Oghbaie, M. | bioRxiv Science

bioRxiv2019

LINE-1 ORF2P EXPRESSION IS NEARLY IMPERCEPTIBLE IN HUMAN CANCERS

AO_SCPLOWBSTRACTC_SCPLOWO_ST_ABSBackgroundC_ST_ABSLong interspersed element-1 (LINE-1, L1) is the major driver of mobile DNA activity in modern humans. When expressed, LINE-1 loci produce bicistronic transcripts encoding two proteins essential for retrotransposition, ORF1p and ORF2p. Many types of human cancers are characterized by L1 promoter hypomethylation, L1 transcription, L1 ORF1p protein expression, and somatic L1 retrotransposition. ORF2p encodes the endonuclease and reverse transcriptase activities required for L1 retrotransposition. Its expression is poorly characterized in human tissues and cell lines.\n\nResultsWe report mass spectrometry based tumor proteome profiling studies wherein ORF2p eludes detection. To test whether ORF2p could be detected with specific reagents, we developed and validated five rabbit monoclonal antibodies with immunoreactivity for specific epitopes on the protein. These reagents readily detect ectopic ORF2p expressed from bicistronic L1 constructs. However, endogenous ORF2p is not detected in human tumor samples or cell lines by western blot, immunoprecipitation, or immunohistochemistry despite high levels of ORF1p expression. Moreover, we report endogenous ORF1p-associated interactomes, affinity isolated from colorectal cancers, wherein we similarly fail to detect ORF2p. These samples include primary tumors harboring hundreds of somatically-acquired L1 insertions. The new data are available via ProteomeXchange with identifier PXD013743.\n\nConclusionsAlthough somatic retrotransposition provides unequivocal genetic evidence for the expression of ORF2p in human cancers, we are unable to directly measure its presence using several standard methods. Experimental systems have previously indicated an unequal stoichiometry between ORF1p and ORF2p, but in vivo, the expression of these two proteins may be more strikingly uncoupled. These findings are consistent with observations that ORF2p is not tolerable for cell growth.

cancer biology↗

bioRxiv2019

Complex+: Aided Decision-Making for the Study of Protein Complexes

Proteins are the chief effectors of cell biology and their functions are typically carried out in the context of multi-protein assemblies; large collections of such interacting protein assemblies are often referred to as interactomes. Knowing the constituents of protein complexes is therefore important for investigating their molecular biology. Many experimental methods are capable of producing data of use for detecting and inferring the existence of physiological protein complexes. Each method has associated pros and cons, affecting the potential quality and utility of the data. Numerous informatic resources exist for the curation, integration, retrieval, and processing of protein interactions data. While each resource may possess different merits, none are definitive and few are wieldy, potentially limiting their effective use by non-experts. In addition, contemporary analyses suggest that we may still be decades away from a comprehensive map of a human protein interactome. Taken together, we are currently unable to maximally impact and improve biomedicine from a protein interactome perspective - motivating the development of experimental and computational techniques that help investigators to address these limitations. Here, we present a resource intended to assist investigators in (i) navigating the cumulative knowledge concerning protein complexes and (ii) forming hypotheses concerning protein interactions that may yet lack conclusive evidence, thus (iii) directing future experiments to address knowledge gaps. To achieve this, we integrated multiple data-types/different properties of protein interactions from multiple sources and after applying various methods of regularization, compared the protein interaction networks computed to those available in the EMBL-EBI Complex Portal, a manually curated, gold-standard catalog of macromolecular complexes. As a result, our resource provides investigators with reliable curation of bona fide and candidate physical interactors of their protein or complex of interest, prompting due scrutiny and further validation when needed. We believe this information will empower a wider range of experimentalists to conduct focused protein interaction studies and to better select research strategies that explicitly target missing information.

bioinformatics↗