Towards 50% Efficiency in Solar Cells

International audienc

The Snail repressor recruits EZH2 to specific genomic sites through the enrollment of the lncRNA HOTAIR in epithelial-to-mesenchymal transition

The transcription factor Snail is a master regulator of cellular identity and epithelial-to-mesenchymal transition (EMT) directly repressing a broad repertoire of epithelial genes. How chromatin modifiers instrumental to its activity are recruited to Snail-specific binding sites is unclear. Here we report that the long non-coding RNA (lncRNA) HOTAIR (for HOX Transcript Antisense Intergenic RNA) mediates a physical interaction between Snail and enhancer of zeste homolog 2 (EZH2), an enzymatic subunit of the polycomb-repressive complex 2 and the main writer of chromatin-repressive marks. The Snail-repressive activity, here monitored on genes with a pivotal function in epithelial and hepatic morphogenesis, differentiation and cell-type identity, depends on the formation of a tripartite Snail/HOTAIR/EZH2 complex. These results demonstrate an lncRNA-mediated mechanism by which a transcriptional factor conveys a general chromatin modifier to specific genes, thereby allowing the execution of hepatocyte transdifferentiation; moreover, they highlight HOTAIR as a crucial player in the Snail-mediated EMT.Oncogene advance online publication, 25 July 2016; doi:10.1038/onc.2016.260

Methods for epidemiological studies in competitive cycling:an extension of the IOC consensus statement on methods for recording and reporting of epidemiological data on injury and illness in sport 2020

In 2020, the IOC released a consensus statement that provides overall guidelines for the recording and reporting of epidemiological data on injury and illness in sport. Some aspects of this statement need to be further specified on a sport-by-sport basis. To extend the IOC consensus statement on methods for recording and reporting of epidemiological data on injury and illness in sports and to meet the sport-specific requirements of all cycling disciplines regulated by the Union Cycliste Internationale (UCI). A panel of 20 experts, all with experience in cycling or cycling medicine, participated in the drafting of this cycling-specific extension of the IOC consensus statement. In preparation, panel members were sent the IOC consensus statement, the first draft of this manuscript and a list of topics to be discussed. The expert panel met in July 2020 for a 1-day video conference to discuss the manuscript and specific topics. The final manuscript was developed in an iterative process involving all panel members. This paper extends the IOC consensus statement to provide cycling-specific recommendations on health problem definitions, mode of onset, injury mechanisms and circumstances, diagnosis classifications, exposure, study population characteristics and data collection methods. Recommendations apply to all UCI cycling disciplines, for both able-bodied cyclists and para-cyclists. The recommendations presented in this consensus statement will improve the consistency and accuracy of future epidemiological studies of injury and illness in cycling

Cell Surface Sialylation and Fucosylation Are Regulated by L1 via Phospholipase Cγ and Cooperate to Modulate Neurite Outgrowth, Cell Survival and Migration

BACKGROUND: Cell surface glycosylation patterns are markers of cell type and status. However, the mechanisms regulating surface glycosylation patterns remain unknown. METHODOLOGY/PRINCIPAL FINDINGS: Using a panel of carbohydrate surface markers, we have shown that cell surface sialylation and fucosylation were downregulated in L1(-/y) neurons versus L1(+/y) neurons. Consistently, mRNA levels of sialyltransferase ST6Gal1, and fucosyltransferase FUT9 were significantly reduced in L1(-/y) neurons. Moreover, treatment of L1(+/y) neurons with L1 antibodies, triggering signal transduction downstream of L1, led to an increase in cell surface sialylation and fucosylation compared to rat IgG-treated cells. ShRNAs for both ST6Gal1 and FUT9 blocked L1 antibody-mediated enhancement of neurite outgrowth, cell survival and migration. A phospholipase Cgamma (PLCgamma) inhibitor and shRNA, as well as an Erk inhibitor, reduced ST6Gal1 and FUT9 mRNA levels and inhibited effects of L1 on neurite outgrowth and cell survival. CONCLUSIONS: Neuronal surface sialylation and fucosylation are regulated via PLCgamma by L1, modulating neurite outgrowth, cell survival and migration

Biallelic mutations in SORD cause a common and potentially treatable hereditary neuropathy with implications for diabetes

Here we report biallelic mutations in the sorbitol dehydrogenase gene (SORD) as the most frequent recessive form of hereditary neuropathy. We identified 45 individuals from 38 families across multiple ancestries carrying the nonsense c.757delG (p.Ala253GlnfsTer27) variant in SORD, in either a homozygous or compound heterozygous state. SORD is an enzyme that converts sorbitol into fructose in the two-step polyol pathway previously implicated in diabetic neuropathy. In patient-derived fibroblasts, we found a complete loss of SORD protein and increased intracellular sorbitol. Furthermore, the serum fasting sorbitol levels in patients were dramatically increased. In Drosophila, loss of SORD orthologs caused synaptic degeneration and progressive motor impairment. Reducing the polyol influx by treatment with aldose reductase inhibitors normalized intracellular sorbitol levels in patient-derived fibroblasts and in Drosophila, and also dramatically ameliorated motor and eye phenotypes. Together, these findings establish a novel and potentially treatable cause of neuropathy and may contribute to a better understanding of the pathophysiology of diabetes

A Novel Bayesian DNA Motif Comparison Method for Clustering and Retrieval

Characterizing the DNA-binding specificities of transcription factors is a key problem in computational biology that has been addressed by multiple algorithms. These usually take as input sequences that are putatively bound by the same factor and output one or more DNA motifs. A common practice is to apply several such algorithms simultaneously to improve coverage at the price of redundancy. In interpreting such results, two tasks are crucial: clustering of redundant motifs, and attributing the motifs to transcription factors by retrieval of similar motifs from previously characterized motif libraries. Both tasks inherently involve motif comparison. Here we present a novel method for comparing and merging motifs, based on Bayesian probabilistic principles. This method takes into account both the similarity in positional nucleotide distributions of the two motifs and their dissimilarity to the background distribution. We demonstrate the use of the new comparison method as a basis for motif clustering and retrieval procedures, and compare it to several commonly used alternatives. Our results show that the new method outperforms other available methods in accuracy and sensitivity. We incorporated the resulting motif clustering and retrieval procedures in a large-scale automated pipeline for analyzing DNA motifs. This pipeline integrates the results of various DNA motif discovery algorithms and automatically merges redundant motifs from multiple training sets into a coherent annotated library of motifs. Application of this pipeline to recent genome-wide transcription factor location data in S. cerevisiae successfully identified DNA motifs in a manner that is as good as semi-automated analysis reported in the literature. Moreover, we show how this analysis elucidates the mechanisms of condition-specific preferences of transcription factors