It is still unclear how the MCC:APC complex falls apart and how the APC:Cdc20 complex is formed afterwards

the primary antibody or mouse isotype IgG control in Leica Primary antibody diluent was applied for 30 minutes at RT. Leica Bond post primary was then applied for 8 minutes at RT. Antibody complexes were visualized using Leica Bond Polymer DAB Refine for 8 minutes at RT and then Leica Bond Mixed Refine detection 2X for 10 minutes at RT. Tissues were counterstained with hematoxylin counterstain for 10 second followed by two rinses in H20. Unless otherwise specified all reagents were obtained from Leica Microsystems. Gene Set Analysis Next, the data was analyzed using GSA in order to investigate categories of genes. GSA assesses the statistical significance of pre-defined gene sets/pathways as a whole rather than of single genes, which allows for the identification of modest but concordant changes in 80321-63-7 expression of individual genes that may be missed by single gene analysis. GSA software is available as R code. GSA considers all the genes in the experiment and allows for the identification of gene sets with strong cross-correlation by boosting the signal-to-noise ratio, which makes it possible to detect modest changes in gene expression. In GSA, the p-values that are calculated to test the null hypothesis are based on permutations of the sample labels. We used four gene set databases for the GSA: three from Gene Ontology , and the functional C2 gene set from the Molecular Signature Database . RNA Extraction and Microarray Processing Choriodecidual Infection Induces 10336422 Fetal Lung Injury IPA Analysis We used the Ingenuity Pathway Analysis software to discover pathways and transcriptional networks in the gene expression microarray data. Our data set containing 17876302 gene identifiers and corresponding expression changes between the experimental groups and p-values was uploaded into the IPA application. Each identifier was mapped to its corresponding object in the IngenuityH Knowledge Base. The Functional Analysis identified the biological functions and/or diseases that were most significant to the data set. Genes from the data set with more than 1.5-fold differential expression and p,0.05 that were associated with biological functions and/or diseases in the Ingenuity Knowledge Base were considered for the analysis. The categories ��Top Canonical Pathways��and ��Top Transcription Factors��were primarily used in this analysis. Right-tailed Fisher’s exact test was used to calculate a p-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone. The IPA Path Designer Graphical Representation was used to generate figures. Molecules are represented as nodes, and the biological relationship between two nodes is represented as an edge. All edges are supported by at least one reference from the literature, from a textbook, or from canonical information stored in the Ingenuity Knowledge Base. Human, mouse, and rat orthologs of a gene are stored as separate objects in the Ingenuity Knowledge Base, but are represented as a single node in the network. The intensity of the node color indicates the degree of up- or down- regulation. Nodes are displayed using various shapes that represent the functional class of the gene product. Edges are displayed with various labels that describe the nature of the relationship between the nodes. IPA also allows prediction of the activation or inhibition of transcription factors involved in the gene expression patterns seen in our study. Validation of cDNA Microarray

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