Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria

Omics data analysis reveals the system-level constraint on cellular amino acid composition

Proteins play a pivotal role in coordinating the functions of organisms, essentially governing their traits, as the dynamic arrangement of diverse amino acids leads to a multitude of folded configurations within peptide chains. Despite dynamic changes in amino acid composition of an individual protein (referred to as AAP) and great variance in protein expression levels under different conditions, our study, utilizing transcriptomics data from four model organisms uncovers surprising stability in the overall amino acid composition of the total cellular proteins (referred to as AACell). Although this value may vary between different species, we observed no significant differences among distinct strains of the same species. This indicates that organisms enforce system-level constraints to maintain a consistent AACell, even amid fluctuations in AAP and protein expression. Further exploration of this phenomenon promises insights into the intricate mechanisms orchestrating cellular protein expression and adaptation to varying environmental challenges.

Construction of a broad-host-range Anderson promoter series and particulate methane monooxygenase promoter variants expand the methanotroph genetic toolbox

Methanotrophic bacteria are currently used industrially for the bioconversion of methane-rich natural gas and anaerobic digestion-derived biogas to valuable products. These bacteria may also serve to mitigate the negative effects of climate change by capturing atmospheric greenhouse gases. Several genetic tools have previously been developed for genetic and metabolic engineering of methanotrophs. However, the available tools for use in methanotrophs are significantly underdeveloped compared to many other industrially relevant bacteria, which hinders genetic and metabolic engineering of these biocatalysts. As such, expansion of the methanotroph genetic toolbox is needed to further our understanding of methanotrophy and develop biotechnologies that leverage these unique microbes for mitigation and conversion of methane to valuable products. Here, we determined the copy number of three broad-host-range plasmids in Methylococcus capsulatus Bath and Methylosinus trichosporium OB3b, representing phylogenetically diverse Gammaproteobacterial and Alphaproteobacterial methanotrophs, respectively. Further, we show that the commonly used synthetic Anderson series promoters are functional and exhibit similar relative activity in M. capsulatus and M. trichosporium OB3b, but the synthetic series had limited range. Thus, we mutagenized the native M. capsulatus particulate methane monooxygenase promoter and identified variants with activity that expand the activity range of synthetic, constitutive promoters functional not only in M. capsulatus, but also in Escherichia coli. Collectively, the tools developed here advance the methanotroph genetic engineering toolbox and represent additional synthetic genetic parts that may have broad applicability in Pseudomonadota bacteria.

How total mRNA influences cell growth

Experimental observations tracing back to the 1960s imply that ribosome quantities play a prominent role in determining a cell's growth. Nevertheless, in biologically relevant scenarios, growth can also be influenced by the levels of mRNA and RNA polymerase. Here, we construct a quantitative model of biosynthesis providing testable scenarios for these situations. The model explores a theoretically motivated regime where RNA polymerases compete for genes and ribosomes for transcripts and gives general expressions relating growth rate, mRNA concentrations, ribosome, and RNA polymerase levels. On general grounds, the model predicts how the fraction of ribosomes in the proteome depends on total mRNA concentration and inspects an underexplored regime in which the trade-off between transcript levels and ribosome abundances sets the cellular growth rate. In particular, we show that the model predicts and clarifies three important experimental observations, in budding yeast and Escherichia coli bacteria: i) that the growth-rate cost of unneeded protein expression can be affected by mRNA levels, ii) that resource optimization leads to decreasing trends in mRNA levels at slow growth, and iii) that ribosome allocation may increase, stay constant, or decrease, in response to transcription-inhibiting antibiotics. Since the data indicate that a regime of joint limitation may apply in physiological conditions and not only to perturbations, we speculate that this regime is likely self-imposed.

Proteome partitioning constraints in long-term laboratory evolution

Adaptive laboratory evolution experiments provide a controlled context in which the dynamics of selection and adaptation can be followed in real-time at the single-nucleotide level. And yet this precision introduces hundreds of degrees-of-freedom as genetic changes accrue in parallel lineages over generations. On short timescales, physiological constraints have been leveraged to provide a coarse-grained view of bacterial gene expression characterized by a small set of phenomenological parameters. Here, we ask whether this same framework, operating at a level between genotype and fitness, informs physiological changes that occur on evolutionary timescales. Using a strain adapted to growth in glucose minimal medium, we find that the proteome is substantially remodeled over 40 000 generations. The most striking change is an apparent increase in enzyme efficiency, particularly in the enzymes of lower-glycolysis. We propose that deletion of metabolic flux-sensing regulation early in the adaptation results in increased enzyme saturation and can account for the observed proteome remodeling.

RNase E searches for cleavage sites in RNA by linear diffusion: direct evidence from single-molecule FRET

The ability of obstacles in cellular transcripts to protect downstream but not upstream sites en masse from attack by RNase E has prompted the hypothesis that this mRNA-degrading endonuclease may scan 5'-monophosphorylated RNA linearly for cleavage sites, starting at the 5' end. However, despite its proposed regulatory importance, the migration of RNase E on RNA has never been directly observed. We have now used single-molecule FRET to monitor the dynamics of this homotetrameric enzyme on RNA. Our findings reveal that RNase E slides along unpaired regions of RNA without consuming a molecular source of energy such as ATP and that its forward progress can be impeded when it encounters a large structural discontinuity. This movement, which is bidirectional, occurs in discrete steps of variable length and requires an RNA ligand much longer than needed to occupy a single RNase E subunit. These results indicate that RNase E scans for cleavage sites by one-dimensional diffusion and suggest a possible molecular mechanism.

The functional small RNA interactome reveals targets for the vancomycin-responsive sRNA RsaOI in vancomycin-tolerant Staphylococcus aureus

Small RNAs have been found to control a broad range of bacterial phenotypes including tolerance to antibiotics. Vancomycin tolerance in multidrug resistance Staphylococcus aureus is correlated with dysregulation of small RNAs although their contribution to antibiotic tolerance is poorly understood. RNA-RNA interactome profiling techniques are expanding our understanding of sRNA-mRNA interactions in bacteria; however, determining the function of these interactions for hundreds of sRNA-mRNA pairs is a major challenge. At steady-state, protein and mRNA abundances are often highly correlated and lower than expected protein abundance may indicate translational repression of an mRNA. To identify sRNA-mRNA interactions that regulate mRNA translation, we examined the correlation between gene transcript abundance, ribosome occupancy, and protein levels. We used the machine learning technique self-organizing maps (SOMs) to cluster genes with similar transcription and translation patterns and identified a cluster of mRNAs that appeared to be post-transcriptionally repressed. By integrating our clustering with sRNA-mRNA interactome data generated in vancomycin-tolerant S. aureus by RNase III-CLASH, we identified sRNAs that may be mediating translational repression. We have confirmed sRNA-dependant post-transcriptional repression of several mRNAs in this cluster. Two of these interactions are mediated by RsaOI, a sRNA that is highly upregulated by vancomycin. We demonstrate the regulation of HPr and the cell-wall autolysin Atl. These findings suggest that RsaOI coordinates carbon metabolism and cell wall turnover during vancomycin treatment.

IMPORTANCE

The emergence of multidrug-resistant Staphylococcus aureus (MRSA) is a major public health concern. Current treatment is dependent on the efficacy of last-line antibiotics like vancomycin. The most common cause of vancomycin treatment failure is strains with intermediate resistance or tolerance that arise through the acqusition of a diverse repertoire of point mutations. These strains have been shown to altered small RNA (sRNA) expression in response to antibiotic treatment. Here, we have used a technique termed RNase III-CLASH to capture sRNA interactions with their target mRNAs. To understand the function of these interactions, we have looked at RNA and protein abundance for mRNAs targeted by sRNAs. Messenger RNA and protein levels are generally well correlated and we use deviations from this correlation to infer post-transcriptional regulation and the function of individual sRNA-mRNA interactions. Using this approach we identify mRNA targets of the vancomycin-induced sRNA, RsaOI, that are repressed at the translational level. We find that RsaOI represses the cell wall autolysis Atl and carbon transporter HPr suggestion a link between vancomycin treatment and suppression of cell wall turnover and carbon metabolism.

Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis

Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to changing environments, yet the concept of transcriptional plasticity (TP) - the variability of gene expression in response to environmental changes - remains largely unexplored. In this study, we investigate the genome-wide TP profiles of Mycobacterium tuberculosis (Mtb) genes by analyzing 894 RNA sequencing samples derived from 73 different environmental conditions. Our data reveal that Mtb genes exhibit significant TP variation that correlates with gene function and gene essentiality. We also find that critical genetic features, such as gene length, GC content, and operon size independently impose constraints on TP, beyond trans-regulation. By extending our analysis to include two other Mycobacterium species -- M. smegmatis and M. abscessus -- we demonstrate a striking conservation of the TP landscape. This study provides a comprehensive understanding of the TP exhibited by mycobacteria genes, shedding light on this significant, yet understudied, genetic feature encoded in bacterial genomes.

Improved RNA stability estimation through Bayesian modeling reveals most Salmonella transcripts have subminute half-lives

RNA decay is a crucial mechanism for regulating gene expression in response to environmental stresses. In bacteria, RNA-binding proteins (RBPs) are known to be involved in posttranscriptional regulation, but their global impact on RNA half-lives has not been extensively studied. To shed light on the role of the major RBPs ProQ and CspC/E in maintaining RNA stability, we performed RNA sequencing of Salmonella enterica over a time course following treatment with the transcription initiation inhibitor rifampicin (RIF-seq) in the presence and absence of these RBPs. We developed a hierarchical Bayesian model that corrects for confounding factors in rifampicin RNA stability assays and enables us to identify differentially decaying transcripts transcriptome-wide. Our analysis revealed that the median RNA half-life in Salmonella in early stationary phase is less than 1 min, a third of previous estimates. We found that over half of the 500 most long-lived transcripts are bound by at least one major RBP, suggesting a general role for RBPs in shaping the transcriptome. Integrating differential stability estimates with cross-linking and immunoprecipitation followed by RNA sequencing (CLIP-seq) revealed that approximately 30% of transcripts with ProQ binding sites and more than 40% with CspC/E binding sites in coding or 3' untranslated regions decay differentially in the absence of the respective RBP. Analysis of differentially destabilized transcripts identified a role for ProQ in the oxidative stress response. Our findings provide insights into posttranscriptional regulation by ProQ and CspC/E, and the importance of RBPs in regulating gene expression.

Beyond genome: Advanced omics progress of Panax ginseng

Ginseng is a perennial herb of the genus Panax in the family Araliaceae as one of the most important traditional medicine. Genomic studies of ginseng assist in the systematic discovery of genes related to bioactive ginsenosides biosynthesis and resistance to stress, which are of great significance in the conservation of genetic resources and variety improvement. The transcriptome reflects the difference and consistency of gene expression, and transcriptomics studies of ginseng assist in screening ginseng differentially expressed genes to further explore the powerful gene source of ginseng. Protein is the ultimate bearer of ginseng life activities, and proteomic studies of ginseng assist in exploring the biosynthesis and regulation of secondary metabolites like ginsenosides and the molecular mechanism of ginseng adversity adaptation at the overall level. In this review, we summarize the current status of ginseng research in genomics, transcriptomics and proteomics, respectively. We also discuss and look forward to the development of ginseng genome allele mapping, ginseng spatiotemporal, single-cell transcriptome, as well as ginseng post-translational modification proteome. We hope that this review will contribute to the in-depth study of ginseng and provide a reference for future analysis of ginseng from a systems biology perspective.

Contribution of different macromolecules to the diffusion of a 40 nm particle in Escherichia coli

Due to the high concentration of proteins, nucleic acids, and other macromolecules, the bacterial cytoplasm is typically described as a crowded environment. However, the extent to which each of these macromolecules individually affects the mobility of macromolecular complexes, and how this depends on growth conditions, is presently unclear. In this study, we sought to quantify the crowding experienced by an exogenous 40 nm fluorescent particle in the cytoplasm of E. coli under different growth conditions. By performing single-particle tracking measurements in cells selectively depleted of DNA and/or mRNA, we determined the contribution to crowding of mRNA, DNA, and remaining cellular components, i.e., mostly proteins and ribosomes. To estimate this contribution to crowding, we quantified the difference of the particle's diffusion coefficient in conditions with and without those macromolecules. We found that the contributions of the three classes of components were of comparable magnitude, being largest in the case of proteins and ribosomes. We further found that the contributions of mRNA and DNA to crowding were significantly larger than expected based on their volumetric fractions alone. Finally, we found that the crowding contributions change only slightly with the growth conditions. These results reveal how various cellular components partake in crowding of the cytoplasm and the consequences this has for the mobility of large macromolecular complexes.

A coarse-grained bacterial cell model for resource-aware analysis and design of synthetic gene circuits

Within a cell, synthetic and native genes compete for expression machinery, influencing cellular process dynamics through resource couplings. Models that simplify competitive resource binding kinetics can guide the design of strategies for countering these couplings. However, in bacteria resource availability and cell growth rate are interlinked, which complicates resource-aware biocircuit design. Capturing this interdependence requires coarse-grained bacterial cell models that balance accurate representation of metabolic regulation against simplicity and interpretability. We propose a coarse-grained E. coli cell model that combines the ease of simplified resource coupling analysis with appreciation of bacterial growth regulation mechanisms and the processes relevant for biocircuit design. Reliably capturing known growth phenomena, it provides a unifying explanation to disparate empirical relations between growth and synthetic gene expression. Considering a biomolecular controller that makes cell-wide ribosome availability robust to perturbations, we showcase our model's usefulness in numerically prototyping biocircuits and deriving analytical relations for design guidance.

Low temperature promotes the production and efflux of terpenoids in yeast

Altering the fermentation environment provides an effective approach to optimizing the production efficiency of microbial cell factories globally. Here, lower fermentation temperatures of yeast were found to significantly improve the synthesis and efflux of terpenoids, including glycyrrhetinic acid (GA), β-caryophyllene, and α-amyrin. The production of GA at 22°C increased by 5.5 times compared to 30°C. Yeast subjected to lower temperature showed substantial changes at various omics levels. Certain genes involved in maintaining cellular homeostasis that were upregulated under the low temperature conditions, leading to enhanced GA production. Substituting Mvd1, a thermo-unstable enzyme in mevalonate pathway identified by transcriptome and proteome, with a thermo-tolerant isoenzyme effectively increased GA production. The lower temperature altered the composition of phospholipids and increased the unsaturation of fatty acid chains, which may influence GA efflux. This study presents a strategy for optimizing the fermentation process and identifying key targets of cell factories for terpenoid production.

Metabolic Engineering of Escherichia coli for Production of 2,5-Dimethylpyrazine

2,5-Dimethylpyrazine (2,5-DMP) is a high-value-added alkylpyrazine compound with important applications in both the food and pharmaceutical fields. In response to the increasing consumer preference for natural products over chemically synthesized ones, efforts have been made to develop efficient microbial cell factories for the production of 2,5-DMP. However, the previously reported recombinant strains have exhibited low yields and relied on expensive antibiotics and inducers. In this study, we employed metabolic engineering strategies to develop an Escherichia coli strain capable of producing 2,5-DMP at high levels without the need for inducers or antibiotics. Initially, the biosynthesis pathway of 2,5-DMP was constructed that realized 2,5-DMP production from glucose. Subsequently, efforts focused on enhancing 2,5-DMP production by improving the availability of the cofactor NAD+ and precursor l-threonine. Additionally, the supply and conversion of l-threonine were balanced by optimizing the copy number of the key gene tdh on the chromosome and by modifying the l-threonine transport system. The final engineering strain D19 produced 3.1 g/L of 2,5-DMP, which is the highest titer for fermentative production of 2,5-DMP using glucose as the carbon source up to date. The strategies used in this study lay a good foundation for the production of 2,5-DMP on a large scale.

An Automatic Method for Generation of CFD-Based 3D Compartment Models: Towards Real-Time Mixing Simulations

This article aims to develop a method to automatically generate CFD-based compartment models. This effort to simplify mixing models aims at capturing the interactions between material transport and chemical/biochemical conversions in large-scale reactors. The proposed method converts the CFD results into a system of mass balance equations for each defined component. The compartmentalization method is applied to two bioreactor geometries and was able to replicate tracer mixing profiles observed in CFD simulations. The generated compartment models were successfully coupled with, a simple Monod-type biokinetic model describing microbial growth, substrate consumption and product formation. The coupled model was used to simulate a four-hour fermentation in a 190 L reactor and a 10 m3 reactor. Resolving the substrate gradients had a clear impact on the biokinetics, increasing with the scale of the reactor. Moreover, the coupled model could simulate the fermentation faster than real-time. Having a real-time-solvable model is essential for implementations in digital twins and other real-time applications using the models as predictive tools.

Transcription-replication interactions reveal bacterial genome regulation

Organisms determine the transcription rates of thousands of genes through a few modes of regulation that recur across the genome1. In bacteria, the relationship between the regulatory architecture of a gene and its expression is well understood for individual model gene circuits2,3. However, a broader perspective of these dynamics at the genome scale is lacking, in part because bacterial transcriptomics has hitherto captured only a static snapshot of expression averaged across millions of cells4. As a result, the full diversity of gene expression dynamics and their relation to regulatory architecture remains unknown. Here we present a novel genome-wide classification of regulatory modes based on the transcriptional response of each gene to its own replication, which we term the transcription-replication interaction profile (TRIP). Analysing single-bacterium RNA-sequencing data, we found that the response to the universal perturbation of chromosomal replication integrates biological regulatory factors with biophysical molecular events on the chromosome to reveal the local regulatory context of a gene. Whereas the TRIPs of many genes conform to a gene dosage-dependent pattern, others diverge in distinct ways, and this is shaped by factors such as intra-operon position and repression state. By revealing the underlying mechanistic drivers of gene expression heterogeneity, this work provides a quantitative, biophysical framework for modelling replication-dependent expression dynamics.

The proteome is a terminal electron acceptor

Microbial metabolism is impressively flexible, enabling growth even when available nutrients differ greatly from biomass in redox state. E. coli, for example, rearranges its physiology to grow on reduced and oxidized carbon sources through several forms of fermentation and respiration. To understand the limits on and evolutionary consequences of metabolic flexibility, we developed a mathematical model coupling redox chemistry with principles of cellular resource allocation. Our integrated model clarifies key phenomena, including demonstrating that autotrophs grow slower than heterotrophs because of constraints imposed by intracellular production of reduced carbon. Our model further indicates that growth is improved by adapting the redox state of biomass to nutrients, revealing an unexpected mode of evolution where proteins accumulate mutations benefiting organismal redox balance.

Noise robustness and metabolic load determine the principles of central dogma regulation

The processes of gene expression are inherently stochastic, even for essential genes required for growth. How does the cell maximize fitness in light of noise? To answer this question, we build a mathematical model to explore the trade-off between metabolic load and growth robustness. The model predicts novel principles of central dogma regulation: Optimal protein expression levels are vastly overabundant. Essential genes are transcribed above a lower limit of one message per cell cycle. Gene expression is achieved by load balancing between transcription and translation. We show that each of these novel regulatory principles is observed. These results reveal that robustness and metabolic load determine the global regulatory principles that govern central dogma processes, and these principles have broad implications for cellular function.

High level food-grade expression of maltogenic amylase in Bacillus subtilis through dal gene auxotrophic selection marker

As a food-safe microorganism, Bacillus subtilis has been widely utilized in the production of food enzyme, where a food-grade expression system without antibiotic is required. However, there is no mature system for such expression, since the recombinant plasmid in existing food-grade expression system is unstable especially in high-density fermentation. In this study, we constructed a food-grade expression system based on the dal gene auxotrophic selection marker. Specifically, maltogenic amylase (AmyM) was expressed in dal deletion strain without antibiotic, yielding an activity of 519 U/mL. To increase the expression of AmyM, the promoter of amyM (gene encoding AmyM) was optimized. Furthermore, we found that excessive expression of dal gene was detrimental to the stability of plasmid, and the ribosome binding site (RBS) of dal was mutated with the reduced synthesis of D-alanine. After that, AmyM activity increased to 1364 U/mL with the 100 % stability of plasmid. The 3-L fermentor cultivation was performed with the highest value ever reported in food-grade microorganisms, an activity of 2388 U/mL, showing the scale-up production capability of this system. Besides, it is also able to apply the system for other food enzymes, which indicating the great generalizability of this system for different application.

Deep Learning-Assisted Design of Novel Promoters in Escherichia coli

Deep learning (DL) approaches have the ability to accurately recognize promoter regions and predict their strength. Here, the potential for controllably designing active Escherichia coli promoter is explored by combining multiple deep learning models. First, "DRSAdesign," which relies on a diffusion model to generate different types of novel promoters is created, followed by predicting whether they are real or fake and strength. Experimental validation showed that 45 out of 50 generated promoters are active with high diversity, but most promoters have relatively low activity. Next, "Ndesign," which relies on generating random sequences carrying functional -35 and -10 motifs of the sigma70 promoter is introduced, and their strength is predicted using the designed DL model. The DL model is trained and validated using 200 and 50 generated promoters, and displays Pearson correlation coefficients of 0.49 and 0.43, respectively. Taking advantage of the DL models developed in this work, possible 6-mers are predicted as key functional motifs of the sigma70 promoter, suggesting that promoter recognition and strength prediction mainly rely on the accommodation of functional motifs. This work provides DL tools to design promoters and assess their functions, paving the way for DL-assisted metabolic engineering.

Reconstruction and metabolic profiling of the genome-scale metabolic network model of Pseudomonas stutzeri A1501

Pseudomonas stutzeri A1501 is a non-fluorescent denitrifying bacteria that belongs to the gram-negative bacterial group. As a prominent strain in the fields of agriculture and bioengineering, there is still a lack of comprehensive understanding regarding its metabolic capabilities, specifically in terms of central metabolism and substrate utilization. Therefore, further exploration and extensive studies are required to gain a detailed insight into these aspects. This study reconstructed a genome-scale metabolic network model for P. stutzeri A1501 and conducted extensive curations, including correcting energy generation cycles, respiratory chains, and biomass composition. The final model, iQY1018, was successfully developed, covering more genes and reactions and having higher prediction accuracy compared with the previously published model iPB890. The substrate utilization ability of 71 carbon sources was investigated by BIOLOG experiment and was utilized to validate the model quality. The model prediction accuracy of substrate utilization for P. stutzeri A1501 reached 90 %. The model analysis revealed its new ability in central metabolism and predicted that the strain is a suitable chassis for the production of Acetyl CoA-derived products. This work provides an updated, high-quality model of P. stutzeri A1501for further research and will further enhance our understanding of the metabolic capabilities.

Differentiating the roles of proteins and polysomes in nucleoid size homeostasis in Escherichia coli

A defining feature of the bacterial cytosolic interior is a distinct membrane-less organelle, the nucleoid, that contains the chromosomal DNA. Although increasing experimental evidence indicates that macromolecular crowding is the dominant mechanism for nucleoid formation, it has remained unclear which crowders control nucleoid volume. It is commonly assumed that polyribosomes play a dominant role, yet the volume fraction of soluble proteins in the cytosol is comparable with that of polyribosomes. Here, we develop a free energy-based model for the cytosolic interior of a bacterial cell to distinguish contributions arising from polyribosomes and cytosolic proteins in nucleoid volume control. The parameters of the model are determined from the existing experimental data. We show that, while the polysomes establish the existence of the nucleoid as a distinct phase, the proteins control the nucleoid volume in physiologically relevant conditions. Our model explains experimental findings in Escherichia coli that the nucleoid compaction curves in osmotic shock measurements do not depend on cell growth rate and that dissociation of polysomes in slow growth rates does not lead to significant nucleoid expansion, while the nucleoid phase disappears in fastest growth rates. Furthermore, the model predicts a cross-over in the exclusion of crowders by their linear dimensions from the nucleoid phase: below the cross-over of 30-50 nm, the concentration of crowders in the nucleoid phase decreases linearly as a function of the crowder diameter, while decreasing exponentially above the cross-over size. Our work points to the possibility that bacterial cells maintain nucleoid size and protein concentration homeostasis via feedback in which protein concentration controls nucleoid dimensions and the nucleoid dimensions control protein synthesis rate.

High-throughput, fluorescent-aptamer-based measurements of steady-state transcription rates for the Mycobacterium tuberculosis RNA polymerase

The first step in gene expression is the transcription of DNA sequences into RNA. Regulation at the level of transcription leads to changes in steady-state concentrations of RNA transcripts, affecting the flux of downstream functions and ultimately cellular phenotypes. Changes in transcript levels are routinely followed in cellular contexts via genome-wide sequencing techniques. However, in vitro mechanistic studies of transcription have lagged with respect to throughput. Here, we describe the use of a real-time, fluorescent-aptamer-based method to quantitate steady-state transcription rates of the Mycobacterium tuberculosis RNA polymerase. We present clear controls to show that the assay specifically reports on promoter-dependent, full-length RNA transcription rates that are in good agreement with the kinetics determined by gel-resolved, α-32P NTP incorporation experiments. We illustrate how the time-dependent changes in fluorescence can be used to measure regulatory effects of nucleotide concentrations and identity, RNAP and DNA concentrations, transcription factors, and antibiotics. Our data showcase the ability to easily perform hundreds of parallel steady-state measurements across varying conditions with high precision and reproducibility to facilitate the study of the molecular mechanisms of bacterial transcription.

Gene Activity as the Predictive Indicator for Transcriptional Bursting Dynamics

Transcription commonly occurs in bursts, with alternating productive (ON) and quiescent (OFF) periods, governing mRNA production rates. Yet, how transcription is regulated through bursting dynamics remains unresolved. In this study, we conduct real-time measurements of endogenous transcriptional bursting with single-mRNA sensitivity. Leveraging the diverse transcriptional activities in early fly embryos, we uncover stringent relationships between bursting parameters. Specifically, we find that the durations of ON and OFF periods are linked. Regardless of the developmental stage or body-axis position, gene activity levels predict the average ON and OFF periods of individual alleles. Lowly transcribing alleles predominantly modulate OFF durations (burst frequency), while highly transcribing alleles primarily tune ON durations (burst size). Importantly, these relationships persist even under perturbation of cis-regulatory elements or trans-factors. This suggests a novel mechanistic constraint governing bursting dynamics rather than a modular control of distinct parameters by distinct regulatory processes. Our study provides a foundation for future investigations into the molecular mechanisms underpinning spatiotemporal transcriptional control.

Metabolic engineering of Paracoccus denitrificans for dual degradation of sulfamethoxazole and ammonia nitrogen

Sulfamethoxazole (SMX), as one of the most widely used sulfonamide antibiotics, has been frequently detected in the aqueous environment, posing potential risks to the environment and human health. Although microbial degradation methods have been widely applied, some issues remain, including low degradation efficiency and poor environmental adaptability. In this regard, constructing efficient degrading bacteria by metabolic engineering is an ideal solution to these challenges. In this study, we used Paracoccus denitrificans DYTN-1, a superior nitrogen removal environment strain, as chassis to construct an SMX degradation pathway, obtaining a new bacteria for simultaneous degradation of SMX and removal of ammonia nitrogen. In doing this, we first identified and characterized four native promoters of P. denitrificans DYTN-1 with gradient strength to control the expression of the SMX degradation pathway. After degradation pathway expression level optimization and FMN reductase optimization, SMX degradation efficiency was significantly improved. The constructed P. d-pIAB4-PCS-sutR strain exhibited superior co-degradation of SMX and ammonia nitrogen contaminants with degradation rates of 44% and 71%, respectively. This study could pave the way for SMX degradation engineered strain design and evolution of environmental bioremediation. IMPORTANCE The abuse of sulfamethoxazole (SMX) had led to an increased accumulation in the environment, resulting in the disruption of the structure of microbial communities, further disrupting the bio-degradation process of other pollutants, such as ammonia nitrogen. To solve this challenge, we first identified and characterized four native promoters of Paracoccus denitrificans DYTN-1 with gradient strength to control the expression of the SMX degradation pathway. Then SMX degradation efficiency was significantly improved with degradation pathway expression level optimization and FMN reductase optimization. Finally, the superior nitrogen removal environment strain, P. denitrificans DYTN-1, obtained an SMX degradation function. This pioneering study of metabolic engineering to enhance the SMX degradation in microorganisms could pave the way for designing the engineered strains of SMX and nitrogen co-degradation and the environmental bioremediation.

Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis

Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to changing environments, yet the concept of transcriptional plasticity (TP) remains largely unexplored. In this study, we investigate the genome-wide TP profiles of Mycobacterium tuberculosis (Mtb) genes by analyzing 894 RNA sequencing samples derived from 73 different environmental conditions. Our data reveal that Mtb genes exhibit significant TP variation that correlates with gene function and gene essentiality. We also found that critical genetic features, such as gene length, GC content, and operon size independently impose constraints on TP, beyond trans-regulation. By extending our analysis to include two other Mycobacterium species -- M. smegmatis and M. abscessus -- we demonstrate a striking conservation of the TP landscape. This study provides a comprehensive understanding of the TP exhibited by mycobacteria genes, shedding light on this significant, yet understudied, genetic feature encoded in bacterial genomes.

A kinetic dichotomy between mitochondrial and nuclear gene expression drives OXPHOS biogenesis

Oxidative phosphorylation (OXPHOS) complexes, encoded by both mitochondrial and nuclear DNA, are essential producers of cellular ATP, but how nuclear and mitochondrial gene expression steps are coordinated to achieve balanced OXPHOS biogenesis remains unresolved. Here, we present a parallel quantitative analysis of the human nuclear and mitochondrial messenger RNA (mt-mRNA) life cycles, including transcript production, processing, ribosome association, and degradation. The kinetic rates of nearly every stage of gene expression differed starkly across compartments. Compared to nuclear mRNAs, mt-mRNAs were produced 700-fold higher, degraded 5-fold faster, and accumulated to 170-fold higher levels. Quantitative modeling and depletion of mitochondrial factors, LRPPRC and FASTKD5, identified critical points of mitochondrial regulatory control, revealing that the mitonuclear expression disparities intrinsically arise from the highly polycistronic nature of human mitochondrial pre-mRNA. We propose that resolving these differences requires a 100-fold slower mitochondrial translation rate, illuminating the mitoribosome as a nexus of mitonuclear co-regulation.

Inherited chitinases enable sustained growth and rapid dispersal of bacteria from chitin particles

Many biogeochemical functions involve bacteria utilizing solid substrates. However, little is known about the coordination of bacterial growth with the kinetics of attachment to and detachment from such substrates. In this quantitative study of Vibrio sp. 1A01 growing on chitin particles, we reveal the heterogeneous nature of the exponentially growing culture comprising two co-existing subpopulations: a minority replicating on chitin particles and a non-replicating majority which was planktonic. This partition resulted from a high rate of cell detachment from particles. Despite high detachment, sustained exponential growth of cells on particles was enabled by the enrichment of extracellular chitinases excreted and left behind by detached cells. The 'inheritance' of these chitinases sustains the colonizing subpopulation despite its reduced density. This simple mechanism helps to circumvent a trade-off between growth and dispersal, allowing particle-associated marine heterotrophs to explore new habitats without compromising their fitness on the habitat they have already colonized.

Genetically encoded transcriptional plasticity underlies stress adaptation in Mycobacterium tuberculosis

Transcriptional regulation is a critical adaptive mechanism that allows bacteria to respond to changing environments, yet the concept of transcriptional plasticity (TP) remains largely unexplored. In this study, we investigate the genome-wide TP profiles of Mycobacterium tuberculosis (Mtb) genes by analyzing 894 RNA sequencing samples derived from 73 different environmental conditions. Our data reveal that Mtb genes exhibit significant TP variation that correlates with gene function and gene essentiality. We also found that critical genetic features, such as gene length, GC content, and operon size independently impose constraints on TP, beyond trans-regulation. By extending our analysis to include two other Mycobacterium species -- M. smegmatis and M. abscessus -- we demonstrate a striking conservation of the TP landscape. This study provides a comprehensive understanding of the TP exhibited by mycobacteria genes, shedding light on this significant, yet understudied, genetic feature encoded in bacterial genomes.

Unified Workflow for the Rapid and In-Depth Characterization of Bacterial Proteomes

Bacteria are the most abundant and diverse organisms among the kingdoms of life. Due to this excessive variance, finding a unified, comprehensive, and safe workflow for quantitative bacterial proteomics is challenging. In this study, we have systematically evaluated and optimized sample preparation, mass spectrometric data acquisition, and data analysis strategies in bacterial proteomics. We investigated workflow performances on six representative species with highly different physiologic properties to mimic bacterial diversity. The best sample preparation strategy was a cell lysis protocol in 100% trifluoroacetic acid followed by an in-solution digest. Peptides were separated on a 30-min linear microflow liquid chromatography gradient and analyzed in data-independent acquisition mode. Data analysis was performed with DIA-NN using a predicted spectral library. Performance was evaluated according to the number of identified proteins, quantitative precision, throughput, costs, and biological safety. With this rapid workflow, over 40% of all encoded genes were detected per bacterial species. We demonstrated the general applicability of our workflow on a set of 23 taxonomically and physiologically diverse bacterial species. We could confidently identify over 45,000 proteins in the combined dataset, of which 30,000 have not been experimentally validated before. Our work thereby provides a valuable resource for the microbial scientific community. Finally, we grew Escherichia coli and Bacillus cereus in replicates under 12 different cultivation conditions to demonstrate the high-throughput suitability of the workflow. The proteomic workflow we present in this manuscript does not require any specialized equipment or commercial software and can be easily applied by other laboratories to support and accelerate the proteomic exploration of the bacterial kingdom.

Functional decomposition of metabolism allows a system-level quantification of fluxes and protein allocation towards specific metabolic functions

Quantifying the contribution of individual molecular components to complex cellular processes is a grand challenge in systems biology. Here we establish a general theoretical framework (Functional Decomposition of Metabolism, FDM) to quantify the contribution of every metabolic reaction to metabolic functions, e.g. the synthesis of biomass building blocks. FDM allowed for a detailed quantification of the energy and biosynthesis budget for growing Escherichia coli cells. Surprisingly, the ATP generated during the biosynthesis of building blocks from glucose almost balances the demand from protein synthesis, the largest energy expenditure known for growing cells. This leaves the bulk of the energy generated by fermentation and respiration unaccounted for, thus challenging the common notion that energy is a key growth-limiting resource. Moreover, FDM together with proteomics enables the quantification of enzymes contributing towards each metabolic function, allowing for a first-principle formulation of a coarse-grained model of global protein allocation based on the structure of the metabolic network.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

The one-message-per-cell-cycle rule: A conserved minimum transcription level for essential genes

The inherent stochasticity of cellular processes leads to significant cell-to-cell variation in protein abundance. Although this noise has already been characterized and modeled, its broader implications and significance remain unclear. In this paper, we revisit the noise model and identify the number of messages transcribed per cell cycle as the critical determinant of noise. In yeast, we demonstrate that this quantity predicts the non-canonical scaling of noise with protein abundance, as well as quantitatively predicting its magnitude. We then hypothesize that growth robustness requires an upper ceiling on noise for the expression of essential genes, corresponding to a lower floor on the transcription level. We show that just such a floor exists: a minimum transcription level of one message per cell cycle is conserved between three model organisms: Escherichia coli, yeast, and human. Furthermore, all three organisms transcribe the same number of messages per gene, per cell cycle. This common transcriptional program reveals that robustness to noise plays a central role in determining the expression level of a large fraction of essential genes, and that this fundamental optimal strategy is conserved from E. coli to human cells.

A Reduction of Transcriptional Regulation in Aquatic Oligotrophic Microorganisms Enhances Fitness in Nutrient-Poor Environments

In this review, we consider the regulatory strategies of aquatic oligotrophs, microbial cells that are adapted to thrive under low-nutrient concentrations in oceans, lakes, and other aquatic ecosystems. Many reports have concluded that oligotrophs use less transcriptional regulation than copiotrophic cells, which are adapted to high nutrient concentrations and are far more common subjects for laboratory investigations of regulation. It is theorized that oligotrophs have retained alternate mechanisms of regulation, such as riboswitches, that provide shorter response times and smaller amplitude responses and require fewer cellular resources. We examine the accumulated evidence for distinctive regulatory strategies in oligotrophs. We explore differences in the selective pressures copiotrophs and oligotrophs encounter and ask why, although evolutionary history gives copiotrophs and oligotrophs access to the same regulatory mechanisms, they might exhibit distinctly different patterns in how these mechanisms are used. We discuss the implications of these findings for understanding broad patterns in the evolution of microbial regulatory networks and their relationships to environmental niche and life history strategy. We ask whether these observations, which have emerged from a decade of increased investigation of the cell biology of oligotrophs, might be relevant to recent discoveries of many microbial cell lineages in nature that share with oligotrophs the property of reduced genome size.

Screening and Constructing a Library of Promoter-5'-UTR Complexes with Gradient Strength in Pediococcus acidilactici

The GRAS (generally recognized as safe) strain Pediococcus acidilactici is well known for its antibacterial and probiotic functions. Furthermore, as P. acidilactici has excellent high temperature and salt resistance, it is an ideal host for the production of food enzymes, food additives, and pharmaceuticals. In this regard, it is desirable and feasible to enhance the production of these products through the metabolic engineering of P. acidilactici. However, the rare gene expression elements greatly obstruct the development of engineering P. acidilactici. In this study, we screened and constructed a library of promoter-5'-UTR (PUTR) complexes in P. acidilactici DY15 for regulating gene expression at the transcription and translation levels. In the post-log phase, the mRNA and protein expression level ranges of the 90 screened native PUTRs were 0.059-2010% and 0.77-245%, respectively, of the P32 promoter. Besides, several PUTRs exhibited great expression stability under high temperature, salt, and ethanol stress. We analyzed the structure of PUTRs and obtained the conserved regions of the promoter and 5'-UTR. Based on the identified core regions of PUTRs, we constructed a panel of combinatorial PUTRs with higher and stable protein expression levels. The strongest combinatorial PUTR was 853% of the P32 promoter in the protein expression level. Finally, the obtained PUTRs were applied to optimize the expression level of aminotransferase and improve the phenyllactic acid (PLA) production in P. acidilactici DY15. The achieved yield was 950.6 mg/L, which was 79.2% higher than the wild-type strain. These results indicated that the obtained PUTRs with gradient strength had great potential for precisely regulating gene expression to achieve various goals in P. acidilactici.

Whole-cell modeling of E. coli colonies enables quantification of single-cell heterogeneity in antibiotic responses

Antibiotic resistance poses mounting risks to human health, as current antibiotics are losing efficacy against increasingly resistant pathogenic bacteria. Of particular concern is the emergence of multidrug-resistant strains, which has been rapid among Gram-negative bacteria such as Escherichia coli. A large body of work has established that antibiotic resistance mechanisms depend on phenotypic heterogeneity, which may be mediated by stochastic expression of antibiotic resistance genes. The link between such molecular-level expression and the population levels that result is complex and multi-scale. Therefore, to better understand antibiotic resistance, what is needed are new mechanistic models that reflect single-cell phenotypic dynamics together with population-level heterogeneity, as an integrated whole. In this work, we sought to bridge single-cell and population-scale modeling by building upon our previous experience in "whole-cell" modeling, an approach which integrates mathematical and mechanistic descriptions of biological processes to recapitulate the experimentally observed behaviors of entire cells. To extend whole-cell modeling to the "whole-colony" scale, we embedded multiple instances of a whole-cell E. coli model within a model of a dynamic spatial environment, allowing us to run large, parallelized simulations on the cloud that contained all the molecular detail of the previous whole-cell model and many interactive effects of a colony growing in a shared environment. The resulting simulations were used to explore the response of E. coli to two antibiotics with different mechanisms of action, tetracycline and ampicillin, enabling us to identify sub-generationally-expressed genes, such as the beta-lactamase ampC, which contributed greatly to dramatic cellular differences in steady-state periplasmic ampicillin and was a significant factor in determining cell survival.

High-throughput, fluorescent-aptamer-based measurements of steady-state transcription rates for Mycobacterium tuberculosis RNA polymerase

The first step in gene expression is the transcription of DNA sequences into RNA. Regulation at the level of transcription leads to changes in steady-state concentrations of RNA transcripts, affecting the flux of downstream functions and ultimately cellular phenotypes. Changes in transcript levels are routinely followed in cellular contexts via genome-wide sequencing techniques. However, in vitro mechanistic studies of transcription have lagged with respect to throughput. Here, we describe the use of a real-time, fluorescent-aptamer-based method to quantitate steady-state transcription rates of the Mycobacterium tuberculosis RNA polymerase. We present clear controls to show that the assay specifically reports on promoter-dependent, full-length RNA transcription rates that are in good agreement with the kinetics determined by gel-resolved, α- 32 P NTP incorporation experiments. We illustrate how the time-dependent changes in fluorescence can be used to measure regulatory effects of nucleotide concentrations and identity, RNAP and DNA concentrations, transcription factors, and antibiotics. Our data showcase the ability to easily perform hundreds of parallel steady-state measurements across varying conditions with high precision and reproducibility to facilitate the study of the molecular mechanisms of bacterial transcription.

Significance Statement

RNA polymerase transcription mechanisms have largely been determined from in vitro kinetic and structural biology methods. In contrast to the limited throughput of these approaches, in vivo RNA sequencing provides genome-wide measurements but lacks the ability to dissect direct biochemical from indirect genetic mechanisms. Here, we present a method that bridges this gap, permitting high-throughput fluorescence-based measurements of in vitro steady-state transcription kinetics. We illustrate how an RNA-aptamer-based detection system can be used to generate quantitative information on direct mechanisms of transcriptional regulation and discuss the far-reaching implications for future applications.

Enzyme expression kinetics by Escherichia coli during transition from rich to minimal media depends on proteome reserves

Bacterial fitness depends on adaptability to changing environments. In rich growth medium, which is replete with amino acids, Escherichia coli primarily expresses protein synthesis machineries, which comprise ~40% of cellular proteins and are required for rapid growth. Upon transition to minimal medium, which lacks amino acids, biosynthetic enzymes are synthesized, eventually reaching ~15% of cellular proteins when growth fully resumes. We applied quantitative proteomics to analyse the timing of enzyme expression during such transitions, and established a simple positive relation between the onset time of enzyme synthesis and the fractional enzyme 'reserve' maintained by E. coli while growing in rich media. We devised and validated a coarse-grained kinetic model that quantitatively captures the enzyme recovery kinetics in different pathways, solely on the basis of proteomes immediately preceding the transition and well after its completion. Our model enables us to infer regulatory strategies underlying the 'as-needed' gene expression programme adopted by E. coli.