Spurious regulatory connections dictate the expression‐fitness landscape of translation factors
2021; Springer Nature; Volume: 17; Issue: 4 Linguagem: Inglês
10.15252/msb.202110302
ISSN1744-4292
AutoresJean‐Benoît Lalanne, Darren J. Parker, Gene‐Wei Li,
Tópico(s)Genomics and Phylogenetic Studies
ResumoArticle26 April 2021Open Access Transparent process Spurious regulatory connections dictate the expression-fitness landscape of translation factors Jean-Benoît Lalanne orcid.org/0000-0001-8753-0669 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Darren J Parker Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Gene-Wei Li Corresponding Author [email protected] orcid.org/0000-0001-7036-8511 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Jean-Benoît Lalanne orcid.org/0000-0001-8753-0669 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Darren J Parker Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Gene-Wei Li Corresponding Author [email protected] orcid.org/0000-0001-7036-8511 Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA Search for more papers by this author Author Information Jean-Benoît Lalanne1,2,†, Darren J Parker1,† and Gene-Wei Li *,1 1Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA 2Department of Physics, Massachusetts Institute of Technology, Cambridge, MA, USA †Present address: Department of Genome Sciences, University of Washington, Seattle, WA, USA †Present address: Biosciences Division, Oak Ridge National Laboratory, Oak Ridge, TN, USA *Corresponding author. Tel: +1 617 324 6703; E-mail: [email protected] Mol Syst Biol (2021)17:e10302https://doi.org/10.15252/msb.202110302 PDFDownload PDF of article text and main figures. Peer ReviewDownload a summary of the editorial decision process including editorial decision letters, reviewer comments and author responses to feedback. ToolsAdd to favoritesDownload CitationsTrack CitationsPermissions ShareFacebookTwitterLinked InMendeleyWechatReddit Figures & Info Abstract During steady-state cell growth, individual enzymatic fluxes can be directly inferred from growth rate by mass conservation, but the inverse problem remains unsolved. Perturbing the flux and expression of a single enzyme could have pleiotropic effects that may or may not dominate the impact on cell fitness. Here, we quantitatively dissect the molecular and global responses to varied expression of translation termination factors (peptide release factors, RFs) in the bacterium Bacillus subtilis. While endogenous RF expression maximizes proliferation, deviations in expression lead to unexpected distal regulatory responses that dictate fitness reduction. Molecularly, RF depletion causes expression imbalance at specific operons, which activates master regulators and detrimentally overrides the transcriptome. Through these spurious connections, RF abundances are thus entrenched by focal points within the regulatory network, in one case located at a single stop codon. Such regulatory entrenchment suggests that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts. SYNOPSIS Quantitative multiscale measurements of the responses to varied expression of translation termination factors in Bacillus subtilis reveal 'regulatory entrenchment', whereby expression-fitness landscapes are dictated by idiosyncratic interactions in the regulatory network. Genetic tools and high-throughput measurements are used to map translation termination factor (release factor) expression-fitness landscapes in B. subtilis. Release factor depletion leads to imbalanced translation for co-transcribed gene pairs. Imbalanced translation induces unintended regulons to the detriment of cell fitness. Swapping a single stop codon rewires global susceptibility to RF perturbation. The observed regulatory entrenchment presents a challenge for predictive bottom-up models. Introduction Formulating predictive models connecting genetic information to phenotypes constitutes an overarching goal in genomics and systems biology (Ostrov et al, 2019; Shendure et al, 2019; Lopatkin & Collins, 2020). In single-celled microbes, the relationship between genotype and phenotype can be conceptually decomposed into two distinct maps: the first, relating genome sequence to gene expression and the second, connecting gene expression to whole-cell properties such as proliferation. Rapid progress on the characterization of cis-regulatory elements, spurred by integration of massively parallel reporter assays (Patwardhan et al, 2009, 2012; Sharon et al, 2012) with novel computational frameworks (Rosenberg et al, 2015; Jaganathan et al, 2019; Bogard et al, 2019; de Boer et al, 2020), has achieved headway in predicting expression features from DNA sequence (Cambray et al, 2018; Sample et al, 2019; Agarwal & Shendure, 2020; preprint: Urtecho et al, 2020). By contrast, expression-fitness landscapes, defined as the distinct relationships between the expression level of individual genes and the cell fitness, remain understudied despite being the basis of selective pressures on protein abundances. This information gap limits both the engineering of complex biological functions and the interpretability of genetic variation. Predicting the shape of expression-fitness landscapes requires quantitative characterization of the cellular state at multiple levels. Although numerous expression-fitness landscapes have been previously mapped, e.g., (Tubulekas & Hughes, 1993; Dekel & Alon, 2005; Chou et al, 2014; Li et al, 2014; Keren et al, 2016; Knöppel et al, 2016; Duveau et al, 2017; Palmer et al, 2018; Schober et al, 2019; Hawkins et al, 2020; Jost et al, 2020; Kavčič et al, 2020; Parker et al, 2020; preprint: Arita et al, 2021; Mathis et al, 2021), these measurements rarely include concomitant assessment of the internal cell state following perturbations (but see Jost et al, 2020; Parker et al, 2020). With limited information bridging the molecular to cellular scales, the root causes of observed fitness defects are challenging to pinpoint (Fig 1A). In particular, changes in enzyme levels not only directly affect flux and growth (Fig 1A, inset i for the case of translation factors) (Ehrenberg & Kurland, 1984; Klumpp et al, 2013; Li et al, 2014), but can also have indirect pleiotropic effects that take the form of damage propagation across three connected levels of biological organization (Fig 1A, inset ii). First, a reduced enzymatic flux could affect the expression of other genes via specific molecular mechanisms (mechanistic level). Second, these proximal changes in expression could ripple through the regulatory network, leading to further changes in expression genome-wide (regulatory level). Third, each terminal downstream expression change could have an impact on fitness (systemic level). Whether selective pressures on the abundance of proteins predominantly relate to direct impacts on flux or are rather dominated by indirect cellular responses remains unresolved. Figure 1. Mapping the underlying determinants of the release factor expression-fitness landscapes Expression-fitness landscapes, which connect enzyme expression (microscopic variable) to the growth rate (cellular phenotype), can be dictated by direct or indirect effects. Inset (i): direct effects correspond to reduction in the flux cognate to the perturbed enzyme (protein synthesis rate in the case of translation factors). Inset (ii): indirect effects result from pleiotropic propagation across mechanistic, regulatory, and systemic levels. As a case study, the expression of peptide chain release factors (RFs: RF1, RF2, and associated methyltransferase PrmC), involved in the first step of mRNA translation termination, was tuned around endogenous levels. Strains with inducible copies of RFs, and deleted endogenous genes, were used to systematically vary RF expression. The resulting impacts on the cell internal state (RNA-seq, ribosome profiling) and relative growth rate s (competition experiments) were measured, leading to precise mapping of expression-fitness landscapes. Data information: See also Figs EV1 and EV2 for details on strains and measurement platform. Download figure Download PowerPoint Here as a case study, we systematically vary the expression of enzymes involved in the core process of mRNA translation termination (Fig 1B) in Gram-positive bacterium Bacillus subtilis. We focus on peptide chain release factors, RF1, RF2, and their methyltransferase PrmC (hereafter collectively referred to as release factors, RFs). RF1 and RF2 catalyze the first step of translation termination (Bertram et al, 2001), recognizing stop codons and releasing completed peptides from the ribosome (Fig 1B). RF1 and RF2 directly interact with the ribosome (Scolnick et al, 1968; Petry et al, 2005) and have partially overlapping specificities (Scolnick et al, 1968) (RF1 recognizes stops UAA/UAG, and RF2 stops UAA/UGA). PrmC post-translationally modifies RF1 and RF2, thereby increasing their catalytic activity (Heurgué-Hamard et al, 2002; Nakahigashi et al, 2002; Mora et al, 2007). We targeted translation termination because the resulting downstream changes in expression were anticipated to be modest: given that translation is initiation-limited on most mRNAs (Laursen & Sørensen, 2005), mildly decreasing termination rate was expected to reduce global ribosome availability without altering protein production on a gene-by-gene basis. A better understanding of the physiology of translation termination stress has relevance in synthetic biology, for example in the context of genome-wide stop codon reassignment (Johnson et al,2011, 2012; Lajoie et al, 2013; Wannier et al, 2018; Fredens et al, 2019). Through precise measurements of transcriptomes, global translational responses, and cell fitness (defined here as the population growth rate in exponential phase), we elucidate the underlying determinants of the RF expression-fitness landscapes (Fig 1C). We find that idiosyncratic and indirect inductions of regulatory programs are associated with decreases in growth rate in multiple directions of the RF expression subspace. We term such situation "regulatory entrenchment", whereby the fitness defect caused by perturbing a protein's expression is strongly and spuriously aggravated by the gene regulatory network. Further, we reconstruct links that connect the initial microscopic perturbation to system-wide effects. At the mechanistic level, we find that occlusion of ribosome-binding sites during RF depletion is a common phenomenon leading to changes in expression stoichiometries between adjacent, co-transcribed genes. In particular, we identify the stop codon of a single regulator as a molecular Achilles heel, sensitizing the entire cell to specific RF perturbations. At the regulatory level, we show that removing one gene can mute pleiotropic changes and liberate RF from regulatory entrenchment. Finally, at the systemic level, we report passive proteome compression as a quantitatively tractable cause of growth defects upon massive activation of a regulon. Our multiscale characterization provides a concrete example of how distal yet focal events triggered by targeted molecular perturbations can have system-wide impacts. Here, the cells' susceptibility to expression perturbations of specific enzymes is not simply related to the magnitude of the changes in the associated cognate flux, but is instead dictated and amplified by sensitive nodes in the regulatory network. These results underscore the viewpoint that a quantitative understanding of the full system (Karr et al, 2012; Boyle et al, 2017; Liu et al, 2019) might be necessary to predict even qualitatively the shape of expression-fitness landscapes. Results Linking RF perturbations to changes in genome-wide expression and fitness We used a combination of genetic tools and high-throughput measurements to map RF expression-fitness landscapes, as well as the underlying mechanistic, regulatory, and systemic responses. We created strains in which two of the three factors (PrmC, RF1, and RF2) can be tuned orthogonally (Fig EV1E and F): PrmC and RF1 in one strain, and PrmC and RF2 in another (with the autoregulatory frameshift removed for RF2 (Craigen et al, 1985; Craigen & Caskey, 1986), Fig EV1A–C). The range of these tunable expression systems spanned 31- to 111-fold and was centered near their respective endogenous levels (Fig EV1D). The global gene expression changes resulting from these perturbations were probed with RNA-seq (Parker et al, 2019) (Materials and Methods), with highly reproducible approaches (Appendix Fig S1). Expression levels were converted to fractions of the proteome using ribosome profiling (Ingolia et al, 2009; Li et al, 2014) (Materials and Methods for details and assumptions). Ribosome profiling further provided a high-resolution view of translation state in a subset of conditions. In order to precisely measure fitness, defined here as relative population growth rate in exponential phase, we designed a DNA-barcoded competition assay (Smith et al, 2009; Parker et al, 2020) with ± 1% precision (± 2σ, Fig EV2, Materials and Methods). This combination of targeted proteome perturbation with precision transcriptomic and fitness measurements enabled a multiscale assessment of the RF expression-fitness landscapes. Click here to expand this figure. Figure EV1. Details of RF-inducible expression constructs A. Schematic tunable expression system. Inducible constructs are added at safe harbor loci, together with a barcode for competition experiments. The endogenous gene copy is then deleted in a scarless fashion. B, C. Details of loci with tunable expression cassettes for the orthogonally tunable (B) RF2 and PrmC strain, and (C) RF1 and PrmC strain. Disrupted safe harbor endogenous loci are amyE, lacA, and levB. Control strains were also constructed with blank expression cassettes at these locations (Materials and Methods, Fig EV2F, G, I and J). Inducible repressors XylR and LacI, respectively, responsive to IPTG and xylose are shown in black. Resistance cassettes are shown in yellow. The location of the 8-nt chromosomal barcode in one arm of the amyE homology region is shown in purple. D. Fold-change in RF levels as a function of inducers, with fitted Hill curve, serves as a guide to the eye. Endogenous expression is shown as the horizontal dashed line (fold-change of 1) and the full attainable dynamic range indicated on the right. See Materials and Methods for calibration to proteome fraction. E, F. 3D RF expression space for all phenotypically profiled conditions (shown separately in Fig 2A–D) shown in (E), and orthogonal projection in 2D subspaces in (F). Dashed lines mark endogenous expression levels. Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Details of relative growth rate measurement A. Schematic of competition experiments. Pools of barcoded strains competed for ≈ 30 generations with five samplings, barcode frequencies are quantified, and changes in barcode frequencies over time determined. Relative growth rate is λinducible/λWT = 1 + s, where s is the slope of the log2 barcode ratio vs. time (number of generations). This process was performed for all induction conditions shown in Fig 2A–D. B. Schematic the barcode readout procedure, carried out in two PCR steps from genomic DNA extracted from pools of competing strains, with UMI and first index added at the first PCR, and a second index added at the second PCR. Details of the final amplicon for barcode readout are in Dataset EV6. C, D. Examples of barcode frequency ratios over time for isogenic strain pairs from a single competition experiment, (C) wild-type vs. wild-type, and (D) RF2 overexpression vs. wild-type. Representative strain pairs from experiment E1-C9 are shown. Inferred s: = λinducible/λWT − 1 from the linear fit (black line) is shown on the graph. Range of slopes smin–smax of subsampled bootstraps (gray lines) is reported as s s min s max . Dashed lines correspond to 95% confidence interval. Error bars correspond to estimated noise attributable from Poisson counting noise in UMI counts ( 1 log ( 2 ) 1 N 1 + 1 N 2 , where N1 and N2 are the respective UMI barcode counts for the compared strain pairs at the corresponding time point). E. Distribution of measured s for pairs of strains with identical genotype apart from barcode across all experiments (n = 1,253 comparisons, e.g., 4/1,253 experimentally determined s are shown in panel C). The shaded gray area corresponds to the ± 2σs = ± 1.2% shown in Fig 2E–H. F, G. Measured fitness difference to wild-type for strains with blank expression cassettes. (F) Blank Pxyl at amyE & blank PspankHy at lacA, strains GLB434–437, n = 644 comparisons. (G) Blank PspankHy at amyE & blank Pxyl at levB, strains GLB446–449, n = 252 comparisons. Both control strain series show minimal effect of ectopic insertions on cell fitness. s values displayed correspond to median with 25th and 75th percentile of values across isogenic pairs. H. Hand mixing experiment with two strains with different barcodes, showing accurate (slopes 1.00 and 0.98 for observed vs. expected ratio of barcodes from two technical replicates) readout of cell frequencies in pool over nearly four orders of magnitude. Median difference between readout from two technical replicates is 20%. Error bars are as in (C and D). I, J. Representative examples of mRNA level (RNA-seq) comparison between wild-type and control strains with blank expression cassette insertions: (I) GLB434 vs. wild-type (experiment E1-C5), and (J) GLB446 vs. wild-type (experiment E2-C1). Cumulative distributions of fold-changes are shown as insets as in Fig 3A and B. Download figure Download PowerPoint Perturbing the expression of different RFs decreases fitness through distinct physiological routes Using our measurement platform, we directly confirmed that endogenous RF expression in exponential phase maximizes growth rate. For conditions at or near endogenous levels of RF1, RF2, and PrmC (dashed lines, Fig 2E–G), the fitness is indistinguishable from wild-type strains (shaded gray area in Fig 2E–H corresponds to experimental precision in fitness, |s| < 2σs = 1.2%, Materials and Methods). On the other hand, perturbing RF expression away from endogenous level led to growth defects in most directions of the RF subspace (Fig 2E–H). Only RF1 overexpression did not cause a measurable decrease in fitness, in part because of the limited maximal achievable level for this particular inducible construct (≈ 3×, Fig EV1D). These results suggest that RF expression is optimized for exponential phase growth, akin to the expression of several other factors (Ehrenberg & Kurland, 1984; Dekel & Alon, 2005; Li et al, 2014; Parker et al, 2020), and is consistent with the tight evolutionary conservation of expression stoichiometry in biological pathways (Lalanne et al, 2018). Figure 2. Diverse fitness landscapes and physiological trajectories upon RF expression perturbation A–D. Profiled orthogonal directions of the RF expression subspace, respectively, scanning along the dimension of (A) RF1, (B) RF2, (C) PrmC, and (D) PrmC with RF2 overexpression. Axes correspond to expression levels of RF1, RF2, and PrmC. E–H. Cell exponential growth rate s measured by competition (relative to wild-type) at corresponding RF levels (reported in units of proteome fraction, derived from a ribosome profiling calibration, Materials and Methods) shown in (A–D). Endogenous levels of RFs are indicated with dashed vertical lines. Gray shadings mark the precision of our fitness measurement, defined as ± 2σs = ± 1.2%, where σs is the standard deviation in the measured relative growth rate among isogenic redundantly barcoded strain (see Fig EV2E), with the distribution shown as inset in panel (H). Relative fitness value reported corresponds to the median across isogenic barcoded pairs, and vertical black bars delineate 25th to 75th percentile among such pairs (between 21 and 28 isogenic pairs, see Dataset EV6) for a single experiment and is typically smaller than the plot symbol. I–L. Trajectories following RF perturbation in the space of relative growth rate vs. estimated proteome fraction to translation proteins (translation sector). Data information: Matched arrows across panels (E–H) and (I–L) show direction of increasing perturbations in the fitness landscape. Lines in (J–L) correspond to least-square fits. Lines in panels (K and L) have been reproduced in panel (J) to highlight of additivity of trajectories under PrmC perturbation with RF2 overexpression. See also Figs EV1, EV2, EV3, EV4. Download figure Download PowerPoint Away from the optimum, distinct RFs displayed expression-fitness landscapes of different shapes. For example, PrmC has a much more severe growth defect than RF2 at the same levels of overexpression (Fig 2F vs. 2G), and RF1 knockdown leads to a near-vertical drop-in fitness (Fig 2E), which is more severe than RF2 or PrmC knockdown (though our expression constructs had higher baseline expression for the latter two). Beyond these qualitative differences, our global expression quantification in all these conditions provides a way to assess whether RF-specific fitness defects nevertheless correspond to similar underlying physiological states generic to translation termination defects. Projecting complex phenotypic data onto low-dimensional manifolds can provide insight on the physiological state underlying growth defects (Scott et al, 2010, 2014; You et al, 2013; Hui et al, 2015). Two cellular-level quantities are of particular interest: (i) the growth rate λ and (ii) the total expression of the mRNA translation machinery, quantified as the summed proteome fraction of translation proteins, termed the translation sector, ϕR. Hwa et al have shown (Scott et al, 2010; You et al, 2013; Zhu et al, 2016, 2019) that trajectories in the space of growth λ vs. translation sector ϕR upon a series of increasingly severe perturbations are intimately related to global regulation (Appendix Supplementary Methods, Appendix Fig S2). In Escherichia coli, they found that under the numerous ways to inhibit translation, the translation sector ϕR increases concomitantly with a decrease in the growth rate (along the "translation line", Appendix Fig S2) as a compensation mechanism (Scott et al, 2010). By contrast, decreasing the nutritional quality of the medium leads to a decrease in growth paralleled by a decrease in the translation sector ϕR (along the "nutrition line", Appendix Fig S2), a long-known property in bacterial physiology (Schaechter et al, 1958). Under the bacterial growth laws established in E. coli, the states of cells with RF-specific fitness defects were anticipated to collapse on the translation line. In effect, perturbing RF expression was expected to reduce the translation termination rate and thus to reduce ribosome availability. Surprisingly, the different RF expression perturbations displayed diverse physiological trajectories (Fig 2I–L). The sharp drop in growth under RF1 knockdown led to little change in the translation sector ϕR (Fig 2I), analogously to the trajectories associated with non-translation-targeting antibiotics (Scott et al, 2010). By contrast, the growth defects upon PrmC expression perturbations were associated with a decrease in the translation sector (Fig 2K and L), leading to movement along the nutrition line, which was unexpected given the fixed quality of the growth medium. RF2-perturbed cells moved slightly in different directions following overexpression (translation line) and knockdown (nutrition line, although high basal activity of our expression construct limited the magnitude of accessible growth defects; Fig 2J). Interestingly, physiological changes were additive under combined RF perturbations: tuning PrmC expression in combination with RF2 overexpression led to movement along the nutrition line, but shifted by the movement along the translation line by the RF2 perturbation (dashed vs. dotted lines in Fig 2J–L, Appendix Supplementary Methods). The physiological divergences observed for the different RFs perturbations were intriguing given the similar involvement of these three proteins in translation termination and pointed to possible RF-specific pleiotropic effects. Analyzing the transcriptomes following perturbations revealed distinct responses along the orthogonal RF expression directions. PrmC perturbations massively induced the general stress σB regulon (Figs 3A and B, and EV3A–D), RF2 perturbation led to little changes across the full range of expression, except for a modest σB induction at maximal knockdown (Fig EV3O and P). RF1 knockdown led to changes in motility and biofilm genes (Fig EV4C and D). Together, these results suggest that each RF has a mechanistically distinct relationship between expression and fitness. Figure 3. σB regulon induction upon PrmC overexpression compresses the translation sector mRNA levels (reads per million mapped reads per kilobase, rpkm, genes with > 5 reads mapped shown) for maximal PrmC overexpression (green arrow) versus unperturbed cells (average across control datasets, Materials and Methods). σB regulon members and translation-related proteins are marked in red × and dark gray +, respectively. Targets for RT–qPCR measurements of σB induction (ygxB, ywzA, Fig 5) are highlighted in dark red. mRNA levels for RF1 (blue), RF2 (orange), and PrmC (green) are marked by dots (corrected for translation efficiency of ectopic expression constructs, Materials and Methods). σB regulon activation is marked by dashed red polygon. Inset shows cumulative distribution of fold-changes in mRNA levels (red σB regulon, dark gray translation, pale gray rest of proteins). σB activation and translation compression are highlighted by arrows indicating shift in median expression. As a comparison, distribution of fold-changes for all genes among unperturbed replicates are show in light blue. Same as (A), but with a strain harboring a deletion of gene sigB, which abrogates σB regulon activation, and restores genome-wide expression levels despite PrmC overexpression (light gray line in inset). Quantification of the proteome fraction to the σB regulon (σB sector) as a function of PrmC (see Materials and Methods for calibration from transcriptome to proteome fraction). Dashed vertical line marks endogenous PrmC level. Inset reproduces broader context of data in RF expression subspace (Fig 2C). Similar to (C), but quantifying proteome fraction to the translation sector. Proteome fraction of the translation sector as a function of the excess proteome fraction to the σB regulon, denoted ϕ U . Dashed line corresponds to growth laws prediction (Appendix Supplementary Methods, equation 1, using parameters κ n , κ t , ϕ ∘ , and ϕ R max obtained from fits in Fig 2J and K), full line corresponds to decrease by factor 1 − ϕU. Schematic illustration of passive proteome fraction compression under σB activation (increase in regulon expression). Relative growth rate as a function of PrmC level, with and without sigB. Growth difference with and without sigB (corresponding to ∆s panel G), as a function of excess proteome fraction to the σB regulon. Dashed lines are growth law prediction (Appendix Supplementary Methods, equation 2), full line corresponds to −ϕU. Data information: In panels (C–E and G), open light green triangles correspond to cells with sigB, and filled dark green diamonds to cells without sigB (deletion). See also Figs EV3 and EV4. Download figure Download PowerPoint Click here to expand this figure. Figure EV3. Interplay between expression of RF2 and PrmC, fitness and σB regulon activation A–I. Analogous to Fig 3, but for varying PrmC levels in conjunction with RF2 overexpression (conditions shown in Fig 2D). In (E–H), open light green pentagrams correspond to cells with sigB, and filled dark green hexagrams to cells without sigB (deletion). Blue shadings mark the region of the expression space for which the growth defect is not rescued by sigB deletion, indicating a different underlying cause for the decrease in translation sector. J. Comparison of expression at maximal PrmC expression for endogenous and overexpressed RF2 levels (respective comparisons to unperturbed conditions in Fig 3A and current panel C), showing highly reproducible σB induction independent of RF2 levels (the two outliers marked by black circles are xylA and xylB, which are responsive to xylose). K. Expression-fitness landscape for RF2 and PrmC. L–N. orthogonal projections from K showing the (L) RF2, and (M, N) PrmC directions. Panel (N) is the PrmC fitness landscape, with the fitness defect caused by RF2 overexpression defect subtracted out (arrows in panels L and M). Fitness defect at overexpressed PrmC is independent of RF2 (dashed black line in N). Knockdown defect is exacerbated by RF2 overexpression (black arrows in N). O, P. Transcriptome under RF2 expression perturbation. (O) RF2 knockdown shows modest σB induction, whereas (P) maximal RF2 overexpression displays little expression changes. Q. Growth rate difference for strain with inducible RF2, with and without sigB. A mild but significant (P < 10−5, bootstrap subsampling, Materials and Methods) improvement in fitness upon sigB deletion at lowest RF2 levels is seen. Measured s for each of 12 strain pairs, inducible RF2 (GLB42
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