IKKε phosphorylates kindlin-2 to induce invadopodia formation and promote colorectal cancer metastasis.

Invadopodia formation is a key driver of cancer metastasis. The noncanonical IkB-related kinase IKKε has been implicated in cancer metastasis, but its roles in invadopodia formation and colorectal cancer (CRC) metastasis are unclear. Methods: Immunofluorescence, gelatin-degradation assay, wound healing assay and transwell invasion assay were used to determine the influence of IKKε over-expression, knockdown and pharmacological inhibition on invadopodia formation and the migratory and invasive capacity of CRC cells in vitro. Effects of IKKε knockdown or pharmacological inhibition on CRC metastasis were examined in mice. Immunohistochemistry staining was used to detect expression levels of IKKε in CRC patient tissues, and its association with prognosis in CRC patients was also analyzed. Immunoprecipitation, western blotting and in vitro kinase assay were constructed to investigate the molecular mechanisms. Results: IKKε co-localizes with F-actin and the invadopodia marker Tks5 at the gelatin-degrading sites of CRC cells. Genetic over-expression/knockdown or pharmacological inhibition of IKKε altered invadopodia formation and the migratory and invasive capacity of CRC cells in vitro. In vivo, knockdown or pharmacological inhibition of IKKε significantly suppressed metastasis of CRC cells in mice. IKKε knockdown also inhibited invadopodia formation in vivo. Clinical investigation of tumor specimens from 191 patients with CRC revealed that high IKKε expression correlates with metastasis and poor prognosis of CRC. Mechanistically, IKKε directly binds to and phosphorylates kindlin-2 at serine 159; this effect mediates the IKKε-induced invadopodia formation and promotion of CRC metastasis. Conclusions: We identify IKKε as a novel regulator of invadopodia formation and a unique mechanism by which IKKε promotes the metastasis of CRC. Our study suggests that IKKε is a potential target to suppress CRC metastasis.

GLP-catalyzed H4K16me1 promotes 53BP1 recruitment to permit DNA damage repair and cell survival.

The binding of p53-binding protein 1 (53BP1) to damaged chromatin is a critical event in non-homologous DNA end joining (NHEJ)-mediated DNA damage repair. Although several molecular pathways explaining how 53BP1 binds damaged chromatin have been described, the precise underlying mechanisms are still unclear. Here we report that a newly identified H4K16 monomethylation (H4K16me1) mark is involved in 53BP1 binding activity in the DNA damage response (DDR). During the DDR, H4K16me1 rapidly increases as a result of catalyzation by the histone methyltransferase G9a-like protein (GLP). H4K16me1 shows an increased interaction level with 53BP1, which is important for the timely recruitment of 53BP1 to DNA double-strand breaks. Differing from H4K16 acetylation, H4K16me1 enhances the 53BP1-H4K20me2 interaction at damaged chromatin. Consistently, GLP knockdown markedly attenuates 53BP1 foci formation, leading to impaired NHEJ-mediated repair and decreased cell survival. Together, these data support a novel axis of the DNA damage repair pathway based on H4K16me1 catalysis by GLP, which promotes 53BP1 recruitment to permit NHEJ-mediated DNA damage repair.

Bifunctional Crosslinking Agents Enhance Anion Exchange Membrane Efficacy for Vanadium Redox Flow Batteries.

A series of cross-linked fluorinated poly (aryl ether oxadiazole) membranes (FPAEOM) derivatized with imidazolium groups were prepared. Poly (N-vinylimidazole) (PVI) was used as the bifunctional cross-linking agent to: a) lower vanadium permeability, b) enhance dimensional stability, and c) concomitantly provide added ion exchange capacity in the resultant anion exchange membranes. At a molar ratio of PVI to FPAEOM of 1.5, the resultant membrane (FPAEOM-1.5 PVI) had an ion exchange capacity of 2.2 meq g-1, a vanadium permeability of 6.8×10-7 cm2 min-1, a water uptake of 68 wt.%, and an ionic conductivity of 22.0 mS cm-1, all at 25°C. Single cells prepared with the FPAEOM-1.5 PVI membrane exhibited a higher coulombic efficiency (> 92%) and energy efficiency (> 86%) after 40 test cycles in vanadium redox flow battery. The imidazolium cation showed high chemical stability in highly acidic and oxidizing vanadium solution as opposed to poor stability in alkaline solutions. Based on our DFT studies, this was attributed to the lower HOMO energy (-7.265 eV) of the HSO4- ion (compared to the OH- ion; -5.496 eV) and the larger HOMO-LUMO energy gap (6.394 eV) of dimethylimidazolium bisulfate ([DMIM] [HSO4]) as compared to [DMIM] [OH] (5.387 eV).

A synthetic framework for modeling the genetic basis of phenotypic plasticity and its costs.

The phenotype of an individual is controlled not only by its genes, but also by the environment in which it grows. A growing body of evidence shows that the extent to which phenotypic changes are driven by the environment, known as phenotypic plasticity, is also under genetic control, but an overall picture of genetic variation for phenotypic plasticity remains elusive. Here, we develop a model for mapping quantitative trait loci (QTLs) that regulate environment-induced plastic response. This model enables geneticists to test whether there exist actual QTLs that determine phenotypic plasticity and, if there are, further test how plasticity QTLs control the costs of plastic response by dissecting the genetic correlation of phenotypic plasticity and trait value. The model was used to analyze real data for grain yield of winter wheat (Triticum aestivum), leading to the detection of pleiotropic QTLs and epistatic QTLs that affect phenotypic plasticity and its cost in this crop.

Structural mapping: how to study the genetic architecture of a phenotypic trait through its formation mechanism.

Traditional approaches for genetic mapping are to simply associate the genotypes of a quantitative trait locus (QTL) with the phenotypic variation of a complex trait. A more mechanistic strategy has emerged to dissect the trait phenotype into its structural components and map specific QTLs that control the mechanistic and structural formation of a complex trait. We describe and assess such a strategy, called structural mapping, by integrating the internal structural basis of trait formation into a QTL mapping framework. Electrical impedance spectroscopy (EIS) has been instrumental for describing the structural components of a phenotypic trait and their interactions. By building robust mathematical models on circuit EIS data and embedding these models within a mixture model-based likelihood for QTL mapping, structural mapping implements the EM algorithm to obtain maximum likelihood estimates of QTL genotype-specific EIS parameters. The uniqueness of structural mapping is to make it possible to test a number of hypotheses about the pattern of the genetic control of structural components. We validated structural mapping by analyzing an EIS data collected for QTL mapping of frost hardiness in a controlled cross of jujube trees. The statistical properties of parameter estimates were examined by simulation studies. Structural mapping can be a powerful alternative for genetic mapping of complex traits by taking account into the biological and physical mechanisms underlying their formation.

A dynamic framework for quantifying the genetic architecture of phenotypic plasticity.

Despite its central role in the adaptation and microevolution of traits, the genetic architecture of phenotypic plasticity, i.e. multiple phenotypes produced by a single genotype in changing environments, remains elusive. We know little about the genes that underlie the plastic response of traits to the environment, their number, chromosomal locations and genetic interactions as well as environment impact on their effects. Here we review key statistical approaches for analyzing the genetic variation of phenotypic plasticity due to genotype-environment interactions and describe the implementation of a dynamic model to map specific quantitative trait loci (QTLs) that affect the gradient expression of a quantitative trait across a range of environments. This dynamic model is distinct by incorporating mathematical aspects of phenotypic plasticity into a QTL mapping framework, thereby better unraveling the quantitative attribute of trait response to the environment. By testing the curve parameters that specify environment-dependent trajectories of the trait, the model allows a series of fundamental hypotheses to be tested in a quantitative way about the interplay between gene action/interaction and environmental sensitivity. The model can also make the dynamic prediction of genetic control over phenotypic plasticity within the context of changing environments. We demonstrate the usefulness of the model by reanalyzing a QTL data set for rice, gleaning new insights into the genetic basis for phenotypic plasticity in plant height growth.

A statistical procedure to map high-order epistasis for complex traits.

Genetic interactions or epistasis have been thought to play a pivotal role in shaping the formation, development and evolution of life. Previous work focused on lower-order interactions between a pair of genes, but it is obviously inadequate to explain a complex network of genetic interactions and pathways. We review and assess a statistical model for characterizing high-order epistasis among more than two genes or quantitative trait loci (QTLs) that control a complex trait. The model includes a series of start-of-the-art standard procedures for estimating and testing the nature and magnitude of QTL interactions. Results from simulation studies and real data analysis warrant the statistical properties of the model and its usefulness in practice. High-order epistatic mapping will provide a routine procedure for charting a detailed picture of the genetic regulation mechanisms underlying the phenotypic variation of complex traits.

A unifying framework for bivalent multilocus linkage analysis of allotetraploids.

An allotetraploid has four paired sets of chromosomes derived from different diploid species, whose meiotic behavior is qualitatively different from the underlying diploids. According to a traditional view, meiotic pairing occurs only between homologous chromosomes, but new evidence indicates that homoeologous chromosomes may also pair to a lesser extent compared with homolog pairing. Here, we describe and assess a unifying analytical framework that incorporates differential chromosomal pairing into a multilocus linkage model. The preferential pairing factor is used to quantify the probability difference of pairing occurring between homologous chromosomes and homoeologous chromosomes. The unifying framework allows simultaneous estimation of the linkage, genetic interference and preferential pairing factor using commonly existing multiplex markers. We compared the unifying approach and traditional approaches assuming random chromosomal pairing by analyzing marker data collected in a full-sib family of tetraploid switchgrass, a bioenergy species whose diploid origins are undefined, but with subgenomes that are genetically well differentiated. The unifying framework provides a better tool for estimating the meiotic linkage and constructing a genetic map for allotetraploids.