Every bacterium races to become bacteria, and the one with the quickest strategy will emerge the victor. The species Escherichia coli is a case in point. It can continuously double without intermission, and counterintuitively, this bug has evolved to divide faster than it can replicate its genome.1 Given that E. coli pushes its replication into overdrive, scientists hypothesized that this might interfere with another process that runs without rest: gene expression. However, scientists struggled to study replication’s effects on gene expression because the bugs in a bacterial population are like shift workers in a sleepless city.2 Each one occupies a different stage in the cell cycle, producing too much noise to sift out patterns.
Now, reporting in Nature, researchers leveraged a technique that measures gene expression within the individual bacterium, allowing them to focus the analysis on single cells.3 The patterns that emerged from the data revealed how these bustling bugs regulate their genomes.
Microbiologist and study coauthor Andrew Pountain, who is a member of Itai Yanai's systems biology research team at New York University, recognized that at any given time, an individual bacterium expresses a distinct set of genes that could get lost in his analysis if he averaged across all members of the population, guiding him toward single-cell transcriptomics.4 Yet, Pountain said, “The raw data that you work with is terrible” because it’s still very noisy.
“It’s as though someone is telling you to recreate a movie, but they’re giving you the frames individually, and each frame is extremely shadowy or blurry,” said Yanai. The team had to find a way to clean up and align the frames to produce a coherent biopic about the bacterium’s gene expression trends.
To uncover potential links between replication and gene expression, the team developed a novel way to organize the data that they collected from unsynchronized cells that they froze in time using formaldehyde. They hypothesized that if replication affects gene expression, it will do so in the order it copies the genes along the chromosome. While sorting through the data in search of patterns that linked gene expression to the gene’s chromosomal position, suddenly all chaos turned to order. “If you impose a structure on it, which is this replication-driven cell cycle structure, then suddenly it goes from being incredibly noisy and very difficult to incredibly logical,” Pountain said.
E. coli has a circular chromosome, but replication doesn’t occur clockwise around the chromosome. Instead, it simultaneously replicates two 180° segments in half the time, both emanating from the same 12 o’clock starting point. One segment replicates in the clockwise direction while the other does so counterclockwise, connecting up at the six o’clock region.5
The team noticed a similar pattern for gene expression: Genes located on opposite segments but at equal distances from the 12 o’clock starting point expressed at the same time. These findings suggested that those genes shared an activation cue that was timed by the synchronized replication of the two segments.
Other expression patterns in the data hinted at replication’s influence. E. coli doesn’t take a break in between one round of replication and the next. At the same time the two segments merge at the six o’clock finish line, a new wave of replication begins at the 12 o’clock starting point. Similarly, they found that genes near the start and finish switched on at the same time, revealing that replication controlled their expression.
Finally, the noisy gene expression data collected from bacterial populations made sense, though it came as a surprise to the team. “We never really expected to find a system that was much more general and much more global in scope,” Pountain said.
Kuanwei Sheng, a systems biologist at Harvard University who was not involved with the work, said, “There are several studies demonstrating, for example, a single gene that is highly affected by replication,” but he noted that this is the first demonstration that these effects are systemic.
Having shown that replication influences overall gene expression, the team next generated graphs showing expression patterns for single genes, which they named transcription-replication interaction profiles (TRIP). These graphs revealed changes in the timing and level of gene expression that occurred in tandem with replication cycles.
Pountain likened TRIP to an electrocardiogram (ECG). In the same way that the waves of an ECG carry information about heart health, the curves on a TRIP reveal how replication switches a gene on and off. The research team still needs to decode the TRIP for each gene to understand how much replication controls its expression versus other regulators, like transcription factors.
“It’s like we discovered this new dictionary, but we don’t know what each word means yet,” Yanai said. With time, they aim to define the various TRIP in the genome.
The team also found similar gene expression trends in Staphylococcus aureus, a distantly related species, suggesting that the process might be conserved across bacteria. However, “how much you can extrapolate to [other bacteria] is currently unknown. I think that’s worth further study,” Sheng added.
Whether archaea or eukaryotes have a similar process also remains an open question. “Eukaryotic cells are much more complicated in different ways,” Sheng said. They have multiple, larger chromosomes, multiple origins of replication on each chromosome, and some regions of the DNA are more tightly packaged than others, so this form of gene regulation might be strictly prokaryotic. Even so, “Bacteria are important pathogens for us to deal with,” Yanai said. “This tool could be of a big biomedical relevance just there alone.”
- Youngren B, et al. The multifork Escherichia coli chromosome is a self-duplicating and self-segregating thermodynamic ring polymer. Genes Dev. 2014;28(1):71-84.
- Thanbichler M. Synchronization of chromosome dynamics and cell division in bacteria. Cold Spring Harb Perspect Biol. 2010;2(1).
- Pountain AW, et al. Transcription–replication interactions reveal bacterial genome regulation. Nature. 2024;626(7999).
- Blattman SB, et al. Prokaryotic single-cell RNA sequencing by in situ combinatorial indexing. Nat Microbiol. 2020;5(10):1192-1201.
- Rudolph C, et al. Avoiding and resolving conflicts between DNA replication and transcription. DNA Repair. 2007;6(7):981-993.
