Mechanisms of drug interactions between translation-inhibiting antibiotics

Predicting the evolution of antibiotic resistance

Predicting the evolution of antibiotic resistance is critical for realizing precision antibiotic therapies. How exactly to achieve such predictions is a theoretical challenge. Insights from mathematical models that reflect future behavior of microbes under antibiotic stress can inform intervention protocols. However, this requires going beyond heuristic approaches by modeling ecological and evolutionary responses linked to metabolic pathways and cellular functions. Developing such models is now becoming possible due to increasing data availability from systematic experiments with microbial systems. Here, I review recent theoretical advances promising building blocks to piece together a predictive theory of antibiotic resistance evolution. I focus on the conceptual framework of eco-evolutionary response models grounded on quantitative laws of bacterial physiology. These forward-looking models can predict previously unknown behavior of bacteria upon antibiotic exposure. With current developments covering mostly the case of ribosome-targeting antibiotics, I write this Opinion piece as an invitation to generalize the principles discussed here to a broader range of drugs and context dependencies.

Development of a luciferase-based Gram-positive bacterial reporter system for the characterization of antimicrobial agents

Mechanistic investigations are of paramount importance in elucidating the modes of action of antibiotics and facilitating the discovery of novel drugs. We reported a luciferase-based reporter system using bacterial cells to unveil mechanisms of antimicrobials targeting transcription and translation. The reporter gene Nluc encoding NanoLuciferase (NanoLuc) was integrated into the genome of the Gram-positive model organism, Bacillus subtilis, to generate a reporter strain BS2019. Cellular transcription and translation levels were assessed by quantifying the amount of Nluc mRNA as well as the luminescence catalyzed by the enzyme NanoLuc. We validated this system using three known inhibitors of transcription (rifampicin), translation (chloramphenicol), and cell wall synthesis (ampicillin). The B. subtilis reporter strain BS2019 successfully revealed a decline in Nluc expression by rifampicin and NanoLuc enzyme activity by chloramphenicol, while ampicillin produced no observable effect. The assay was employed to characterize a previously discovered bacterial transcription inhibitor, CUHK242, with known antimicrobial activity against drug-resistant Staphylococcus aureus. Production of Nluc mRNA in our reporter BS2019 was suppressed in the presence of CUHK242, demonstrating the usefulness of the construct, which provides a simple way to study the mechanism of potential antibiotic candidates at early stages of drug discovery. The reporter system can also be modified by adopting different promoters and reporter genes to extend its scope of contribution to other fields of work.

IMPORTANCE

Discovering new classes of antibiotics is desperately needed to combat the emergence of multidrug-resistant pathogens. To facilitate the drug discovery process, a simple cell-based assay for mechanistic studies is essential to characterize antimicrobial candidates. In this work, we developed a luciferase-based reporter system to quantify the transcriptional and translational effects of potential compounds and validated our system using two currently marketed drugs. Reporter strains generated in this study provide readily available means for identifying bacterial transcription inhibitors as prospective novel antibacterials. We also provided a series of plasmids for characterizing promoters under various conditions such as stress.

Within-species variability of antibiotic interactions in Gram-negative bacteria

Treatments with antibiotic combinations are becoming increasingly important even though the supposed clinical benefits of combinations are, in many cases, unclear. Here, we systematically examined how several clinically used antibiotics interact and affect the antimicrobial efficacy against five especially problematic Gram-negative pathogens. A total of 232 bacterial isolates were tested against different pairwise antibiotic combinations spanning five classes, and the ability of all combinations in inhibiting growth was quantified. Descriptive statistics, principal component analysis (PCA), and Spearman's rank correlation matrix were used to determine the correlations between the different combinations on interaction outcome. Several important conclusions can be drawn from the 696 examined interactions. Firstly, within a species, the interactions are in general conserved but can be isolate-specific for a given antibiotic combination and can range from antagonistic to synergistic. Secondly, additive and antagonistic interactions are the most common observed across species and antibiotics, with 87.1% of isolate-antibiotic combinations being additive, 11.6% antagonistic, and only 0.3% showing synergy. These findings suggest that to achieve the highest precision and efficacy of combination therapy, not only isolate-specific interaction profiling ought to be routinely performed, in particular to avoid using drug combinations that show antagonistic interaction and an expected associated reduction in efficacy, but also discovering rare and potentially valuable synergistic interactions.IMPORTANCEAntibiotic combinations are often used to treat bacterial infections, which aim to increase treatment efficacy and reduce resistance evolution. Typically, it is assumed that one specific antibiotic combination has the same effect on different isolates of the same species, i.e., the interaction is conserved. Here, we tested this idea by examining how several clinically used antibiotics interact and affect the antimicrobial efficacy against several bacterial pathogens. Our results show that, even though within a species the interactions are often conserved, there are also isolate-specific differences for a given antibiotic combination that can range from antagonistic to synergistic. These findings suggest that isolate-specific interaction profiling ought to be performed in clinical microbiology routine to avoid using antagonistic drug combinations that might reduce treatment efficacy.

Systematic analysis of drug combinations against Gram-positive bacteria

Drug combinations can expand options for antibacterial therapies but have not been systematically tested in Gram-positive species. We profiled ~8,000 combinations of 65 antibacterial drugs against the model species Bacillus subtilis and two prominent pathogens, Staphylococcus aureus and Streptococcus pneumoniae. Thereby, we recapitulated previously known drug interactions, but also identified ten times more novel interactions in the pathogen S. aureus, including 150 synergies. We showed that two synergies were equally effective against multidrug-resistant S. aureus clinical isolates in vitro and in vivo. Interactions were largely species-specific and synergies were distinct from those of Gram-negative species, owing to cell surface and drug uptake differences. We also tested 2,728 combinations of 44 commonly prescribed non-antibiotic drugs with 62 drugs with antibacterial activity against S. aureus and identified numerous antagonisms that might compromise the efficacy of antimicrobial therapies. We identified even more synergies and showed that the anti-aggregant ticagrelor synergized with cationic antibiotics by modifying the surface charge of S. aureus. All data can be browsed in an interactive interface ( https://apps.embl.de/combact/ ).

Photodynamic Cationic Ultrasmall Copper Oxide Nanoparticles-Loaded Liposomes for Alleviation of MRSA Biofilms

Introduction

As we enter the post-antibiotic era, the rise of antibiotic-resistant pathogenic bacteria is becoming a serious threat to public health. This problem is further complicated by antibiotic-resistant biofilms, for which current treatment options are limited.

Methods

To tackle this challenge, we propose a novel approach that involves the use of photodynamic cationic pH-sensitive liposomes loaded with ultra-small copper oxide (Ce6@Lipo/UCONs) to effectively eliminate drug-resistant bacteria and eradicate biofilms while minimizing safety concerns and the risk of resistance development.

Results

Our study demonstrates that Ce6@Lipo/UCONs have minimal toxicity to mammalian cells and can significantly enhance the association affinity with methicillin-resistant Staphylococcus aureus (MRSA) as confirmed by fluorescent microscope and flow cytometry, thereby greatly improving the bactericidal effect against planktonic MRSA. The cationic nature of Ce6@Lipo/UCONs also enables them to penetrate MRSA biofilms and respond to the acidic microenvironment within the biofilm, effectively releasing the loaded UCONs. Our results indicate that Ce6@Lipo/UCONs could effectively eliminate biofilms under light irradiation conditions, as evidenced by both biomass analysis and scanning electron microscopy observations. In addition, significant antibacterial effects and abscess healing were observed in MRSA-infected mice treated with Ce6@Lipo/UCONs upon light irradiation, while good biocompatibility was achieved in vivo.

Conclusion

Taken together, our findings suggest that photodynamic cationic ultrasmall copper oxide nanoparticles-loaded liposomes are a highly promising nano platform for combating antibiotic-resistant microbial pathogens and biofilms. The effective biofilm penetration and synergistic effect between photodynamic inactivation and metal sterilization make them a valuable tool for overcoming the challenges posed by antibiotic resistance.

Quantitative approaches to study phenotypic effects of large-scale genetic perturbations

How microbes interact with their environment and how the complex interplay of their genes enables them to survive and thrive under stress is a fundamental question in microbial system biology, which is also important from a public health perspective. Large-scale studies of gene-gene, gene-drug, and drug-drug interactions have proven to be powerful tools for elucidating gene function and functional modules in the cell. Approaches that systematically quantify phenotypes in libraries of microbial strains with genome-wide genetic perturbations are crucial for progress in this area. Here, we review recent advances in this field, and point out applications to the study of gene-drug interactions. We highlight newly developed techniques for the rapid generation of genome-wide mutant libraries and the high-throughput measurement of more complex phenotypes and other observables, such as cell morphology or thermal stability of the proteome.

Evolutionary origins and interactomes of human, young microproteins and small peptides translated from short open reading frames

All species continuously evolve short open reading frames (sORFs) that can be templated for protein synthesis and may provide raw materials for evolutionary adaptation. We analyzed the evolutionary origins of 7,264 recently cataloged human sORFs and found that most were evolutionarily young and had emerged de novo. We additionally identified 221 previously missed sORFs potentially translated into peptides of up to 15 amino acids-all of which are smaller than the smallest human microprotein annotated to date. To investigate the bioactivity of sORF-encoded small peptides and young microproteins, we subjected 266 candidates to a mass-spectrometry-based interactome screen with motif resolution. Based on these interactomes and additional cellular assays, we can associate several candidates with mRNA splicing, translational regulation, and endocytosis. Our work provides insights into the evolutionary origins and interaction potential of young and small proteins, thereby helping to elucidate this underexplored territory of the human proteome.

The Extent of Antimicrobial Resistance Due to Efflux Pump Regulation

A key mechanism driving antimicrobial resistance (AMR) stems from the ability of bacteria to up-regulate efflux pumps upon exposure to drugs. The resistance gained by this up-regulation is pliable because of the tight regulation of efflux pump levels. This leads to temporary enhancement in survivability of bacteria due to higher efflux pump levels in the presence of antibiotics, which can be reversed when the cells are no longer exposed to the drug. Knowledge of the extent of resistance thus gained would inform intervention strategies aimed at mitigating AMR. Here, we combine mathematical modeling and experiments to quantify the maximum extent of resistance that efflux pump up-regulation can confer via phenotypic induction in the presence of drugs and genotypic abrogation of regulation. Our model describes the dynamics of drug transport in and out of cells coupled with the associated regulation of efflux pump levels and predicts the increase in the minimum inhibitory concentration (MIC) of drugs due to such regulation. To test the model, we measured the uptake and efflux as well as the MIC of the compound ethidium bromide (EtBr), a substrate of the efflux pump LfrA, in wild-type Mycobacterium smegmatis mc²155, as well as in two laboratory-generated strains. Our model captured the observed EtBr levels and MIC fold-changes quantitatively. Further, the model identified key parameters associated with the resulting resistance, variations in which could underlie the extent to which such resistance arises across different drug-bacteria combinations, potentially offering tunable handles to optimize interventions aimed at minimizing AMR.

Recombinant Actifensin and Defensin-d2 Induce Critical Changes in the Proteomes of Multidrug-Resistant Pseudomonas aeruginosa and Candida albicans

Drug-resistant strains of Pseudomonas aeruginosa and Candida albicans pose serious threats to human health because of their propensity to cause fatal infections. Defensin and defensin-like antimicrobial peptides (AMPs) are being explored as new lines of antimicrobials, due to their broad range of activity, low toxicity, and low pathogen resistance. Defensin-d2 and actifensin are AMPs from spinach and Actinomyces ruminicola, respectively, whose mechanisms of action are yet to be clearly elucidated. This study investigated the mechanisms of action of the recombinant AMPs through label-free quantitative proteomics. The data are available at PRIDE with accession number PXD034169. A total of 28 and 9 differentially expressed proteins (DEPs) were identified in the treated P. aeruginosa and C. albicans, respectively, with a 2-fold change threshold and P values of <0.05. Functional analysis revealed that the DEPs were involved in DNA replication and repair, translation, and membrane transport in P. aeruginosa, while they were related mainly to oxidative phosphorylation, RNA degradation, and energy metabolism in C. albicans. Protein-protein interactions showed that the DEPs formed linear or interdependent complexes with one another, indicative of functional interaction. Subcellular localization indicated that the majority of DEPs were cytoplasmic proteins in P. aeruginosa, while they were of nuclear or mitochondrial origin in C. albicans. These results show that recombinant defensin-d2 and actifensin can elicit complex multiple organism responses that cause cell death in P. aeruginosa and C. albicans. IMPORTANCE AMPs are considered essential alternatives to conventional antimicrobials because of their broad-spectrum efficacy and low potential for resistance by target cells. In this study, we established that the recombinant AMPs defensin-d2 and actifensin exert proteomic changes in P. aeruginosa and C. albicans within 1 h after treatment. We also found that the DEPs in peptide-treated P. aeruginosa are related to ion transport and homeostasis, molecular functions including nucleic and amino acid metabolism, and structural biogenesis and activity, while the DEPs in treated C. albicans are mainly involved in membrane synthesis and mitochondrial metabolism. Our results also highlight ATP synthase as a potential drug target for multidrug-resistant P. aeruginosa and C. albicans.

Overview on Strategies and Assays for Antibiotic Discovery

The increase in antibiotic resistance poses a major threat to global health. Actinomycetes, the Gram-positive bacteria of the order Actinomycetales, are fertile producers of bioactive secondary metabolites, including antibiotics. Nearly two-thirds of antibiotics that are used for the treatment of bacterial infections were originally isolated from actinomycetes strains belonging to the genus Streptomyces. This emphasizes the importance of actinomycetes in antibiotic discovery. However, the identification of a new antimicrobial compound and the exploration of its mode of action are very challenging tasks. Therefore, different approaches that enable the "detection" of an antibiotic and the characterization of the mechanisms leading to the biological activity are indispensable. Beyond bioinformatics tools facilitating the identification of biosynthetic gene clusters (BGCs), whole cell-screenings-in which cells are exposed to actinomycete-derived compounds-are a common strategy applied at the very early stage in antibiotic drug development. More recently, target-based approaches have been established. In this case, the drug candidates were tested for interactions with usually validated targets. This review focuses on the bioactivity-based screening methods and provides the readers with an overview on the most relevant assays for the identification of antibiotic activity and investigation of mechanisms of action. Moreover, the article includes examples of the successful application of these methods and suggestions for improvement.

“Upcycling” known molecules and targets for drug-resistant TB

Despite reinvigorated efforts in Tuberculosis (TB) drug discovery over the past 20 years, relatively few new drugs and candidates have emerged with clear utility against drug resistant TB. Over the same period, significant technological advances and learnings around target value have taken place. This has offered opportunities to re-assess the potential for optimization of previously discovered chemical matter against Mycobacterium tuberculosis (M.tb) and for reconsideration of clinically validated targets encumbered by drug resistance. A re-assessment of discarded compounds and programs from the “golden age of antibiotics” has yielded new scaffolds and targets against TB and uncovered classes, for example beta-lactams, with previously unappreciated utility for TB. Leveraging validated classes and targets has also met with success: booster technologies and efforts to thwart efflux have improved the potential of ethionamide and spectinomycin classes. Multiple programs to rescue high value targets while avoiding cross-resistance are making progress. These attempts to make the most of known classes, drugs and targets complement efforts to discover new chemical matter against novel targets, enhancing the chances of success of discovering effective novel regimens against drug-resistant TB.

Growth-mediated negative feedback shapes quantitative antibiotic response

Dose-response relationships are a general concept for quantitatively describing biological systems across multiple scales, from the molecular to the whole-cell level. A clinically relevant example is the bacterial growth response to antibiotics, which is routinely characterized by dose-response curves. The shape of the dose-response curve varies drastically between antibiotics and plays a key role in treatment, drug interactions, and resistance evolution. However, the mechanisms shaping the dose-response curve remain largely unclear. Here, we show in Escherichia coli that the distinctively shallow dose-response curve of the antibiotic trimethoprim is caused by a negative growth-mediated feedback loop: Trimethoprim slows growth, which in turn weakens the effect of this antibiotic. At the molecular level, this feedback is caused by the upregulation of the drug target dihydrofolate reductase (FolA/DHFR). We show that this upregulation is not a specific response to trimethoprim but follows a universal trend line that depends primarily on the growth rate, irrespective of its cause. Rewiring the feedback loop alters the dose-response curve in a predictable manner, which we corroborate using a mathematical model of cellular resource allocation and growth. Our results indicate that growth-mediated feedback loops may shape drug responses more generally and could be exploited to design evolutionary traps that enable selection against drug resistance.

Computational models, databases and tools for antibiotic combinations

Antibiotic combination is a promising strategy to extend the lifetime of antibiotics and thereby combat antimicrobial resistance. However, screening for new antibiotic combinations is both time-consuming and labor-intensive. In recent years, an increasing number of researchers have used computational models to predict effective antibiotic combinations. In this review, we summarized existing computational models for antibiotic combinations and discussed the limitations and challenges of these models in detail. In addition, we also collected and summarized available data resources and tools for antibiotic combinations. This study aims to help computational biologists design more accurate and interpretable computational models.

Challenges and potential solutions for studying the genetic and phenotypic architecture of adaptation in microbes

All organisms are defined by the makeup of their DNA. Over billions of years, the structure and information contained in that DNA, often referred to as genetic architecture, have been honed by a multitude of evolutionary processes. Mutations that cause genetic elements to change in a way that results in beneficial phenotypic change are more likely to survive and propagate through the population in a process known as adaptation. Recent work reveals that the genetic targets of adaptation are varied and can change with genetic background. Further, seemingly similar adaptive mutations, even within the same gene, can have diverse and unpredictable effects on phenotype. These challenges represent major obstacles in predicting adaptation and evolution. In this review, we cover these concepts in detail and identify three emerging synergistic solutions: higher-throughput evolution experiments combined with updated genotype-phenotype mapping strategies and physiological models. Our review largely focuses on recent literature in yeast, and the field seems to be on the cusp of a new era with regard to studying the predictability of evolution.

Type 1 piliated uropathogenic Escherichia coli hijack the host immune response by binding to CD14

A key attribute of persistent or recurring bacterial infections is the ability of the pathogen to evade the host’s immune response. Many Enterobacteriaceae express type 1 pili, a pre-adapted virulence trait, to invade host epithelial cells and establish persistent infections. However, the molecular mechanisms and strategies by which bacteria actively circumvent the immune response of the host remain poorly understood. Here, we identified CD14, the major co-receptor for lipopolysaccharide detection, on mouse dendritic cells (DCs) as a binding partner of FimH, the protein located at the tip of the type 1 pilus of Escherichia coli. The FimH amino acids involved in CD14 binding are highly conserved across pathogenic and non-pathogenic strains. Binding of the pathogenic strain CFT073 to CD14 reduced DC migration by overactivation of integrins and blunted expression of co-stimulatory molecules by overactivating the NFAT (nuclear factor of activated T-cells) pathway, both rate-limiting factors of T cell activation. This response was binary at the single-cell level, but averaged in larger populations exposed to both piliated and non-piliated pathogens, presumably via the exchange of immunomodulatory cytokines. While defining an active molecular mechanism of immune evasion by pathogens, the interaction between FimH and CD14 represents a potential target to interfere with persistent and recurrent infections, such as urinary tract infections or Crohn’s disease.

Nanomaterials-Based Combinatorial Therapy as a Strategy to Combat Antibiotic Resistance

Since the discovery of antibiotics, humanity has been able to cope with the battle against bacterial infections. However, the inappropriate use of antibiotics, the lack of innovation in therapeutic agents, and other factors have allowed the emergence of new bacterial strains resistant to multiple antibiotic treatments, causing a crisis in the health sector. Furthermore, the World Health Organization has listed a series of pathogens (ESKAPE group) that have acquired new and varied resistance to different antibiotics families. Therefore, the scientific community has prioritized designing and developing novel treatments to combat these ESKAPE pathogens and other emergent multidrug-resistant bacteria. One of the solutions is the use of combinatorial therapies. Combinatorial therapies seek to enhance the effects of individual treatments at lower doses, bringing the advantage of being, in most cases, much less harmful to patients. Among the new developments in combinatorial therapies, nanomaterials have gained significant interest. Some of the most promising nanotherapeutics include polymers, inorganic nanoparticles, and antimicrobial peptides due to their bactericidal and nanocarrier properties. Therefore, this review focuses on discussing the state-of-the-art of the most significant advances and concludes with a perspective on the future developments of nanotherapeutic combinatorial treatments that target bacterial infections.

Decoding the Mechanism of Specific RNA Targeting by Ribosomal Methyltransferases

Methylation of specific nucleotides is integral for ribosomal biogenesis and also serves as a common mechanism to confer antibiotic resistance by pathogenic bacteria. Here, by determining the high-resolution structure of the 30S-KsgA complex by cryo-electron microscopy, a state was captured, where KsgA juxtaposes between helices h44 and h45 of the 30S ribosome, separating them, thereby enabling remodeling of the surrounded rRNA and allowing the cognate site to enter the methylation pocket. With the structure as a guide, several mutant versions of the ribosomes, where interacting bases in the catalytic helix h45 and surrounding helices h44, h24, and h27, were mutated and evaluated for their methylation efficiency revealing factors that direct the enzyme to its cognate site with high fidelity. The biochemical studies show that the three-dimensional environment of the ribosome enables the interaction of select loop regions in KsgA with the ribosome helices paramount to maintain selectivity.

The physiology and genetics of bacterial responses to antibiotic combinations

Several promising strategies based on combining or cycling different antibiotics have been proposed to increase efficacy and counteract resistance evolution, but we still lack a deep understanding of the physiological responses and genetic mechanisms that underlie antibiotic interactions and the clinical applicability of these strategies. In antibiotic-exposed bacteria, the combined effects of physiological stress responses and emerging resistance mutations (occurring at different time scales) generate complex and often unpredictable dynamics. In this Review, we present our current understanding of bacterial cell physiology and genetics of responses to antibiotics. We emphasize recently discovered mechanisms of synergistic and antagonistic drug interactions, hysteresis in temporal interactions between antibiotics that arise from microbial physiology and interactions between antibiotics and resistance mutations that can cause collateral sensitivity or cross-resistance. We discuss possible connections between the different phenomena and indicate relevant research directions. A better and more unified understanding of drug and genetic interactions is likely to advance antibiotic therapy.

Box C/D small nucleolar ribonucleoproteins regulate mitochondrial surveillance and innate immunity

Monitoring mitochondrial function is crucial for organismal survival. This task is performed by mitochondrial surveillance or quality control pathways, which are activated by signals originating from mitochondria and relayed to the nucleus (retrograde response) to start transcription of protective genes. In Caenorhabditis elegans, several systems are known to play this role, including the UPRmt, MAPKmt, and the ESRE pathways. These pathways are highly conserved and their loss compromises survival following mitochondrial stress. In this study, we found a novel interaction between the box C/D snoRNA core proteins (snoRNPs) and mitochondrial surveillance and innate immune pathways. We showed that box C/D, but not box H/ACA, snoRNPs are required for the full function of UPRmt and ESRE upon stress. The loss of box C/D snoRNPs reduced mitochondrial mass, mitochondrial membrane potential, and oxygen consumption rate, indicating overall degradation of mitochondrial function. Concomitantly, the loss of C/D snoRNPs increased immune response and reduced host intestinal colonization by infectious bacteria, improving host resistance to pathogenesis. Our data may indicate a model wherein box C/D snoRNP machinery regulates a "switch" of the cell's activity between mitochondrial surveillance and innate immune activation. Understanding this mechanism is likely to be important for understanding multifactorial processes, including responses to infection and aging.

Independent Drug Action in Combination Therapy: Implications for Precision Oncology

Combination therapies are superior to monotherapy for many cancers. This advantage was historically ascribed to the ability of combinations to address tumor heterogeneity, but synergistic interaction is now a common explanation as well as a design criterion for new combinations. We review evidence that independent drug action, described in 1961, explains the efficacy of many practice-changing combination therapies: it provides populations of patients with heterogeneous drug sensitivities multiple chances of benefit from at least one drug. Understanding response heterogeneity could reveal predictive or pharmacodynamic biomarkers for more precise use of existing drugs and realize the benefits of additivity or synergy.

Translation complex stabilization on messenger RNA and footprint profiling to study the RNA responses and dynamics of protein biosynthesis in the cells

During protein biosynthesis, ribosomes bind to messenger (m)RNA, locate its protein-coding information, and translate the nucleotide triplets sequentially as codons into the corresponding sequence of amino acids, forming proteins. Non-coding mRNA features, such as 5' and 3' untranslated regions (UTRs), start sites or stop codons of different efficiency, stretches of slower or faster code and nascent polypeptide interactions can alter the translation rates transcript-wise. Most of the homeostatic and signal response pathways of the cells converge on individual mRNA control, as well as alter the global translation output. Among the multitude of approaches to study translational control, one of the most powerful is to infer the locations of translational complexes on mRNA based on the mRNA fragments protected by these complexes from endonucleolytic hydrolysis, or footprints. Translation complex profiling by high-throughput sequencing of the footprints allows to quantify the transcript-wise, as well as global, alterations of translation, and uncover the underlying control mechanisms by attributing footprint locations and sizes to different configurations of the translational complexes. The accuracy of all footprint profiling approaches critically depends on the fidelity of footprint generation and many methods have emerged to preserve certain or multiple configurations of the translational complexes, often in challenging biological material. In this review, a systematic summary of approaches to stabilize translational complexes on mRNA for footprinting is presented and major findings are discussed. Future directions of translation footprint profiling are outlined, focusing on the fidelity and accuracy of inference of the native in vivo translation complex distribution on mRNA.

First-principles model of optimal translation factors stoichiometry

Enzymatic pathways have evolved uniquely preferred protein expression stoichiometry in living cells, but our ability to predict the optimal abundances from basic properties remains underdeveloped. Here, we report a biophysical, first-principles model of growth optimization for core mRNA translation, a multi-enzyme system that involves proteins with a broadly conserved stoichiometry spanning two orders of magnitude. We show that predictions from maximization of ribosome usage in a parsimonious flux model constrained by proteome allocation agree with the conserved ratios of translation factors. The analytical solutions, without free parameters, provide an interpretable framework for the observed hierarchy of expression levels based on simple biophysical properties, such as diffusion constants and protein sizes. Our results provide an intuitive and quantitative understanding for the construction of a central process of life, as well as a path toward rational design of pathway-specific enzyme expression stoichiometry.

Spurious regulatory connections dictate the expression-fitness landscape of translation factors

During steady-state cell growth, individual enzymatic fluxes can be directly inferred from growth rate by mass conservation, but the inverse problem remains unsolved. Perturbing the flux and expression of a single enzyme could have pleiotropic effects that may or may not dominate the impact on cell fitness. Here, we quantitatively dissect the molecular and global responses to varied expression of translation termination factors (peptide release factors, RFs) in the bacterium Bacillus subtilis. While endogenous RF expression maximizes proliferation, deviations in expression lead to unexpected distal regulatory responses that dictate fitness reduction. Molecularly, RF depletion causes expression imbalance at specific operons, which activates master regulators and detrimentally overrides the transcriptome. Through these spurious connections, RF abundances are thus entrenched by focal points within the regulatory network, in one case located at a single stop codon. Such regulatory entrenchment suggests that predictive bottom-up models of expression-fitness landscapes will require near-exhaustive characterization of parts.

Translational remodeling by RNA-binding proteins and noncoding RNAs

Responsible for generating the proteome that controls phenotype, translation is the ultimate convergence point for myriad upstream signals that influence gene expression. System-wide adaptive translational reprogramming has recently emerged as a pillar of cellular adaptation. As classic regulators of mRNA stability and translation efficiency, foundational studies established the concept of collaboration and competition between RNA-binding proteins (RBPs) and noncoding RNAs (ncRNAs) on individual mRNAs. Fresh conceptual innovations now highlight stress-activated, evolutionarily conserved RBP networks and ncRNAs that increase the translation efficiency of populations of transcripts encoding proteins that participate in a common cellular process. The discovery of post-transcriptional functions for long noncoding RNAs (lncRNAs) was particularly intriguing given their cell-type-specificity and historical definition as nuclear-functioning epigenetic regulators. The convergence of RBPs, lncRNAs, and microRNAs on functionally related mRNAs to enable adaptive protein synthesis is a newer biological paradigm that highlights their role as "translatome (protein output) remodelers" and reinvigorates the paradigm of "RNA operons." Together, these concepts modernize our understanding of cellular stress adaptation and strategies for therapeutic development. This article is categorized under: RNA Interactions with Proteins and Other Molecules > Protein-RNA Interactions: Functional Implications Translation > Translation Regulation Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

Minimal biophysical model of combined antibiotic action

Phenomenological relations such as Ohm's or Fourier's law have a venerable history in physics but are still scarce in biology. This situation restrains predictive theory. Here, we build on bacterial "growth laws," which capture physiological feedback between translation and cell growth, to construct a minimal biophysical model for the combined action of ribosome-targeting antibiotics. Our model predicts drug interactions like antagonism or synergy solely from responses to individual drugs. We provide analytical results for limiting cases, which agree well with numerical results. We systematically refine the model by including direct physical interactions of different antibiotics on the ribosome. In a limiting case, our model provides a mechanistic underpinning for recent predictions of higher-order interactions that were derived using entropy maximization. We further refine the model to include the effects of antibiotics that mimic starvation and the presence of resistance genes. We describe the impact of a starvation-mimicking antibiotic on drug interactions analytically and verify it experimentally. Our extended model suggests a change in the type of drug interaction that depends on the strength of resistance, which challenges established rescaling paradigms. We experimentally show that the presence of unregulated resistance genes can lead to altered drug interaction, which agrees with the prediction of the model. While minimal, the model is readily adaptable and opens the door to predicting interactions of second and higher-order in a broad range of biological systems.