Global transcriptome analysis of Clostridium thermocellum ATCC 27405 during growth on dilute acid pretreated Populus and switchgrass

Abstract

Background: The thermophilic anaerobe Clostridium thermocellum is a candidate consolidated bioprocessing (CBP) biocatalyst for cellulosic ethanol production. The aim of this study was to investigate C. thermocellum genes required to ferment biomass substrates and to conduct a robust comparison of DNA microarray and RNA sequencing (RNA-seq) analytical platforms.

Results: C. thermocellum ATCC 27405 fermentations were conducted with a 5 g/L solid substrate loading of either pretreated switchgrass or Populus. Quantitative saccharification and inductively coupled plasma emission spectroscopy (ICP-ES) for elemental analysis revealed composition differences between biomass substrates, which may have influenced growth and transcriptomic profiles. High quality RNA was prepared for C. thermocellum grown on solid substrates and transcriptome profiles were obtained for two time points during active growth (12 hours and 37 hours postinoculation). A comparison of two transcriptomic analytical techniques, microarray and RNA-seq, was performed and the data analyzed for statistical significance. Large expression differences for cellulosomal genes were not observed. We updated gene predictions for the strain and a small novel gene, Cthe_3383, with a putative AgrD peptide quorum sensing function was among the most highly expressed genes. RNA-seq data also supported different small regulatory RNA predictions over others. The DNA microarray gave a greater number (2,351) of significant genes relative to RNA-seq (280 genes when normalized by the kernel density mean of M component (KDMM) method) in an analysis of variance (ANOVA) testing method with a 5% false discovery rate (FDR). When a 2-fold difference in expression threshold was applied, 73 genes were significantly differentially expressed in common between the two techniques. Sulfate and phosphate uptake/utilization genes, along with genes for a putative efflux pump system were some of the most differentially regulated transcripts when profiles for C. thermocellum grown on either pretreated switchgrass or Populus were compared.

Conclusions: Our results suggest that a high degree of agreement in differential gene expression measurements between transcriptomic platforms is possible, but choosing an appropriate normalization regime is essential.

Figure 1

Two-way clustering of normalized RNA-seq and microarray log₂ transformed read counts or probe fluorescent intensities for all genes, respectively, for C. thermocellum grown on switchgrass or Populus 12 hours and 37 hours postinoculation. RNA-seq read counts normalized by RPM, RPKM, KDMM, UQS, or TMM in the JMP Genomics 6 software suite were plotted with the microarray probe fluorescent intensities normalized by the LOESS method. Genes were clustered into a default of ten clusters based on similarity of expression patterns across all transcriptomic platforms and normalization techniques. KDMM, kernel density mean of M component; RNA-seq, RNA sequencing; RPKM, reads per kilobase per million; RPM, reads per million; TMM, trimmed mean of M component; UQS, upper quartile scaling.

Figure 2

Venn diagram of genes identified as significantly (FDR <0.05) differentially expressed (± 1, log₂ scale) by microarray and RNA-seq data normalized by KDMM or RPM. Those genes common between the KDMM and microarray strategies but not present in the RPM analytical strategy are outlined in accompanying table. FDR, false discovery rate; KDMM, kernel density mean of M component; RNA-seq, RNA sequencing; RPM, reads per million.