Additional file 1: of SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Supplementary Methods. Figure S1 Proportions of RNA-seq reads from T. gondii-infected HFFs aligning to a composite hg19/Toxoplasma genome. Figure S2 Comparison of distance weighting effect on gene scores. Figure S3 Representation of simulations demonstrating the effects on high scoring genes of variation of weightings. Figure S4 Comparison of gene scores with reduced and full SMITE models. Figure S5 Examples of modules generated by full and reduced SMITE models. Figure S6 KS test results comparing SMITE and FEM module genes and a random sampling of 10,000 genes. Figure S7 Comparison of the performance of the full SMITE model with the FEM model. Table S1 Criteria for defining genomic contexts in HFFs. Table S2 Weighting criteria used for SMITE analysis of the T. gondii HFF dataset. Appendix 1 R code for analyzing T. gondii HFF dataset with SMITE. Appendix 2 R code for analyzing T. gondii HFF dataset with FEM. Supplementary references (PDF 5642 kb

Additional file 2: of SMITE: an R/Bioconductor package that identifies network modules by integrating genomic and epigenomic information

Supplementary Tables. Table S3 Gene symbol and score of the high scoring genes using three different methods: SMITE full model, SMITE reduced model, and FEM. Table S4 Modules discovered using FEM and genes composing the modules with their DNA methylation, expression, and overall statistics. Table S5 Modules discovered using the reduced model of SMITE (SMITE-R) with spin-glass. Table S6 Modules discovered using the full model of SMITE (SMITE-F) with spin-glass. Table S7 Pathways associated with the genes composing the modules discovered by FEM. Table S8 Pathways associated with the genes composing the modules discovered by the reduced model of SMITE (SMITE-R) using spin-glass. Table S9 Pathways associated with the genes composing the modules discovered by the full model of SMITE (SMITE-F) using spin-glass. Table S10 Quantifying the number of times pathways were found to be associated the modules discovered by either FEM, the reduced model of SMITE (SMITE-R) using spin-glass, or the full model of SMITE(SMITE-F) using spin-glass. Table S11 Genes composing the “summary network” found by either the reduced (SMITE-R) or full (SMITE-F) SMITE models using the Heinz algorithm. Table S12 Pathways associated with the genes composing the “summary network” discovered by the reduced model of SMITE(SMITE-R) using the Heinz algorithm. Table S13 Pathways associated with the genes composing the “summary network” discovered by the full model of SMITE (SMITE-F) using the Heinz algorithm. Table S14 Genes composing the “modules” found using no weights instead of weighting by distance. Table S15 Pathways associated with the genes in the modules identified without using distance weighting. (XLSX 269 kb

ATAC-seq sequencing data metrics for the human and <i>T</i>. <i>gondii</i> genomes.

(XLSX)</p

S6 Fig -

The expected nucleosomal periodicity pattern of chromatin is revealed from the insert size plots of panel (A), representing the reads aligning to the T. gondii nuclear genome, whereas in (B) we see no evidence for such nucleosomal organization in reads mapping to the T. gondii apicoplast DNA. (TIF)</p

Project overview and host gene transcriptional response to <i>T</i>. <i>gondii</i> infection.

We show in (A) the overview of the experiments performed, depicting the in vitro infection of human fibroblasts with T. gondii, with transcriptional (RNA-seq) and chromatin accessibility (ATAC-seq) studies performed before and 24 hours after infection, and alignment of the reads obtained to both the human and T. gondii genomes. The RNA-seq results in (B) show more human genes upregulated (yellow) than downregulated (blue) with infection, with ADAMTS15 strongly upregulated. Using network analysis, we extracted genes interacting with ADAMTS15 (C) and showed that other metalloproteases were upregulated with infection (D). (E-G) show the major groups of gene ontologies in the remaining upregulated genes.</p

A model derived from genomic assay data for the host cellular response to <i>T</i>. <i>gondii</i> infection.

By inferring the TFs mediating the host cell response, we can further predict the cell signaling pathways induced by T. gondii infection in human fibroblasts. How the known GRA24 induction of p38 MAPK signaling influences the transcriptional response remains uncertain, and we include the possibility that T. gondii TFs may contribute to host cell transcriptional dysregulation.</p

S3 Fig -

The location of ATAC-seq peaks (left) and differentially-accessible regions (DARs, right) relative to annotated transcription start sites (TSS) in the human genome. Whereas ATAC-seq peaks are strongly enriched at TSS, only 51 (9.6%) of DARs are located within 5 kb of annotated transcription start sites (yellow shading). (TIF)</p

The gene set enrichment analysis-derived Gene Ontology (GO) terms characterizing the properties of the genes increasing expression with <i>T</i>. <i>gondii</i> infection.

(TIF)</p

Chromatin accessibility and transcriptional profiling identifies new features in the <i>T</i>. <i>gondii</i> genome.

The gene expression and chromatin accessibility information combine to allow new T. gondii genomic annotations. In (A) we show the RNA expression at ATAC-seq peaks that are not within 5 kb of an annotated TSS. We sorted these peaks by clustering the characteristics of the RNA-seq data nearby, with the top clusters (1–3 especially) showing evidence for adjacent transcripts. Examples of the loci are shown on the right, including what appears to be a new gene (B), an antisense transcript from the 3’ end of an annotated gene (C), the use of an alternative intragenic promoter (D) and a locus of open chromatin with no nearby genes or RNA expression, potentially representing a distal cis-regulatory element.</p

RNA-seq sequencing data metrics for the human and <i>T</i>. <i>gondii</i> genomes.

(XLSX)</p