Adaptive evolution of hybrid bacteria by horizontal gene transfer

Abstract

Horizontal gene transfer (HGT) is an important factor in bacterial evolution that can act across species boundaries. Yet, we know little about rate and genomic targets of cross-lineage gene transfer and about its effects on the recipient organism's physiology and fitness. Here, we address these questions in a parallel evolution experiment with two Bacillus subtilis lineages of 7% sequence divergence. We observe rapid evolution of hybrid organisms: gene transfer swaps ∼12% of the core genome in just 200 generations, and 60% of core genes are replaced in at least one population. By genomics, transcriptomics, fitness assays, and statistical modeling, we show that transfer generates adaptive evolution and functional alterations in hybrids. Specifically, our experiments reveal a strong, repeatable fitness increase of evolved populations in the stationary growth phase. By genomic analysis of the transfer statistics across replicate populations, we infer that selection on HGT has a broad genetic basis: 40% of the observed transfers are adaptive. At the level of functional gene networks, we find signatures of negative, positive, and epistatic selection, consistent with hybrid incompatibilities and adaptive evolution of network functions. Our results suggest that gene transfer navigates a complex cross-lineage fitness landscape, bridging epistatic barriers along multiple high-fitness paths.

Keywords: experimental evolution; fitness landscape; horizontal gene transfer.

Fig. 1.

Evolution of B. subtilis hybrids. (A) Experimental design. A single 2-d (10 generation) cycle contains six steps, including two growth phases with irradiation and HGT, respectively. (B) Transferred segments (green) are broadly distributed over the recipient core genome (light shading) with two hot spots (red) and two cold spots (blue). De novo mutations occur throughout the recipient genome, and there are a few deletions from the recipient genome (orange). Data are shown for run four; see SI Appendix, Fig. S3 and Datasets S1 and S2 for data from all runs and from intermediate time points. (C) Time dependence of HGT. Fraction of core genes (light green) and of core genome (dark green) affected by HGT after 9, 15, and 21 cycles (mean and SD across the seven parallel runs). (D) Length histogram of transferred genome segments (bars: count histogram, black line: exponential fit for segments > 1,000 bp). Inset: number of segments versus time (mean and SD across runs). (E) Inferred distribution of donor–recipient sequence divergence, $d$, in 100 bp windows around the recombination start site of transferred segments (green) and in scrambled 100 bp windows (gray). (F) Distribution of the transfer frequency per gene, $\widehat{Q}\left(\text{θ}\right)$, in the seven parallel runs, counted across the recipient core genome (green); corresponding distribution $P_0\left(\text{θ}\right)$ from simulations of the null model (gray).

Fig. 2.

Selective effects of HGT. (A) Transfer affects hybrid fitness. Selection coefficient of hybrids compared to the ancestral recipient strain in the exponential phase and in the stationary phase (colored dots: data from individual runs, bars and boxes: mean and SD over all seven runs); control data of runs without donor DNA (gray). (B) Genome-wide selection on transfer. The relative likelihood of transfer, $\hat{Q}\left(\text{θ}|p_0\right)/P_0\left(\text{θ}|p_0\right)$, is shown for genes binned by transfer frequency (eight bins, $\text{θ}=0/7,\hspace{0.17em}1/7,\dots ,\hspace{0.17em}7/7$) and by local sequence similarity, $p_0$ (four bins, $p_0$ values are averages in each bin). The underlying count histograms are shown in SI Appendix, Fig. S5. (C) The relative likelihood of transfer aggregated over $p_0$ bins, $\widehat{Q}\left(\text{θ}\right)/P_0\left(\text{θ}\right)$, is shown together with the corresponding likelihood ratio $Q^{\ast }\left(\text{θ}\right)/P_0\left(\text{θ}\right)$ of the maximum likelihood selection model (green circles); see SI Appendix. Relative to the neutral null model (gray baseline), the selection model has an enhanced transfer probability $\left(p_{+}/p_0=\hspace{0.17em}1.9\right)$ in a fraction $c\hspace{0.17em}=\hspace{0.17em}0.2$ of the recipient core genes and a reduced probability $\left(p_{-}/p_0\hspace{0.17em}=\hspace{0.17em}0.75\right)$ in the remainder of core genes.

Fig. 3.

Gene expression in hybrids. Log₂ RNA fold changes, $\mathrm{\Delta }R$, with respect to the ancestral recipient (whisker plots, blue line: mean, box: first and third quartiles, bars: 99% percentiles, dots: outliers) are shown for different gene classes: all genome, genes with low transfer frequency $\left(0\le \theta \le 3/7\right)$, and genes with high transfer frequency $\left(4/7\le \theta \le 1\right)$; cf. Fig. 1F. In each gene class, we show $\mathrm{\Delta }R$ whisker plots separately for nontransferred recipient genes (R; orange) and for transferred orthologous donor genes (D; green), together with the corresponding changes in the control experiments without donor DNA (0; gray). For example, a gene subject to HGT in replicates one, two, and five is in the low transfer class; its $\mathrm{\Delta }R$ values from replicates 1, 2, 5 (3, 4, 6, 7) contribute to the low/D (low/R) statistics. Asterisks mark highly significant changes of the average $\mathrm{\Delta }R$ for up-regulation of high/D and high/R genes ($P<10$⁻³, t test) compared to the ancestor.

Fig. 4.

Cross-lineage selection in gene networks. (A) Cross-lineage fitness landscapes of the form of Eq. 2 with different parameters $\left(a,b\right)$ contain predominantly negative directional selection ($a<0$, orange), predominantly positive directional selection ($a>0$, magenta), disruptive epistasis due to hybrid incompatibilities ($b<0$, blue), stabilizing epistasis due to advantageous cross-lineage combinations ($b>0$, dark blue), and approximately neutral evolution (gray). (B) Posterior fitness parameters $\left(a^{\ast },\hspace{0.17em}{b}^{\ast }\right)$ inferred for complex operons, colored by predominant selection component. (C–G) Examples of operons with different types of maximum likelihood cross-lineage selection. (Left) Protein–protein interaction link diagram, darker lines indicating links with stronger support (35). (Center) HGT trajectories recorded at cycle 9, 15, and 21 for seven experimental runs (solid lines) and 40 simulation runs (dashed). Dots and error bars mark the transfer frequency after cycle 21, $\theta$, and the SD of experimental and simulation runs, respectively. (Right) Measured values $\left(\theta ,h\right)$ (green dots) and the corresponding distribution from simulations of the selection model, $Q\left(\theta ,h|a^{\ast },b^{\ast }\right)$. (C) Negative directional selection (iol). (D and E) Positive directional selection (leu, eps). (F and G) Disruptive epistasis, hybrid incompatibilities (rps, ylo). See also Dataset S5.