Composability of regulatory sequences controlling transcription and translation in Escherichia coli

Ribosome-footprint profiling provides genome-wide snapshots of translation, but technical challenges can confound its analysis. Here, we use improved methods to obtain ribosome-footprint profiles and mRNA abundances that more faithfully reflect geneexpression in Saccharomyces cerevisiae. Our results support proposals that both the beginning of coding regions and codons matching rare tRNAs are more slowly translated. They also indicate that emergent polypeptides with as few as three basic residues within a ten-residue window tend to slow translation. With the improved mRNA measurements, the variation attributable to translational control in exponentially growing yeast was less than previously reported, and most of this variation could be predicted with a simple model that considered mRNA abundance, upstream open reading frames, cap-proximal structure and nucleotide composition, and lengths of the coding and 5' UTRs. Collectively, our results provide a framework for executing and interpreting ribosome-profiling studies and reveal key features of translational control in yeast.

Several studies have established that in addition to age, sex, body mass index and trauma, genetic background also contributes to the risk of osteoarthritis (OA). ADAM12, one of the main proteolytic enzymes that regulates extracellular matrix turnover in OA joint tissues, is likely to be one of such genes, because genetic association studies have indicated a link between ADAM12 genetic variants and OA susceptibility and progression traits (Valdes et al, 2004). However, the functional impact of ADAM12 polymorphisms in osteoarthritic joint tissues has not yet been studied. Previously, the allele-specific expression of several OA-associated genes (GDF5, DIO2) has been reported in cartilage and other tissue of the synovial joints, emphasizing the need to consider the OA as involving the entire joint (van Meurs and Uitterlinden, 2012). One of the key factors in the OA pathophysiology is inflammation of the synovial membrane, which is associated with risk of progressive cartilage degradation and signs and symptoms of diseases (Sellam and Berenbaum, 2010). We aimed to evaluate the influence of the ADAM12 SNP on mRNA expression in the synovial tissue of patients with early knee OA (KOA). The synovial tissue samples were harvested in 44 middle-aged subjects (aged 32–60, mean 46.7 years, 24 women) who had undergone arthroscopy due to chronic knee complaints. X-rays of the knee joints were performed on all participants for the estimation of radiographic KOA using the Nagaosa et Doherty grading system (Nagaosa et al, 2000). The expression of ADAM12 mRNA, assessed by 2-ΔΔCT method, was measured in synovial samples by TaqMan® Gene Expression Assay (Hs01106104; Applied Biosystems, Foster City, CA). The synovial tissue harvested from macroscopically intact synovia was used as a control sample. Four SNPs in the ADAM12 gene (rs3740199, rs1871054, rs1044122, rs1278279) were genotyped in all subjects using TaqMan® SNP Genotyping Assays. The expression of ADAM12 mRNA in synovia was compared with genotypes of investigated polymorphisms in the ADAM12 gene, as well as with detailed phenotypical features of OA (presence of osteophytes, joint space narrowing). The Wilcox exact test (WET) was applied for evaluation of the association between expression level (assessed by 2-ΔΔCT) and polymorphisms of the ADAM12 gene. We found that the synonymous polymorphism rs1278279 (p. c.1515G > A, p.N505N) in exon 14 of the ADAM12 gene influences the overall expression of ADAM12 mRNA in synovial tissue. The rs1278279 genotypes in the study groups are distributed as follows: 36 GG homozygotes and 8 GA heterozygotes. In our study group, GG homozygotes had a lower relative expression level of ADAM12 mRNA in synovia compared to subjects with AG genotype (Wilcox exact test; p = 0.03, Figure 1). Separate analyses in men and women did not reveal statistically significant differences in mRNA expression, probably due to the smaller number of subjects in the groups; however, a trend for higher expression in AG heterozygotes was noticed in males (p= 0.06, WET). For other SNPs, no difference was observed in ADAM12 mRNA expression between distinct genotypes. Additionally, there was no association between severity of radiographic KOA and expression level of ADAM12 mRNA. Figure 1. Relative expression of ADAM12 mRNA in synovial tissue in different genotypes of rs1278279 SNP. GG homozygotes had significant lower ADAM12 expression level compared to AG genotype. The rs1278279 is a synonymous polymorphism in exon 14 of the ADAM12 gene, which results in asparagine-coding triplet substitution (AAC→AAT). As our previous data showed rs1278279 and rs1044122 demonstrate strong linkage disequilibrium (LD) across (D`>80%) (Kerna et al, 2013). The same study revealed that rs1044122 carried the higher risk for early knee OA, whereas no statistically significant associations were found for rs1278279. The possible reason for that could be insufficient power of the study regarding detection of associations for rs1278279 with low minor allele frequency (MAF 16%); however, reported associations of several polymorphisms of ADAM12 with OA could suggest the putative relation of this gene region to specific pathophysiological pathways of OA. The mechanism, which could be responsible for regulation of mRNA expression level by rs1278279, is presently unknown. Most frequently, the spectrum of the action by which cis-acting polymorphisms could influence gene expression includes transcriptional control, relative isoform expression, and mRNA stability (Pastinen et al, 2006). Synonymous mutations do not alter the encoded protein, but they can influence gene expression via changes in secondary structures of mRNA and thereby alter the length of pause cycles during translation, the overall rate of translation, or protein folding (Kimchi-Sarfaty et al, 2007; Bartoszewski et al, 2010). Indeed, the ubiquitous long pauses can lead to translational frame shifting and to protein misfolding (Wen et al, 2008). The assumption that triplet AAT (AG heterozygote in our study) may result in shorter pause time during translation could putatively explain higher expression levels of ADAM12 mRNA. Briefly, our results suggest that synonymous variant rs1278279 in the ADAM12 gene could influence ADAM12 mRNA expression in the synovial membrane of the knee joint. The mechanism of regulation for ADAM12 mRNA and protein expression is currently unclear and needs to be clarified by further investigation. A better understanding of molecular mechanisms of ADAM12 mRNA/protein expression regulation in OA joint tissue could potentially help in the development of new therapeutic approaches in the field of OA.

The adapter kinase receptor interacting protein-like interacting caspase-like apoptosis regulatory protein kinase (RICK, also called RIP2 and CARDIAK) was found to be elevated at both the protein and RNA levels during human cytomegalovirus (HCMV) replication, suggesting either that the virus may require RICK for replication or that RICK is part of an unsuccessful host attempt to inhibit HCMV replication. It is demonstrated here that forced expression of RICK in either a kinase active or inactive form activates nuclear factor (NF)-kappaB by means of its intermediate domain and potently blocks HCMV replication in human fibroblasts. Importantly, NF-kappaB activation, which exerted a modestly positive effect on the early phase of infection, clearly had a strongly negative impact during later viral steps. A stable inhibitor of NF-kappaB (IkappaB) reverses the RICK inhibitory effect, and activation of NF-kappaB by IkappaB kinase beta expression is inhibitory to HCMV, demonstrating that NF-kappaB activation is part of a potent anti-HCMV response. Supernatant transfer experiments identified interferon-beta as a downstream component of the RICK inhibitory pathway. RICK expression was found to synergize with HCMV infection in the induction of interferon-beta expression. This study identifies an endogenous RICK-activated, NF-kappaB- and interferon-beta-dependent antiviral pathway that is either inhibited or faulty under normal HCMV replication conditions; efforts to bolster this pathway may lead to novel anti-viral approaches.

Messenger RNA initiate the process of translation, by transferring the code of DNA to transfer RNA. A poly purine rich sequence called Shine-Dalgarno sequence help to determine the position of the start codon, the Shine-Dalgarno sequence is different because it allows the ribosome to be built at an interior position on the mRNA through direct binding to this sequence. Ribosomal binding site at the 5’end of translation initiation site used to bind mRNA secondary structure, distance between RBS and start codon effects translational efficiency of a gene. In this study we change the distance between ribosome binding site and start codon to get the different ratios of translational expression. For this purpose, proinsulin gene cloned in pET21a vector, the distance between the binding site and starting codon has been retained to 8 nucleotides. At this distance between ribosome binding site (RBS) and start codon, the expression of proinsulin was 30% of total cellular proteins. When there are 10 nucleotides between, expression decreased up to 2-4%. With 12 nucleotides between RBS and start codon, expression further decreased up to 1-2%. As these attempts are made random, so by checking the mRNA secondary structures by M-fold showed a binding between ribosome binding site and start of proinsulin gene. Another attempt was made by incorporating ten different nucleotides at the start of proinsulin gene to make the secondary structure of mRNA less stable (ΔG=-5.5) does not significantly alter the expression of proinsulin in BL21 codon plus cells. There are other controlling factors of protein expression i.e., metabolic instability, rapid degradation of mRNA or accumulation of protein may downregulate expression of mRNA.

mRNA carries a wealth of the structural and regulatory information in addition to the encoded amino acid sequence. This information defines mRNAs secondary structure and stability, pre-mRNA splicing efficiency, regulates rate of translation and affects folding and posttranslational modifications of the nascent polypeptide (1-3). Emerging evidence suggests important biological functions for synonymous nucleotides and “silent” mutations in the protein coding genes that do not change the amino acid sequences of the proteins. Using our software Afold, we performed the first transcriptome-wide analysis of the mRNA folding in different organisms, and demonstrated that the structure of the genetic code and the unequal use of synonymous codons create a periodic pattern of nucleotide involvement in mRNA secondary structure in the protein coding regions (CDSs) (4-5). We also showed how RNA secondary structure might regulate gene expression and suggested that a periodic pattern in the CDS is likely responsible for translation frame monitoring (Figure 1 - low centre panel, 1-2). The degenerate codon sites make the greatest contribution to mRNA stability. Our results support the hypothesis that redundancies in the genetic code enable mRNA sequences to satisfy requirements for both protein and RNA structure, and suggest that selection in favor of G and C may be operating in synonymous codons to maintain a more stable and ordered mRNA secondary structure, which is likely to be important for transcript stability and translation. Functional domains of the mRNA (5’UTR, CDS and 3’UTR) preferentially fold onto themselves, while domain boundaries are characterized by relaxed secondary structures, as compared to the overall mRNA folding. Relaxed secondary structures in the vicinity of the start and stop codon regions could facilitate the initiation and termination of translation. Comparative analysis of mRNA secondary structure patterns for eukaryotes and prokaryotes revealed the ubiquity of periodic mRNA secondary structures and RNA level selection pressure acting at the level of mRNA secondary structure in different organisms. Systematic differences in selection pressure exist between synonymous and nonsynonymous positions in mRNA coding regions (2). Selection pressure on the coding gene regions follows a three-nucleotide periodic pattern of nucleotide base pairing in

The rate of translational elongation is non-uniform. mRNA secondary structure, codon usage and mRNA associated proteins may alter ribosome movement on the message(for review see 1). However, it's now widely accepted that synonymous codon usage is the primary cause of non-uniform translational elongation rates(1). Synonymous codons are not used with identical frequency. A bias exists in the use of synonymous codons with some codons used more frequently than others(2). Codon bias is organism as well as tissue specific(2,3). Moreover, frequency of codon usage is directly proportional to the concentrations of cognate tRNAs(4). Thus, a frequently used codon will have higher multitude of corresponding tRNAs, which further implies that a frequent codon will be translated faster than an infrequent one. Thus, regions on mRNA enriched in rare codons (potential pause sites) will as a rule slow down ribosome movement on the message and cause accumulation of nascent peptides of the respective sizes(5-8). These pause sites can have functional impact on the protein expression, mRNA stability and protein folding(for review see 9). Indeed, it was shown that alleviation of such pause sites can alter ribosome movement on mRNA and subsequently may affect the efficiency of co-translational (in vivo) protein folding(1,7,10,11). To understand the process of protein folding in vivo, in the cell, that is ultimately coupled to the process of protein synthesis it is essential to gain comprehensive insights into the impact of codon usage/tRNA content on the movement of ribosomes along mRNA during translational elongation. Here we describe a simple technique that can be used to locate major translation pause sites for a given mRNA translated in various cell-free systems(6-8). This procedure is based on isolation of nascent polypeptides accumulating on ribosomes during in vitro translation of a target mRNA. The rationale is that at low-frequency codons, the increase in the residence time of the ribosomes results in increased amounts of nascent peptides of the corresponding sizes. In vitro transcribed mRNA is used for in vitro translational reactions in the presence of radioactively labeled amino acids to allow the detection of the nascent chains. In order to isolate ribosome bound nascent polypeptide complexes the translation reaction is layered on top of 30% glycerol solution followed by centrifugation. Nascent polypeptides in polysomal pellet are further treated with ribonuclease A and resolved by SDS PAGE. This technique can be potentially used for any protein and allows analysis of ribosome movement along mRNA and the detection of the major pause sites. Additionally, this protocol can be adapted to study factors and conditions that can alter ribosome movement and thus potentially can also alter the function/conformation of the protein.

p53 protein levels have been shown to increase in a number of cells after treatment with genotoxic agents through a post-transcriptional mechanism. In gamma-irradiated human cells, the accumulation of p53 protein is accompanied by an increase in the association of p53 mRNA with large polysomes without any change in the level of p53 mRNA. This redistribution of p53 mRNA on polysomes in response to irradiation is consistent with enhanced translational activity of p53 mRNA. We demonstrate that a region of the p53 3'-untranslated region (3'UTR) inhibits translation of a chimeric reporter mRNA in vivo. Induced elevation of reporter activity after gamma-irradiation was seen in cells expressing chimeric reporter-p53 3'UTR transcripts. These data taken together demonstrate translational control of p53 gene expression after gamma-irradiation and denote a previously unsuspected and novel role for the p53 3'UTR in controlling translation.

Implementation of the megaprojects with large-scale engineering and construction projects are risky in nature and evaluating the associated risks of those large projects is a critical success factor. The systematic approaches and empirical studies related to the visualisation and communicating risks of these projects remain missing. This paper aims to develop a systematic approach to managing and visualising the risk of these mega-projects using joint application of fuzzy group decision-making, analytic network process and mapping the resulting network of dependencies together with proximity information, graph theory, and mutual information theory. We have applied the model in a real case study of megaprojects in the oil and gas industry. The methodology proposed in this study could be used in the other large-scale engineering and construction projects considering the contracts features and the contextual factors.

Microbial cells optimally structure their proteomes in order to mutually maximize metabolism and translation, as established by an extensive comparison between data and a low-dimensional model of cellular physiology.

Article Figures and data Abstract Editor's evaluation Introduction Discussion Methods Appendix 1 Data availability References Decision letter Author response Article and author information Metrics Abstract Effective coordination of cellular processes is critical to ensure the competitive growth of microbial organisms. Pivotal to this coordination is the appropriate partitioning of cellular resources between protein synthesis via translation and the metabolism needed to sustain it. Here, we extend a low-dimensional allocation model to describe the dynamic regulation of this resource partitioning. At the core of this regulation is the optimal coordination of metabolic and translational fluxes, mechanistically achieved via the perception of charged- and uncharged-tRNA turnover. An extensive comparison with ≈ 60 data sets from Escherichia coli establishes this regulatory mechanism's biological veracity and demonstrates that a remarkably wide range of growth phenomena in and out of steady state can be predicted with quantitative accuracy. This predictive power, achieved with only a few biological parameters, cements the preeminent importance of optimal flux regulation across conditions and establishes low-dimensional allocation models as an ideal physiological framework to interrogate the dynamics of growth, competition, and adaptation in complex and ever-changing environments. Editor's evaluation This valuable study provides a synthesis of sector models for cellular resource partitioning in microbes and shows how a simple flux balance model can quantitatively explain growth phenomena from numerous published experimental data sets. The evidence is convincing, and the study should be of interest to the microbial physiology community. https://doi.org/10.7554/eLife.84878.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Growth and reproduction is central to life. This is particularly true of microbial organisms where the ability to quickly accumulate biomass is critical for competition in ecologically diverse habitats. Understanding which cellular processes are key in defining growth has thus become a fundamental goal in the field of microbiology. Pioneering physiological and metabolic studies throughout the 20th century laid the groundwork needed to answer this question (Monod, 1935; Monod, 1937; Monod, 1941; Monod, 1947; Monod, 1966; Campbell, 1957; Schaechter et al., 1958; Kjeldgaard et al., 1958; Cooper and Helmstetter, 1968; Donachie et al., 1976; Jun et al., 2018), with the extensive characterization of cellular composition across growth conditions at both the elemental (Heldal et al., 1985; Loferer-Krößbacher et al., 1998; Lawford and Rousseau, 1996) and molecular (Schaechter et al., 1958; Kjeldgaard et al., 1958; Watson, 1976; Britten and Mcclure, 1962) levels showing that the dry mass of microbial cells is primarily composed of proteins and RNA. Seminal studies further revealed that the cellular RNA content is strongly correlated with the growth rate (Schaechter et al., 1958; Kjeldgaard et al., 1958; Gausing, 1977), an observation which has held for many microbial species (Karpinets et al., 2006). As the majority of RNAs are ribosomal, these observations suggested that protein synthesis via ribosomes is a major determinant of biomass accumulation in nutrient replete conditions (Koch, 1988; Hernandez and Bremer, 1993; Magasanik et al., 1959). Given that the cellular processes involved in biosynthesis, particularly those of protein synthesis, are well conserved between species and domains (Doris et al., 2015; Davidovich et al., 2009; Bruell et al., 2008), these findings have inspired hope that fundamental principles of microbial growth can be found despite the enormous diversity of microbial species and the variety of habitats they occupy. The past decade has seen a flurry of experimental studies further establishing the importance of protein synthesis in defining growth. Approaches include modern '-omics' techniques with molecular-level resolution (Taniguchi et al., 2010; Bennett et al., 2009; Schmidt et al., 2016; Valgepea et al., 2013; Peebo et al., 2015; Li et al., 2014; Balakrishnan et al., 2021b; Mori et al., 2021; Belliveau et al., 2021; Metzl-Raz et al., 2017; Paulo et al., 2015; Paulo et al., 2016; Xia et al., 2021; Jahn et al., 2018), measurements of many core physiological processes and their coordination (Dai et al., 2016; Basan et al., 2015; You et al., 2013; Wu et al., 2022; Di Bartolomeo et al., 2020; Li et al., 2018; Jahn et al., 2018; Zavřel et al., 2019; Parker et al., 2020), and the perturbation of major cellular processes like translation (Scott et al., 2010; Hui et al., 2015; Dai et al., 2016; Towbin et al., 2017). Together, these studies advanced a more thorough description of how cells allocate their ribosomes to the synthesis of different proteins depending on their metabolic state and the environmental conditions they encounter, called ribosomal allocation. Tied to the experimental studies, different theoretical ribosomal allocation models have further been formulated to dissect how ribosomal allocation influences growth (Molenaar et al., 2009; Karr et al., 2012; Scott et al., 2014; Weiße et al., 2015; Maitra and Dill, 2015; Giordano et al., 2016; Mori et al., 2017; Erickson et al., 2017; Towbin et al., 2017; Mori et al., 2017; Korem Kohanim et al., 2018; Macklin et al., 2020; Hu et al., 2020; Dourado and Lercher, 2020; Roy et al., 2021; Mori et al., 2021; Serbanescu et al., 2020; Balakrishnan et al., 2021a; Balakrishnan et al., 2021b). For example, high-dimensional models have been formulated which simulate hundreds to thousands of biological reactions (Karr et al., 2012; Macklin et al., 2020) providing a detailed view of the emergence of distinct internal physiological states and the underlying processes which sustain them. Alternatively, other theoretical considerations follow coarse-grained approaches of moderate dimensionality which group different classes metabolic reactions together and mathematizicing their dynamics (Roy et al., 2021; Hu et al., 2020). Distinct from these is an array of extremely low-dimensional models, pioneered by Molenaar et al., 2009, which have been developed to describe growth phenomena in varied conditions and physiological limits that rely on only a few parameters (Molenaar et al., 2009; Scott et al., 2014; Bosdriesz et al., 2015; Giordano et al., 2016; Towbin et al., 2017; Korem Kohanim et al., 2018; Erickson et al., 2017; Mairet et al., 2021; Balakrishnan et al., 2021a) (a more detailed overview of the different modeling approaches is provided in Appendix 1 - Allocation models to study microbial growth). In this work, we build on low-dimensional allocation models (Scott et al., 2014; Giordano et al., 2016; Bosdriesz et al., 2015; Dourado and Lercher, 2020; Hu et al., 2020) and the results from dozens of experimental studies to synthesize a self-consistent and quantitatively predictive description of resource allocation and growth. At the core of our model is the dynamic reallocation of resources between the translational and metabolic machinery, which is sensitive to the metabolic state of the cell. We demonstrate how 'optimal allocation'—meaning an allocation towards ribosomes which contextually maximizes the steady-state growth rate—emerges when the flux of amino acids through translation to generate new proteins and the flux of uncharged-tRNA through metabolism to provide charged-tRNA required for translation are mutually maximized, given the environmental conditions and corresponding physiological constraints. This regulatory scheme, which we term flux-parity regulation, can be mechanistically achieved by a global regulator (e.g., guanosine tetraphosphate, ppGpp, in bacteria) capable of simultaneously measuring the turnover of charged- and uncharged-tRNA pools and routing protein synthesis. The explanatory power of the flux-parity regulation circuit is confirmed by extensive comparison of model predictions with ≈ 60 data sets from Escherichia coli, spanning more than half a century of studies using varied methodologies. This comparison demonstrates that a simple argument of flux-sensitive regulation is sufficient to predict bacterial growth phenomena in and out of steady state and across diverse physiological perturbations. The accuracy of the predictions, coupled with the minimalism of the model, establishes the optimal regulation and cements the centrality of protein synthesis in defining microbial growth. The mechanistic nature of the theory—predicated on a minimal set of biologically meaningful parameters—provides a low-dimensional framework that can be used to explore complex phenomena at the intersection of physiology, ecology, and evolution without requiring extensive characterization of the myriad biochemical processes which drive them. A simple allocation model describes translation-limited growth We begin by formulating a simplified model of growth which follows the flow of mass from nutrients in the environment to biomass by building upon and extending the general logic of low-dimensional resource allocation models (Molenaar et al., 2009; Scott et al., 2010; Scott et al., 2014; Dai et al., 2016; Giordano et al., 2016). Specifically, we focus on the accumulation of protein biomass, as protein constitutes the majority of microbial dry mass (Churchward et al., 1982; Feijó Delgado et al., 2013) and peptide bond formation commonly accounts for ≈80% of the cellular energy budget (Stouthamer, 1973; Belliveau et al., 2021). Furthermore, low-dimensional allocation models utilize a simplified representation of the proteome where proteins can be categorized into only a few functional classes (Molenaar et al., 2009; Scott et al., 2014; Hui et al., 2015; Maitra and Dill, 2015; Dourado and Lercher, 2020). In this work, we consider proteins to be either ribosomal (i.e., a structural component of the ribosome, excluding ternary complex members like EF-Tu), metabolic (i.e., enzymes catalyzing synthesis of charged-tRNA molecules from environmental nutrients), or being involved in all other biological processes (e.g., lipid synthesis, DNA replication, energy generation, and chemotaxis) Molenaar et al., 2009; Scott et al., 2010; Scott et al., 2014; Hui et al., 2015; Figure 1—figure supplement 1; in Appendix 1 What makes the fraction of 'other' proteins?, we outline in more detail how individual protein species are partitioned between the 'metabolic' and 'other' sectors depending on their functional annotations. Simple allocation models further do not distinguish between different cells but only consider the overall turnover of nutrients and biomass. To this end, we explicitly consider a well-mixed batch culture growth as reference scenario where the nutrients are considered to be in abundance. This low-dimensional view of living matter may at first seem like an unfair approximation, ignoring the decades of work interrogating the multitudinous biochemical and biophysical processes of cell-homeostasis and growth (Macklin et al., 2020; Karr et al., 2012; Hui et al., 2015; Grigaitis et al., 2021; Noree et al., 2019). However, at least in nutrient replete conditions, many of these processes appear not to impose a fundamental limit on the rate of growth in the manner that protein synthesis does (Belliveau et al., 2021). In Appendix 1 The major simplifications of low-dimensional allocation models and why they might work we discuss this along with other simplifications in more detail. To understand protein synthesis and biomass growth within the low-dimensional allocation framework, consider the flux diagram (Figure 1A, Molenaar et al., 2009; Giordano et al., 2016; Belliveau et al., 2021; Balakrishnan et al., 2021b; Scott et al., 2014) showing the masses of the three protein classes, precursors which are required for protein synthesis (including charged-tRNA molecules, free amino acids, cofactors, etc.), nutrients which are required for the synthesis of precursors, and the corresponding fluxes through the key biochemical processes (arrows). This diagram emphasizes that growth is autocatalytic in that the synthesis of ribosomes is undertaken by ribosomes which imposes a strict speed limit on growth (Dill et al., 2011; Belliveau et al., 2021; Kafri et al., 2016). While this may imply that the rate of growth monotonically increases with increasing ribosome abundance, it is important to remember that metabolic proteins are needed to supply the ribosomes with the precursors needed to form peptide bonds. Herein lies the crux of ribosomal allocation models: the abundance of ribosomes is constrained by the need to synthesize other proteins and growth is a result of how new protein synthesis is partitioned between ribosomal, metabolic, and other proteins. How is this partitioning determined, and how does it affect growth? Figure 1 with 4 supplements see all Download asset Open asset A simple model of ribosomal allocation and hypothetical regulatory strategies. (A) The flow of mass through the self-replicating system. Biomolecules and biosynthetic processes are shown as gray and white boxes, respectively. Nutrients in the environment passed through cellular metabolism to produce 'precursor' molecules which are then consumed through the process of translation to produce new protein biomass, either as metabolic proteins (purple arrow), ribosomal proteins (gold arrow), or 'other' proteins (gray arrow). (B) Annotated equations of the model with key parameters highlighted in blue. An interactive figure where these equations can be numerically integrated is provided on paper website (cremerlab.github.io/flux_parity). (C) Key model parameters, their units, typical values in E. coli, and their appropriate references. This is also provided as Supplementary file 1. The steady-state values of (D) the growth rate λ and (E) the relative translation rate γ⁢(cp⁢c*)/γm⁢a⁢x, are plotted as functions of the allocation towards ribosomes for different metabolic rates (colored lines). (F) Analytical solutions for candidate scenarios for regulation of ribosomal allocation with fixed allocation, allocation to prioritize translation rate, and allocation to optimal growth rate highlighted in gray, green, and blue respectively. (G) A list of collated data sets of E. coli ribosomal allocation and translation speed measurements spanning 55 years of research. Details regarding these sources and method of data collation is provided in Supplementary file 2. A comparison of the observations with predicted growth-rate dependence of ribosomal allocation (H) and translation speeds (I) for the three allocation strategies. An interactive version of the panels allowing the free adjustment of parameters is available on the associated paper website (cremerlab.github.io/flux_parity). Figure 1—source data 1 Collated measurements of ribosomal mass fractions in E. coli. https://cdn.elifesciences.org/articles/84878/elife-84878-fig1-data1-v2.csv Download elife-84878-fig1-data1-v2.csv Figure 1—source data 2 Collated measurements of translation speeds per ribosome in E. coli. https://cdn.elifesciences.org/articles/84878/elife-84878-fig1-data2-v2.csv Download elife-84878-fig1-data2-v2.csv To answer these questions, we must understand how these different fluxes interact at a quantitative level and thus must mathematize the biology underlying the boxes and arrows in Figure 1A. Taking inspiration from previous models of allocation (Molenaar et al., 2009; Scott et al., 2010; Scott et al., 2014; Giordano et al., 2016; Dourado and Lercher, 2020), we enumerate a minimal set of coupled differential equations which captures the flow of mass through metabolism and translation (Figure 1B, with the dimensions and value ranges of the parameters listed in Figure 1C and Supplementary file 1). While we present a step-by-step introduction of this model in 'Methods,' we here focus on a summary of the underlying biological intuition and implications of the approach. We begin by codifying the assertion that protein synthesis is key in determining growth. The synthesis of new total protein mass M depends on the total proteinaceous mass of ribosomes MR⁢b present in the system and their corresponding average translation rate γ (Figure 1Bi). As ribosomes rely on precursors to work, it is reasonable to assert that this translation rate must be dependent on the concentration of precursors cp⁢c such that γ≡γ⁢(cp⁢c) (Scott et al., 2014; Giordano et al., 2016), for which a simple Michaelis–Menten relation is biochemically well motivated (Figure 1Bii). With changing precursor concentrations, the translation rate γ varies between a maximum value γm⁢a⁢x, representing rapid synthesis, and a minimum value γm⁢i⁢n, representing the slowest achievable translation rate. In our model, this minimum rate γm⁢i⁢n is zero and corresponds to the condition where there are no available precursors to support translation. The standing precursor concentration cp⁢c is set by a combination of processes (Figure 1Biii), namely the production of new precursors through metabolism (synthesis), their degradation through translation (consumption), and their dilution as the total cell volume grows. The synthesis is driven by the abundance of metabolic proteins MM⁢b in the system and the speed by which they convert nutrients into novel precursors. As the metabolic networks at play are complex, low-dimensional allocation models describe the process of metabolism using an average metabolic rate ν in lieu of mathematicizing the network's individual components. As such, the metabolic rate is difficult to directly measure but generally depends on the quality and concentration of nutrients in the environment (see below, Figure 1—figure supplement 2 and 'Methods'). In the following, we focus on a growth regime in which nutrient concentrations are saturating. In such a scenario, metabolism operates at a nutrient-specific maximal metabolic rate ν≡νm⁢a⁢x. Finally, the relative magnitude of the ribosomal, metabolic, and 'other' protein masses is dictated by ϕR⁢b, ϕM⁢b, and ϕO, three allocation parameters which range between zero and one to describe the fraction of ribosomes being utilized in synthesizing the corresponding protein pools. Importantly, as ribosomes only translate one protein at a time, the allocation parameters follow the constraint ϕR⁢b+ϕM⁢b+ϕO=1 (Figure 1Biv). For readers familiar with allocation models, we emphasize that we here use ϕX to denote allocation parameters rather than mass fractions, MX/M; both quantities are only equivalent in the steady-state regime. Together, the introduced equations provide a full mathematicization of the mass flow diagram shown in Figure 1A. For constant allocation parameters (ϕR⁢b*,ϕM⁢b*), a steady-state regime emerges from this system of differential equation. Particularly, the precursor concentration is stationary in time (cp⁢c=cp⁢c*), meaning the rate of synthesis is exactly equal to the rate of consumption and dilution. Furthermore, the translation rate γ⁢(cp⁢c*) is constant during steady-state growth and the mass abundances of ribosomes and metabolic proteins are equivalent to the corresponding allocation parameters, e.g. MR⁢bM≡ϕR⁢b*. As a consequence, biomass is increasing exponentially d⁢Md⁢t=λ⁢M, with the growth rate λ=γ⁢(cp⁢c*)⁢ϕR⁢b*. The emergence of a steady state and analytical solutions describing steady growth are further discussed in Figure 1—figure supplement 2 and Figure 1—figure supplement 3. Notably, dilution is important to obtain a steady state as has been highlighted previously by Giordano et al., 2016 and Dourado and Lercher, 2020 but is often neglected (Appendix Precursors concentrations and the importance of dilution by cell growth). Figure 1D and E show how the steady-state growth rate λ and translation rate γ⁢(cp⁢c*) are dependent on the allocation towards ribosomes ϕR⁢b*. The figures also show the dependence on the metabolic rate νm⁢a⁢x which we here assert to be a proxy for the 'quality' of the nutrients in the environment (with increasing νm⁢a⁢x, less metabolic proteins are required to obtain the same synthesis of precursors). The non-monotonic dependence of the steady-state growth rate on the ribosome allocation and the metabolic rate poses a critical question: What biological mechanisms determine the allocation towards ribosomes in a particular environment and what criteria must be met for the allocation to ensure efficient growth? Different strategies for regulation of allocation predicts different phenomenological behavior While cells might employ many different ways to regulate allocation, we here consider three specific allocation scenarios to illustrate the importance of allocation on growth. These candidate scenarios either strictly maintain the total ribosomal content (scenario maintain a rate of translation (scenario or the steady-state growth rate (scenario We analytical solutions for these scenarios has been previously for scenario Giordano et al., 2016; Dourado and Lercher, 2020; Figure and and these predictions to observations with E. coli to show this optimal allocation of The and regulatory scenario is one in which the allocation towards ribosomes is fixed and of the environmental This (scenario in Figure a physiological state where a specific constant fraction of all proteins is This imposes a strict speed limit for growth when all ribosomes are to their maximal rate, the fixed allocation is (e.g., then this speed limit be at moderate metabolic A more complex regulatory scenario is one in which the allocation towards ribosomes is to prioritize the translation rate. This (scenario in Figure that the ribosomal allocation is such that a constant internal concentration of precursors is across environmental conditions, of the metabolic rate. In the where this standing precursor concentration is all ribosomes be to their maximal rate. The and regulatory scenario is one in which the allocation towards ribosomes is such that the steady-state growth rate is The analytical which describes this scenario (scenario in Figure previous analytical solutions by Giordano et al., 2016; Dourado and Lercher, the can be of as one in which the allocation towards ribosomes is across conditions such that the growth rate at the of the in Figure Notably, this does not imply that the translation rate is across conditions in scenario the translation rate is also and approaches maximal value only in conditions metabolic allocation scenarios and their on growth are discussed in further detail in Figure 1—figure supplement 4 and the corresponding interactive figure on the paper website (cremerlab.github.io/flux_parity). E. coli ribosome content to growth our modeling of microbial growth has without parameters to the of one To the predictive power of this simple allocation model and the of the three different strategies for regulation of ribosomal allocation, we a and of data from a array quantitative studies of the E. coli. This of studies spanning 55 years of in Supplementary file 2 and as Figure 1—source data 1 and Figure 1—source data using varied experimental well previous to allocation models to data (Scott et al., 2010; Hui et al., 2015; Erickson et al., 2017; Giordano et al., 2016; Bosdriesz et al., 2015; Hu et al., 2020; Dourado and Lercher, 2020; Serbanescu et al., 2020; Hu et al., 2020; Roy et al., 2021; Maitra and Dill, 2015; Weiße et al., These shown in Figure and present a view of E. coli physiology where the allocation towards ribosomes to ribosomal mass fraction in steady-state and the translation rate demonstrate a dependence on the steady-state growth rate in different The between the allocation towards ribosomes and the steady-state growth rate out scenario where allocation is as a regulatory used by E. coli, of the of a dependence of the translation speed on the growth rate out scenario where the translation rate is across growth rates and at a constant The for both the ribosomal allocation and the translation speed is only with the logic of regulatory scenario where the allocation towards ribosomes is to growth rate. This logic is quantitatively confirmed when we the predicted of these quantities on the steady-state growth rate for the three scenarios in Figure on values for key parameters in Supplementary file 1). from the for scenario are only for the ribosomal content at steady growth which are in ecologically conditions and can be to biological and experimental protein degradation et al., and which have not steady we discuss in Appendix 1 considerations at growth. The of ribosomes is such a growth fraction is not sufficient to explain the Appendix 1 Importantly, the between and observations with a minimal of parameters and does not the of fixed model parameters such as the maximum translation rate and the Michaelis–Menten constant for translation have distinct biological meaning and can be either directly or from data file 1). Furthermore, we discuss the of other parameters such as the protein (Appendix What makes the fraction of 'other' with the maximum metabolic rate νm⁢a⁢x, and of ribosome and minimal ribosome content (Appendix provide an interactive figure on the paper website where the of these regulatory scenarios and the with data can be directly there is no combination of values that scenario or to describe both the ribosomal allocation and the translation speed as a of growth rate. These findings are in with a modeling study et al., 2020), on the of a with the in translation speed with growth as a of efficient protein synthesis. Together, these results that scenario can describe observations a range of conditions, in support of the but often that E. coli ribosomal content to growth et al., 2016; Bosdriesz et al., 2015; Towbin et al., 2017). In Appendix 1 of the model to we present a for in with previous studies et al., 2017; Xia et al., 2021; Paulo et al., 2015; Paulo et al., 2016; and that this follows a optimal allocation data for ribosomal content and the translation rate is The between ribosome content and growth rate has further been for other microbial organisms in with an optimal allocation (Karpinets et al., Jahn et al., 2018; Zavřel et al., 2019; Jahn et al., the of translation rate measurements An is the which to maintain constant allocation, in with scenario et al., 2021). The thus that E. coli and many other microbes follow an optimal ribosome allocation behavior to support efficient growth. the between and data establishes that a simple low-dimensional allocation model can describe growth with quantitative accuracy. However, this the question: how do cells their complex to ensure optimal allocation results from a of translational and metabolic flux To the steady-state growth rate, cells must have of the flow of mass through metabolism and protein synthesis. In the ribosomal allocation model, this to a regulatory in which the allocation

The expression of neutrophil-specific chemokines is known to be regulated via adenine-uridine-rich sequence elements in the 3'-untranslated regions of their mRNAs that confer a high degree of mRNA instability. Although the presence of intron sequences in eukaryotic genes is known to enhance expression, the effect of intron content on the rate of mature, translatable mRNA degradation has not been demonstrated. In this study, we have determined the effects of intron content on the rate of decay of the chemokine CXCL1 (KC) mRNA. The half-life of KC mRNA was markedly prolonged when the primary transcript was obtained from a genomic clone containing three introns as compared with the half-life observed with sequence-identical KC mRNA derived from an intron-free cDNA construct. The effect of intron content was achieved with a single intron, and neither the intron sequences nor the intron positions were critical determinants of the outcome. The intron content produced the same effect when expressed in multiple cell types and when the sequences were stably integrated into the genome. The differential decay rates were not a consequence of differential nuclear to cytoplasmic transport. The intron content of the primary transcript did not influence the rate of KC mRNA translation and did not modulate the ability of interleukin-1 stimulation to stabilize the otherwise unstable mRNA. The intron effect on mRNA decay was seen with mRNAs containing two distinct instability determinants. These findings document that intron content marks the mRNA sequence leading to enhanced stability that is particularly evident in short lived ARE-containing mRNAs.

It was necessary to have a tool that could predict the amount of protein and optimize the gene sequences to produce recombinant proteins efficiently. The Transim model published by Tuller et al. in 2018 can calculate the translation rate in E. coli using features on the mRNA sequence, achieving a Spearman correlation with the amount of protein per mRNA of 0.36 when tested on the dataset of operons’ first genes in E. coli K-12 MG1655 genome. However, this Spearman correlation was not high, and the model did not fully consider the features of mRNA and protein sequences. Therefore, to enhance the prediction capability, our study firstly tried expanding the testing dataset, adding genes inside the operon, and using the microarray of the mRNA expression data set, thereby helping to improve the correlation of translation rate with the amount of protein with more than 0.42. Next, the applicability of 6 traditional machine learning models to calculate a "new translation rate" was examined using initiation rate and elongation rate as inputs. The result showed that the SVR algorithm had the most correlated new translation rates, with Spearman correlation improving to R = 0.6699 with protein level output and to R = 0.6536 with protein level per mRNA. Finally, the study investigated the degree of improvement when combining more features with the new translation rates. The results showed that the model’s predictive ability to produce a protein per mRNA reached R = 0.6660 when using six features, while the correlation of this model’s final translation rate to protein level was up to R = 0.6729. This demonstrated the model’s capability to predict protein expression of a gene, rather than being limited to predicting expression by an mRNA and showed the model’s potential for development into gene expression predicting tools.

It was necessary to have a tool that could predict the amount of protein and optimize the gene sequences to produce recombinant proteins efficiently. The Transim model published by Tuller et al. in 2018 can calculate the translation rate in E. coli using features on the mRNA sequence, achieving a Spearman correlation with the amount of protein per mRNA of 0.36 when tested on the dataset of operons' first genes in E. coli K-12 MG1655 genome. However, this Spearman correlation was not high, and the model did not fully consider the features of mRNA and protein sequences. Therefore, to enhance the prediction capability, our study firstly tried expanding the testing dataset, adding genes inside the operon, and using the microarray of the mRNA expression data set, thereby helping to improve the correlation of translation rate with the amount of protein with more than 0.42. Next, the applicability of 6 traditional machine learning models to calculate a "new translation rate" was examined using initiation rate and elongation rate as inputs. The result showed that the SVR algorithm had the most correlated new translation rates, with Spearman correlation improving to R = 0.6699 with protein level output and to R = 0.6536 with protein level per mRNA. Finally, the study investigated the degree of improvement when combining more features with the new translation rates. The results showed that the model's predictive ability to produce a protein per mRNA reached R = 0.6660 when using six features, while the correlation of this model's final translation rate to protein level was up to R = 0.6729. This demonstrated the model's capability to predict protein expression of a gene, rather than being limited to predicting expression by an mRNA and showed the model's potential for development into gene expression predicting tools.

Protein degradation plays an important role in modulating ethylene signal transduction in plants. Here we show that the ethylene receptor ETR2 is one such target for degradation and that its degradation is dependent upon perception of the signaling ligand ethylene. The ETR2 protein is initially induced by ethylene treatment, consistent with an increase in transcript levels. At ethylene concentrations above 1 mul/liter, however, ETR2 protein levels subsequently decrease in a post-transcriptional fashion. Genetic and chemical approaches indicate that ethylene perception by the receptors initiates the reduction in ETR2 protein levels. The ethylene-induced decrease in ETR2 levels is not affected by cycloheximide, an inhibitor of protein biosynthesis, but is affected by proteasome inhibitors, indicating a role for the proteasome in ETR2 degradation. Ethylene-induced degradation still occurs in seedlings treated with brefeldin A, indicating that degradation of ETR2 does not require exit from its subcellular location at the endoplasmic reticulum. These data support a model in which ETR2 is degraded by a proteasome-dependent pathway in response to ethylene binding. Implications of this model for ethylene signaling are discussed.

Single and Codon-Optimized Synonymous Mutations in Factor IX Alter Protein Properties