Origin and ion charge state evolution of solar wind transients during 4 - 7 August 2011

This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No. 647214). The computational work for this article was carried out on the joint STFC and SFC (SRIF) funded clusters at the University of St Andrews (Scotland, UK). The work is partially supported by RFBR grants 17-02-00787, 14-02-00945 and the P7 Program of the Russian Academy of Sciences.We present a study of the complex event consisting of several solar wind transients detected by the Advanced Composition Explorer (ACE) on 4 - 7 August 2011, which caused a geomagnetic storm with Dst=-110 nT. The supposed coronal sources, three flares and coronal mass ejections (CMEs), occurred on 2 - 4 August 2011 in active region (AR) 11261. To investigate the solar origin and formation of these transients, we study the kinematic and thermodynamic properties of the expanding coronal structures using the Solar Dynamics Observatory/Atmospheric Imaging Assembly (SDO/AIA) EUV images and differential emission measure (DEM) diagnostics. The Helioseismic and Magnetic Imager (HMI) magnetic field maps were used as the input data for the 3D magnetohydrodynamic (MHD) model to describe the flux rope ejection (Pagano, Mackay, and Poedts, 2013b). We characterize the early phase of the flux rope ejection in the corona, where the usual three-component CME structure formed. The fluxrope was ejected with a speed of about 200 km s-1 to the height of 0.25 R⊙. The kinematics of the modeled CME front agrees well with the Solar Terrestrial Relations Observatory (STEREO) EUV measurements. Using the results of the plasma diagnostics and MHD modeling, we calculate the ion charge ratios of carbon and oxygen as well as the mean charge state of iron ions of the 2 August 2011 CME, taking into account the processes of heating, cooling, expansion, ionization, and recombination of the moving plasma in the corona up to the frozen-in region. We estimate a probable heating rate of the CME plasma in the low corona by matching the calculated ion composition parameters of the CME with those measured in situ for the solar wind transients. We also consider the similarities and discrepancies between the results of the MHD simulation and the observations.PostprintPeer reviewe

Serine-threonine kinases and transcription factors active in signal transduction are detected at high levels of phosphorylation during mitosis in preimplantation embryos and trophoblast stem cells

Serine-threonine kinases and transcription factors play important roles in the G1-S phase progression of the cell cycle. Assays that use quantitative fluorescence by immunocytochemical means, or that measure band strength during Western blot analysis, may have confused interpretations if the intention is to measure G1-S phase commitment of a small subpopulation of phosphorylated proteins, when a larger conversion of the same population of proteins can occur during late G2 and M phases. In mouse trophoblast stem cells (TSC), a human placental cell line (HTR), and/or mouse preimplantation embryos, 8/19 serine-threonine and tyrosine kinases, 3/8 transcription factors, and 8/14 phospho substrate and miscellaneous proteins were phosphorylated at higher levels in M phase than in interphase. Most phosphoproteins appeared to associate with the spindle complex during M phase, but one (p38MAPK) associated with the spindle pole and five (Cdx2, MEK1, 2, p27, and RSK1) associated with the DNA. Phosphorylation was detected throughout apparent metaphase, anaphase and telophase for some proteins, or for only one of these segments for others. The phosphorylation was from 2.1- to 6.2-fold higher during M phase compared with interphase. These data suggest that, when planning and interpreting quantitative data and perturbation experiments, consideration must be given to the role of serine-threonine kinases and transcription factors during decision making in M phase as well as in G1-S phase

Cancer LncRNA Census reveals evidence for deep functional conservation of long noncoding RNAs in tumorigenesis

Long non-coding RNAs (lncRNAs) are a growing focus of cancer genomics studies, creating the need for a resource of lncRNAs with validated cancer roles. Furthermore, it remains debated whether mutated lncRNAs can drive tumorigenesis, and whether such functions could be conserved during evolution. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, we introduce the Cancer LncRNA Census (CLC), a compilation of 122 GENCODE lncRNAs with causal roles in cancer phenotypes. In contrast to existing databases, CLC requires strong functional or genetic evidence. CLC genes are enriched amongst driver genes predicted from somatic mutations, and display characteristic genomic features. Strikingly, CLC genes are enriched for driver mutations from unbiased, genome-wide transposon-mutagenesis screens in mice. We identified 10 tumour-causing mutations in orthologues of 8 lncRNAs, including LINC-PINT and NEAT1, but not MALAT1. Thus CLC represents a dataset of high-confidence cancer lncRNAs. Mutagenesis maps are a novel means for identifying deeply-conserved roles of lncRNAs in tumorigenesis.</p

Genomic basis for RNA alterations in cancer

Transcript alterations often result from somatic changes in cancer genomes1. Various forms of RNA alterations have been described in cancer, including overexpression2, altered splicing3 and gene fusions4; however, it is difficult to attribute these to underlying genomic changes owing to heterogeneity among patients and tumour types, and the relatively small cohorts of patients for whom samples have been analysed by both transcriptome and whole-genome sequencing. Here we present, to our knowledge, the most comprehensive catalogue of cancer-associated gene alterations to date, obtained by characterizing tumour transcriptomes from 1,188 donors of the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA)5. Using matched whole-genome sequencing data, we associated several categories of RNA alterations with germline and somatic DNA alterations, and identified probable genetic mechanisms. Somatic copy-number alterations were the major drivers of variations in total gene and allele-specific expression. We identified 649 associations of somatic single-nucleotide variants with gene expression in cis, of which 68.4% involved associations with flanking non-coding regions of the gene. We found 1,900 splicing alterations associated with somatic mutations, including the formation of exons within introns in proximity to Alu elements. In addition, 82% of gene fusions were associated with structural variants, including 75 of a new class, termed ‘bridged’ fusions, in which a third genomic location bridges two genes. We observed transcriptomic alteration signatures that differ between cancer types and have associations with variations in DNA mutational signatures. This compendium of RNA alterations in the genomic context provides a rich resource for identifying genes and mechanisms that are functionally implicated in cancer.</p

Analyses of non-coding somatic drivers in 2,658 cancer whole genomes

The discovery of drivers of cancer has traditionally focused on protein-coding genes1–4. Here we present analyses of driver point mutations and structural variants in non-coding regions across 2,658 genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium5 of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). For point mutations, we developed a statistically rigorous strategy for combining significance levels from multiple methods of driver discovery that overcomes the limitations of individual methods. For structural variants, we present two methods of driver discovery, and identify regions that are significantly affected by recurrent breakpoints and recurrent somatic juxtapositions. Our analyses confirm previously reported drivers6,7, raise doubts about others and identify novel candidates, including point mutations in the 5′ region of TP53, in the 3′ untranslated regions of NFKBIZ and TOB1, focal deletions in BRD4 and rearrangements in the loci of AKR1C genes. We show that although point mutations and structural variants that drive cancer are less frequent in non-coding genes and regulatory sequences than in protein-coding genes, additional examples of these drivers will be found as more cancer genomes become available.</p