LTP-Related Gene Networks calcium and calmodulin. Enrichment score 1.42. Associations between the 5 h LRG sets and their annotations are illustrated above. The region in grey illustrates the common annotation across the cluster with areas in black illustrating the differences in annotation. (TIF) Figure S2 Functional Annotation Clustering (DAVID Analysis) of LRGs at 5 h. Genes with annotations related to ion channels. Enrichment score 1.59. Associations between the 5 h LRG sets and their annotations are illustrated above. The region in grey illustrates the common annotation across the cluster with areas in black illustrating the differences in annotation. (TIF) Figure S3 Functional Annotation Clustering (DAVID Analysis) of LRGs at 5 h. Genes with annotations related to the synapse. Enrichment score 1.37. Associations between the 5 h LRG sets and their annotations are illustrated above. The region in grey illustrates the common annotation across the cluster with areas in black illustrating the differences in annotation. (TIF) Figure S4 Functional Annotation Clustering (DAVID Analysis) of LRGs at 24 h. Genes with annotations related to protein kinases, Enrichment score 1.98. Associations between the 24 h LRG sets and their annotations are illustrated above. The region in grey illustrates the common annotation across the cluster with areas in black illustrating the differences in annotation. (TIF) Figure S5 Functional Annotation Clustering (DAVID Analysis) of LRGs at 24 h. Genes with annotations related to phosphorylation. Enrichment score 2.58. Associations between the 24 h LRG sets and their annotations are illustrated above. The region in grey illustrates the common annotation across the cluster with areas in black illustrating the differences in annotation. (TIF) Table S1 Annotated genes differentially expressed at 5 h post-tetanization ranked by p-value. (XLS) Table S2 Annotated genes differentially expressed at 24 h post-tetanization ranked by p-value. (XLSX) Expression of significant LRG genes across Real-time quantitative PCR: microRNA TaqManH MicroRNA Assays were used to analyse MIR-34-a, MIR-24-1 and the endogenous control, Y1. RNA was extracted using TRIzol reagent (Invitrogen) followed by purification through Norgen Total RNA purification columns (Biotek Corporation) and reverse-transcribed using the TaqManH MicroRNA Reverse Transcription Kit (Applied Biosystems) with an input of 10 ng total RNA per reaction. PCR reactions were carried out as per manufacturer’s instructions. Ct values were normalized using the 2-DDCT method [66]. Significance was assessed using Student’s t- tests with the significance criterion set at p,0.05. Identification of biologically relevant networks and biological pathways Ingenuity Pathway Analysis, version 7 (IPA; Ingenuity Systems, USA; https://www.analysis.ingenuity.com) The individual LRG sets, containing Affymetrix identifiers and corresponding expression values (p,0.05) were uploaded to the IPA application and analysed, as described previously [13], to identify biological functions, canonical pathways and gene networks that may contribute to LTP maintenance and stabilization. IPA builds gene networks from input data utilizing the IPA knowledge base, which contains manually curated information sourced from published literature regarding the functions and interactions of molecules. A graphical representation of the predicted interactions of so-called focus genes is produced, which may contain molecules important to the structure of the network, but not identified as differentially expressed in the input data set (e.g. transcription factors activated by phosphorylation, or microRNA not assessed by mRNA array analysis). Each focus gene is represented as a node, where shape indicates functional groups (e.g. protein kinases) and colour indicates direction of regulation of the gene in the input dataset (green indicates downregulated; red indicates upregulated). Nodes are linked by lines (edges), which indicate proposed interactions. Solid lines indicate direct interactions while dotted lines indicate indirect interactions. In our analysis each network contained up to 35 genes and has an associated score derived from a p-value that indicates the expected likelihood of the focus genes being present in a network compared to that expected by chance. Scores of 3 or above have at least a 99.9% likelihood of not being generated by chance. The Database for Annotation, Visualization and Integrated Discovery (DAVID; https://david.abcc.ncifcrf.gov/; [67]) was used for functional annotation clustering. This measures relationships among annotation terms on the basis of the degree of their co- association with genes within the inputted list. Enrichment scores of $1.3 are equivalent to non-log scale #0.05. A 2D-associated view tool was also used to examine the internal relationships among the clustered terms and genes. microRNA target prediction The following computational algorithms were used to predict mRNA that may be targeted by microRNA of interest: TargetScan (www.targetscan.org/), PicTar (pictar.mdc-berlin.de/ ), and miRanda (http://www.ebi.ac.uk/). Lists of putative mRNA targets generated by each of the three algorithms were compared. To investigate microRNA seed sequence enrichment in our data we used the web application miRvestigator Framework (http:// mirvestigator.systemsbiology.net/). Supporting Information Figure S1 Functional Annotation Clustering (DAVID Analysis) of LRGs at 5 h. Genes with annotations related to PLoS ONE | www.plosone.org 12 July 2012 | Volume 7 | Issue 7 | e40538 Table S4 Annotated genes associated with 24 h IPA Biological Function Protein Synthesis. (XLSX) Table S5 Annotated genes associated with 5 h IPA Canonical Pathways. (XLSX) List of primers used for real-time qPCR. Table S3 time. (DOC) Table S6 (DOC) Table S7 Targets of miR-34a predicted by TargetScan, miRanda and PicTar. (XLSX) Table S8 Targets of miR-24-1 predicted by TargetScan, miRanda and PicTar. (XLSX)