Development of novel orthogonal genetic circuits, based on extracytoplasmic function (ECF) σ factors

The synthetic biology field aims to apply the engineering 'design-build-test-learn' cycle for the implementation of synthetic genetic circuits modifying the behavior of biological systems. In order to reach this goal, synthetic biology projects use a set of fully characterized biological parts that subsequently are assembled into complex synthetic circuits following a rational, model-driven design. However, even though the bottom-up design approach represents an optimal starting point to assay the behavior of the synthetic circuits under defined conditions, the rational design of such circuits is often restricted by the limited number of available DNA building blocks. These usually consist only of a handful of transcriptional regulators that additionally are often borrowed from natural biological systems. This, in turn, can lead to cross-reactions between the synthetic circuit and the host cell and eventually to loss of the original circuit function. Thus, one of the challenges in synthetic biology is to design synthetic circuits that perform the designated functions with minor cross-reactions (orthogonality). To overcome the restrictions of the widely used transcriptional regulators, this project aims to apply extracytoplasmic function (ECF) σ factors in the design novel orthogonal synthetic circuits. ECFs are the smallest and simplest alternative σ factors that recognize highly specific promoters. ECFs represent one of the most important mechanisms of signal transduction in bacteria, indeed, their activity is often controlled by anti-σ factors. Even though it was shown that the overexpression of heterologous anti-σ factors can generate an adverse effect on cell growth, they represent an attractive solution to control ECF activity. Finally, to date, we know thousands of ECF σ factors, widespread among different bacterial phyla, that are identifiable together with the cognate promoters and anti-σ factors, using bioinformatic approaches. All the above-mentioned features make ECF σ factors optimal candidates as core orthogonal regulators for the design of novel synthetic circuits. In this project, in order to establish ECF σ factors as standard building blocks in the synthetic biology field, we first established a high throughput experimental setup. This relies on microplate reader experiments performed using a highly sensitive luminescent reporter system. Luminescent reporters have a superior signal-to-noise ratio when compared to fluorescent reporters since they do not suffer from the high auto-fluorescence background of the bacterial cell. However, they also have a drawback represented by the constant light emission that can generate undesired cross-talk between neighboring wells on a microplate. To overcome this limitation, we developed a computational algorithm that corrects for luminescence bleed-through and estimates the “true” luminescence activity for each well of a microplate. We show that the correcting algorithm preserves low-level signals close to the background and that it is universally applicable to different experimental conditions. In order to simplify the assembly of large ECF-based synthetic circuits, we designed an ECF toolbox in E. coli. The toolbox allows for the combinatorial assembly of circuits into expression vectors, using a library of reusable genetic parts. Moreover, it also offers the possibility of integrating the newly generated synthetic circuits into four different phage attachment (att) sites present in the genome of E. coli. This allows for a flawless transition between plasmid-encoded and chromosomally integrated genetic circuits, expanding the possible genetic configurations of a given synthetic construct. Moreover, our results demonstrate that the four att sites are orthogonal in terms of the gene expression levels of the synthetic circuits. With the purpose of rationally design ECF-based synthetic circuits and taking advantage of the ECF toolbox, we characterized the dynamic behavior of a set of 15 ECF σ factors, their cognate promoters, and relative anti-σs. Overall, we found that ECFs are non-toxic and functional and that they display different binding affinities for the cognate target promoters. Moreover, our results show that it is possible to optimize the output dynamic range of the ECF-based switches by changing the copy number of the ECFs and target promoters, thus, tuning the input/output signal ratio. Next, by combining up to three ECF-switches, we generated a set of “genetic-timer circuits”, the first synthetic circuits harboring more than one ECF. ECF-based timer circuits sequentially activate a series of target genes with increasing time delays, moreover, the behavior of the circuits can be predicted by a set of mathematical models. In order to improve the dynamic response of the ECF-based constructs, we introduced anti-σ factors in our synthetic circuits. By doing so we first confirmed that anti-σ factors can exert an adverse effect on the growth of E. coli, thus we explored possible solutions. Our results demonstrate that anti-σ factors toxicity can be partially alleviated by generating truncated, soluble variants of the anti-σ factors and, eventually, completely abolished via chromosomal integration of the anti-σ factor-based circuits. Finally, after demonstrating that anti-σ factors can be used to generate a tunable time delay among ECF expression and target promoter activation, we designed ECF/AS-suicide circuits. Such circuits allow for the time-delayed cell-death of E. coli and will serve as a prototype for the further development of ECF/AS-based lysis circuits

The genetic basis for adaptation of model-designed syntrophic co-cultures.

Understanding the fundamental characteristics of microbial communities could have far reaching implications for human health and applied biotechnology. Despite this, much is still unknown regarding the genetic basis and evolutionary strategies underlying the formation of viable synthetic communities. By pairing auxotrophic mutants in co-culture, it has been demonstrated that viable nascent E. coli communities can be established where the mutant strains are metabolically coupled. A novel algorithm, OptAux, was constructed to design 61 unique multi-knockout E. coli auxotrophic strains that require significant metabolite uptake to grow. These predicted knockouts included a diverse set of novel non-specific auxotrophs that result from inhibition of major biosynthetic subsystems. Three OptAux predicted non-specific auxotrophic strains-with diverse metabolic deficiencies-were co-cultured with an L-histidine auxotroph and optimized via adaptive laboratory evolution (ALE). Time-course sequencing revealed the genetic changes employed by each strain to achieve higher community growth rates and provided insight into mechanisms for adapting to the syntrophic niche. A community model of metabolism and gene expression was utilized to predict the relative community composition and fundamental characteristics of the evolved communities. This work presents new insight into the genetic strategies underlying viable nascent community formation and a cutting-edge computational method to elucidate metabolic changes that empower the creation of cooperative communities

Global Functional Atlas of \u3cem\u3eEscherichia coli\u3c/em\u3e Encompassing Previously Uncharacterized Proteins

One-third of the 4,225 protein-coding genes of Escherichia coli K-12 remain functionally unannotated (orphans). Many map to distant clades such as Archaea, suggesting involvement in basic prokaryotic traits, whereas others appear restricted to E. coli, including pathogenic strains. To elucidate the orphans’ biological roles, we performed an extensive proteomic survey using affinity-tagged E. coli strains and generated comprehensive genomic context inferences to derive a high-confidence compendium for virtually the entire proteome consisting of 5,993 putative physical interactions and 74,776 putative functional associations, most of which are novel. Clustering of the respective probabilistic networks revealed putative orphan membership in discrete multiprotein complexes and functional modules together with annotated gene products, whereas a machine-learning strategy based on network integration implicated the orphans in specific biological processes. We provide additional experimental evidence supporting orphan participation in protein synthesis, amino acid metabolism, biofilm formation, motility, and assembly of the bacterial cell envelope. This resource provides a “systems-wide” functional blueprint of a model microbe, with insights into the biological and evolutionary significance of previously uncharacterized proteins

Binary particle swarm optimization for operon prediction

An operon is a fundamental unit of transcription and contains specific functional genes for the construction and regulation of networks at the entire genome level. The correct prediction of operons is vital for understanding gene regulations and functions in newly sequenced genomes. As experimental methods for operon detection tend to be nontrivial and time consuming, various methods for operon prediction have been proposed in the literature. In this study, a binary particle swarm optimization is used for operon prediction in bacterial genomes. The intergenic distance, participation in the same metabolic pathway, the cluster of orthologous groups, the gene length ratio and the operon length are used to design a fitness function. We trained the proper values on the Escherichia coli genome, and used the above five properties to implement feature selection. Finally, our study used the intergenic distance, metabolic pathway and the gene length ratio property to predict operons. Experimental results show that the prediction accuracy of this method reached 92.1%, 93.3% and 95.9% on the Bacillus subtilis genome, the Pseudomonas aeruginosa PA01 genome and the Staphylococcus aureus genome, respectively. This method has enabled us to predict operons with high accuracy for these three genomes, for which only limited data on the properties of the operon structure exists

Adaptations of Escherichia coli strains to oxidative stress are reflected in properties of their structural proteomes.

BACKGROUND:The reconstruction of metabolic networks and the three-dimensional coverage of protein structures have reached the genome-scale in the widely studied Escherichia coli K-12 MG1655 strain. The combination of the two leads to the formation of a structural systems biology framework, which we have used to analyze differences between the reactive oxygen species (ROS) sensitivity of the proteomes of sequenced strains of E. coli. As proteins are one of the main targets of oxidative damage, understanding how the genetic changes of different strains of a species relates to its oxidative environment can reveal hypotheses as to why these variations arise and suggest directions of future experimental work. RESULTS:Creating a reference structural proteome for E. coli allows us to comprehensively map genetic changes in 1764 different strains to their locations on 4118 3D protein structures. We use metabolic modeling to predict basal ROS production levels (ROStype) for 695 of these strains, finding that strains with both higher and lower basal levels tend to enrich their proteomes with antioxidative properties, and speculate as to why that is. We computationally assess a strain's sensitivity to an oxidative environment, based on known chemical mechanisms of oxidative damage to protein groups, defined by their localization and functionality. Two general groups - metalloproteins and periplasmic proteins - show enrichment of their antioxidative properties between the 695 strains with a predicted ROStype as well as 116 strains with an assigned pathotype. Specifically, proteins that a) utilize a molybdenum ion as a cofactor and b) are involved in the biogenesis of fimbriae show intriguing protective properties to resist oxidative damage. Overall, these findings indicate that a strain's sensitivity to oxidative damage can be elucidated from the structural proteome, though future experimental work is needed to validate our model assumptions and findings. CONCLUSION:We thus demonstrate that structural systems biology enables a proteome-wide, computational assessment of changes to atomic-level physicochemical properties and of oxidative damage mechanisms for multiple strains in a species. This integrative approach opens new avenues to study adaptation to a particular environment based on physiological properties predicted from sequence alone

Bradyrhizobium elkanii nod regulon: insights through genomic analysis

A successful symbiotic relationship between soybean [Glycine max (L.) Merr.] and Bradyrhizobium species requires expression of the bacterial structural nod genes that encode for the synthesis of lipochitooligosaccharide nodulation signal molecules, known as Nod factors (NFs). Bradyrhizobium diazoefficiens USDA 110 possesses a wide nodulation gene repertoire that allows NF assembly and modification, with transcription of the nodYABCSUIJnolMNOnodZ operon depending upon specific activators, i.e., products of regulatory nod genes that are responsive to signaling molecules such as flavonoid compounds exuded by host plant roots. Central to this regulatory circuit of nod gene expression are NodD proteins, members of the LysR-type regulator family. In this study, publicly available Bradyrhizobium elkanii sequenced genomes were compared with the closely related B. diazoefficiens USDA 110 reference genome to determine the similarities between those genomes, especially with regards to the nod operon and nod regulon. Bioinformatics analyses revealed a correlation between functional mechanisms and key elements that play an essential role in the regulation of nod gene expression. These analyses also revealed new genomic features that had not been clearly explored before, some of which were unique for some B. elkanii genomes

Operon prediction using both genome-specific and general genomic information

We have carried out a systematic analysis of the contribution of a set of selected features that include three new features to the accuracy of operon prediction. Our analyses have led to a number of new insights about operon prediction, including that (i) different features have different levels of discerning power when used on adjacent gene pairs with different ranges of intergenic distance, (ii) certain features are universally useful for operon prediction while others are more genome-specific and (iii) the prediction reliability of operons is dependent on intergenic distances. Based on these new insights, our newly developed operon-prediction program achieves more accurate operon prediction than the previous ones, and it uses features that are most readily available from genomic sequences. Our prediction results indicate that our (non-linear) decision tree-based classifier can predict operons in a prokaryotic genome very accurately when a substantial number of operons in the genome are already known. For example, the prediction accuracy of our program can reach 90.2 and 93.7% on Bacillus subtilis and Escherichia coli genomes, respectively. When no such information is available, our (linear) logistic function-based classifier can reach the prediction accuracy at 84.6 and 83.3% for E.coli and B.subtilis, respectively