins, Not-Translated Transcripts, and Untranscribed Genes

Abstract

The Chromosome-centric Human Proteome Project aims at characterizing the expression of proteins encoded in each chromosome at the tissue, cell, and subcellular levels. The proteomic profiling of a particular tissue or cell line commonly results in a substantial portion of proteins that are not observed (the "missing" proteome). The concurrent transcriptome profiling of the analyzed tissue/cells samples may help define the set of untranscribed genes in a given type of tissue or cell, thus narrowing the size of the "missing" proteome and allowing us to focus on defining the reasons behind undetected proteins, namely, whether they are technical (insufficient sensitivity of protein detection) or biological (correspond to not-translated transcripts). We believe that the quantitative polymerase chain reaction (qPCR) can provide an efficient approach to studying low-abundant transcripts related to undetected proteins due to its high sensitivity and the possibility of ensuring the specificity of detection via the simple Sanger sequencing of PCR products. Here we illustrated the feasibility of such an approach on a set of low-abundant transcripts. Although inapplicable to the analysis of whole transcriptome, qPCR can successfully be utilized to profile a limited cohort of transcripts encoded on a particular chromosome, as we previously demonstrated for human chromosome 18.

Genome of the Single Human Chromosome 18 as a “Gold Standard” for Its Transcriptome

“…Regardless of the chosen measurement unit, there is a tendency for an increase in the cutoff level to cause a decrease in the number of registered transcripts, thereby increasing the reliability of detection (Łabaj and Kreil, 2016;Zhao et al, 2020). This tendency has also been confirmed in targeted polymerase chain reaction (PCR)-based transcriptome mining, in which increasing the number of cycles in droplet digital PCR transcriptome profiling confirmed the presence of transcripts that scored below the cutoff level in the sample (Radko et al, 2019).…”

Human CHR18: “Stakhanovite” Genes, Missing and uPE1 Proteins in Liver Tissue and HepG2 Cells

“…The absence of some proteins could be reasonably attributed to alterations in the primary structure [5], when proteotypic peptides can accidentally fall into the splice junctions, or carry nonsynonymous polymorphisms, affecting the peptide retention time and mass-to-charge ratio in the LC-MS/MS analysis. Thus, the increased accuracy of transcriptome data became ultimately essential, despite many studies already published on the chromosome-centric transcriptome to proteome mapping, including those from our group [6,8], from the Spanish Proteome society [9], reports on Chr9 [10] and Chr17 [11], and also the report on the transcriptome-to-translatometo-proteome contribution from the Chinese consortium [12]. Inspired by C-HPP work tasks a corpus of bioinformatics tools [13,14] and databases [15,16] was developed to manage the transcriptomes.…”

“…Actually, studies in the field of Chr18 transcriptome profiling and targeted proteome mapping in liver tissue and HepG2 cells [6] revealed a poor correlation between transcriptome and proteome data. Radko et al [8] investigated to which extent the targeted PCRbased transcriptome mining could contribute to the problem of the missing proteins, encoded by human Chr18 genes. A summary of these chromosome-centric efforts revealed the unexpectedly low quantitative correlation, with no satisfactory explanation [5].…”

Human Chr18: “Stakhanovite” Genes, Missing and uPE1 Proteins in Liver Tissue and HepG2 Cells

“…The absence of some proteins could be reasonably attributed to alterations in the primary structure 5 , when proteotypic peptides can accidentally fall into the splice junctions, or carry nonsynonymous polymorphisms, affecting the peptide retention time and mass-to-charge ratio in LC-MS/MS analysis. Thus, the increased accuracy of transcriptome data became ultimately essential, despite many studies already published on the chromosome-centric transcriptome to proteome mapping, including those from our group [6][7][8] , from the Spanish Proteome society 9 , reports on Chr9 10 and Chr17 11 , and also the report on the transcriptome-to-translatome-to-proteome contribution from the Chinese consortium 12 . Inspired by C-HPP work tasks a corpus of bioinformatics tools 13,14 and databases 15,16 was developed to manage the transcriptomes.…”

“…Actually, studies in the field of Chr18 transcriptome profiling and targeted proteome mapping in liver tissue and HepG2 cells 6 revealed a poor correlation between transcriptome and proteome data. Radko et al 8 investigated to which extent the targeted PCR-based transcriptome mining can contribute to the problem of the missing proteins of human Chr18. A summary of these chromosome-centric efforts revealed the unexpectedly low quantitative correlation, with no satisfactory explanation 5 .…”