SHERPA Position Paper - Empowering rural areas in multi-level governance processes

This SHERPA Position Paper builds on the contributions of all 41 SHERPA Multi-Actor Platforms (MAPs) involved in the fourth (and final) cycle of the SHERPA project. During this final cycle, MAPs were asked to reflect on how to empower regional and local institutions and actors in multi-level decision-making processes in rural areas, and propose recommendations for policy and future research on this topic. Each MAP discussed the elements they found most relevant for their geographical area in relation to multi-level governance in rural areas, and used this as their MAP input for the development of this Position Paper. More information on this topic from each individual MAP can be found in the MAP Fiches

Predicting the influence of climate induced temperature changes on the production of polysaccharides in Arabidopsis seed mucilage

International audienceIn recent years, the climate trend in temperate zones such as Europe and North America has been towards episodes of severe hot weather which are projected to occur more frequently and/or severely over coming decades (IPCC fifth assessment report, 2014). This is predicted to have major effects on ecosystems and the biodiversity of wild species, such as Arabidopsis. Polysaccharides play a key role in plant growth and resistance to stress and how their production will be affected by climate change remains an open question. Mucilage is a hydrogel of polysaccharides formed around imbibed seeds of certain species and is an amenable model system for studying polysaccharide production. We have previously found that natural accessions of Arabidopsis show variation in mucilage production that may be locally adapted to particular climates. To determine how climate change can impact polysaccharide synthesis, we have examined the effect of temperature on the production of seed mucilage polymers. Polysaccharide composition and properties in natural mucilage variants exhibiting divergent traits under standard growth conditions have been compared after seed production at different temperatures. How these are related to the modulation of genes encoding key enzymes in mucilage polysaccharide synthesis will also be presented

Impact of delay to cryopreservation on RNA integrity and genome-wide expression profiles in resected tumor samples.

The quality of tissue samples and extracted mRNA is a major source of variability in tumor transcriptome analysis using genome-wide expression microarrays. During and immediately after surgical tumor resection, tissues are exposed to metabolic, biochemical and physical stresses characterized as "warm ischemia". Current practice advocates cryopreservation of biosamples within 30 minutes of resection, but this recommendation has not been systematically validated by measurements of mRNA decay over time. Using Illumina HumanHT-12 v3 Expression BeadChips, providing a genome-wide coverage of over 24,000 genes, we have analyzed gene expression variation in samples of 3 hepatocellular carcinomas (HCC) and 3 lung carcinomas (LC) cryopreserved at times up to 2 hours after resection. RNA Integrity Numbers (RIN) revealed no significant deterioration of mRNA up to 2 hours after resection. Genome-wide transcriptome analysis detected non-significant gene expression variations of -3.5%/hr (95% CI: -7.0%/hr to 0.1%/hr; p = 0.054). In LC, no consistent gene expression pattern was detected in relation with warm ischemia. In HCC, a signature of 6 up-regulated genes (CYP2E1, IGLL1, CABYR, CLDN2, NQO1, SCL13A5) and 6 down-regulated genes (MT1G, MT1H, MT1E, MT1F, HABP2, SPINK1) was identified (FDR <0.05). Overall, our observations support current recommendation of time to cryopreservation of up to 30 minutes and emphasize the need for identifying tissue-specific genes deregulated following resection to avoid misinterpreting expression changes induced by warm ischemia as pathologically significant changes

Megakaryocytes form linear podosomes devoid of digestive properties to remodel medullar matrix

International audienceBone marrow megakaryocytes (MKs) undergo a maturation involving contacts with the microenvironment before extending proplatelets through sinusoids to deliver platelets in the bloodstream. We demonstrated that MKs assemble linear F-actin-enriched podosomes on collagen I fibers. Microscopy analysis evidenced an inverse correlation between the number of dot-like versus linear podosomes over time. Confocal videomicroscopy confirmed that they derived from each-other. This dynamics was dependent on myosin IIA. Importantly, MKs progenitors expressed the Tks4/5 adaptors, displayed a strong gelatinolytic ability and did not form linear podosomes. While maturing, MKs lost Tks expression together with digestive ability. However, those MKs were still able to remodel the matrix by exerting traction on collagen I fibers through a collaboration between GPVI, ß1 integrin and linear podosomes. Our data demonstrated that a change in structure and composition of podosomes accounted for the shift of function during megakaryopoiesis. These data highlight the fact that members of the invadosome family could correspond to different maturation status of the same entity, to adapt to functional responses required by differentiation stages of the cell that bears them

Average log-expression profiles of the 12 genes with significant up- or down-regulation over harvesting time (FDR<0.05) in HCC.

<p>The BRB-ArrayTools v4.2 time course analysis model was applied to whole-genome expression microarray data (HCC and LC samples) to identify significant individual deregulated genes over harvesting time. No significant deregulated genes in LC were observed. Individual log-expression profiles () and average log-expression line plots (3 HCC samples taken at the center and at the periphery) in relation to delay to tumor cryopreservation are displayed.</p

Hierarchical cluster analysis of all samples following log-transformation and quantile normalization of the microarray data.

<p>Dendrogram for clustering experiments was created using centred correlation and average linkage method. Length of nodes corresponds to correlation between samples. HCC4_5P: HCC from patient 4 taken at the periphery of the tumor and maintained at room temperature and then frozen in liquid nitrogen at t5 (min).</p

Samples description and microarray quality.

<p>Type of tumor and delay to tumor freezing are shown. RNA integrity is evaluated through the RIN number. The ratio of centiles P95/P05 reflects the overall strength of the signal compared to the background. The Pearson correlation coefficient (r²) shows the correlation between log-expression levels of the central and peripheral samples, for each tumor and each time to cryopreservation.</p><p>HCC: HepatoCellular Carcinoma.</p><p>LC: Lung Carcinoma.</p><p>ND: Not Determined.</p

List of 34 HCC specific genes: comparison of gene expression data from the Liverome database and experimental dataset.

<p>Expression trend of 34 HCC specific genes reported as deregulated in more than 4 studies in the public Liverome database was compared to experimental expression trend.</p>a<p>average rate of expression from different Illumina probes.</p>b<p>discrepancies between Liverome studies results.</p><p><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0079826#pone-0079826-t003" target="_blank">Table 3</a> References.</p><p>1) Chan, K.Y., Lai, P.B., Squire, J.A., Beheshti, B., Wong, N.L., Sy, S.M., Wong, N., 2006. Positional expression profiling indicates candidate genes in deletion hotspots of hepatocellular carcinoma. Mod Pathol 19, 1546–1554.</p><p>2) Chung, E.J., Sung, Y.K., Farooq, M., Kim, Y., Im, S., Tak, W.Y., Hwang, Y.J., Kim, Y.I., Han, H.S., Kim, J.C., Kim, M.K., 2002. Gene expression profile analysis in human hepatocellular carcinoma by cDNA microarray. Mol Cells 14, 382–387.</p><p>3) Cui, X.D., Lee, M.J., Yu, G.R., Kim, I.H., Yu, H.C., Song, E.Y., Kim, D.G., 2010. EFNA1 ligand and its receptor EphA2: potential biomarkers for hepatocellular carcinoma. Int J Cancer 126, 940–949.</p><p>4) De Giorgi, V., Monaco, A., Worchech, A., Tornesello, M., Izzo, F., Buonaguro, L., Marincola, F.M., Wang, E., Buonaguro, F.M., 2009. Gene profiling, biomarkers and pathways characterizing HCV-related hepatocellular carcinoma. J Transl Med 7, 85.</p><p>5) Delpuech, O., Trabut, J.B., Carnot, F., Feuillard, J., Brechot, C., Kremsdorf, D., 2002. Identification, using cDNA macroarray analysis, of distinct gene expression profiles associated with pathological and virological features of hepatocellular carcinoma. Oncogene 21, 2926–2937.</p><p>6) Dong, H., Ge, X., Shen, Y., Chen, L., Kong, Y., Zhang, H., Man, X., Tang, L., Yuan, H., Wang, H., Zhao, G., Jin, W., 2009. Gene expression profile analysis of human hepatocellular carcinoma using SAGE and LongSAGE. BMC Med Genomics 2, 5.</p><p>7) Goldenberg, D., Ayesh, S., Schneider, T., Pappo, O., Jurim, O., Eid, A., Fellig, Y., Dadon, T., Ariel, I., de Groot, N., Hochberg, A., Galun, E., 2002. Analysis of differentially expressed genes in hepatocellular carcinoma using cDNA arrays. Mol Carcinog 33, 113–124.</p><p>8) Iizuka, N., Tsunedomi, R., Tamesa, T., Okada, T., Sakamoto, K., Hamaguchi, T., Yamada-Okabe, H., Miyamoto, T., Uchimura, S., Hamamoto, Y., Oka, M., 2006. Involvement of c-myc-regulated genes in hepatocellular carcinoma related to genotype-C hepatitis B virus. J Cancer Res Clin Oncol 132, 473–481.</p><p>9) Kato, K., Yamashita, R., Matoba, R., Monden, M., Noguchi, S., Takagi, T., Nakai, K., 2005. Cancer gene expression database (CGED): a database for gene expression profiling with accompanying clinical information of human cancer tissues. Nucleic Acids Res 33, D533–536.</p><p>10) Kim, B.Y., Lee, J.G., Park, S., Ahn, J.Y., Ju, Y.J., Chung, J.H., Han, C.J., Jeong, S.H., Yeom, Y.I., Kim, S., Lee, Y.S., Kim, C.M., Eom, E.M., Lee, D.H., Choi, K.Y., Cho, M.H., Suh, K.S., Choi, D.W., Lee, K.H., 2004. Feature genes of hepatitis B virus-positive hepatocellular carcinoma, established by its molecular discrimination approach using prediction analysis of microarray. Biochim Biophys Acta 1739, 50–61.</p><p>11) Kurokawa, Y., Matoba, R., Takemasa, I., Nakamori, S., Tsujie, M., Nagano, H., Dono, K., Umeshita, K., Sakon, M., Ueno, N., Kita, H., Oba, S., Ishii, S., Kato, K., Monden, M., 2003. Molecular features of non-B, non-C hepatocellular carcinoma: a PCR-array gene expression profiling study. J Hepatol 39, 1004–1012.</p><p>12) Lee, M.J., Yu, G.R., Park, S.H., Cho, B.H., Ahn, J.S., Park, H.J., Song, E.Y., Kim, D.G., 2008. Identification of cystatin B as a potential serum marker in hepatocellular carcinoma. Clin Cancer Res 14, 1080–1089.</p><p>13) Li, Y., Tang, R., Xu, H., Qiu, M., Chen, Q., Chen, J., Fu, Z., Ying, K., Xie, Y., Mao, Y., 2002. Discovery and analysis of hepatocellular carcinoma genes using cDNA microarrays. J Cancer Res Clin Oncol 128, 369–379.</p><p>14) Okabe, H., Satoh, S., Kato, T., Kitahara, O., Yanagawa, R., Yamaoka, Y., Tsunoda, T., Furukawa, Y., Nakamura, Y., 2001. Genome-wide analysis of gene expression in human hepatocellular carcinomas using cDNA microarray: identification of genes involved in viral carcinogenesis and tumor progression. Cancer Res 61, 2129–2137.</p><p>15) Patil, M.A., Chua, M.S., Pan, K.H., Lin, R., Lih, C.J., Cheung, S.T., Ho, C., Li, R., Fan, S.T., Cohen, S.N., Chen, X., So, S., 2005. An integrated data analysis approach to characterize genes highly expressed in hepatocellular carcinoma. Oncogene 24, 3737–3747.</p><p>16) Shirota, Y., Kaneko, S., Honda, M., Kawai, H.F., Kobayashi, K., 2001. Identification of differentially expressed genes in hepatocellular carcinoma with cDNA microarrays. Hepatology 33, 832–840.</p><p>17) Tackels-Horne, D., Goodman, M.D., Williams, A.J., Wilson, D.J., Eskandari, T., Vogt, L.M., Boland, J.F., Scherf, U., Vockley, J.G., 2001. Identification of differentially expressed genes in hepatocellular carcinoma and metastatic liver tumors by oligonucleotide expression profiling. Cancer 92, 395–405.</p><p>18) Xu, L., Hui, L., Wang, S., Gong, J., Jin, Y., Wang, Y., Ji, Y., Wu, X., Han, Z., Hu, G., 2001a. Expression profiling suggested a regulatory role of liver-enriched transcription factors in human hepatocellular carcinoma. Cancer Res 61, 3176–3181.</p><p>19) Xu, X.R., Huang, J., Xu, Z.G., Qian, B.Z., Zhu, Z.D., Yan, Q., Cai, T., Zhang, X., Xiao, H.S., Qu, J., Liu, F., Huang, Q.H., Cheng, Z.H., Li, N.G., Du, J.J., Hu, W., Shen, K.T., Lu, G., Fu, G., Zhong, M., Xu, S.H., Gu, W.Y., Huang, W., Zhao, X.T., Hu, G.X., Gu, J.R., Chen, Z., Han, Z.G., 2001b. 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Time-course scatter-plots of HCC and LC genome-wide expression profiling quantile normalized data.

<p>Scatter plots for each tumor pair at t5 (HCC_5_AVG_Signal and LC_5_AVG_Signal on the X Axis) versus harvested tumor pairs at t15, t30 and t120 (on the Y Axis) were generated on a logarithmic scale. Genes showing greater than 2-fold change relative to the t5 sample from the same tumor were highlighted.</p

Gene expression for all probes and for restricted sets of probes.

<p>Over-all rate of expression changes for all probes in all samples combined, in LC and HCC samples and in peripheral and central samples are estimated as percent-change per hour. Expression levels changes are also estimated for different sets of probes (lowest and highest 5% of geometric mean expression, probes in warm ischemia genes and probes in HCC genes).</p