Suboptimal resource allocation in changing environments constrains response and growth in bacteria
2021; Springer Nature; Volume: 17; Issue: 12 Linguagem: Inglês
10.15252/msb.202110597
ISSN1744-4292
AutoresRohan Balakrishnan, R. Silva, Terence Hwa, Jonas Cremer,
Tópico(s)Gene Regulatory Network Analysis
ResumoArticle20 December 2021Open Access Source DataTransparent process Suboptimal resource allocation in changing environments constrains response and growth in bacteria Rohan Balakrishnan Corresponding Author Rohan Balakrishnan [email protected] orcid.org/0000-0002-7547-8565 Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Roshali T de Silva Roshali T de Silva Department of Biology, Stanford University, Stanford, CA, USA Search for more papers by this author Terence Hwa Terence Hwa orcid.org/0000-0003-1837-6842 Department of Physics, University of California at San Diego, La Jolla, CA, USA Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Jonas Cremer Corresponding Author Jonas Cremer [email protected] orcid.org/0000-0003-2328-5152 Department of Biology, Stanford University, Stanford, CA, USA Search for more papers by this author Rohan Balakrishnan Corresponding Author Rohan Balakrishnan [email protected] orcid.org/0000-0002-7547-8565 Department of Physics, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Roshali T de Silva Roshali T de Silva Department of Biology, Stanford University, Stanford, CA, USA Search for more papers by this author Terence Hwa Terence Hwa orcid.org/0000-0003-1837-6842 Department of Physics, University of California at San Diego, La Jolla, CA, USA Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA Search for more papers by this author Jonas Cremer Corresponding Author Jonas Cremer [email protected] orcid.org/0000-0003-2328-5152 Department of Biology, Stanford University, Stanford, CA, USA Search for more papers by this author Author Information Rohan Balakrishnan *,1, Roshali T Silva2, Terence Hwa1,3 and Jonas Cremer *,2 1Department of Physics, University of California at San Diego, La Jolla, CA, USA 2Department of Biology, Stanford University, Stanford, CA, USA 3Division of Biological Sciences, University of California at San Diego, La Jolla, CA, USA *Corresponding author. Tel: +1 858 534 5817; E-mail: [email protected] *Corresponding author. Tel: +1 650 724 7178; E-mail: [email protected] Molecular Systems Biology (2021)17:e10597https://doi.org/10.15252/msb.202110597 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 To respond to fluctuating conditions, microbes typically need to synthesize novel proteins. As this synthesis relies on sufficient biosynthetic precursors, microbes must devise effective response strategies to manage depleting precursors. To better understand these strategies, we investigate the active response of Escherichia coli to changes in nutrient conditions, connecting transient gene expression to growth phenotypes. By synthetically modifying gene expression during changing conditions, we show how the competition by genes for the limited protein synthesis capacity constrains cellular response. Despite this constraint cells substantially express genes that are not required, trapping them in states where precursor levels are low and the genes needed to replenish the precursors are outcompeted. Contrary to common modeling assumptions, our findings highlight that cells do not optimize growth under changing environments but rather exhibit hardwired response strategies that may have evolved to promote fitness in their native environment. The constraint and the suboptimality of the cellular response uncovered provide a conceptual framework relevant for many research applications, from the prediction of evolution to the improvement of gene circuits in biotechnology. SYNOPSIS Analyses of how allocation of cellular resources to different genes shapes Escherichia coli's response to changing nutrient conditions show that growth transitions are determined by the competition between genes that are directly required in the encountered conditions and those that are not. Synthetic titration constructs are used to characterize how growth recovery during diauxic shifts varies with allocation of gene expression resources towards different genes. A low-dimensional model predicts how the allocation of resources affects the dynamics of growth transitions. Bacterial response includes the substantial expression of non-required genes explaining the diauxic transitions to many different carbon sources. Introduction Changing environmental conditions are a hallmark of microbial habitats, and microbes have to respond appropriately to thrive (Stanier, 1951; Roszak & Colwell, 1987; Siegal, 2015; Bertrand, 2019; Erez et al, 2020; Moreno-Gámez et al, 2020). For instance, the depletion of a preferred carbon source requires the efficient transitioning to the consumption of another carbon source (Monod, 1949, 1966). Several studies have characterized the response to such diauxic shifts by identifying the up- and downregulation of hundreds of genes (Chang et al, 2002; Kao et al, 2005; Mostovenko et al, 2011), and implicating major regulators such as cAMP (Loomis & Magasanik, 1967; Ullmann & Monod, 1968; Inada et al, 1996; Kimata et al, 1997) and ppGpp (Traxler et al, 2006; Fernández-Coll & Cashel, 2018). Executing these different processes is a major challenge for the cell, especially when biosynthetic precursor levels drop during shifts, yet how cells navigate these challenges and strategize an optimal response remains poorly understood. To decipher the fundamental principles shaping the cellular response and growth kinetics, we here present a quantitative study on cell physiology connecting gene expression to growth phenotypes. Results We first studied the shift from growth in glucose to growth in acetate, a shift previously used to study growth transitions (Kao et al, 2005; Kotte et al, 2014; Enjalbert et al, 2015; Basan et al, 2020). We grew Escherichia coli–K-12 cells in batch cultures and tracked growth by measuring optical density (Fig 1A). The shift from growth on glucose (blue zone) to acetate (red zone) is accompanied by a period of growth arrest (lag-time τlag) lasting ~3.5 h (gray zone). The lag is also illustrated by the drop of the instantaneous growth rate during the shift (Fig EV1A). From the metabolic perspective, this transition requires a switch from glycolytic pathways to the activation of the glyoxylate shunt and gluconeogenesis pathways(Oh et al, 2002; Kao et al, 2005; Wolfe, 2005; Enjalbert et al, 2015) so that the synthesis of amino acids and other growth precursors (green arrows) can continue (Figs 1B and EV1B). Hence, following glucose depletion, the synthesis of the glyoxylate shunt enzymes (AceB, AceA) and gluconeogenesis enzymes (MaeA, MaeB, Pck, PpsA) is required before growth can resume on acetate (Fig EV1B). Indeed, maintaining enzyme reserves of the glyoxylate shunt pathway by pre-expressing aceBA prior to glucose runout reduces the lag-time (Fig EV1C and D). Yet, why does it take so long for the few required enzyme types to reach sufficient concentrations for growth to resume? Figure 1. Diauxic shift from glucose to acetate Diauxic growth of WT Escherichia coli (NCM3722) in minimal media containing glucose and acetate, bacterial density measured as optical density (OD600). Growth on glucose is captured by the exponential fit (blue dashed line) and proceeds until glucose runs out (black dashed line) at time = 0 h. This is followed by a period of growth lag lasting ~3.5 h before exponential growth resumes on acetate (red dashed line). The central carbon metabolism pathways are illustrated along with the nodes branching out into amino acid precursor synthesis (green arrows). Glucose to acetate diauxie requires switching from the pathways facilitating glucose consumption (represented in blue) to those responsible for acetate consumption (represented in red). More details in Fig EV1B. The mRNA fractional abundances for aceB, aceA, and the gluconeogenesis genes (summed abundance of maeA, maeB, pck, and ppsA genes) were estimated by RNA sequencing performed at various time points during the diauxic transition as indicated on the x-axis. The x-axis is truncated 2.3 h into the shift, and the “post-shift” (pink) regime represents transcript levels when growth fully resumes on acetate, measured under steady-state growth on acetate minimal medium. The series of RNA-Seq through the growth transition was performed once. Lag-times for controlled titration of aceBA expression using an inducer construct in strain NQ1350 (inset). Addition of chlortetracycline (cTc) removes the tetR repression and induces aceBA expression. As the expression of aceB/aceA during the response (inducer added at time = 0 h) is increased, lag-times decrease. Bar plot shows mean lag-times (N = 3 biological repeats) for different inducer concentration with error bars denoting the standard deviations (SD). Of the overall transcription and translation fluxes (arrows), a strong allocation toward the expression of shunt and gluconeogenesis genes (red) increases the novel synthesis of required enzymes and should thus lead to faster growth recovery. Source data are available online for this figure. Source Data for Figure 1 [msb202110597-sup-0003-SDataFig1.xlsx] Download figure Download PowerPoint Click here to expand this figure. Figure EV1. Diauxic growth on glucose and acetate: transition kinetics and metabolic requirements A. Instantaneous growth rate of the WT (NCM3722) is derived by calculating the derivative of the growth rate divided by the optical density (growth curve shown in Fig 1A). Cells first consume glucose and grow exponentially at a rate of ~0.9/h (blue horizontal line). Following glucose depletion (defined as time 0; left vertical dashed line), the instantaneous growth rate immediately falls and growth stops. After a phase of no growth, growth gradually begins to approach a rate of 0.4/h, the steady-state growth rate for growth on acetate (red horizontal line). Right vertical dashed line indicates the time of growth recovery as determined by the fitting of an exponential curve (see Lag-time quantification in Materials and Methods). B. To resume growth on acetate following glucose depletion, the supply of amino acids, the major precursors required for biomass synthesis, must be re-established. The various nodes along the central carbon metabolism that branch into the synthesis of different amino acids are indicated in green. For the successful recycling of the 2 carbon molecules per acetate into amino acid precursors, the most essential step is the activation of the glyoxylate shunt (Wolfe, 2005): To prevent the loss of the 2 CO2 molecules occurring along the TCA cycle, the carbon flux has to be bypassed. Instead of being converted to alpha-KG, isocitrate is split into succinate and glyoxylate by the enzyme isocitrate lyase (AceA). Glyoxylate is then converted to malate by the malate synthase (AceB). Succinate and malate generated as a result of the shunt subsequently fuel gluconeogenesis (MaeA, MaeB, Pck, PpsA), making available the carbon precursors required for the synthesis of many amino acids (green arrows). Besides amino acids as precursors, protein synthesis also requires substantial amounts of energy, primarily to charge tRNA and drive translation. However, energy supply is unlikely to be the major bottleneck during this shift since cells already express and utilize TCA enzymes during the pre-shift growth, which can thus ensure a continuous production of ATP over the course of the transition. C, D. Effect of maintaining pre-expressed aceBA reserves on growth transitions. Diauxic transitions of NQ1350 in which the native aceBA promoter is replaced by the inducible Ptet promoter are shown when 0, 5, or 10 ng/ml inducer cTc is added to the medium already at the pre-culture stage, well before glucose depletion. Bar plot (D) shows the mean lag-times of N = 2 biological replicates. Error bars indicate SD. Source data are available online for this figure. Download figure Download PowerPoint To tackle this question, we next followed gene expression during the course of the shift. Translation rates are known to severely fall with growth arrest upon glucose depletion (Madar & Zaritsky, 1983; Erickson et al, 2017). Given these low rates, the high stability of proteins synthesized before the glucose runout, and the technical challenges to detect low levels of novel proteins, it is difficult to analyze the proteome response in high resolution. In contrast, given the fast turnover of mRNA (Chen et al, 2015; preprint: Balakrishnan et al, 2021), transcriptomics and the pool of mRNA species provide a good readout of momentary gene expression during the shift. Using RNA sequencing (RNA-Seq), we determined mRNA abundances at six different time points. The mRNAs of the glyoxylate shunt and gluconeogenesis genes, represented as fraction of total mRNA, increase immediately (< 5 min) following glucose depletion, and these increased levels are maintained through the duration of the growth lag (Fig 1C). Given such a rapid regulatory response, the speed at which the transcriptional program changes is likely not the reason for long lag-times, but it is rather the expression strength that could be important. To test this idea, we first employed a strain in which the native promoter of the aceBAK operon is replaced by the titratable promoter Ptet (Basan et al, 2020). In this strain, as increasing concentrations of the inducer chlortetracycline (cTc) are added at the moment of glucose depletion, growth recovery is progressively faster, from no recovery for over 10 h in the absence of induction to ~3-h recovery at the highest cTc concentration used (Fig 1D). Since the aceBA expression levels prior to glucose depletion are unperturbed, and thus uniform among the cultures, the decrease in lag-times with increasing cTc concentrations highlights the significance of the active response to changing conditions in determining the transition kinetics. Following these results, we wondered whether lag-times emerge due to a fundamental competition for shared resources such as RNA polymerase and ribosomal activity, which could be particularly limited during the shift: If a larger portion of the limited transcriptional and translational fluxes are allocated to the synthesis of the required mRNAs and proteins (Fig 1E top), the shunt and gluconeogenesis enzymes become available to replenish precursors earlier than in the case with a lower allocation of resources toward these genes (Fig 1E bottom). To probe this allocation picture, we next employed a titration construct to overexpress lacZ (Scott et al, 2010), the product of which hydrolyses lactose and is thus useless for growth in glucose and acetate (Fig 2A, inset): When transcriptional and translational resources are diverted toward LacZ synthesis during the response to changing conditions, the protein itself adds no benefit to the cell and thus acts as a sink for shared resources, which should extend lag-times. In line with this expectation, when inducing lacZ expression by adding various levels of the inducer chlortetracycline (cTc) at the moment of glucose depletion, we observed that lag-times increase strongly from τlag = 3.9 h at 0 ng/ml cTc to τlag = 12.2 h at 7 ng/ml cTc (Figs 2A and EV2A). To further explore this effect, we measured the mRNA levels of lacZ and the required shunt genes aceB and aceA by qPCR, 10 min after the shift. The abundance of lacZ mRNA increases with inducer concentration (Fig EV2B) in direct relation to the lag-time (Fig 2B). Notably, as lacZ mRNA is dialed up, aceB and aceA expression is reduced (Figs 2C and EV2C and D), explaining the longer lag-times based on a lower expression of these required enzymes (Fig 2D). Hence, upon synthetically introducing a resource scarcity during an environmental shift, these observations indeed suggest that the allocation of limited shared resources determines the cellular response and thus lag-times. Figure 2. Expression of a non-needed gene inhibits expression of required genes and elongates lag-times A. A plasmid system (inset) is used to control the expression of the non-required gene lacZ using the strain NQ1389. lacZ expression was induced using cTc to varying degrees at the moment of glucose depletion (time = 0 h) using the indicated range of cTc concentrations. Diauxic growth conditions with glucose and acetate, same as in Fig 1. B–D. lacZ mRNA resulting from the different degrees of induction and aceB mRNA in the same cultures were measured by qPCR and plotted as fold change increase compared with that in the absence of induction. The lag-times observed in panel A are plotted against the respective change in lacZ abundances (B), where the dashed line represents the 3.5-h lag observed for the WT strain (no induction, Fig 1A). Changes in lacZ and aceB mRNA levels are inversely related (C), shown both as linear (left plot) and as log2 (right plot) scales. The dashed line in the left plot shows a linear fit. aceB mRNA abundance is inversely related to the lag-time (D). Means of N = 3 and N = 5 biological replicates are shown for lag-times and expression levels, respectively, in panels B–D. Error bars denote SD. Source data are available online for this figure. Source Data for Figure 2 [msb202110597-sup-0004-SDataFig2.xlsx] Download figure Download PowerPoint Click here to expand this figure. Figure EV2. Lag-times and gene expression when overexpressing lacZ A. Increase in lag-times with increasing inducer levels (strain NQ1389). The inducer chlortetracycline (cTc) is added when glucose runs out (Fig 2A, time = 0). Dashed horizontal line indicates lag-time for the WT strain (NCM3722). B–D. mRNA levels of lacZ, aceB, and aceA at different cTc levels are quantified by qPCR 10 min after the depletion of glucose. mRNA levels of each gene are normalized to the 16S rRNA level, which is known to remain constant during the lag phase (Bosdriesz et al, 2015; Erickson et al, 2017). These normalized expression levels are shown relative to the expression level in the absence of induction (0 cTc). Data information: Mean of N = 3–5 biological repeats shown. Error bars indicate SD.Source data are available online for this figure. Download figure Download PowerPoint To better understand how the competition for shared transcription and translation resources can have such drastic impacts on growth transitions, we next formulated a kinetic model of growth, which focuses on protein synthesis as the most resource demanding process of biomass synthesis (detailed description in Materials and Methods). The model builds on recent advances to describe growth (Molenaar et al, 2009; Scott et al, 2014; Hermsen et al, 2015; Erickson et al, 2017; Allen & Waclaw, 2018; Korem Kohanim et al, 2018) and explicitly considers amino acid precursors, their synthesis by metabolic enzymes, and their utilization by ribosomes in form of charged tRNA (Fig EV3). A key feature of the model is that only a fraction of the ribosomes synthesizes the enzymes (e.g., AceB) that supply the precursors, while the rest of the translation flux is diverted to the synthesis of other proteins (Fig 3A). The consumption of amino acid precursors, however, depends on the (total) protein synthesis, leading to a feedback between protein synthesis and precursor supply. During the diauxic transition, where there is a sudden depletion of cellular amino acid pools following the runout of the preferred carbon source, this can lead to cells being “trapped” in a low precursor state. The mathematical analysis shows that such states can persist for hours when (i) the required proteins such as AceB have not been synthesized in sufficient numbers yet, and (ii) the remaining amino acid levels are insufficient to support the synthesis of new proteins (Fig EV4A–E). A direct way to mitigate this trap is to allocate a larger fraction of the translation flux toward the synthesis of the required enzymes (Fig EV4F–J). Accordingly, lag-times fall drastically with a higher allocation toward the synthesis of required enzymes (Fig 3B), reflecting the lag-time changes observed when overexpressing the required or non-required genes aceBA and lacZ (Figs 1D and 2). A quantitative comparison between the model prediction and the observed lag-times upon non-required gene (lacZ) expression is shown in Fig 3C. Taken together, our experiments and theoretical analyses establish mechanistically how the allocation of limited resources during the shift can shape growth transitions, outlining a range of possible allocational behaviors with varying consequences on the growth transition kinetics. Click here to expand this figure. Figure EV3. Modeling growth in changing environments A, B. To model growth during transitions, we here build on replicator/allocation models, which have been established previously for growth in steady-state conditions and for specific shifts. Replicator/allocation models consider protein synthesis by ribosomes toward different proteins (Scott et al, 2014), and central to their approach is the allocation of ribosome activity toward the synthesis of different proteins. To illustrate the concept, we here consider steady growth on one carbon source first (A). The pool of ribosomes is allocated toward different protein classes such as the proteins for novel ribosomes (A, arrow 3), metabolic proteins needed to provide the precursors when utilizing the carbon source (A, arrow 1) and other proteins (A, arrow 2). Growth depends on the availability of nutrients and how the ribosomal activity is allocated to the different protein classes: High growth rates are achieved with allocation ratios that balance precursor influx provided by the metabolic enzymes and their utilization by ribosomes, such that as many ribosomes as possible can translate at maximum speed. This logic is formulated mathematically in Materials and Methods, and Appendix Supplementary Text. Notably, for Escherichia coli the allocation of ribosomal activity toward the synthesis of new ribosomes follows indeed a close to optimal regulation scheme preventing the synthesis of idle ribosomes (as manifested in the ribosome content changing with growth rate). To describe growth during shifts, we extend this modeling approach and explicitly consider glucose and acetate as two nutrient sources (B). In this case, two metabolic protein classes (arrows 1a and 1b), which provide the precursors when cells grow on the two carbon sources (blue and red arrows) and perform glycolysis (blue) or gluconeogenesis (red), respectively. As such, this model structure shares similarities with recent modeling approaches to describe growth during nutrient shifts (Molenaar et al, 2009; Erickson et al, 2017; Korem Kohanim et al, 2018). However, our approach is distinguished from those studies by the inclusion of two key aspects, which are central to the cellular response during growth shifts: (i) We consider the highly responsive regulation of transcription and integrate our transcription measurements (Fig EV5), which quantifies the immediate expression response of the cell during the shift. (ii) We explicitly vary the allocation toward other proteins shift (arrow 2) during the shift, thereby bringing the focus of the study to the allocational constraints acting during the response itself. Notably, right after the shift the model simplifies to the scenario shown in Fig 3A and the consideration of metabolic enzymes (arrows 1 and 2) together with the precursors required to drive novel protein synthesis is sufficient to investigate how long lag-times can emerge and how they relate to the expression of non-required proteins. The mathematical formulation of the full model and additional context is provided in Materials and Methods, and results for the switch from growth on glucose to growth on acetate are provided in Fig EV4. The central model output, the curve describing how lag-times decrease with an increasing allocation to required proteins, is shown in Fig 3B. Download figure Download PowerPoint Figure 3. Modeling growth kinetics during the shift Essential dynamics during the shift from growth on glucose to growth on acetate: Protein synthesis by ribosomes depends on the availability of biosynthetic precursors, which itself depends on the availability of metabolic enzymes that utilize acetate to provide novel precursors. When ribosomes synthesize more of these required enzymes (red arrow 1) instead of other proteins (gray arrow 2), novel precursors are generated from acetate (black arrow) faster, and growth thus resumes faster (detailed considerations and full model introduction in Figs EV3 and EV4 and Materials and Methods). Lag-times fall reciprocally with the allocation toward required proteins (the fraction of mRNA encoding for required proteins) during the shift. Allocation of translation activity toward different proteins is represented by the weight of red and gray arrows (top). Comparison of model prediction with observed changes in lag-time when overexpressing the non-required gene lacZ (Fig 2). The model has one major free parameter, the metabolic rate describing precursor influx. We determined this parameter by comparing predicted and observed lag-times in the absence of induction (Materials and Methods). The model then predicts the change in lag-time without further fitting when lacZ expression was induced and the fraction of required genes fell as a consequence. Data and error bars represent mean ± SD of N = 3–5 biological replicates, as in Fig 2D. All model parameters are provided in Appendix Table S3. Results and figures can be regenerated using the Jupyter Notebook available on GitHub. Download figure Download PowerPoint Click here to expand this figure. Figure EV4. Modeling growth transitions and the consequence of non-required protein expression Based on our model of growth transitions (Fig EV3, Materials and Methods), we analyzed growth kinetics during the transition from growth on glucose to growth on acetate. A–E. Change of major model variables (cell density, nutrients, precursors, and required metabolic enzymes) during the shift for a reference condition with a specific allocation toward the metabolic enzymes required for growth on acetate. Growth and nutrient abundance: When glucose is available, cells grow fast (A, blue region) consuming only glucose, and not acetate (B, blue region). Following glucose depletion, precursors fall dramatically (D, gray region) and growth thus stops (A and C, gray region). Cells then only slowly synthesize the required metabolic enzymes such as AceB (E), which then slowly support higher precursor concentrations (D, gray region). The case where no metabolic proteins for growth on acetate are synthesized is shown for comparison (D, dashed line); precursor concentrations continue to fall. Growth only recovers to post-shift growth rates (A and C, red regions) once precursors reach concentrations comparable again to the Michaelis–Menten constant (D, dashed horizontal line), which describes the concentration of precursors above which ribosomes can work efficiently with close to maximum translation rates. The growth recovery is slow and spans several hours since cells are trapped in a state where precursor concentrations and the abundance of the metabolic enzymes that can generate new precursors are both low. Accordingly, novel metabolic proteins can only be generated slowly and precursor concentrations thus also recover slowly. F–J. Effect on changing allocation toward the metabolic enzymes required for growth on acetate. Plots show the same variables as in (A–E) but for different allocational behavior toward the synthesis of novel proteins required during the shift (model parameter α M b , a c e , m a x describing the allocation of overall transcription (mRNA fraction) to the required enzymes; see color legend). A higher allocation toward the metabolic enzymes (darker colors) substantially decreases the lag during the growth transition as it leads to much faster accumulation of required metabolic enzymes (J), preventing a dramatic decrease in precursor concentrations right after the shift (I). This thus also leads to a much faster recovery of growth (I). Growth stops once acetate is also consumed (G, H). The change in lag-times for different allocations toward the required metabolic enzymes is shown in Fig 3B. Data information: All times shown are relative to the time point where glucose is depleted, and colored regions in (A–E) indicate different growth phases as defined by the intersection of exponential growth curves (dashed lines in A and F) and the density values during the shift, following what was done for the experiments (Figs 1A and EV1A). Model parameters as listed in Appendix Table S3. Download figure Download PowerPoint We next ask where in this range of allocational behaviors does native E. coli (no synthetic overexpression) fall, and whether the long lag-times observed for WT E. coli (Fig 1A) also emerge from the synthesis of non-required proteins during the shift. It has long been known that E. coli growing steadily on poor carbon sources (such as acetate and glycerol) express several catabolic enzymes, despite the absence of their specific substrates (Hui et al, 2015; Schmidt et al, 2015). Allocating resources toward such “
Referência(s)