Redox crisis underlies conditional light-dark lethality in cyanobacterial mutants that lack the circadian regulator, RpaA

Abstract

Cyanobacteria evolved a robust circadian clock, which has a profound influence on fitness and metabolism under daily light-dark (LD) cycles. In the model cyanobacterium Synechococcus elongatus PCC 7942, a functional clock is not required for diurnal growth, but mutants defective for the response regulator that mediates transcriptional rhythms in the wild-type, regulator of phycobilisome association A (RpaA), cannot be cultured under LD conditions. We found that rpaA-null mutants are inviable after several hours in the dark and compared the metabolomes of wild-type and rpaA-null strains to identify the source of lethality. Here, we show that the wild-type metabolome is very stable throughout the night, and this stability is lost in the absence of RpaA. Additionally, an rpaA mutant accumulates excessive reactive oxygen species (ROS) during the day and is unable to clear it during the night. The rpaA-null metabolome indicates that these cells are reductant-starved in the dark, likely because enzymes of the primary nighttime NADPH-producing pathway are direct targets of RpaA. Because NADPH is required for processes that detoxify ROS, conditional LD lethality likely results from inability of the mutant to activate reductant-requiring pathways that detoxify ROS when photosynthesis is not active. We identified second-site mutations and growth conditions that suppress LD lethality in the mutant background that support these conclusions. These results provide a mechanistic explanation as to why rpaA-null mutants die in the dark, further connect the clock to metabolism under diurnal growth, and indicate that RpaA likely has important unidentified functions during the day.

Keywords: circadian clock; cyanobacteria; diurnal; metabolism; metabolomics.

Fig. 1.

Absorbance and viability data from WT and the ΔrpaA mutant. (A) Mean absorbance of WT subtracted from the ΔrpaA mutant at 0 h, immediately before entering darkness. Shaded area indicates SD of mean, significance of difference between WT and ΔrpaA calculated by Student’s t test (n = 8). *P < 0.05; ***P < 0.001. (B) Change in absorbance of WT and ΔrpaA from 0 h immediately before entering darkness to 8 h of dark exposure. Shaded area indicates SD of mean, significance of difference between 0 and 8 h for each strain calculated by Student’s t test (n = 8). *P < 0.05. (C) Mean viable cells counted at time points after WT and the ΔrpaA mutant entered the dark. Error bars indicate SEM. Significance was calculated by using Student’s t test (n = 4).

Fig. S1.

Summary of phenotypic effects of darkness on WT and the ΔrpaA mutant. (A) Representative photographs of WT and ΔrpaA before and after 8 h of dark exposure. Chlorotic bleaching is evident in the ΔrpaA strain after incubation in darkness (A, Lower). (B) Representative data collected from the photobioreactor optical density sensor (900 nm) over the course of an experiment where WT and the ΔrpaA mutant were exposed to darkness. Time is given from the inoculation of photobioreactors, and gray bars indicate 12-h periods of darkness. Turbidostatic growth of both cultures can be observed at ∼72–84 h from the start of the experiment. Both cultures show a decrease in optical density in the final dark period; however, the WT culture resumes growth in the following light period (B, Upper, black arrow), whereas the optical density of the ΔrpaA mutant continues to decrease despite being in a light period (B, Lower, black arrow).

Fig. 2.

Summary of metabolic changes in WT and ΔrpaA. (A) Heatmap showing the autoscaled abundances of all metabolites where a significant difference was detected between WT and ΔrpaA over the time course as analyzed by two-way ANOVA and Tukey’s honest significant difference (n = 4 for WT; n = 5 for ΔrpaA; P < 0.05). Autoscaling represents a Z-score difference from the mean value of the metabolite across all time points. (B) Plot of PLS-DA components 1 and 2 for all metabolomics samples. Components 1 and 2 account for 35.5% of the variance in the dataset and are significant predictors of class membership (Materials and Methods). Ellipses indicate the 95% confidence interval (CI) for each sample grouping (n = 4 for WT; n = 5 for ΔrpaA; 114 metabolites per sample).

Fig. S2.

Comparison of metabolite abundance between WT and ΔrpaA before entering the dark. This volcano plot of metabolites highlights metabolites that show a significant difference in abundance between WT and ΔrpaA at 0 h (named red points) vs. those without a detectable difference (gray points). Dotted lines indicate required thresholds for significance. Metabolites on the right and left sides of the plot were elevated and decreased in ΔrpaA relative to WT, respectively. Significance was calculated using Student’s t test (n = 4 for WT and n = 5 for ΔrpaA), and correction for multiple testing used the method of Benjamini–Hochberg.

Fig. 3.

Metabolic changes in the context of central carbon and nitrogen metabolism. (A) Diagram of relevant reactions in central carbon and nitrogen metabolism. Also included is a model of lipid recycling from photosynthetic membranes (right side). Metabolites are colored based on whether they were elevated (red text) or decreased (blue text) in ΔrpaA relative to WT at some point during the 6-h time course. Data for RpaA gene regulation were taken from Markson et al. (20). Dotted lines indicate that metabolites are linked, but details are not displayed. (B) Plot of AKG abundance in ΔrpaA and WT across the metabolic time course. Error bars indicate SD (n = 4 for WT; n = 5 for ΔrpaA).

Fig. S3.

Changes in expression of glnN. Relative expression levels of the glnN transcript at 0 h (before entering darkness) and 2 h after exposure to darkness as measured by qRT-PCR. Log₂ values were calculated relative to WT at 0 h. +MSM sample indicates ∆rpaA treated with 25 µM MSM for a 12-h period in the light before entering the dark. Error bars indicate SEM. Significance calculated using one-way ANOVA and Tukey’s HSD (n = 3). *P < 0.05; **P < 0.01. Additionally, there was no significant difference between ∆rpaA treated with MSM, a WT control treated with MSM, and WT untreated with MSM at any time point, indicating that transcript abundance of glnN was not generally affected by MSM treatment.

Fig. S4.

Supporting data for EMS mutagenesis of the ΔrpaA mutant. (A) A representative photo of an older ΔrpaA culture that was plated as a serial dilution and grown in an LD cycle. Black arrows indicate ΔrpaA colonies showing robust growth even under normally restrictive LD conditions. (B) Photographs of plates of unmutagenized and EMS-mutagenized ΔrpaA cultures after incubation in an LD cycle for 15 d. The photographs show that hundreds of colonies form on the EMS-mutagenized ΔrpaA plate (B, Right), but not on the plate that received unmutagenized cells (B, Left). (C) PCR amplification of the rpaA locus from ΔrpaA cells carrying second-site mutations and from WT controls. The recombination at the rpaA locus with the pAM4420 vector to produce the ΔrpaA strain results in an expected amplified fragment of 1.8 kb. This region is larger than the 1.2-kb amplicon expected from WT cells. Comparison with amplification from WT cells (white arrows) shows that all ΔrpaA strains tested in this study produce the expected increased amplicon size for a strain that carries the ΔrpaA mutation. Additionally, no WT-size bands are present in the ΔrpaA strains, indicating that the mutation is fully segregated.

Fig. 4.

Summary of enriched KEGG functional categories identified by suppressor mutations and metabolic pathway topology represented by mutated genes. (A) Plot of KEGG metabolic categories that were enriched in the gene set of suppressor mutations. The x axis indicates the number of times a specific KEGG pathway was matched to genes in the set; dots scale from small to large with increasing number of matches, and color of dots scales from yellow to red with increasing significance. Significance was calculated by using the binomial distribution, corrected for multiple testing using the method of Benjamini–Hochberg, and significance cutoff is indicated with a gray dotted line (FDR < 0.05). (B) Subpathway diagram of VLI biosynthesis indicating locations of ΔrpaA suppressor mutations and average abundance of compounds in the ΔrpaA strain relative to WT over the metabolomics time course. Genes were named for reactions where a suppressing mutation was identified, and colors are detailed in the key.

Fig. 5.

Summary of data on MSM- and light intensity-mediated suppression of the ΔrpaA LD lethality phenotype. (A) Representative photo of dilution series of WT and ΔrpaA cells treated (Left) and not treated (Right) with 100 nM MSM (n = 6 biological replications of experiment). Pictured samples were grown in an LD cycle with a light intensity of 120 μE⋅m⁻²⋅s⁻¹. (B) Difference in absorbance of ΔrpaA cells treated with 25 μM MSM between 0 and 8 h after dark exposure. Shaded region indicates SD of mean, and black arrow points to absorbance at 630 nm highlighting no significant change. Significance was calculated by using Student’s t test for absorbance at 440, 630, and 680 nm, with no significant change observed (n = 3). (C) Mean absorbance values of WT and ΔrpaA untreated (solid line) and treated (dotted line) with 25 μM MSM after 12 h in the light. Shaded area indicates SEM. MSM-exposed cells show significantly lower absorption values at 440, 630, and 680 nm, as calculated by a one-sided Student’s t test (n = 3 for MSM treated samples and n = 4 for untreated samples). *P < 0.05. (D) Representative photo of dilution series of WT and ΔrpaA cells grown in an LD cycle with decreasing daytime light intensity (indicated below each image; n = 2).

Fig. S5.

Supporting data for potential mechanisms that suppress LD lethality in the ΔrpaA mutant. (A) Representative data collected from the photobioreactor optical density sensor (900 nm; y axis) over the course of an experiment where ΔrpaA mutants were exposed to darkness and not treated (Upper) or treated (Lower) with MSM. Time (x axis) starts at inoculation of photobioreactors, gray bars indicate 12-h periods of darkness, and light intensity during the light periods is noted with black text. The black arrow in the bottom panel indicates when 25 μM MSM was added to one culture. The ΔrpaA mutant receiving MSM was able to continue growth even after an LD cycle (Lower) that was lethal to the ΔrpaA mutant not receiving MSM (Upper). (B) Photographs of EMS-mutagenized ΔrpaA strains before genomic DNA extraction. Strains are organized by the type of mutation they were found to carry, and only strains with mutations that affected amino acid metabolism in some way are included. The general metabolic pathway affected is indicated above each photograph panel and the specific EMS mutant number is indicated below each test tube (Dataset S2). The photos highlight the altered pigmentation that is present in these strains relative to WT cells.

Fig. S6.

Controls for LD plating and ROS experiments. (A) Serial dilutions of WT and ΔrpaA grown at a light intensity of 150 μE·m⁻²·s⁻¹ using 24-h LL (Upper) and 12 h:12 h LD (Lower) light regimes for 7 d. These data show the ability of ΔrpaA to tolerate high light conditions as long as a 24-h constant light regime is used. (B) Plot of H₂DCFDA fluorescence over a 24-h LD cycle indicating total cellular ROS in WT untreated (solid line) and treated (dashed line) with 25 μM MSM. Curves are best-fit lines calculated by using LOESS regression to all data points in a given sample; the gray-shaded area indicates the 95% CI of the regression line (n = 21 data points for day samples; n = 42 data points for night samples). The data show that treatment of WT with 25 μM MSM does not significantly affect levels of ROS over the 24-h LD cycle.

Fig. 6.

Plot of H₂DCFDA fluorescence over a 24-h LD cycle indicating total cellular ROS in WT, ΔrpaA, and ΔrpaA treated with 25 μM MSM. Cells were grown at a light intensity in bioreactors empirically determined to support ΔrpaA growth in LD, and the experiment began after a 12-h dark period for all cells. Curves shown are best-fit lines calculated using LOESS regression to all data points in a given sample; the gray shaded area indicates the 95% CI of the regression line (n = 21 data points for day samples; n = 42 data points for night samples). Day and night from the same experiment were split to more effectively fit regressions. Places where the CI does not overlap indicate a statistically significant difference in the model.

Fig. S7.

Diagram of the growth and sampling scheme used for experiments conducted in photobioreactors. Each box indicates a 12-h period with white boxes corresponding to periods of light and black boxes corresponding to periods of darkness. Where indicated, only the WT strain was exposed to darkness. Light intensity during each light period is indicated in black text within each white box. Times when samples were taken during the metabolomics experiment are indicated by red arrows. Red arrows correspond to time points ZT12 (0 h), ZT13 (1 h), ZT14 (2 h), ZT16 (4 h), and ZT18 (6 h), from left to right.

Fig. S8.

Supporting data for metabolomics statistical analysis. (A) Plot of PCA components 1 and 2 for log₂ autoscaled metabolite abundance data for all WT samples. Ellipses indicate the 95% CI for each sample group. The black arrow indicates all time points collected for WT biological replicate A. The statistically significant separation of the WT_A replicate indicates that this sample is an outlier relative to the other WT samples collected. (B) Plot showing result from LOOCV performed on the PLS-DA model. The red star indicates the accuracy of the model is highest when it includes only two components. Due to a high degree of variability in the data, which is typical of metabolomics datasets, we chose prediction accuracy as a metric to select a number of components over other metrics such as model fit (Q2). (C) Plot showing the results of accuracy permutation testing on the PLS-DA model. The red arrow indicates the test statistic. The data indicate that our PLS-DA model is significantly better at predicting class membership than a random model (P < 0.01; n = 1,000 permutations).