Growth condition-dependent differences in methylation imply transiently differentiated DNA methylation states in Escherichia coli

Abstract

DNA methylation in bacteria frequently serves as a simple immune system, allowing recognition of DNA from foreign sources, such as phages or selfish genetic elements. However, DNA methylation also affects other cell phenotypes in a heritable manner (i.e. epigenetically). While there are several examples of methylation affecting transcription in an epigenetic manner in highly localized contexts, it is not well-established how frequently methylation serves a more general epigenetic function over larger genomic scales. To address this question, here we use Oxford Nanopore sequencing to profile DNA modification marks in three natural isolates of Escherichia coli. We first identify the DNA sequence motifs targeted by the methyltransferases in each strain. We then quantify the frequency of methylation at each of these motifs across the entire genome in different growth conditions. We find that motifs in specific regions of the genome consistently exhibit high or low levels of methylation. Furthermore, we show that there are replicable and consistent differences in methylated regions across different growth conditions. This suggests that during growth, E. coli transiently differentiate into distinct methylation states that depend on the growth state, raising the possibility that measuring DNA methylation alone can be used to infer bacterial growth states without additional information such as transcriptome or proteome data. These results show the utility of using Oxford Nanopore sequencing as an economic means to infer DNA methylation status. They also provide new insights into the dynamics of methylation during bacterial growth and provide evidence of differentiated cell states, a transient analog to what is observed in the differentiation of cell types in multicellular organisms.

Keywords: E coli; DNA methylation; nanopore.

Fig. 1.

Experimental design for sampling native (possibly modified) and unmodified DNA. To sample native DNA, we grew cultures until exponential phase (for the minimal M9 media, rich LB media, 42°C and 25°C growth conditions); or late stationary phase (for the 96-h growth condition). For whole genome amplification, we isolated DNA from early stationary phase (24 h of growth). After purification of genomic DNA (and whole-genome amplification when necessary), we sequenced the samples using the ONT platform. To infer DNA modifications, we compared the signals from native and WGA DNA using Nanodisco.

Fig. 2.

The P-values resulting from Mann–Whitney U tests for signal deviations at DAM and DCM sites are correlated with the fraction of methylated molecules. We mixed known fractions of WGA reads (unmethylated) and native reads (possibly methylated) in silico and used Nanodisco to determine the P-value of a Mann–Whitney U test at each position in the genome. We then determined the lowest P-value in a 3-bp window surrounding each hypothetically modified base in DAM (GATC) or DCM (GGCC) motif. For both methyltransferases, the sensitivity of the test increases as the fraction of native reads increases, with the DCM P-values decreasing to a much larger extent.

Fig. 3.

The fraction of DAM 6 mA and DCM 5 mC methylated sites within 10-Kbp windows varies according to strain and growth condition. The histograms in each panel indicate the distribution of 10-Kbp windows in which a certain fraction of sites are DAM (left panel) or DCM (right panel) methylated. This fraction ranges from almost 100% of all sites in all windows (e.g. for SC419 DCM in the 42°C growth condition) to less than 50% of all sites in the majority of windows (e.g. for SC469 DAM in the 42°C growth condition). With the exception of the LB-rich media sample, all cultures were grown in M9 minimal glucose media.

Fig. 4.

Top row: The fraction of methylated sites in 10-Kbp windows across the genome is correlated across growth conditions. The three panels indicate the fraction of methylated DCM sites within a 10-Kbp window that we inferred as methylated for strain SC469. We observed strong positive correlations in methylation patterns in replicate cultures of minimal M9 glucose media, slightly weaker correlations between M9 media and 96-h stationary phase cultures, and almost no correlation between patterns in rich LB media and 96 h of stationary phase. Pearson partial correlations and corresponding P-values are indicated in each plot. Middle and bottom rows, respectively: Pairwise partial correlations in DAM and DCM methylation patterns between all growth environments accounting for genome coverage. Each panel shows all pairwise Pearson partial correlations between growth conditions in the fraction of methylated sites for all 10-Kbp windows in the genome, controlling for genome and WGA coverage in each of the growth conditions.

Fig. 5.

Genome-wide patterns in the fraction of methylated sites. Each panel shows the fraction of methylated sites in 10-Kbp windows across the entire genome, with different growth conditions indicated in different colours. No strong long-range correlations, such as higher methylation at the replication terminus, were apparent (although see main text).