3) KEGG pathway information (http://www.genome.jp/kegg/).
The platform was constructed using the open source web framework CakePHP
(https://cakephp.org/) and the open-source relational database management system MySQL (https://www.mysql.com/). The visualization function of the regulatory networks was imple-mented using D3.js (https://d3js.org/). The database was provided at: http://biosystem.bt1.tu-harburg.de:1555/homes/. Figure 3.8 shows two screen shots of the StrepReg database.
3.8 Conclusion
The genomes of 8 mutans streptococci strains, including sixS. mutansstrains, oneS. ratti strain and one S. sobrinusstrain were sequenced, annotated and compared together with S. mutansUA159 and NN2025. Multiple genome alignment showed extensive genome rearrangement among the eight strains ofS. mutans. The core-genome size ofS. mutanswas determined to be around 1,370 genes by including 67S. mutansgenomes available in the NCBI database. A possibly open pan-genome ofS. mutanswas inferred.
Systematic comparative analyses were focused on competence regulation, bacteriocin (mutacin) production, antibiotic resistance, oxidative stress resistance, as well as central carbon metabolism and energy production pathways. Most of these cellular functional systems show remarkable differences between the strains, especially between the species with the mutans group streptococci, except for oxidative stress resistance systems which are well conserved. For example, CSP-dependent and independent competence regulation systems are highly diverse in mutans streptococci while no comC-like genes could be identified in S. rattiand S. sobrinus; putative ComC amino acid sequences of S. mutans strains show clear variations; ComS and ComR are also absent inS. sobrinuswhich well explains the fact that it was not able to obtain genetic competence state of S. sobrinusby experiment, even though the ComX and the downstream competence development genes are well reserved. Furthermore, the response regulators of the HdrMR and BsrRM systems, which are known to be also involved in competence development, are missing in bothS. ratti andS. sobrinus.
Variation in the presence/absence of mutacin-encoding genes is accompanied with the conservation of mutacin immunity proteins, which indicates apparently important roles of the mutacin immunity proteins for the survival of these mutans streptococci in a bacteriocin rich environment. The presence of various antibiotic resistance factors, together with the open pan-genome inferred, implies that attention should be paid to the potential of mutans group streptococci in the development of antibiotic resistance.
66 Genome-scale comparative studies of mutans streptococci The sizes of the genome-scale metabolic networks of the 10 strains are very close to each other. Comparative analysis of sub-pathways usingS. mutansUA159 as reference reveals that 46 sub-pathways of all 416 sub-pathways as defined in KEGG pathway database show variations between the strains. By identifying lactate oxidases to be uniquely present inS.
sobrinusDSM 20742, for the first time a novel energy production pathway inS. sobrinus is proposed. Additional functions of the lactate oxidases in connection with the proposed energy production pathway are also discussed.
An online regulation database for S. mutans, named StrepReg, was constructed by integrating transcription factor-based gene regulatory network, which was derived from time-series transcriptome analysis, with information from STRING interaction database and KEGG pathway database (http://biosystem.bt1.tu-harburg.de:1555/homes/).
In conclusion, the genomes of mutans group streptococci display remarkable differ-ences, especially between different species. The strain-specific information provided in this study can be helpful in understanding the evolution and adaptive mechanisms of those oral pathogens.
3.8 Conclusion 67
Fig. 3.6 Example of visualized genome-scale metabolic networks constructed based on genome annotations and KEGG pathway
The blue rectangle nodes represent the reactions and the circle green nodes represent the metabolites.
68 Genome-scale comparative studies of mutans streptococci
Fig. 3.7 Glycolysis/Gluconeogenesis and TCA cycle pathway in mutans streptococci
The rectangle nodes represent the metabolites. The yellow lines represent enzymes and the blue line represent enzymes with diversities across mutans streptococci strains studied here. The yellow line with cross means this enzyme is not present in all strains. Dotted blue line means this enzyme is absent inS. sobrinusDSM20742 and solid blue line means this enzyme is uniquely present inS. sobrinusDSM20742. Malate dehydrogenase represented in green line was absent inS. mutansNN2025 andS. mutansAC4446.
3.8 Conclusion 69
Fig. 3.8 Screenshots of StrepReg database
Chapter 4
Development of a multiple IO system for biological engineering in E. coli
4.1 Introduction
Although cells are composed of molecules and their viability relies on extracting and using energy to maintain them, they are not ‘just’ matter and energy [155]. Cells can respond to their environment, make decisions, build structures, and coordinate tasks based on com-putational operations performed by networks of regulatory proteins that integrate signals and control the timing of gene expression [155]. It has been shown that cells can be pro-grammed using synthetic genetic circuits composed of regulators organized to generate desired operations [155–159]. Stimulated by the great potential of engineering biological systems to achieve novel tasks, an emerging discipline termed synthetic biology is drawing more and more attentions [160–184]. It focuses on designing and building novel biological functions and systems by combining science and engineering principles, including the design and construction of new biological parts, devices, and systems, as well as the re-design of existing, natural biological systems for useful purposes. In general, the overall process of biological engineering is similar to programming in computer science. However, unlike programming on a computer, "programming" a biological system is much more time- and labor-intensive. One reason is that changing the "biological codes" is much more difficult than changing digital codes on a computer. It always takes days or even weeks to enable the editing of the "genetic codes". Recently, this process has been greatly simplified by the recently emerged CRISPR/Cas9 based genetic editing tools [185–188]. Another more crucial
This chapter was a modified and extended version of a recent publication: Song, Lifu; Zeng, An-Ping (2017): Engineering ’cell robots’ for parallel and highly sensitive screening of biomolecules underin vivo conditions. Scientific Reports 7 (1), p. 15145.
72 Development of a multiple IO system for biological engineering inE. coli reason is the inherent complexity and uncertainty of the genotype-phenotype relationships of biological systems. Despite the complicate interactions among the metabolic, gene regu-latory and signaling networks at the cellular level, it is not possible to precisely predict the consequences of even a single base change at the single gene level. Hence, the biological engineering process is held by the time- and labor- intensive design–build–test cycles as shown in Figure 1.1, in which many designs have to be evaluated and iterated on in order to improve the performance of target system. The rate of improvements is directly related to the throughput and rounds of the design cycles, with higher throughputs and more rounds resulting in reduced development period. Although recent advances have enabled the design and construction of billions of genetic variants per day, but evaluation capacity is still limited to thousands of variants per day.
Inspired by the debugging system in computer science, a versatile diagnosis system was proposed to reduce the development burden for biological engineering. This was achieved by a novel multiple input-output (IO) system which can interact with the cells and output multiple signals corresponding to various perturbations (inputs). Despite impressive progress in systems metabolic engineering and synthetic biology, there are still unsolved major problems in their practical applications for developing effective microorganisms for biosynthesis, such as identification of relevant targets for pathway engineering, designed elements or devices from synthetic biology often not working well inside cells under industrially relevant conditions. For proof of concept, the IO system used for target identification, evaluation of designs, evolution and selection of key enzymes for bioproduction.