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Abstract Neuroinflammation contributes to impaired cognitive function in brain aging and neurodegenerative disorders like Alzheimer’s disease, which is characterized by the aggregation of pathological tau. One major driver of both age- and tau-associated neuroinflammation is the NF-κB and NLRP3 signaling axis. However, current treatments targeting NF-κB or NLRP3 may have adverse/systemic effects, and most have not been clinically translatable. In this study, we tested the efficacy of a novel, nucleic acid therapeutic (Nanoligomer) cocktail specifically targeting both NF-κB and NLRP3 in the brain for reducing neuroinflammation and improving cognitive function in old (aged 19 months) wildtype mice, and in rTg4510 tau pathology mice (aged 2 months). We found that 4 weeks of NF-κB/NLRP3-targeting Nanoligomer treatment strongly reduced neuro-inflammatory cytokine profiles in the brain and improved cognitive-behavioral function in both old and rTg4510 mice. These effects of NF-κB/NLRP3-targeting Nanoligomers were also associated with reduced glial cell activation and pathology, favorable changes in transcriptome signatures of glia-associated inflammation (reduced) and neuronal health (increased), and positive systemic effects. Collectively, our results provide a basis for future translational studies targeting both NF-κB and NLRP3 in the brain, perhaps using Nanoligomers, to inhibit neuroinflammation and improve cognitive function with aging and neurodegeneration.

Otoupal et al. use a systematic approach to investigate the effects of fitness neutral gene perturbations and antibiotic synergy in Escherichia coli. These neutral fitness interactions worked as co-therapies in a Salmonella enterica infection and informed the design of re-sensitization therapies in multi-drug resistant E. coli and Klebisiella pneumoniae clinical isolates.

Eller et al. develop a Facile Accelerated Specific Therapeutic (FAST) platform of antisense therapeutics that targets MDR bacterial pathogens with peptide nucleic acids (PNAs). This platform designs species and/or sequence specific PNAS based on a bioinformatics toolbox and offers a new delivery approach by repurposing the bacterial Type III secretion system in conjunction with a kill switch to overcome limited transport of PNAs into mammalian cells.

Summary: MicroRNAs (miRNAs) are small non-coding RNAs involved in post-transcriptional gene regulation that have a major impact on many diseases and provide an exciting avenue toward antiviral therapeutics. From patient transcriptomic data, we determined that a circulating miRNA, miR-2392, is directly involved with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) machinery during host infection. Specifically, we show that miR-2392 is key in driving downstream suppression of mitochondrial gene expression, increasing inflammation, glycolysis, and hypoxia, as well as promoting many symptoms associated with coronavirus disease 2019 (COVID-19) infection. We demonstrate that miR-2392 is present in the blood and urine of patients positive for COVID-19 but is not present in patients negative for COVID-19. These findings indicate the potential for developing a minimally invasive COVID-19 detection method. Lastly, using in vitro human and in vivo hamster models, we design a miRNA-based antiviral therapeutic that targets miR-2392, significantly reduces SARS-CoV-2 viability in hamsters, and may potentially inhibit a COVID-19 disease state in humans.

Economically-viable biofuel production is often limited by low levels of microbial tolerance to high biofuel concentrations. Here we demonstrate the first application of deactivated CRISPR perturbations of gene expression to improve Escherichia coli biofuel tolerance. We construct a library of 31 unique CRISPR inhibitions and activations of gene expression in E. coli and explore their impacts on growth during 10 days of exposure to n-butanol and n-hexane. We show that perturbation of metabolism and membrane-related genes induces the greatest impacts on growth in n-butanol, as does perturbation of redox-related genes in n-hexanes. We identify uncharacterized genes yjjZ and yehS with strong potential for improving tolerance to both biofuels. Perturbations demonstrated significant temporal dependencies, suggesting that rationally designing time-sensitive gene circuits can optimize tolerance. We also introduce a sgRNA-specific hyper-mutator phenotype (~2,600-fold increase) into our perturbation strains using error-prone Pol1. We show that despite this change, strains exhibited similar growth phenotypes in n-butanol as before, demonstrating the robustness of CRISPR perturbations during prolonged use. Collectively, these results demonstrate the potential of CRISPR manipulation of gene expression for improving biofuel tolerance and provide constructive starting points for optimization of biofuel producing microorganisms.

Bacteria grown in space experiments under microgravity conditions have been found to undergo unique physiological responses, ranging from modified cell morphology and growth dynamics to a putative increased tolerance to antibiotics. A common theory for this behavior is the loss of gravity-driven convection processes in the orbital environment, resulting in both reduction of extracellular nutrient availability and the accumulation of bacterial byproducts near the cell. To further characterize the responses, this study investigated the transcriptomic response of Escherichia coli to both microgravity and antibiotic concentration. E. coli was grown aboard International Space Station in the presence of increasing concentrations of the antibiotic gentamicin with identical ground controls conducted on Earth. Here we show that within 49 h of being cultured, E. coli adapted to grow at higher antibiotic concentrations in space compared to Earth, and demonstrated consistent changes in expression of 63 genes in response to an increase in drug concentration in both environments, including specific responses related to oxidative stress and starvation response. Additionally, we find 50 stress-response genes upregulated in response to the microgravity when compared directly to the equivalent concentration in the ground control. We conclude that the increased antibiotic tolerance in microgravity may be attributed not only to diminished transport processes, but also to a resultant antibiotic cross-resistance response conferred by an overlapping effect of stress response genes. Our data suggest that direct stresses of nutrient starvation and acid-shock conveyed by the microgravity environment can incidentally upregulate stress response pathways related to antibiotic stress and in doing so contribute to the increased antibiotic stress tolerance observed for bacteria in space experiments. These results provide insights into the ability of bacteria to adapt under extreme stress conditions and potential strategies to prevent antimicrobial-resistance in space and on Earth.

The rapid emergence of superbugs, or multi-drug resistant (MDR) organisms, has prompted a search for novel antibiotics, beyond traditional small-molecule therapies. Nanotherapeutics are being investigated as alternatives, and recently superoxide-generating quantum dots (QDs) have been shown as important candidates for selective light-activated therapy, while also potentiating existing antibiotics against MDR superbugs. Their therapeutic action is selective, can be tailored by simply changing their quantum-confined conduction-valence band (CB-VB) positions and alignment with different redox half-reactions—and hence their ability to generate specific radical species in biological media. Here, we show the design of superoxide-generating QDs using optimal QD material and size well-matched to superoxide redox potential, charged ligands to modulate their uptake in cells and selective redox interventions, and core/shell structures to improve their stability for therapeutic action. We show that cadmium telluride (CdTe) QDs with conduction band (CB) position at −0.5 V with respect to Normal Hydrogen Electron (NHE) and visible 2.4 eV bandgap generate a large flux of selective superoxide radicals, thereby demonstrating the effective light-activated therapy. Although the positively charged QDs demonstrate large cellular uptake, they bind indiscriminately to cell surfaces and cause non-selective cell death, while negatively charged and zwitterionic QD ligands reduce the uptake and allow selective therapeutic action via interaction with redox species. The stability of designed QDs in biologically-relevant media increases with the formation of core-shell QD structures, but an appropriate design of core-shell structures is needed to minimize any reduction in charge injection efficiency to adsorbed oxygen molecules (to form superoxide) and maintain similar quantitative generation of tailored redox species, as measured using electron paramagnetic resonance (EPR) spectroscopy and electrochemical impedance spectroscopy (EIS). Using these findings, we demonstrate the rational design of QDs as selective therapeutic to kill more than 99% of a priority class I pathogen, thus providing an effective therapy against MDR superbugs.

ABSTRACT Antibiotic-resistant bacteria are an increasingly serious public health concern, as strains emerge that demonstrate resistance to almost all available treatments. One factor that contributes to the crisis is the adaptive ability of bacteria, which exhibit remarkable phenotypic and gene expression heterogeneity in order to gain a survival advantage in damaging environments. This high degree of variability in gene expression across biological populations makes it a challenging task to identify key regulators of bacterial adaptation. Here, we research the regulation of adaptive resistance by investigating transcriptome profiles of Escherichia coli upon adaptation to disparate toxins, including antibiotics and biofuels. We locate potential target genes via conventional gene expression analysis as well as using a new analysis technique examining differential gene expression variability. By investigating trends across the diverse adaptation conditions, we identify a focused set of genes with conserved behavior, including those involved in cell motility, metabolism, membrane structure, and transport, and several genes of unknown function. To validate the biological relevance of the observed changes, we synthetically perturb gene expression using clustered regularly interspaced short palindromic repeat (CRISPR)-dCas9. Manipulation of select genes in combination with antibiotic treatment promotes adaptive resistance as demonstrated by an increased degree of antibiotic tolerance and heterogeneity in MICs. We study the mechanisms by which identified genes influence adaptation and find that select differentially variable genes have the potential to impact metabolic rates, mutation rates, and motility. Overall, this work provides evidence for a complex nongenetic response, encompassing shifts in gene expression and gene expression variability, which underlies adaptive resistance. IMPORTANCE Even initially sensitive bacteria can rapidly thwart antibiotic treatment through stress response processes known as adaptive resistance. Adaptive resistance fosters transient tolerance increases and the emergence of mutations conferring heritable drug resistance. In order to extend the applicable lifetime of new antibiotics, we must seek to hinder the occurrence of bacterial adaptive resistance; however, the regulation of adaptation is difficult to identify due to immense heterogeneity emerging during evolution. This study specifically seeks to generate heterogeneity by adapting bacteria to different stresses and then examines gene expression trends across the disparate populations in order to pinpoint key genes and pathways associated with adaptive resistance. The targets identified here may eventually inform strategies for impeding adaptive resistance and prolonging the effectiveness of antibiotic treatment.

Antisense transcription has been extensively recognized as a regulatory mechanism for gene expression across all kingdoms of life. Despite the broad importance and extensive experimental determination of cis-antisense transcription, relatively little is known about its role in controlling cellular switching responses. Growing evidence suggests the presence of non-coding cis-antisense RNAs that regulate gene expression via antisense interaction. Recent studies also indicate the role of transcriptional interference in regulating expression of neighboring genes due to traffic of RNA polymerases from adjacent promoter regions. Previous models investigate these mechanisms independently, however, little is understood about how cells utilize coupling of these mechanisms in advantageous ways that could also be used to design novel synthetic genetic devices. Here, we present a mathematical modeling framework for antisense transcription that combines the effects of both transcriptional interference and cis-antisense regulation. We demonstrate the tunability of transcriptional interference through various parameters, and that coupling of transcriptional interference with cis-antisense RNA interaction gives rise to hypersensitive switches in expression of both antisense genes. When implementing additional positive and negative feed-back loops from proteins encoded by these genes, the system response acquires a bistable behavior. Our model shows that combining these multiple-levels of regulation allows fine-tuning of system parameters to give rise to a highly tunable output, ranging from a simple-first order response to biologically complex higher-order response such as tunable bistable switch. We identify important parameters affecting the cellular switch response in order to provide the design principles for tunable gene expression using antisense transcription. This presents an important insight into functional role of antisense transcription and its importance towards design of synthetic biological switches.

Advances in computational methods that allow for exploration of the combinatorial mutation space are needed to realize the potential of synthetic biology based strain engineering efforts. Here, we present Constrictor, a computational framework that uses flux balance analysis (FBA) to analyze inhibitory effects of genetic mutations on the performance of biochemical networks. Constrictor identifies engineering interventions by classifying the reactions in the metabolic model depending on the extent to which their flux must be decreased to achieve the overproduction target. The optimal inhibition of various reaction pathways is determined by restricting the flux through targeted reactions below the steady state levels of a baseline strain. Constrictor generates unique in silico strains, each representing an "expression state", or a combination of gene expression levels required to achieve the overproduction target. The Constrictor framework is demonstrated by studying overproduction of ethylene in Escherichia coli network models iAF1260 and iJO1366 through the addition of the heterologous ethylene-forming enzyme from Pseudomonas syringae. Targeting individual reactions as well as combinations of reactions reveals in silico mutants that are predicted to have as high as 25% greater theoretical ethylene yields than the baseline strain during simulated exponential growth. Altering the degree of restriction reveals a large distribution of ethylene yields, while analysis of the expression states that return lower yields provides insight into system bottlenecks. Finally, we demonstrate the ability of Constrictor to scan networks and provide targets for a range of possible products. Constrictor is an adaptable technique that can be used to generate and analyze disparate populations of in silico mutants, select gene expression levels and provide non-intuitive strategies for metabolic engineering.

There is increasing recognition that stochasticity involved in gene regulatory processes may help cells enhance the signal or synchronize expression for a group of genes. Thus the validity of the traditional deterministic approach to modeling the foregoing processes cannot be without exception. In this study, we identify a frequently encountered situation, i.e., the biofilm, which has in the past been persistently investigated with intracellular deterministic models in the literature. We show in this paper circumstances in which use of the intracellular deterministic model appears distinctly inappropriate. In Enterococcus faecalis, the horizontal gene transfer of plasmid spreads drug resistance. The induction of conjugation in planktonic and biofilm circumstances is examined here with stochastic as well as deterministic models. The stochastic model is formulated with the Chemical Master Equation (CME) for planktonic cells and Reaction-Diffusion Master Equation (RDME) for biofilm. The results show that although the deterministic model works well for the perfectly-mixed planktonic circumstance, it fails to predict the averaged behavior in the biofilm, a behavior that has come to be known as stochastic focusing. A notable finding from this work is that the interception of antagonistic feedback loops to signaling, accentuates stochastic focusing. Moreover, interestingly, increasing particle number of a control variable could lead to an even larger deviation. Intracellular stochasticity plays an important role in biofilm and we surmise by implications from the model, that cell populations may use it to minimize the influence from environmental fluctuation.

ABSTRACT In bacteria, RNAs regulate gene expression and function via several mechanisms. An RNA may pair with complementary sequences in a target RNA to impact transcription, translation, or degradation of the target. Control of conjugation of pCF10, a pheromone response plasmid of Enterococcus faecalis, is a well-characterized system that serves as a model for the regulation of gene expression in bacteria by intercellular signaling. The prgQ operon, whose products mediate conjugation, is negatively regulated by two products of the prgX operon, Anti-Q, a small RNA, and PrgX, the transcriptional repressor of the prgQ promoter. Here we show that Qs, an RNA from the 5′ end of the prgQ operon, represses expression of PrgX by targeting prgX mRNA for cleavage by RNase III. Our results demonstrate that the prgQ and prgX operons each use RNAs to negatively regulate gene expression from the opposing operon by different mechanisms. Such reciprocal regulation between two operons using RNAs has not been previously demonstrated. Furthermore, these results show that Qs is an unusually versatile RNA, serving three separate functions in the regulation of conjugation. Understanding the potential versatility of RNAs and their various roles in gene regulatory networks will allow us to better understand how cells regulate complex behavior. IMPORTANCE Bacteria use RNA to regulate gene expression by a variety of mechanisms. The prgQ and prgX operons of pCF10, a conjugative plasmid of Enterococcus faecalis, have been shown to negatively regulate one another by a variety of mechanisms. One of these mechanisms involves Anti-Q, a small RNA from the prgX operon that prevents gene expression from the prgQ operon. In this work, we find that Qs, an RNA from the prgQ operon, negatively regulates gene expression from the prgX operon. These findings have a number of implications. (i) The Anti-Q and Qs RNAs act by different mechanisms, highlighting the variety of ways in which bacteria can regulate gene expression using RNAs. (ii) Reciprocal regulation between operons mediated by small RNAs has not been previously described, deepening our understanding of how bacteria regulate complex behavior. (iii) Additional roles for Qs have been described, demonstrating the versatility of this RNA.

In recent times, stochastic treatments of gene regulatory processes have appeared in the literature in which a cell exposed to a signaling molecule in its environment triggers the synthesis of a specific protein through a network of intracellular reactions. The stochastic nature of this process leads to a distribution of protein levels in a population of cells as determined by a Fokker-Planck equation. Often instability occurs as a consequence of two (stable) steady state protein levels, one at the low end representing the "off" state, and the other at the high end representing the "on" state for a given concentration of the signaling molecule within a suitable range. A consequence of such bistability has been the appearance of bimodal distributions indicating two different populations, one in the "off" state and the other in the "on" state. The bimodal distribution can come about from stochastic analysis of a single cell. However, the concerted action of the population altering the extracellular concentration in the environment of individual cells and hence their behavior can only be accomplished by an appropriate population balance model which accounts for the reciprocal effects of interaction between the population and its environment. In this study, we show how to formulate a population balance model in which stochastic gene expression in individual cells is incorporated. Interestingly, the simulation of the model shows that bistability is neither sufficient nor necessary for bimodal distributions in a population. The original notion of linking bistability with bimodal distribution from single cell stochastic model is therefore only a special consequence of a population balance model.

cis-encoded antisense RNAs (cis asRNA) have been reported to participate in gene expression regulation in both eukaryotic and prokaryotic organisms. Its presence in Streptomyces coelicolor has also been reported recently; however, its role has yet to be fully investigated. Using mathematical modeling we explore the role of cis asRNA produced as a result of convergent transcription in scbA-scbR genetic switch. scbA and scbR gene pair, encoding repressor-amplifier proteins respectively, mediates the synthesis of a signaling molecule, the γ-butyrolactone SCB1 and controls the onset of antibiotic production. Our model considers that transcriptional interference caused by convergent transcription of two opposing RNA polymerases results in fatal collision and transcriptional termination, which suppresses transcription efficiency. Additionally, convergent transcription causes sense and antisense interactions between complementary sequences from opposing strands, rendering the full length transcript inaccessible for translation. We evaluated the role of transcriptional interference and the antisense effect conferred by convergent transcription on the behavior of scbA-scbR system. Stability analysis showed that while transcriptional interference affects the system, it is asRNA that confers scbA-scbR system the characteristics of a bistable switch in response to the signaling molecule SCB1. With its critical role of regulating the onset of antibiotic synthesis the bistable behavior offers this two gene system the needed robustness to be a genetic switch. The convergent two gene system with potential of transcriptional interference is a frequent feature in various genomes. The possibility of asRNA regulation in other such gene-pairs is yet to be examined.