Highlights d MethylSight is used to identify candidate methylation sites in the human proteome d 45 histone methylation sites are uncovered by MethylSight and validated d The H2B-K43 methylation site is demethylated by KDM5B

The incorporation of 2,2,2-trichloroethanol in polyacrylamide gels allows for fluorescent visualization of proteins following electrophoresis. Ultraviolet-light exposure, in the presence of this trichlorinated compound, results in a covalent modification of the tryptophan indole ring that shifts the fluorescent emission into the visible range. Based on this principle, we used 2,2,2-trichloroethanol to develop a microplate format protein quantification assay based on the fluorescent signal generated by modified proteins. We also demonstrated a specific fluorescent emission of 2,2,2-trichloroethanol-labeled protein at 450 nm, with a 310 nm excitation, resulting from modification of both tryptophan and tyrosine residues. Following optimization, this protein quantification assay displayed superior sensitivity when compared to UV absorbance at 280 nm (A280), and enabled quantification beyond the linear range permitted by the Bradford method. This 100 μL assay displayed a sensitivity of 10.5 μg in a range up to at least 200 μg. Furthermore, we extended the utility of this method through the development of a 20 μL low-volume assay, with a sensitivity of 8.7 μg tested up to 100 μg, which enabled visualization of proteins following SDS-PAGE. Collectively, these results demonstrate the utility of 2,2,2-trichloroethanol-based protein quantification and demonstrates the protein visualization in polyacrylamide gels based on 2,2,2-trichloroethanol-labeling pre-electrophoresis.

Gliomas are one of the most common and lethal brain tumors among adults. One process that contributes to glioma progression and recurrence is the epithelial to mesenchymal transition (EMT). EMT is regulated by a set of defined transcription factors which tightly regulate this process, among them is the basic helix-loop-helix family member, TWIST1. Here we show that TWIST1 is methylated on lysine-33 at chromatin by SETD6, a methyltransferase with expression levels correlating with poor survival in glioma patients. RNA-seq analysis in U251 glioma cells suggested that both SETD6 and TWIST1 regulate cell adhesion and migration processes. We further show that TWIST1 methylation attenuates the expression of the long-non-coding RNA, LINC-PINT, thereby promoting EMT in glioma. Mechanistically, TWIST1 methylation represses the transcription of LINC-PINT by increasing the occupancy of EZH2 and the catalysis of the repressive H3K27me3 mark at the LINC-PINT locus. Under un-methylated conditions, TWIST1 dissociates from the LINC-PINT locus, allowing the expression of LINC-PINT which leads to increased cell adhesion and decreased cell migration. Together, our findings unravel a new mechanistic dimension for selective expression of LINC-PINT mediated by TWIST1 methylation.

Oxygen sensing is inherent among most animal lifeforms and is critical for organism survival. Oxygen sensing mechanisms collectively trigger cellular and physiological responses that enable adaption to a reduction in ideal oxygen levels. The major mechanism by which oxygen-responsive changes in the transcriptome occur are mediated through the hypoxia-inducible factor (HIF) pathway. Upon reduced oxygen conditions, HIF activates hypoxia-responsive gene expression programs. However, under normal oxygen conditions, the activity of HIF is regularly suppressed by cellular oxygen sensors; prolyl-4 and asparaginyl hydroxylases. Recently, these oxygen sensors have also been found to suppress the function of two lysine methyltransferases, G9a and G9a-like protein (GLP). In this manner, the methyltransferase activity of G9a and GLP are hypoxia-inducible and thus present a new avenue of low-oxygen signaling. Furthermore, G9a and GLP elicit lysine methylation on a wide variety of non-histone proteins, many of which are known to be regulated by hypoxia. In this article we aim to review the effects of oxygen on G9a and GLP function, non-histone methylation events inflicted by these methyltransferases, and the clinical relevance of these enzymes in cancer.

Background Understanding the disease pathogenesis of the novel coronavirus, denoted SARS-CoV-2, is critical to the development of anti-SARS-CoV-2 therapeutics. The global propagation of the viral disease, denoted COVID-19 (“coronavirus disease 2019”), has unified the scientific community in searching for possible inhibitory small molecules or polypeptides. A holistic understanding of the SARS-CoV-2 vs. human inter-species interactome promises to identify putative protein-protein interactions (PPI) that may be considered targets for the development of inhibitory therapeutics. Methods We leverage two state-of-the-art, sequence-based PPI predictors (PIPE4 & SPRINT) capable of generating the comprehensive SARS-CoV-2 vs. human interactome, comprising approximately 285,000 pairwise predictions. Three prediction schemas (all, proximal, RP-PPI) are leveraged to obtain our highest-confidence subset of PPIs and human proteins predicted to interact with each of the 14 SARS-CoV-2 proteins considered in this study. Notably, the use of the Reciprocal Perspective (RP) framework demonstrates improved predictive performance in multiple cross-validation experiments. Results The all schema identified 279 high-confidence putative interactions involving 225 human proteins, the proximal schema identified 129 high-confidence putative interactions involving 126 human proteins, and the RP-PPI schema identified 539 high-confidence putative interactions involving 494 human proteins. The intersection of the three sets of predictions comprise the seven highest-confidence PPIs. Notably, the Spike-ACE2 interaction was the highest ranked for both the PIPE4 and SPRINT predictors with the all and proximal schemas, corroborating existing evidence for this PPI. Several other predicted PPIs are biologically relevant within the context of the original SARS-CoV virus. Furthermore, the PIPE-Sites algorithm was used to identify the putative subsequence that might mediate each interaction and thereby inform the design of inhibitory polypeptides intended to disrupt the corresponding host-pathogen interactions. Conclusion We publicly released the comprehensive sets of PPI predictions and their corresponding PIPE-Sites landscapes in the following DataVerse repository: https://www.doi.org/10.5683/SP2/JZ77XA. The information provided represents theoretical modeling only and caution should be exercised in its use. It is intended as a resource for the scientific community at large in furthering our understanding of SARS-CoV-2.