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Physiopathology
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User name	Nicolas Tchitchek
Email	nicolas.tchitchek@ihes.fr
Phone / Fax number	0033610887604 /
Location	Institut des Hautes Ã‰tudes Scientifiques (Systems Epigenomics Group) - 35, route de Chartres - 91440 Bures-sur-Yvette, FRANCE

Scientific description

From abstract of the published article:

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions.

Technical description

From materials and methods section of the published article:

Cell culture, siRNA transfection and tumour necrosis factor treatment:
Umbilical cords were collected after written informed consent was given and approval of the study received from the Cambridge Research Ethics Committee. Human umbilical vein endothelial cells (HUVECs) were isolated from umbilical cords by collagenase digestion and cultured at 37Â°C/5% CO2 in basal culture medium supplemented with a proprietary mixture of heparin, hydrocortisone, epidermal growth factor, fibroblast growth factor, 2% fetal calf serum (FCS) (EGM-2, Cambrex, Workingham, UK). Equal numbers of HUVECs from 10 individuals were pooled, plated at 2.5â€‰Ã—â€‰105 cells/well in six-well plates and allowed to recover for 24â€‰h, at which time they were âˆ¼70% confluent.
To support the choice of pooling 10 biological isolates to minimize individual variation in our dataset, we carried out an in silico pooling experiment. Using microarray expression data from 15 different HUVEC isolates, we assessed how individual variance in the dataset decreased when the mean expression value for an increasing number of isolates was calculated. Mean expression values were calculated for all combinations of the 15 different HUVEC isolates on a gene by gene basis. The variance between all combinations of the mean values was then calculated and the ratios between all variance values above an arbitrarily set minimum threshold value calculated for all combinations of the mean. The mean value of the variance ratios for each â€˜meanedâ€™ group of isolates was then plotted against the mean number of isolates (Supplementary File S1). It can be seen from the curve that even when a more stringent variance threshold of 1.5 is used, increasing the number of isolates pooled beyond 10 individuals will not result in an additional marked decrease in the individual variability in the dataset.
Four hundred transcription factors, signalling molecules, receptors and ligands were selected as siRNA targets based on their relevance to endothelial biology and pathology. siRNA â€˜smartpoolsâ€™ from Dharmacon Inc. (Lafayette, CO, USA) were transfected into the cells using the siFectamine transfection reagent (ICVEC, London, UK) used according to the manufacturer's instructions.
To generate a time course dataset related to inflammatory conditions, pools of 70% confluent HUVECs isolated and cultured as above were treated for 24â€‰h with 10â€‰ng/ml tumour necrosis factor (TNF-Î±).

RNA preparation and gene array analysis:
Total RNA was prepared using Trizol reagent (Invitrogen, London, UK) and assessed using an Agilent 2100 bioanalyser. Biotin-labelled complex cRNAs were prepared and hybridized to CodeLink UniSet Human 20K Bioarray microarrays according to the manufacturer's protocols (GE Healthcare, Amersham, UK). The quality of the expression data from all chips was confirmed using CodeLink Expression Analysis Software (version 4.1). To ensure that expression levels were comparable between the arrays the data was normalized using the cyclic Loess method (42).

Reference

Hurley D, Araki H, Tamada Y, Dunmore B et al. Gene network inference and visualization tools for biologists: application to new human transcriptome datasets. Nucleic Acids Res 2012 Mar 1;40(6):2377-2398. PMID: 22121215

Pubmed : http://www.ncbi.nlm.nih.gov/pubmed/22121215