Emerging roles of ATF2 and the dynamic AP1 network in cancer

Cooperation among transcription factors is central for their ability to execute specific transcriptional programmes. The AP1 complex exemplifies a network of transcription factors that function in unison under normal circumstances and during the course of tumour development and progression. This Perspective summarizes our current understanding of the changes in members of the AP1 complex and the role of ATF2 as part of this complex in tumorigenesis.Fil: Lopez Bergami, Pablo Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Biología y Medicina Experimental (i); Argentina; ArgentinaFil: Lau, Eric . Burnham Institute for Medical Research; Estados UnidosFil: Ronai, Zeev . Burnham Institute for Medical Research; Estados Unido

Unravelling higher order chromatin organisation through statistical analysis

Recent technological advances underpinned by high throughput sequencing have given new insights into the three-dimensional structure of mammalian genomes. Chromatin conformation assays have been the critical development in this area, particularly the Hi-C method which ascertains genome-wide patterns of intra and inter-chromosomal contacts. However many open questions remain concerning the functional relevance of such higher order structure, the extent to which it varies, and how it relates to other features of the genomic and epigenomic landscape. Current knowledge of nuclear architecture describes a hierarchical organisation ranging from small loops between individual loci, to megabase-sized self-interacting topological domains (TADs), encompassed within large multimegabase chromosome compartments. In parallel with the discovery of these strata, the ENCODE project has generated vast amounts of data through ChIP-seq, RNA-seq and other assays applied to a wide variety of cell types, forming a comprehensive bioinformatics resource. In this work we combine Hi-C datasets describing physical genomic contacts with a large and diverse array of chromatin features derived at a much finer scale in the same mammalian cell types. These features include levels of bound transcription factors, histone modifications and expression data. These data are then integrated in a statistically rigorous way, through a predictive modelling framework from the machine learning field. These studies were extended, within a collaborative project, to encompass a dataset of matched Hi-C and expression data collected over a murine neural differentiation timecourse. We compare higher order chromatin organisation across a variety of human cell types and find pervasive conservation of chromatin organisation at multiple scales. We also identify structurally variable regions between cell types, that are rich in active enhancers and contain loci of known cell-type specific function. We show that broad aspects of higher order chromatin organisation, such as nuclear compartment domains, can be accurately predicted in a variety of human cell types, using models based upon underlying chromatin features. We dissect these quantitative models and find them to be generalisable to novel cell types, presumably reflecting fundamental biological rules linking compartments with key activating and repressive signals. These models describe the strong interconnectedness between locus-level patterns of local histone modifications and bound factors, on the order of hundreds or thousands of basepairs, with much broader compartmentalisation of large, multi-megabase chromosomal regions. Finally, boundary regions are investigated in terms of chromatin features and co-localisation with other known nuclear structures, such as association with the nuclear lamina. We find boundary complexity to vary between cell types and link TAD aggregations to previously described lamina-associated domains, as well as exploring the concept of meta-boundaries that span multiple levels of organisation. Together these analyses lend quantitative evidence to a model of higher order genome organisation that is largely stable between cell types, but can selectively vary locally, based on the activation or repression of key loci

Light-driven charge accumulation of a molecular Cu(I) complex for storage of photoredox equivalents

The diurnal day/night cycle is presently of great interest for harvesting solar energy aimed at rendering suitable energy storage schemes. To this end we present a noble-metal free system based on a Cu(I) 4H-imidazolate complex, that is efficiently photoreduced in the presence of a sacrificial donor. The two-electron reduced species obtained can be stored in the dark for more than 14 hours. In a dark reaction, the photoredox equivalents can subsequently be transferred to the electron acceptors methyl viologen or oxygen, while the starting Cu(I) complex is almost completely regained. Repetition of this process revealed a charging capacity of 72% after four cycles. The implications of light-driven charge accumulation and prolonged storage times for solar battery and photoredox catalysis are discusse

A Modified Calculation Improves the Accuracy of Predicted Postoperative Lung Function Values in Lung Cancer Patients

Purpose Preoperative pulmonary function testing is mandatory for non-small cell lung cancer (NSCLC) surgery. The predicted postoperative FEV1 (ppoFEV1) is used for further risk stratification. We compared the ppoFEV1 with the postoperative FEV1 (postFEV1) in order to improve the calculation of the ppoFEV1. Methods 87 patients voluntarily received an FEV1 assessment 1 year after surgery. ppoFEV1 was calculated according to the Brunelli calculation. Baseline characteristics and surgical procedure were compared in a uni- and multivariate analysis between different accuracy levels of the ppoFEV1. Parameters which remained significant in the multinominal regression analysis were evaluated for a modification of the ppoFEV1 calculation. Results Independent factors for a more inaccurate ppoFEV1 were preoperative active smoking (odds ratio (OR) 4.1, confidence interval (CI) 3.6-6.41; p = 0.01), packyears (OR 4.1, CI 3.6-6.41; p = 0.008), younger age (OR 1.1, CI 1.01-1.12; p = 0.03), and patients undergoing pneumectomy (OR 5.55, CI 1.35-23.6; p = 0.01). For the customized ppoFEV1 we excluded pneumonectomies. For patients < 60 years, an additional lung segment was added to the calculation. ppoFEV1 = preFEV1 x 1 - (Lung segments resected+1/Total number of segments). For actively smoking patients with more than 30 packyears we subtracted one lung segment from the calculation ppoFEV1 = PreFEV1 x 1 - (Lung segments resected-1/Total number of segments). Conclusion We were able to enhance the predictability of the ppoFEV1 with modifications. The modified ppoFEV1 (1.828 1 +/- 0.479 1) closely approximates the postFEV1 of 1.823 1 +/- 0.476 1, (0.27%) while the original ppoFEV1 calculation is at 1.78 1 +/- 0.53 (2.19%). However, if patients require pneumectomy, more complex techniques to determine the ppoFEV1 should be included to stratify risk

Die Sanierungsfoerderung als Instrument der Landesentwicklungsplanung Dargestellt am Beispiel des Landes Nordrhein-Westfalen

SIGLEDEGerman