2 research outputs found
Expression Profiling of Calcium Channels and Calcium-Activated Potassium Channels in Colorectal Cancer
No full textSajida Ibrahim and Hassan Dakik contributed equally to this work as the first author ; Maxime Guéguinou and William Raoul contributed equally to this work as the last author.International audienceBackground: Colorectal cancer (CRC) is a highly devastating cancer. Ca 2+-dependent channels are now considered key regulators of tumor progression. In this study, we aimed to investigate the association of non-voltage gated Ca 2+ channels and Ca 2+-dependent potassium channels (KCa) with CRC using the transcriptional profile of their genes. Methods: We selected a total of 35 genes covering KCa channels KCNN1-4, KCNMA1 and their subunits KCNMB1-4, endoplasmic reticulum (ER) calcium sensors STIM1 and STIM2, Ca 2+ channels ORAI1-3 and the family of cation channels TRP (TRPC1-7, TRPA1, TRPV1/2,4-6 and TRPM1-8). We analyzed their expression in two public CRC datasets from The Cancer Genome Atlas (TCGA) and GSE39582. Results: KCNN4 and TRPM2 were induced while KCNMA1 and TRPM6 were downregulated in tumor tissues comparing to normal tissues. In proximal tumors, STIM2 and KCNN2 were upregulated while ORAI2 and TRPM6 were downregulated. ORAI1 decreased in lymph node metastatic tumors. TRPC1 and ORAI3 predicted poor prognosis in CRC patients. Moreover, we found that ORAI3/ORAI1 ratio is increased in CRC progression and predicted poor prognosis. Conclusions: KCa and Ca 2+ channels could be important contributors to CRC initiation and progression. Our results provide new insights on KCa and Ca 2+ channels remodeling in CRC
Large-Scale Phenotyping Inferences: From Trees to Forest through Machine Learning
No full textThe genomics revolution has provided rapid gains in crop productivity by shortening the breeding
cycle of many commercial species. Genomic data is most useful when carefully linked to crop
phenotypic expression and environmental conditions (Dungey, et al., 2018). Within forests, individual
tree phenotyping is exceptionally challenging due to their considerable size, the long breeding cycle,
and the high variability of growing conditions (Pont, 2016). Advances in remote sensing and the
emergence of sophisticated methods for large data analytics provide a means for describing and
analysing phenotypic and environmental variation at a forest scale. This study outlines the development and implementation of a phenotyping system that provides
spatial estimates of stand productivity across a large plantation forest. Using a machine learning
method forest productivity was modelled from an extensive set of 18 million observations of 93
variables describing climate, forest management, genetics and terrain, extracted from environmental
surfaces, management records and LiDAR data (Watt, et al., 2013). The most important determinants
of productivity were the genetic information and seasonal air temperatures, followed by variables
describing the silvicultural treatment. The phenotyping method developed here can be used to
identify superior and inferior genotypes and estimate a productivity index for each site, which will
improve tree breeding and increase overall productivity across the forest. References Dungey, H. S., Dash, J. P., Pont, D., Clinton, P. W., Watt, M. S., & Telfer, E. J. (2018). Phenotyping Whole Forests Will Help to Track Genetic Performance. Trends in plant science. Pont, D. (2016). Assessment of individual trees using aerial laser scanning in New Zealand radiata pine forests. Watt, P., & Watt, M. S. (2013). Development of a national model of Pinus radiata stand volume from LiDAR metrics for New Zealand. International journal of remote sensing, 34(16), 5892-5904.ABOUT THE AUTHOR Maxime Bombrun is a data scientist in the Data Analytics team at Scion. He received a PhD in
image processing and geology from the University of Clermont-Ferrand, France, in 2015, and
completed a two-year post-doctoral fellowship in biomedicine at the University of Uppsala, Sweden.
His research interests include image processing, statistical learning and their application in data
science. He has published more than 15 papers, mostly in the field of image processing and data
analysis.</div
