The role of the stratosphere in Iberian Peninsula rainfall: a preliminary approach in February

This paper attempts to establish a connection between stratospheric anomalies in the North Pole and rainfall on the Iberian Peninsula through the occurrence of major midwinter warmings (MMWs) and cold events (CEs), taking February as a preliminary approach. We define the MMWs as the warmings which break down the polar vortex, whereas the CEs are the episodes in which the polar vortex remains cold and undisturbed. Both anomalies lead to a wind anomaly around the north polar stratosphere, which is connected with a shortly lagged tropospheric anomaly through a stratosphere-troposphere coupling in winter. A T-mode principal component analysis (PCA) was used as an objective pattern classification method for identifying the main daily surface-level pressure (SLP) patterns for February for the 1961-1990 reference period. Subsequently, those February months with an MMW or a CE influence in the troposphere are identified in the whole study period (1958-2000) by means of the Arctic Oscillation Index (AOI). Thus, performing the same analysis for the selected February months, new principal patterns for detecting changes in surface circulation structure and morphology are obtained. The results show a significant decrease in the westerlies and a southward shift of the storm tracks in Western Europe some weeks after an MMW occurrence, leading to an increase in precipitation in western Iberia and a slight decrease on the eastern Mediterranean fringe. The results are quite the opposite under a CE influence: the westerlies are strengthened and shifted northwards due to the displacement of the Atlantic anticyclone towards Central Europe; dry conditions are established throughout Iberia, except for the Mediterranean fringe, where precipitation shows a considerable increase due to the greater frequency of the northeasterly winds. Finally, an 11-year sunspot cycle-quasibiennial oscillation (QBO) modulation might be demonstrated in Iberian rainfall in February through the occurrence of these stratospheric anomalies

PDL1 Signals through Conserved Sequence Motifs to Overcome Interferon-Mediated Cytotoxicity

PDL1 blockade produces remarkable clinical responses, thought to occur by T cell reactivation through prevention of PDL1-PD1 T cell inhibitory interactions. Here, we find that PDL1 cell-intrinsic signaling protects cancer cells from interferon (IFN) cytotoxicity and accelerates tumor progression. PDL1 inhibited IFN signal transduction through a conserved class of sequence motifs that mediate crosstalk with IFN signaling. Abrogation of PDL1 expression or antibody-mediated PDL1 blockade strongly sensitized cancer cells to IFN cytotoxicity through a STAT3/caspase-7-dependent pathway. Moreover, somatic mutations found in human carcinomas within these PDL1 sequence motifs disrupted motif regulation, resulting in PDL1 molecules with enhanced protective activities from type I and type II IFN cytotoxicity. Overall, our results reveal a mode of action of PDL1 in cancer cells as a first line of defense against IFN cytotoxicity

Prediction of epigenetically regulated genes in breast cancer cell lines

Methylation of CpG islands within the DNA promoter regions is one mechanism that leads to aberrant gene expression in cancer. In particular, the abnormal methylation of CpG islands may silence associated genes. Therefore, using high-throughput microarrays to measure CpG island methylation will lead to better understanding of tumor pathobiology and progression, while revealing potentially new biomarkers. We have examined a recently developed high-throughput technology for measuring genome-wide methylation patterns called mTACL. Here, we propose a computational pipeline for integrating gene expression and CpG island methylation profles to identify epigenetically regulated genes for a panel of 45 breast cancer cell lines, which is widely used in the Integrative Cancer Biology Program (ICBP). The pipeline (i) reduces the dimensionality of the methylation data, (ii) associates the reduced methylation data with gene expression data, and (iii) ranks methylation-expression associations according to their epigenetic regulation. Dimensionality reduction is performed in two steps: (i) methylation sites are grouped across the genome to identify regions of interest, and (ii) methylation profles are clustered within each region. Associations between the clustered methylation and the gene expression data sets generate candidate matches within a fxed neighborhood around each gene. Finally, the methylation-expression associations are ranked through a logistic regression, and their significance is quantified through permutation analysis. Our two-step dimensionality reduction compressed 90% of the original data, reducing 137,688 methylation sites to 14,505 clusters. Methylation-expression associations produced 18,312 correspondences, which were used to further analyze epigenetic regulation. Logistic regression was used to identify 58 genes from these correspondences that showed a statistically signifcant negative correlation between methylation profles and gene expression in the panel of breast cancer cell lines. Subnetwork enrichment of these genes has identifed 35 common regulators with 6 or more predicted markers. In addition to identifying epigenetically regulated genes, we show evidence of differentially expressed methylation patterns between the basal and luminal subtypes. Our results indicate that the proposed computational protocol is a viable platform for identifying epigenetically regulated genes. Our protocol has generated a list of predictors including COL1A2, TOP2A, TFF1, and VAV3, genes whose key roles in epigenetic regulation is documented in the literature. Subnetwork enrichment of these predicted markers further suggests that epigenetic regulation of individual genes occurs in a coordinated fashion and through common regulators