Efforts to Minimise the Bacterial Genome as a Free-Living Growing System

Abstract

Exploring the minimal genetic requirements for cells to maintain free living is an exciting topic in biology. Multiple approaches are employed to address the question of the minimal genome. In addition to constructing the synthetic genome in the test tube, reducing the size of the wild-type genome is a practical approach for obtaining the essential genomic sequence for living cells. The well-studied Escherichia coli has been used as a model organism for genome reduction owing to its fast growth and easy manipulation. Extensive studies have reported how to reduce the bacterial genome and the collections of genomic disturbed strains acquired, which were sufficiently reviewed previously. However, the common issue of growth decrease caused by genetic disturbance remains largely unaddressed. This mini-review discusses the considerable efforts made to improve growth fitness, which was decreased due to genome reduction. The proposal and perspective are clarified for further accumulated genetic deletion to minimise the Escherichia coli genome in terms of genome reduction, experimental evolution, medium optimization, and machine learning.

Keywords: culture medium; experimental evolution; genome reduction; growth fitness; machine learning; minimal genome.

Figure 1

Schematic drawing of genome reduction. A reduced genome is constructed by removing redundant genomic sequences from the parent genome (wild-type genome).

Figure 2

Coordination of genome size to growth and mutation rates. (A) Contribution of genome reduction to fitness and evolvability. The changes in growth and mutation rates caused by the genome reduction are indicated with arrows. (B) Relative values of genome, growth, and mutation. Gray gradation indicates the variation in growth media. The scatter plots are newly made using previously reported data [37,38]. The panels from left to right represent the nutritional richness of culture media from poor to rich.

Figure 3

An overview of experimental evolution. (A) Experimental evolution. Repeated culture and dilution are performed with the ancestor to acquire the evolved genome with an improved growth rate. (B) Temporal changes in growth rate during experimental evolution. (C) Growth rate changes between the ancestor and evolved E. coli cells. Dark and light circles represent the full-length and reduced genomes, respectively. The previously reported data [49] were used to make the graph.

Figure 4

Changes caused by genome reduction and rescued via experimental evolution. Genome reduction causes decreased growth fitness and increased mutation rate (left panel), which is restored via experimental evolution (right panel). The transcriptome architecture maintains homeostasis regardless of genome reduction or experimental evolution.

Figure 5

Compensation of medium composition to gene loss. The wild-type strain (full-length genome) grows regularly (a). Gene loss caused by genome reduction leads to nutritional auxotrophs when the genes are essential (b). However, once the component (C) in response to the absent metabolite (C) is supplied in the medium, the growth fitness recovers (c). Decreases in growth fitness associated with the accumulation of genetic deletions can be avoided by changes in environmental conditions, i.e., medium optimization (right panel).

Figure 6

Scheme of machine learning-assisted medium optimization. The learning and prediction processes are illustrated in the upper and bottom boxes, respectively. The learning process constructs a model by training the machine learning algorithm with the experimental dataset that links medium combinations to growth fitness. The prediction process outputs the predicted growth fitness using the constructed model by inputting the novel data generated artificially.

Figure 7

Proposed future challenges. (A) Active learning. Repeated experimental tests and machine learning improve prediction accuracy and advanced medium or genome combinations. (B) Blueprint for a minimal cell. Combination of genetic and environmental information benefits.