T cell balance gene expression, compositions of matters and methods of use thereof
A cellular, balanced technology that can be used in drug combinations, animal cells, genetic material components, etc., and can solve problems such as the inability to easily apply primary T cells
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example 1
[0207] Example 1: Materials and methods
[0208] Briefly, gene expression profiles were measured at 18 time points (0.5 hours to 72 days) under Th17 conditions (IL-6, TGF-β1) or control (Th0) using the Affymetrix microarray HT_MG-430A . Differentially expressed genes were detected using the common parts of the four inference methods, and the genes were clustered using k-means with automatically derived k. By looking for a significant (p-5 and fold enrichment >1.5) overlap to infer temporal regulatory interactions. Candidates for perturbation are lexicographically ranked using network-based and expression-based features. Perturbation for siRNA delivery using SiNWs. These methods are described in more detail below.
[0209] Mice: C57BL / 6 wild type (wt), Mt - / - , Irf1 - / - 、Fas - / - , Irf4 fl / fl , and Cd4 CreMice were obtained from The Jackson Laboratory (Bar Harbor, ME). Stat1 - / - and 129 / Sv control mice were purchased from Taconic ((Hudson, NY). IL-12rβ1 - / - Mice we...
example 2
[0312] Example 2: Transcriptional time course of Th17 differentiation
[0313] Differentiation of naive CD4+ T-cells into Th17 cells was induced using TGF-β1 and IL-6, and at eighteen time points along a 72-hour time course during the differentiation of naive CD4+ T-cells into Th17 cells Using microarrays to measure transcriptional profiles, this differentiation was induced by combining the anti-inflammatory cytokine TGF-β1 and the pro-inflammatory cytokine IL-6 (Fig. Figure 6A , Figure 6B and Figure 6C , see method in Example 1). As a control, the mRNA profile (Th0) of cells activated without addition of differentiation cytokines was measured. 1,291 genes specifically differentially expressed during Th17 differentiation were identified by comparing these Th17 differentiated cells with control cells (see method in Example 1) and divided into 20 gene clusters showing different time profiles. Expression clusters (k-means clustering, see method in example 1, Fig. 1b and ...
example 3
[0322] Example 3: Inference of Dynamic Regulatory Interactions
[0323] It was hypothesized that each of these clusters (Fig. 1b) encompassed genes sharing regulators that were active at the relevant time point. To predict these regulators, general networks of regulator-target associations from published genome profiles were assembled (Linhart, C., Halperin, Y. & Shamir, R. .Transcription factor and microRNA motif discovery: the Amadeus platform and a compendium of metazoan target sets Zheng (Zheng), G. et al. ITFP: an integrated platform of mammalian transcription factors (ITFP: an integrated platform of mammalian transcription factors). Bioinformatics (Bioinformatics) 24, 2416-2417, doi: 10.1093 / bioinformatics / btn439 (2008); Wilson ( Wilson), N.K. et al. Combinatorial transcriptional control in bloodstem / progenitor cells: genome-wide analysis soften major transcriptional regulators in blood stem / progenitor cells. Cell Stem Cell 7, 532-544, doi: 10.1016 / j. stem.2010.07.01...
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