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Protein ‘Purity’, Proteoforms, and the Albuminome: Critical Observations on Proteome and Systems Complexity

ABSTRACT: The identification of effective, selective biomarkers and therapeutics is dependent on truly deep, comprehensive analysis of proteomes at the proteoform level. Using bovine serum albumin (BSA) isolated by two different protocols, cold ethanol fractionation (CEF) and heat shock fractionation (HSF), the data highlight several concerns regarding proteome analyses using currently popular analytical approaches and what it means to purify a ‘protein’ if the isolate consists of a wide variety of proteoforms and/or co-purifying species. Failure to widely recognize and accept proteome complexity has likely delayed the identification of effective biomarkers and new, more selective drug targets. Integrative top-down proteomics (iTDP) is the most logical available analytical technique to effectively provide the necessary critical depth and breadth for complex proteome analyses. Routine analyses at the level of proteoforms will provide the much-needed data for the development and validation of selective biomarkers and drugs, including biologics.

INSTRUMENT(S): Q Exactive Plus

ORGANISM(S): Bos Taurus (bovine)

TISSUE(S): Blood Serum

SUBMITTER: Breyer-Lynne Woodland

LAB HEAD: Matt Padula

PROVIDER: PXD056316 | Pride | 2024-12-07

REPOSITORIES: Pride

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Source:

			Action	DRS
	BSA_CEF_PeaksStudio_Search_Exports.xlsx	Xlsx
	BSA_HSF_PeaksStudio_Search_Exports.xlsx	Xlsx
	BSA_combined_PeaksStudio_Search_Exports.xlsx	Xlsx
	BW20231208_C1.raw	Raw
	BW20231208_C10.raw	Raw

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Project description:Sense-antisense transcript (SAT) pairs are pairs of transcripts that fully or partially overlap but are transcribed in opposite directions. Although SAT expression occurs in various species, most SAT pairs have not been examined in detail. Because our previous studies revealed some tissue specificity in SAT expression, SAT pairs might be involved in cell differentiation and the maintenance of tissue-specific gene expression programs. To analyze such tissue-specific SAT pairs, we assessed the expression profiles of SATs from 12 tissues of normal mice at a genome-wide scale and found that a considerable number of SAT pairs showed expression patterns unique to testis. This finding prompted us to study the relationship between SAT expression pattern and another epigenetic gene regulatory mechanism, DNA methylation. We conducted a comparative global analysis of the DNA methylation status of CpG island (CGI)-associated SAT loci from various tissues and found that in 99 of the 4911 SAT pairs studied, DNA methylation could cooperatively suppress its downstream “sense” transcription together with the “antisense” transcriptional activity in a tissue-specific manner. The tissue-specific differentially methylated regions (T-DMRs) of these SAT pairs mainly occurred outside of the defined CGIs. In addition, some of these T-DMRs are situated at the 5′ region of the antisense transcripts. This positioning implies a novel mechanism for DNA methylation to regulate gene transcription, in which DNA methylation inhibits an opposing transcriptional activity and therefore maintains stable expression of a tissue-specific gene. Twelve tissues (brain, thymus, heart, lung, liver, spleen, stomach, kidney, small intestine, testis, and placentae [10.5 and 13.5 days postcoitum (dpc)]) of C57BL/6J Jc1 and a mouse fibroblast cell line (SL10) were used for the mouse expression oligo-microarray. The expression array data were obtained by both oligo-dT and random priming methods, except for SL10 (olio-dT only). Twelve tissues (brain, thymus, heart, lung, liver, spleen, stomach, kidney, small intestine, testis, and placentae [10.5 and 13.5 days postcoitum (dpc)]) were used for DIP chip experiments. Nine of twelve tissues were separately analyzed classified on the basis of sex. A technical duplication of DIP chip analysis was performed by using the same genomic DNA of male kidney.

Dataset Information

Protein ‘Purity’, Proteoforms, and the Albuminome: Critical Observations on Proteome and Systems Complexity

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