Selective Translation of Low Abundance and Upregulated Transcripts in Halobacterium salinarum

Abstract

When organisms encounter an unfavorable environment, they transition to a physiologically distinct, quiescent state wherein abundant transcripts from the previous active growth state continue to persist, albeit their active transcription is downregulated. In order to generate proteins for the new quiescent physiological state, we hypothesized that the translation machinery must selectively translate upregulated transcripts in an intracellular milieu crowded with considerably higher abundance transcripts from the previous active growth state. Here, we have analyzed genome-wide changes in the transcriptome (RNA sequencing [RNA-seq]), changes in translational regulation and efficiency by ribosome profiling across all transcripts (ribosome profiling [Ribo-seq]), and protein level changes in assembled ribosomal proteins (sequential window acquisition of all theoretical mass spectra [SWATH-MS]) to investigate the interplay of transcriptional and translational regulation in Halobacterium salinarum as it transitions from active growth to quiescence. We have discovered that interplay of regulatory processes at different levels of information processing generates condition-specific ribosomal complexes to translate preferentially pools of low abundance and upregulated transcripts. Through analysis of the gene regulatory network architecture of H. salinarum, Escherichia coli, and Saccharomyces cerevisiae, we demonstrate that this conditional, modular organization of regulatory programs governing translational systems is a generalized feature across all domains of life.IMPORTANCE Our findings demonstrate conclusively that low abundance and upregulated transcripts are preferentially translated, potentially by environment-specific translation systems with distinct ribosomal protein composition. We show that a complex interplay of transcriptional and posttranscriptional regulation underlies the conditional and modular regulatory programs that generate ribosomes of distinct protein composition. The modular regulation of ribosomal proteins with other transcription, translation, and metabolic genes is generalizable to bacterial and eukaryotic microbes. These findings are relevant to how microorganisms adapt to unfavorable environments when they transition from active growth to quiescence by generating proteins from upregulated transcripts that are in considerably lower abundance relative to transcripts associated with the previous physiological state. Selective translation of transcripts by distinct ribosomes could form the basis for adaptive evolution to new environments through a modular regulation of the translational systems.

Keywords: archaea; proteomics; ribosome heterogeneity; ribosome profiling; selective translation; transcription-translation interplay; transcriptomics; translational regulation.

FIG 1

Coupled transcription-translation regulation across growth phase. (A) Scatterplot of relative changes in transcript abundance (log₂ FC mRNA) and ribosomal footprints (log₂ FC footprints) in stationary phase (TP4) with respect to early exponential phase (TP1). Black dots (TC; n = 875) represent genes regulated at the transcriptional level only. Red (n = 79) and blue (n = 110) dots represent genes under positive (+) and negative (-) compensatory mechanisms, respectively (COMP). Orange (n = 15) and green (n = 24) dots represent genes translationally regulated only (TL). Yellow dots represent genes that are not transcriptionally or translationally regulated (NR; n = 304). (B) Linear regression of transcript abundance (x axis; log₁₀ TPM + 1) and TE (y axis; log₂ Ribo-seq/RNA-seq ratio). Slope, a = −1.10; correlation coefficient R = −0.52; P < 10⁻¹³². The gray area represents the 95% confidence interval. (C) Regression analysis of predicted ribosomal footprints from transcript expression at different growth phases. (D) Deviation distributions from the expected TE given expression (y axis) in the context of transcriptional regulation across growth phase (stationary versus early exponential; TP4 versus TP1). Transcriptionally upregulated genes are shown in red, downregulated genes in blue, nondifferentially expressed (non-DET) genes in white, and all genes in gray. The horizontal axis shows expression levels (x): low, 10 < x ≤ 100; medium, 100 < x ≤ 1,000; high, 1,000 < x ≤ 10,000. The number of transcripts of each boxplot (n) is shown at the top. Asterisks indicate significance: ns, nonsignificant; *, P < 0.05; **, P < 0.01.

FIG 2

Ribosome abundance and composition shifts across growth-related physiological states. (A) Relative RP abundance changes (log₂ FC) with respect to early exponential phase (TP1). Protein detection ranged from n = 49 to n = 50 RPs across samples. (B) Ribosome composition (log₂ RP stoichiometry ratio) changes across growth phase. Small white dots represent RP stoichiometry values, and small black dots represent values outside the 95% confidence interval (95% CI). Large colored dots highlight RPs with two or more deviation events outside the 95% CI threshold. Dashed colored lines assist picturing the trend across time. White horizontal bars inside violin plots indicate 95% CIs.

FIG 3

RP genes are transcriptionally and translationally regulated. (A) Scatterplot of relative abundance changes with respect to TP1. Each dot represents median log₂ FC over three biological replicates for a given RP gene. Colors map to time points. (B) Scatterplot of expression and relative change of RP gene abundance (RNA-seq; left) and ribosomal footprints (Ribo-seq; right) at time point TP4 compared to TP1. (C) Scatterplot of absolute transcript and footprint abundances measured across growth phase. Each dot represents the median log₂ normalized counts over three biological replicates for a given RP transcript. Colors map to time points. R, correlation coefficient; a, slope. (D) Scatterplot and regression model of RP transcript and footprint abundance changes in time point TP4 compared to TP1. The black line represents regression model, the dark gray area represents regression confidence interval, and the light gray area represents the regression prediction interval. The red dot highlights rpl14p outside the prediction interval. R, correlation coefficient; a, slope.

FIG 4

RP genes are organized into distinct functional coregulated classes. (A) Bootstrapped hierarchical clustering of RP genes based on corem membership identified four classes, including three principal classes plus three outlier genes, joined together as class IV. Classes are boxed and colored with red, green, and blue, and outlier genes in magenta. (B) RP genes are depicted on the y axis versus corems on the x axis. Dark gray squares indicate the presence of a particular gene in a given corem. The RP genes are arranged and colored by class on the right side. Corems comprise both neighboring genes in operons but also distal genes. (C) RPs are depicted in the ribosomal 3D structure following the color scheme of the functional classes as shown in panel A. Functional classes of RPs do not follow a restricted pattern of physical interactions, indicative of functional specialization of the ribosome due to coregulation in different environments, rather than coexpression derived from physical interactions at the protein level. Gray sections represent rRNA molecules. Subunits excluded (S12P, S19E, S24E, and S27AE) from the clustering analysis because they were not present in any corem are depicted in orange.

FIG 5

RP genes are coregulated in a condition-specific manner. (A) Expression distribution for two representative examples of the 72 ribosomal corems identified. Vertical bars represent interquartile range (third quartile [Q₃] to first quartile [Q_1]) of expression across genes. n indicates the number of genes in each corem, whereas m refers to the subset that encode RPs. Colors correspond to different environmental conditions (see key). Conditions are ranked based on median expression. Gray background bars correspond to average expression distribution of 10,000 permutations of randomly selected gene sets of the same size as the corem. Note the larger interquartile range of background distributions. (B) Corem similarity based on gene expression. We used uniform manifold approximation and projection (UMAP [77]) to visualize 62 ribosomal corems in a two-dimensional space from an original high-dimensional space of 1,495 environmental conditions. Each dot represents a corem. Color maps to functional classes. (C) Similarity matrix between ribosomal corem classes broken into nine different broad condition categories (n, number of experimental conditions). Similarity values across diagonals may not reach one, as we defined class similarity as the proportion of class corems with no significant expression differences. (D) Promoter architecture of rps13p (VNG1132G) deciphered by MAST (78) alignments of cis-regulatory motifs from each GRE, in this case GRE #7, #9, and #28 discovered by MEME (79) in gene promoters of EGRIN biclusters that include rps13p. The heights of the histograms are proportional to the frequency of GRE alignments to the VNG1132G promoter. (E) Hierarchical clustering of corems enriched in RP genes based on relative importance of each GRE (log₁₀ GRE counts) coregulating corem members. Red and blue bars correspond to class I and III, respectively. (F) Motif sequence logos for two representative GREs.

FIG 6

RPs physically interact with transcription complex components. Diamonds represent RPs; squares represent transcription complex components. Tagged proteins used as bait in the immunoprecipitation experiment are highlighted by a black border. Arrowheads link bait to coimmunoprecipitated proteins. We labeled each of the seven modules obtained by the Newman-Girvan clustering algorithm using a different color.

FIG 7

Synthesis figure. Growth phase H. salinarum transcriptome abundance, ribosomal footprints, and proteome quantification and analysis identified key points of translational regulation. TLR, translational regulation; TE, translational efficiency; TCR, transcriptional regulation; PPI, protein-protein interaction; OD, optical density; RP, ribosomal protein.