Structural and functional differences between KRIT1A and KRIT1B isoforms: a framework for understanding CCM pathogenesis

KRIT1 is a disease gene responsible for Cerebral Cavernous Malformations (CCM). It encodes for a protein containing distinct protein-protein interaction domains, including three NPXY/F motifs and a FERM domain. Previously, we isolated KRIT1B, an isoform characterized by the alternative splicing of the 15th coding exon and suspected to cause CCM when abnormally expressed.Combining homology modeling and docking methods of protein-structure and ligand binding prediction with the yeast two-hybrid assay of in vivo protein-protein interaction and cellular biology analyses we identified both structural and functional differences between KRIT1A and KRIT1B isoforms.We found that the 15th exon encodes for the distal β-sheet of the F3/PTB-like subdomain of KRIT1A FERM domain, demonstrating that KRIT1B is devoid of a functional PTB binding pocket. As major functional consequence, KRIT1B is unable to bind Rap1A, while the FERM domain of KRIT1A is even sufficient for this function. Furthermore, we found that a functional PTB subdomain enables the nucleocytoplasmic shuttling of KRIT1A, while its alteration confers a restricted cytoplasmic localization and a dominant negative role to KRIT1B. Importantly, we also demonstrated that KRIT1A, but not KRIT1B, may adopt a closed conformation through an intramolecular interaction involving the third NPXY/F motif at the N-terminus and the PTB subdomain of the FERM domain, and proposed a mechanism whereby an open/closed conformation switch regulates KRIT1A nuclear translocation and interaction with Rap1A in a mutually exclusive manner.As most mutations found in CCM patients affect the KRIT1 FERM domain, the new insights into the structure-function relationship of this domain may constitute a useful framework for understanding molecular mechanisms underlying CCM pathogenesis.     

Epigenomic profiling of cancer cells

DNA methylation, post-translational modifications of histones and high order organization of chromatin in cell nuclei are the components of the epigenome. Epigenetic regulation of gene expression is specific for each cell type, within different tissues, according to stages of development and (in the adult organism) of differentiation. Almost invariably, this regulation is altered in disease states, including cancer. The complete understanding of the identity of the epigenome of cancer has been so far hampered, due to the technical limitations and costs of the genome-wide analyses required. The recent development of next generation sequencing (NGS) technologies, however, holds the promise of fast, reliable and cost-effective analyses. Here we review the main approaches employed thus far to identify altered epigenetic patterns in cancer cells, and analyse how they are predicted to evolve in the scenario of the ultra high-throughput (UHT) screenings.     

Quantitative Chemical Proteomics Identifies Novel Targets of the Anti-cancer Multi-kinase Inhibitor E-3810

Novel drugs are designed against specific molecular targets, but almost unavoidably they bind non-targets, which can cause additional biological effects that may result in increased activity or, more frequently, undesired toxicity. Chemical proteomics is an ideal approach for the systematic identification of drug targets and off-targets, allowing unbiased screening of candidate interactors in their natural context (tissue or cell extracts).E-3810 is a novel multi-kinase inhibitor currently in clinical trials for its anti-angiogenic and anti-tumor activity. In biochemical assays, E-3810 targets primarily vascular endothelial growth factor and fibroblast growth factor receptors. Interestingly, E-3810 appears to inhibit the growth of tumor cells with low to undetectable levels of these proteins in vitro, suggesting that additional relevant targets exist. We applied chemical proteomics to screen for E-3810 targets by immobilizing the drug on a resin and exploiting stable isotope labeling by amino acids in cell culture to design experiments that allowed the detection of novel interactors and the quantification of their dissociation constant (K_{d imm}) for the immobilized drug. In addition to the known target FGFR2 and PDGFRα, which has been described as a secondary E-3810 target based on in vitro assays, we identified six novel candidate kinase targets (DDR2, YES, LYN, CARDIAK, EPHA2, and CSBP). These kinases were validated in a biochemical assay and—in the case of the cell-surface receptor DDR2, for which activating mutations have been recently discovered in lung cancer—cellular assays.Taken together, the success of our strategy—which integrates large-scale target identification and quality-controlled target affinity measurements using quantitative mass spectrometry—in identifying novel E-3810 targets further supports the use of chemical proteomics to dissect the mechanism of action of novel drugs.The “target deconvolution” process, namely, the identification and characterization of proteins bound by a drug of interest (1), is a crucial step in drug development that allows definition of the compound selectivity and the early detection of potential side effects. Target deconvolution can be achieved by means of systematic in vitro biochemical assays measuring the ability of the drug to interact with candidate binders and, if they are enzymes, interfere with their activity. An alternative approach is chemical proteomics (chemoproteomics), which combines affinity chromatography and proteomic techniques (2, 3). Up-to-date chemical proteomics essentially consists of three main steps: (i) drug immobilization on a solid phase; (ii) drug affinity chromatography to capture drug targets in complex protein mixtures, such as cell or tissue lysates; and (iii) mass spectrometry (MS)-based¹ identification of the proteins retained by the immobilized drug (4–6).In chemical proteomics, the affinity chromatography step is typically performed under mild conditions, to allow the identification of all possible natural binders. The drawback of using mild, non-denaturing conditions is the significant number of proteins nonspecifically binding to the solid phase, which, once identified via MS, can be difficult to discern from genuine drug targets. The relatively high number of such nonspecific binders has limited the widespread use of this strategy.More recently, the development and implementation of quantitative strategies in proteomics based on the use of differentially stable isotopes to label proteomes from distinct functional states, together with significant technological and instrumental developments in the MS field concerning sensitivity and throughput, have largely allowed this limitation to be overcome. One of the most popular labeling techniques is stable isotope labeling by amino acids in cell culture (SILAC) (7). In SILAC, dividing cells are cultured in media supplemented with amino acids containing stable isotopic variants of carbon (¹²C/¹³C), nitrogen (¹⁴N/¹⁵N), or hydrogen (¹H/²H), which are incorporated into newly synthesized proteins during cell division. When extensive labeling (>98%) of cells is achieved upon the appropriate number of replications, light and heavy cells are differentially treated (e.g. exposed to drug versus vehicle), mixed in equal proportion, and subjected to proteomics analysis by means of liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Peptides from the two functional states can be distinguished by their specific delta mass values, and their intensity ratio in MS spectra is directly proportional to the relative abundance of the corresponding proteins in the initial protein extract. Robust analysis of SILAC data is possible with dedicated software, such as MaxQuant (8). The application of SILAC strategies to interactomic studies is an efficient means of discerning specific from background binders (9). When applied to chemical proteomics, quantitative proteomics is crucial, as it offers quality filters to discern genuine drug interactors from proteins binding to the solid phase, with the use of different experimental setups (4, 5).In this study, we successfully coupled SILAC with chemical proteomics to carry out an unbiased screening of protein interactors of the anti-cancer drug E-3810, currently in Phase II clinical trials. E-3810 is a novel multi-kinase inhibitor, a class of targeted drug that comprises different molecules currently used in clinical practice (e.g. imatinib, dasatinib, sunitinib, sorafenib) (10). E-3810 exhibits both anti-tumor and anti-angiogenic properties (11). In preclinical studies, E-3810 showed broad anti-tumor activity in vivo, when used as monotherapy in a variety of human xenografts, or in conjunction with conventional chemotherapy (11, 12).Cellular vascular endothelial growth factor receptors (VEGFRs) and fibroblast growth factor receptors (FGFRs) are the principal targets of E-3810, as previously demonstrated by in vitro kinase assays, which showed that E-3810 inhibited VEGFR-1, -2, and -3 and FGFR-1 and -2 in the nanomolar range (11). Studies performed on several kinase inhibitors demonstrated that these molecules can elicit pleiotropic effects not easily explained by the sole inhibition of their known targets (13, 14). These effects are in most cases due to an inhibitory activity of the drug on additional kinase targets not tested in vitro that may lead to synergistic anti-cancer effects or undesirable toxicity. This could also be the case for E-3810, which was shown to inhibit in vitro additional kinase targets with high affinity, and which is able to inhibit the growth of tumor cells expressing low to undetectable levels of VEGFRs/FGFRs, suggesting that its spectrum of target inhibition has not been fully explored (11).We thus established a SILAC-based chemical proteomic platform composed of a set of affinity chromatography experiments using E-3810 immobilized on agarose resin and incubated with SILAC-labeled extract from the ovarian cancer cell line A2780. We identified proteins interacting with the resin via MS and took advantage of SILAC-based protein quantitation to discern genuine from background binders and derive quantitative information about the specific interactions. Our findings demonstrate that additional targets of E-3810 exist and that these targets may contribute to the anticancer effect of E-3810.