MAGE–KAP1 binding induces ZNF382 ubiquitination in HEK293T cells.

<p>HEK293T cells were transiently transfected with His tagged wild-type MAGE-C2 (WT), mutant (non-binding) MAGE-C2^{L152A L153A} , or empty vectors (Mock) and co-transfected with ZNF382, KAP1, and HA tagged ubiquitin. Cells were incubated for 5 hours in the presence of 25 µM MG132 (C2211, SIGMA). Top two panels: ZNF382 (around 55 kDa), MAGE-C2 (around 50 KD) and KAP1 (110 KD) were detected in whole lysates by immunoblotting with anti-KAP1 (upper panel) and anti-FLAG antibodies (lower panel). Third panel: His tagged ubiquitinated proteins were immunoprecipitated with anti-His and detected with anti-HA. High-molecular-weight ubiquitinated species were seen only in blots of cells transfected with both ZNF382 and MAGE-C2. Note that no ubiquitination occurred when wild type MAGE-C2 was replaced with MAGE-C2^{L152A L153A} which does not bind to KAP1, indicating MAGE–KAP1 binding is required for ZNF382 ubiquitination. Also please note an ubiquitinated ZNF382 degradation product of lesser molecular weight in the presence of wild type MAGE-C2 and KAP1. Lowest panel: immunoprecipitation and immunoblotting confirms expression of ZNF382.</p

Proposed model of MAGE-KAP1 interactions.

<p>KAP1 performs diverse functions by serving as a molecular scaffold that binds multiple proteins which allow it to regulate chromatin environments. KAP1 has an N terminal RING-B-box coiled-coil (RBCC) domain that binds to the KRAB domains of KZNFs, which target KAP1 to specific DNA sequences through their zinc finger DNA binding motifs. KAP1 mediates localized compaction of euchromatin to heterochromatin that is necessary for suppression of specific gene transcription, and that is associated with chromatin modifications including histone de-acetylation, histone 3 tri-methylation on K9, and HP1 binding to both DNA and histones. In some cases, MAGE expression enhances KAP1 E3 ubiquitin ligase activity, resulting in KZNF ubiquitination and degradation, thereby de-repressing KZNF mediated gene repression, shown as (-). In other cases, MAGE enhances KZNF and KAP1 localization to specific gene loci, shown as (+) (After A. Ivanov <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0023747#pone.0023747-Ivanov1" target="_blank">[22]</a>).</p

Molecular modeling and molecular dynamic simulation of the effects of variants in the TGFBR2 kinase domain as a paradigm for interpretation of variants obtained by next generation sequencing

<div><p>Variants in the TGFBR2 kinase domain cause several human diseases and can increase propensity for cancer. The widespread application of next generation sequencing within the setting of Individualized Medicine (IM) is increasing the rate at which TGFBR2 kinase domain variants are being identified. However, their clinical relevance is often uncertain. Consequently, we sought to evaluate the use of molecular modeling and molecular dynamics (MD) simulations for assessing the potential impact of variants within this domain. We documented the structural differences revealed by these models across 57 variants using independent MD simulations for each. Our simulations revealed various mechanisms by which variants may lead to functional alteration; some are revealed energetically, while others structurally or dynamically. We found that the ATP binding site and activation loop dynamics may be affected by variants at positions throughout the structure. This prediction cannot be made from the linear sequence alone. We present our structure-based analyses alongside those obtained using several commonly used genomics-based predictive algorithms. We believe the further mechanistic information revealed by molecular modeling will be useful in guiding the examination of clinically observed variants throughout the exome, as well as those likely to be discovered in the near future by clinical tests leveraging next-generation sequencing through IM efforts.</p></div

Cancer Hallmarks inferred from expression profiles correlate with breast laterality.

<p>In a different cohort of 25 breast tumors, Cancer Hallmarks were inferred from the expression profiles of 32 cancer related genes. CHs are represented in rows, tumors in columns. A color gradient from green to red is used to represent low to high values of CHs (from 5 to 52). By Unsupervised Hierarchical Cluster Analysis even though many clusters are formed, only two groups were established for bootstrapping 90–100% (shown by the red arm). By regression analyses a single association was found between clusters and the clinical variable BL.</p

CpG location, genes and methylation frequencies in 113 breast tumors.

<p>CpG location, genes and methylation frequencies in 113 breast tumors.</p

Translation of Methylation profile to Cancer Hallmark profile.

<p>(A) The scheme describes how the MLPA-derived methylation data was converted into CH profiles, though a translation matrix. A multiplication operation was performed on two matrices: the Methylation Profile Matrix (MPM) and the Translation Matrix (TM). The MPM holds information of 51 CpGs located in 43 genes the MPM, for 51 tumors with complete clinical-pathological information. Green boxes represent the un-methylated status and red boxes the methylated status. The TM contains the <u>A</u>djusted <u>P</u>articipation <u>I</u>ndex (API) which expresses in a rank from 0 to 3, the influence of each of the 43 genes on the 6 studied Cancer Hallmarks (CH). This multiplication of both results in a CH Profile Matrix, which represents for each tumor, the values with which each CH is enhanced. The higher the values of the CH matrix, the more the methylation events have contributed to acquire specific CHs. (B) Results of the matrices multiplication operation performed for the 51 studied tumors. Each tumor (rows) presents a specific CH profile. The CH values are represented in a colored grey gradient (light grey are lower values; dark grey represent higher values).</p

Tumors from left-right breast sides are differentially predicted by cancer hallmarks.

<p>Regression analyses of CHs vs tumor stage in left and right sided tumors. (A) CHs of left sided lesions were better predictors of the tumor stage, revealing that CH1 increased with tumor stage while CH4, CH5 and CH6 decreased (adjusted R² = 0.76). (B) CHs of right sided tumors have no predictable value for tumor stages (adjusted R² = 0.00), supporting thereby the conclusion that the behavior of CHs differs in tumors of different sides.</p

Genes included in the expression analyses.

<p>Genes included in the expression analyses.</p

Variants that are distant from the activation loop or the ligand binding site affect dynamics at these sites.

<p>Variants that resulted in increased dynamics either the activation loop or the ligand binding site are indicated by spheres at their C^α atom position. The activation loop and ligand binding site are highlighted as in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0170822#pone.0170822.g001" target="_blank">Fig 1</a>. We defined an increase by values greater than those observed in benign simulations. Residues that when mutated alter dynamics at these sites are distributed throughout the structure.</p

Application of structural metrics to simulations of observed variants with unknown functional consequences.

<p>Many variants of uncertain significance, with conflicting annotations, or individual reports of disease associations, show alterations in structural features.</p