Negative feedback in genetic circuits confers evolutionary resilience and capacitance

Pervasive gene deregulation underlies adaptation and maladaptation in trimethoprim-resistant E. coli

IMPORTANCE

Bacteria employ a number of mechanisms to adapt to antibiotics. Mutations in transcriptional regulators alter the expression levels of genes that can change the susceptibility of bacteria to antibiotics. Two-component signaling proteins are a major class of signaling molecule used by bacteria to regulate transcription. In previous work, we found that mutations in MgrB, a feedback regulator of the PhoQP two-component system, conferred trimethoprim tolerance to Escherichia coli. Here, we elucidate how mutations in MgrB have a domino-like effect on the gene regulatory network of E. coli. As a result, pervasive perturbation of gene regulation ensues. Depending on the environmental context, this pervasive deregulation is either adaptive or maladaptive. Our study sheds light on how deregulation of gene expression can be beneficial for bacteria when challenged with antibiotics, and why regulators like MgrB may have evolved in the first place.

Euo is a developmental regulator that represses late genes and activates midcycle genes in Chlamydia trachomatis

IMPORTANCE

In this study, we developed a correlative approach that combined DNA immunoprecipitation-seq and RNA-seq analyses to define the regulon of the Chlamydia trachomatis transcription factor Euo. We confirmed the proposed role of Euo as a transcriptional repressor of late chlamydial genes but also showed that Euo activates transcription of a subset of midcycle genes and autoregulates its own expression via negative feedback. This study validates and expands the role of Euo as an important developmental regulator in C. trachomatis. In addition, this genome-wide correlative approach can be applied to study transcription factors in other pathogenic bacteria.

A double-negative feedback loop between NtrBC and a small RNA rewires nitrogen metabolism in legume symbionts

The nitrogen (N) status transduced via the NtrBC two-component system is a major signaling cue in the root nodule endosymbiosis of diazotrophic rhizobia with legumes. NtrBC is upregulated in the N-limiting rhizosphere environment at the onset of nodulation but silenced in nodules to favor the assimilation of the fixed N into plant biomass. We reported that the trans-acting sRNA NfeR1 (Nodule Formation Efficiency RNA) broadly influences the symbiotic performance of the α-rhizobium Sinorhizobium meliloti. Here, we show that NfeR1 is indeed an N-responsive sRNA that fine-tunes NtrBC output during the symbiotic transition. Biochemical and genetic approaches unveiled that NtrC and the LysR-type symbiotic regulator LsrB bind at distinct nearby sites in the NfeR1 promoter, acting antagonistically as repressor and activator of transcription, respectively. This complex transcriptional control specifies peak NfeR1 steady-state levels in N-starved and endosymbiotic bacteria. Furthermore, NfeR1 base pairs the translation initiation region of the histidine kinase coding mRNA ntrB, causing a decrease in both NtrB and NtrC abundance as assessed by double-plasmid genetic assays. In the context of endogenous regulation, NfeR1-mediated ntrBC silencing most likely amends the effective strength of the known operon autorepression exerted by NtrC. Accordingly, a lack of NfeR1 shifts the wild-type NtrBC output, restraining the fitness of free-living rhizobia under N stress and plant growth upon nodulation. The mixed NtrBC-NfeR1 double-negative feedback loop is thus an unprecedented adaptive network motif that helps α-rhizobia adjust N metabolism to the demands of an efficient symbiosis with legume plants. IMPORTANCE Root nodule endosymbioses between diazotrophic rhizobia and legumes provide the largest input of combined N to the biosphere, thus representing an alternative to harmful chemical fertilizers for sustainable crop production. Rhizobia have evolved intricate strategies to coordinate N assimilation for their own benefit with N2 fixation to sustain plant growth. The rhizobial N status is transduced by the NtrBC two-component system, the seemingly ubiquitous form of N signal transduction in Proteobacteria. Here, we show that the regulatory sRNA NfeR1 (nodule formation efficiency RNA) of the alfalfa symbiont Sinorhizobium meliloti is transcribed from a complex promoter repressed by NtrC in a N-dependent manner and feedback silences ntrBC by complementary base-pairing. These findings unveil a more prominent role of NtrC as a transcriptional repressor than hitherto anticipated and a novel RNA-based mechanism for NtrBC regulation. The NtrBC-NfeR1 double-negative feedback loop accurately rewires symbiotic S. meliloti N metabolism and is likely conserved in α-rhizobia.

Adaptive evolvability through direct selection instead of indirect, second-order selection

Can evolvability itself be the product of adaptive evolution? To answer this question is challenging, because any DNA mutation that alters only evolvability is subject to indirect, "second order" selection on the future effects of this mutation. Such indirect selection is weaker than "first-order" selection on mutations that alter fitness, in the sense that it can operate only under restrictive conditions. Here I discuss a route to adaptive evolvability that overcomes this challenge. Specifically, a recent evolution experiment showed that some mutations can enhance both fitness and evolvability through a combination of direct and indirect selection. Unrelated evidence from gene duplication and the evolution of gene regulation suggests that mutations with such dual effects may not be rare. Through such mutations, evolvability may increase at least in part because it provides an adaptive advantage. These observations suggest a research program on the adaptive evolution of evolvability, which aims to identify such mutations and to disentangle their direct fitness effects from their indirect effects on evolvability. If evolvability is itself adaptive, Darwinian evolution may have created more than life's diversity. It may also have helped create the very conditions that made the success of Darwinian evolution possible.

Mitigating Host Burden of Genetic Circuits by Engineering Autonegatively Regulated Parts and Improving Functional Prediction

Mitigating unintended interferences between circuits and host cells is key to realize applications of synthetic regulatory systems both for bacteria and mammalian cells. Here, we demonstrated that growth burden and circuit dysregulation occurred in a concentration-dependent manner for specific transcription factors (CymR*/CymR) in E.coli, and direct negative feedback modules were able to control the concentration of CymR*/CymR, mitigate growth burden, and restore circuit functions. A quantitative design scheme was developed for circuits embedded with autorepression modules. Four key parameters were theoretically identified to determine the performance of autoregulated switches and were experimentally modified by fine-tuning promoter architectures and cooperativity. Using this strategy, we synthesized a number of switches and demonstrated its improvement of product titers and host growth controlling the complex deoxyviolacein biosynthesis pathway. Furthermore, we restored functions of a dysregulated multilayer NOR gate by integrating autorepression modules. Our work provides a blueprint for engineering host-adaptable synthetic systems.

Genome interpretation using in silico predictors of variant impact

Estimating the effects of variants found in disease driver genes opens the door to personalized therapeutic opportunities. Clinical associations and laboratory experiments can only characterize a tiny fraction of all the available variants, leaving the majority as variants of unknown significance (VUS). In silico methods bridge this gap by providing instant estimates on a large scale, most often based on the numerous genetic differences between species. Despite concerns that these methods may lack reliability in individual subjects, their numerous practical applications over cohorts suggest they are already helpful and have a role to play in genome interpretation when used at the proper scale and context. In this review, we aim to gain insights into the training and validation of these variant effect predicting methods and illustrate representative types of experimental and clinical applications. Objective performance assessments using various datasets that are not yet published indicate the strengths and limitations of each method. These show that cautious use of in silico variant impact predictors is essential for addressing genome interpretation challenges.

Comparative transcriptome analysis identified important genes and regulatory pathways for flower color variation in Paphiopedilum hirsutissimum

BACKGROUND

Paphiopedilum hirsutissimum is a member of Orchidaceae family that is famous for its ornamental value around the globe, it is vulnerable due to over-exploitation and was listed in Appendix I of the Convention on International Trade in Endangered Species of Wild Fauna and Flora, which prevents its trade across borders. Variation in flower color that gives rise to different flower patterns is a major trait contributing to its high ornamental value. However, the molecular mechanism underlying color formation in P. hirsutissimum still remains unexplored. In the present study, we exploited natural variation in petal and labellum color of Paphiopedilum plants and used comparative transcriptome analysis as well as pigment measurements to explore the important genes, metabolites and regulatory pathways linked to flower color variation in P. hirsutissimum.

RESULT

We observed that reduced anthocyanin and flavonoid contents along with slightly higher carotenoids are responsible for albino flower phenotype. Comparative transcriptome analysis identified 3287 differentially expressed genes (DEGs) among normal and albino labellum, and 3634 DEGs between normal and albino petals. Two genes encoding for flavanone 3-hydroxylase (F3H) and one gene encoding for chalcone synthase (CHS) were strongly downregulated in albino labellum and petals compared to normal flowers. As both F3H and CHS catalyze essentially important steps in anthocyanin biosynthesis pathway, downregulation of these genes is probably leading to albino flower phenotype via down-accumulation of anthocyanins. However, we observed the downregulation of major carotenoid biosynthesis genes including VDE, NCED and ABA2 which was inconsistent with the increased carotenoid accumulation in albino flowers, suggesting that carotenoid accumulation was probably controlled at post-transcriptional or translational level. In addition, we identified several key transcription factors (MYB73, MYB61, bHLH14, bHLH106, MADS-SOC1, AP2/ERF1, ERF26 and ERF87) that may regulate structural genes involved in flower color formation in P. hirsutissimum. Importantly, over-expression of some of these candidate TFs increased anthocyanin accumulation in tobacco leaves which provided important evidence for the role of these TFs in flower color formation probably via regulating key structural genes of the anthocyanin pathway.

CONCLUSION

The genes identified here could be potential targets for breeding P. hirsutissimum with different flower color patterns by manipulating the anthocyanin and carotenoid biosynthesis pathways.

Genome-Wide Transcription Factor DNA Binding Sites and Gene Regulatory Networks in Clostridium thermocellum

Clostridium thermocellum is a thermophilic bacterium recognized for its natural ability to effectively deconstruct cellulosic biomass. While there is a large body of studies on the genetic engineering of this bacterium and its physiology to-date, there is limited knowledge in the transcriptional regulation in this organism and thermophilic bacteria in general. The study herein is the first report of a large-scale application of DNA-affinity purification sequencing (DAP-seq) to transcription factors (TFs) from a bacterium. We applied DAP-seq to > 90 TFs in C. thermocellum and detected genome-wide binding sites for 11 of them. We then compiled and aligned DNA binding sequences from these TFs to deduce the primary DNA-binding sequence motifs for each TF. These binding motifs are further validated with electrophoretic mobility shift assay (EMSA) and are used to identify individual TFs’ regulatory targets in C. thermocellum. Our results led to the discovery of novel, uncharacterized TFs as well as homologues of previously studied TFs including RexA-, LexA-, and LacI-type TFs. We then used these data to reconstruct gene regulatory networks for the 11 TFs individually, which resulted in a global network encompassing the TFs with some interconnections. As gene regulation governs and constrains how bacteria behave, our findings shed light on the roles of TFs delineated by their regulons, and potentially provides a means to enable rational, advanced genetic engineering of C. thermocellum and other organisms alike toward a desired phenotype.

Single-molecule imaging of LexA degradation in Escherichia coli elucidates regulatory mechanisms and heterogeneity of the SOS response

The bacterial SOS response stands as a paradigm of gene networks controlled by a master transcriptional regulator. Self-cleavage of the SOS repressor, LexA, induces a wide range of cell functions that are critical for survival and adaptation when bacteria experience stress conditions¹, including DNA repair², mutagenesis^3,4, horizontal gene transfer^5–7, filamentous growth, and the induction of bacterial toxins^8–12, toxin-antitoxin systems¹³, virulence factors^6,14, and prophages^15–17. SOS induction is also implicated in biofilm formation and antibiotic persistence^11,18–20. Considering the fitness burden of these functions, it is surprising that the expression of LexA-regulated genes is highly variable across cells^10,21–23 and that cell subpopulations induce the SOS response spontaneously even in the absence of stress exposure^{9,11,12,16,24,25}. Whether this reflects a population survival strategy or a regulatory inaccuracy is unclear, as are the mechanisms underlying SOS heterogeneity. Here, we developed a single-molecule imaging approach based on a HaloTag fusion to directly monitor LexA inside live Escherichia coli cells, demonstrating the existence of 3 main states of LexA: DNA-bound stationary molecules, free LexA and degraded LexA species. These analyses elucidate the mechanisms by which DNA-binding and degradation of LexA regulate the SOS response in vivo. We show that self-cleavage of LexA occurs frequently throughout the population during unperturbed growth, rather than being restricted to a subpopulation of cells, which causes substantial cell-to-cell variation in LexA abundances. LexA variability underlies SOS gene expression heterogeneity and triggers spontaneous SOS pulses, which enhance bacterial survival in anticipation of stress.

Synthetic negative feedback circuits using engineered small RNAs

Negative feedback is known to enable biological and man-made systems to perform reliably in the face of uncertainties and disturbances. To date, synthetic biological feedback circuits have primarily relied upon protein-based, transcriptional regulation to control circuit output. Small RNAs (sRNAs) are non-coding RNA molecules that can inhibit translation of target messenger RNAs (mRNAs). In this work, we modelled, built and validated two synthetic negative feedback circuits that use rationally-designed sRNAs for the first time. The first circuit builds upon the well characterised tet-based autorepressor, incorporating an externally-inducible sRNA to tune the effective feedback strength. This allows more precise fine-tuning of the circuit output in contrast to the sigmoidal, steep input–output response of the autorepressor alone. In the second circuit, the output is a transcription factor that induces expression of an sRNA, which inhibits translation of the mRNA encoding the output, creating direct, closed-loop, negative feedback. Analysis of the noise profiles of both circuits showed that the use of sRNAs did not result in large increases in noise. Stochastic and deterministic modelling of both circuits agreed well with experimental data. Finally, simulations using fitted parameters allowed dynamic attributes of each circuit such as response time and disturbance rejection to be investigated.

Multiscale effects of heating and cooling on genes and gene networks

Fluctuating environments such as changes in ambient temperature represent a fundamental challenge to life. Cells must protect gene networks that protect them from such stresses, making it difficult to understand how temperature affects gene network function in general. Here, we focus on single genes and small synthetic network modules to reveal four key effects of nonoptimal temperatures at different biological scales: (i) a cell fate choice between arrest and resistance, (ii) slower growth rates, (iii) Arrhenius reaction rates, and (iv) protein structure changes. We develop a multiscale computational modeling framework that captures and predicts all of these effects. These findings promote our understanding of how temperature affects living systems and enables more robust cellular engineering for real-world applications.

Most organisms must cope with temperature changes. This involves genes and gene networks both as subjects and agents of cellular protection, creating difficulties in understanding. Here, we study how heating and cooling affect expression of single genes and synthetic gene circuits in Saccharomyces cerevisiae. We discovered that nonoptimal temperatures induce a cell fate choice between stress resistance and growth arrest. This creates dramatic gene expression bimodality in isogenic cell populations, as arrest abolishes gene expression. Multiscale models incorporating population dynamics, temperature-dependent growth rates, and Arrhenius scaling of reaction rates captured the effects of cooling, but not those of heating in resistant cells. Molecular-dynamics simulations revealed how heating alters the conformational dynamics of the TetR repressor, fully explaining the experimental observations. Overall, nonoptimal temperatures induce a cell fate decision and corrupt gene and gene network function in computationally predictable ways, which may aid future applications of engineered microbes in nonstandard temperatures.

NbGIS regulates glandular trichome initiation through GA signaling in tobacco

KEY MESSAGE

A novel gene NbGIS positively regulates glandular trichome initiation through GA Signaling in tobacco. NbMYB123-like regulates glandular trichome initiation by acting downstream of NbGIS in tobacco. Glandular trichome is a specialized multicellular structure which has capability to synthesize and secrete secondary metabolites and protects plants from biotic and abiotic stresses. Our previous results revealed that a C2H2 zinc-finger transcription factor GIS and its sub-family genes act upstream of GL3/EGL3-GL1-TTG1 transcriptional activator complex to regulate trichome initiation in Arabidopsis. In this present study, we found that NbGIS could positively regulate glandular trichome development in Nicotiana benthamiana (tobacco). Our result demonstrated that 35S:NbGIS lines exhibited much higher densities of trichome on leaves, main stems, lateral branches and sepals than WT plants, while NbGIS:RNAi lines had the opposite phenotypes. Furthermore, our results also showed that NbGIS was required in response to GA signal to control glandular trichome initiation in Nicotiana benthamiana. In addition, our results also showed that NbGIS significantly influenced GA accumulation and expressions of marker genes of the GA biosynthesis, might result in the changes of growth and maturation in tobacco. Lastly, our results also showed that NbMYB123-like regulated glandular trichome initiation in tobacco by acting downstream of NbGIS. These findings provide new insights to discover the molecular mechanism by which C2H2 transcriptional factors regulates glandular trichome initiation through GA signaling pathway in tobacco.

CRISPR-FRT targets shared sites in a knock-out collection for off-the-shelf genome editing

CRISPR advances genome engineering by directing endonuclease sequence specificity with a guide RNA molecule (gRNA). For precisely targeting a gene for modification, each genetic construct requires a unique gRNA. By generating a gRNA against the flippase recognition target (FRT) site, a common genetic element shared by multiple genetic collections, CRISPR-FRT circumvents this design constraint to provide a broad platform for fast, scarless, off-the-shelf genome engineering.

Cooperativity of Negative Autoregulation Confers Increased Mutational Robustness

Negative autoregulation is universally found across organisms. In the bacterium Escherichia coli, transcription factors often repress their own expression to form a negative feedback network motif that enables robustness to changes in biochemical parameters. Here we present a simple phenomenological model of a negative feedback transcription factor repressing both itself and another target gene. The strength of the negative feedback is characterized by three parameters: the cooperativity in self-repression, the maximal expression rate of the transcription factor, and the apparent dissociation constant of the transcription factor binding to its own promoter. Analysis of the model shows that the target gene levels are robust to mutations in the transcription factor, and that the robustness improves as the degree of cooperativity in self-repression increases. The prediction is tested in the LexA transcriptional network of E. coli by altering cooperativity in self-repression and promoter strength. Indeed, we find robustness is correlated with the former. Considering the proposed importance of gene regulation in speciation, parameters governing a transcription factor's robustness to mutation may have significant influence on a cell or organism's capacity to evolve.

Nonlinear Dynamics in Gene Regulation Promote Robustness and Evolvability of Gene Expression Levels

Cellular phenotypes underpinned by regulatory networks need to respond to evolutionary pressures to allow adaptation, but at the same time be robust to perturbations. This creates a conflict in which mutations affecting regulatory networks must both generate variance but also be tolerated at the phenotype level. Here, we perform mathematical analyses and simulations of regulatory networks to better understand the potential trade-off between robustness and evolvability. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics, through the creation of regions presenting sudden changes in phenotype with small changes in genotype. For genotypes embedding low levels of nonlinearity, robustness and evolvability correlate negatively and almost perfectly. By contrast, genotypes embedding nonlinear dynamics allow expression levels to be robust to small perturbations, while generating high diversity (evolvability) under larger perturbations. Thus, nonlinearity breaks the robustness-evolvability trade-off in gene expression levels by allowing disparate responses to different mutations. Using analytical derivations of robustness and system sensitivity, we show that these findings extend to a large class of gene regulatory network architectures and also hold for experimentally observed parameter regimes. Further, the effect of nonlinearity on the robustness-evolvability trade-off is ensured as long as key parameters of the system display specific relations irrespective of their absolute values. We find that within this parameter regime genotypes display low and noisy expression levels. Examining the phenotypic effects of mutations, we find an inverse correlation between robustness and evolvability that breaks only with nonlinearity in the network dynamics. Our results provide a possible solution to the robustness-evolvability trade-off, suggest an explanation for the ubiquity of nonlinear dynamics in gene expression networks, and generate useful guidelines for the design of synthetic gene circuits.

Intramolecular phenotypic capacitance in a modular RNA molecule

Phenotypic capacitance refers to the ability of a genome to accumulate mutations that are conditionally hidden and only reveal phenotype-altering effects after certain environmental or genetic changes. Capacitance has important implications for the evolution of novel forms and functions, but experimentally studied mechanisms behind capacitance are mostly limited to complex, multicomponent systems often involving several interacting protein molecules. Here we demonstrate phenotypic capacitance within a much simpler system, an individual RNA molecule with catalytic activity (ribozyme). This naturally occurring RNA molecule has a modular structure, where a scaffold module acts as an intramolecular chaperone that facilitates folding of a second catalytic module. Previous studies have shown that the scaffold module is not absolutely required for activity, but dramatically decreases the concentration of magnesium ions required for the formation of an active site. Here, we use an experimental perturbation of magnesium ion concentration that disrupts the folding of certain genetic variants of this ribozyme and use in vitro selection followed by deep sequencing to identify genotypes with altered phenotypes (catalytic activity). We identify multiple conditional mutations that alter the wild-type ribozyme phenotype under a stressful environmental condition of low magnesium ion concentration, but preserve the phenotype under more relaxed conditions. This conditional buffering is confined to the scaffold module, but controls the catalytic phenotype, demonstrating how modularity can enable phenotypic capacitance within a single macromolecule. RNA's ancient role in life suggests that phenotypic capacitance may have influenced evolution since life's origins.