DOI: 10.6084/m9.figshare.10008914.v3
title: Supplementary Data for Schniete et al 2019.Differential transcription in expanded gene families of central carbon metabolism of Streptomyces coelicolor A3(2)

description: <b>Background: </b>Streptomycete<i> </i>bacteria are prolific producer of specialised metabolites, many of which have clinically relevant bioactivity. A striking feature of their genomes is the expansion of gene families that encode the same enzymatic function. Genes that undergo expansion events, either by HGT or duplication, can have a range of fates: genes can be lost, or they can undergo neo-functionalisation or sub-functionalisation. To test whether expanded gene families in <i>Streptomyces </i>exhibit differential expression an RNA-Seq approach was used to examine cultures of wild-type <i>Streptomyces coelicolor </i>grown with either glucose or tween as the sole carbon source.<b>Results: </b>RNA-Seq analysis showed that two-thirds of genes within expanded gene families show transcriptional differences when strains were grown on tween compared to glucose, in addition, expression of specialised metabolite gene clusters (actinorhodin, isorenieratane, coelichelin and a cryptic NRPS) was also influenced by carbon source. <b>Conclusions: </b>Expression of genes encoding the same enzymatic function exhibited transcriptional differences when grown on different carbon sources. This transcriptional divergence enables partitioning function under different physiological conditions. These<b> </b>approaches can inform metabolic engineering of industrial <i>Streptomyces </i>strains and may help develop cultivation conditions to activate the so-called silent biosynthetic gene clusters. . <br>Supplementary Figure 1 Growth profiles of liquid Streptomyces coelicolor M145 cultures grown in glucose or tween with sampling point indicator (green arrow) at mid log phase. Mean of three biological replicates is shown as ln of cell dry weight (CDW) over time. Each experimental condition was carried out in three biological replicates, points are the mean of three independent experiments and error bars represent the standard deviation of the data. Supplementary Fig. 2 Overview of differential gene expression by functional categories of genes Supplementary Figure 3. Verification of fold change data from RNA-Seq and qPCR obtained from three biological replicates with standard deviation of five genes, one constitutively expressed gene (hrdB), two from glycolysis (pyk1, pyk2) and two from gluconeogenesis (ppdk1, ppdk2). Experimental details are in the Materials and Methods section of the associated manuscript. Pyk= pyruvate kinase; PPDK= pyruvate phosphate dikinase; hrdB = housekeeping sigma factor Supplementary Figure 4 Standard curves of each gene for the quantification of transcripts in qPCR experiments A purified PCR product from a PCR for each gene from genomic DNA served as template (Supp. Table 8), this template was diluted to get in total seven different standards ranging from 101-107 molecules. The standard curves were prepared in triplicate and were used to calculate the concentration in the unknown samples and were then compared to the results obtained in the RNA Sequencing. . Pyk= pyruvate kinase; PPDK= pyruvate phosphate dikinase; hrdB = housekeeping sigma factor Supplementary Table Legends Supplementary Table 1 Differential gene expression (DE) and expression category on tween versus glucose in central carbon metabolism showing all genes annotated for the function Legend: green = up, red = down, yellow highlighted genes = expanded gene in Streptomyces, '-' symbol indicates no significant change in expression detected, expression type meanings: I) same direction of change or no change in all genes II) different direction of change in all genesSupplementary Table 2 Specialised metabolite gene clusters with individual genes showing differential expression on Tween and Glucose given SCO number, gene function, fold change and p-valueSupplementary Table 3 Genes involved in fatty acid metabolism and EM-CoA pathway showing differential expression on Tween and Glucose given SCO number, gene function, fold change and p-valueSupplementary Table 4 Regulatory genes showing differential expression on Tween and Glucose given SCO number, gene function, fold change and p-valueSupplementary Table 5 List of all genes showing differential expression on Tween and Glucose given SCO number, gene function, fold change and p-valueSupplementary Table 6 List of all genes showing differential expression on Tween and Glucose given SCO number, normalised fold change (+1 highest increase in expression to -1 highest decrease in expression), fold change and p-valueSupplementary Table 7 GO enrichment analysis (geneontology.org,https://www.ncbi.nlm.nih.gov/pubmed/23868073)Supplementary Table 8 Raw data output from analysis of RNA-Seq data from CLC Genomics Workbench 7.5 Supplementary Table 9 Primer utilised for qPCR specifying for which gene, direction, sequence,melting temperature, amplicon size