SiRNA profiling of human HEK and HeLa cell lines
Ontology highlight
ABSTRACT:
HeLA siRNA vs. HeLa control
ORGANISM(S): Homo sapiens
SUBMITTER: Christian Thiel
PROVIDER: E-MEXP-2591 | biostudies-arrayexpress |
REPOSITORIES: biostudies-arrayexpress
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Project description:It is currently unknown how extensively the double-stranded RNA binding protein Staufen (Stau)1 is utilized by mammalian cells to regulate gene expression. To date, Stau1 binding to the 3â untranslated region (3âUTR) of ARF1 mRNA has been shown to target ARF1 mRNA for Stau1-mediated mRNA decay (SMD). ARF1 SMD depends on translation and recruitment of the nonsense-mediated mRNA decay factor Upf1 to the ARF1 3âUTR by Stau1. Here, we use microarray analyses to examine changes in the abundance of cellular mRNAs that occur when Stau1 is depleted. Results indicate that 1.1% and 1.0% of the 11,569 HeLa-cell transcripts that were analyzed are, respectively, upregulated and downregulated at least two-fold in three independently performed experiments. Additionally, we localize the Stau1 binding site to the 3âUTR of four mRNAs that we define as natural SMD targets. Together, these and substantiating results suggest that Stau1 influences the expression of a wide variety of physiologic transcripts and metabolic pathways. Experiment Overall Design: We report the results of three independently performed microarray analyses that examined changes in the abundance of transcripts from HeLa-cell genes upon Stau1 depletion. HeLa cells were transiently transfected with either a nonspecific Control small interfering (si)RNA or Stau1 siRNA. Stau1 siRNA reduced the level of cellular Stau1 to as little as 4% of normal, where normal is defined as the level in the presence of Control siRNA (data not shown). RNA from three independently performed transfections was separately hybridized to microarrays.
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Project description:Nonsense-mediated mRNA decay (NMD) is a molecular pathway of mRNA surveillance that ensures rapid degradation of mRNAs containing premature translation termination codons (PTCs) in eukaryotes. Originally, NMD was thought of as a quality control pathway that targets non-functional mRNAs arising from mutations and splicing errors. More recently, NMD has been shown to also regulate normal gene expression and NMD thus emerged as one of the key post-transcriptional mechanisms of gene regulation. We have now systematically analyzed the molecular mechanism of variable NMD efficiency and used different HeLa cell strains as a model system. The results of this analysis show that NMD efficiency can be remarkably variable and represents a stable characteristic of these strains. Low NMD efficiency is shown to be functionally related to the reduced abundance of the exon junction component RNPS1 in one of the HeLa strain analyzed. Furthermore, restoration of functional RNPS1 expression, but not of NMD-inactive mutant proteins, also restores efficient NMD in the RNPS1 deficient cell line. We conclude that cellular concentrations of RNPS1 modify NMD efficiency and propose that the cell type specific co-factor availability represents a novel principle that controls NMD. Experiment Overall Design: HeLa cells were treated with UPF1 siRNA or Luciferase siRNA as a negative control. After 72 hs, cytoplasmic RNA was isolated and the integrity of the RNA was assessed using a Agilent 2100 Bioanalyzer (Agilent, Palo Alto, CA). We performed preparation, processing, and hybridisation of labelled and fragmented cRNA targets to Affymetrix HG_U133A GeneChipsTM according to the manufacturerâs protocols (Affymetrix Inc., Santa Clara, CA). Oligonucleotide arrays were scanned using a confocal laser scanner (GeneArrayTM, Hewlett Packard, Palo Alto, CA). We used the Affymetrix GeneChip Suite 5.0 software (MAS 5.0) to calculate raw expression values for each of the 22,283 probe sets on the U133A oligonucleotide array. Signal intensities were calculated as average intensity difference (AID) between perfect and mismatch probes. Approximately 8,800 probe sets continuously resulting in absent calls were excluded from the analyses. Next, we used GeneSpring 4.2.1 (Silicon Genetics, Redwood City, CA) for scaling, normalisation and background correction of all genes and arrays. We performed Student´s t-test on normalised relative expression ratios to identify significant differentially expressed genes with a minimum factor of difference of >2-fold, within the 95% confidence interval (p<0.05).
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