Transcriptional Profiling of Methyltransferase Genes during Growth of Methanosarcina mazei on Trimethylamine

The genomic expression patterns ofMethanosarcina mazei growing with trimethylamine were measured in comparison to those of cells grown with methanol. We identified a total of 72 genes with either an increased level (49 genes) or a decreased level (23 genes) of mRNA during growth on trimethylamine with methanol-grown cells as the control. Major differences in transcript levels were observed for themta ,mtb ,mtt , andmtm genes, which encode enzymes involved in methane formation from methanol and trimethylamine, respectively. Other differences in mRNA abundance were found for genes encoding enzymes involved in isopentenyl pyrophosphate synthesis and in the formation of aromatic amino acids, as well as a number of proteins with unknown functions. The results were verified by in-depth analysis of methyltransferase genes using specific primers for real-time quantitative reverse transcription-PCR (RT-PCR). The monitored transcript levels of genes encoding corrinoid proteins involved in methyl group transfer from methylated C1 compounds (mta C,mtb C,mtt C, andmtm C) indicated increased amounts of mRNA from themtaBC1 ,mtaBC2 , andmtaBC3 operons in methanol-grown cells, whereas mRNA of themtb1-mtt1 operon was found in high concentrations during trimethylamine consumption. The genes of themtb1-mtt1 operon encode methyltransferases that are responsible for sequential demethylation of trimethylamine. The analysis of product formation of trimethylamine-grown cells at different optical densities revealed that large amounts of dimethylamine and monomethylamine were excreted into the medium. The intermediate compounds were consumed only in the very late exponential growth phase. RT-PCR analysis of key genes involved in methanogenesis led to the conclusion thatM. mazei is able to adapt to changing trimethylamine concentrations and the consumption of intermediate compounds. Hence, we assume that the organism possesses a regulatory network for optimal substrate utilization.