A Programmable CRISPR/Cas9 Toolkit Improves Lycopene Production in Bacillus subtilis.

Bacillus subtilis has been widely used and generally recognized as a safe host for the production of recombinant proteins, high-value chemicals, and pharmaceuticals. Thus, its metabolic engineering attracts significant attention. Nevertheless, the limited availability of selective markers makes this process difficult and time-consuming, especially in the case of multistep biosynthetic pathways. Here, we employ CRISPR/Cas9 technology to build an easy cloning toolkit that addresses commonly encountered obstacles in the metabolic engineering of B. subtilis, including the chromosomal integration locus, promoter, terminator, and guide RNA (gRNA) target. Six promoters were characterized, and the promoter strengths ranged from 0.9- to 23-fold that of the commonly used strong promoter P43. We characterized seven terminators in B. subtilis, and the termination efficiencies (TEs) of the seven terminators are all more than 90%. Six gRNA targets were designed upstream of the promoter and downstream of the terminator. Using a green fluorescent protein (GFP) reporter, we confirmed integration efficiency with the single-locus integration site is up to 100%. We demonstrated the applicability of this toolkit by optimizing the expression of a challenging but industrially important product, lycopene. By heterologous expression of the essential genes for lycopene synthesis on the B. subtilis genome, a total of 13 key genes involved in the lycopene biosynthetic pathway were manipulated. Moreover, our findings showed that the gene cluster ispG-idi-dxs-ispD could positively affect the production of lycopene, while the cluster dxr-ispE-ispF-ispH had a negative effect on lycopene production. Hence, our multilocus integration strategy can facilitate the pathway assembly for production of complex chemicals and pharmaceuticals in B. subtilis. IMPORTANCE We present a toolkit that allows for rapid cloning procedures and one-step subcloning to move from plasmid-based expression to stable chromosome integration and expression in a production strain in less than a week. The utility of the customized tool was demonstrated by integrating the MEP (2C-methyl-d-erythritol-4-phosphate) pathway, part of the pentose phosphate pathway (PPP), and the hetero-lycopene biosynthesis genes by stable expression in the genome. The tool could be useful to engineer B. subtilis strains through diverse recombination events and ultimately improve its potential and scope of industrial application as biological chassis.

Gene regulatory network inference and validation using relative change ratio analysis and time-delayed dynamic Bayesian network.

The Dialogue for Reverse Engineering Assessments and Methods (DREAM) project was initiated in 2006 as a community-wide effort for the development of network inference challenges for rigorous assessment of reverse engineering methods for biological networks. We participated in the in silico network inference challenge of DREAM3 in 2008. Here we report the details of our approach and its performance on the synthetic challenge datasets. In our methodology, we first developed a model called relative change ratio (RCR), which took advantage of the heterozygous knockdown data and null-mutant knockout data provided by the challenge, in order to identify the potential regulators for the genes. With this information, a time-delayed dynamic Bayesian network (TDBN) approach was then used to infer gene regulatory networks from time series trajectory datasets. Our approach considerably reduced the searching space of TDBN; hence, it gained a much higher efficiency and accuracy. The networks predicted using our approach were evaluated comparatively along with 29 other submissions by two metrics (area under the ROC curve and area under the precision-recall curve). The overall performance of our approach ranked the second among all participating teams.

Learning the structure of gene regulatory networks from time series gene expression data.

BACKGROUND: Dynamic Bayesian Network (DBN) is an approach widely used for reconstruction of gene regulatory networks from time-series microarray data. Its performance in network reconstruction depends on a structure learning algorithm. REVEAL (REVerse Engineering ALgorithm) is one of the algorithms implemented for learning DBN structure and used to reconstruct gene regulatory networks (GRN). However, the two-stage temporal Bayes network (2TBN) structure of DBN that specifies correlation between time slices cannot be obtained by score metrics used in REVEAL. METHODS: In this paper, we study a more sophisticated score function for DBN first proposed by Nir Friedman for stationary DBNs structure learning of both initial and transition networks but has not yet been used for reconstruction of GRNs. We implemented Friedman's Bayesian Information Criterion (BIC) score function, modified K2 algorithm to learn Dynamic Bayesian Network structure with the score function and tested the performance of the algorithm for GRN reconstruction with synthetic time series gene expression data generated by GeneNetWeaver and real yeast benchmark experiment data. RESULTS: We implemented an algorithm for DBN structure learning with Friedman's score function, tested it on reconstruction of both synthetic networks and real yeast networks and compared it with REVEAL in the absence or presence of preprocessed network generated by Zou&Conzen's algorithm. By introducing a stationary correlation between two consecutive time slices, Friedman's score function showed a higher precision and recall than the naive REVEAL algorithm. CONCLUSIONS: Friedman's score metrics for DBN can be used to reconstruct transition networks and has a great potential to improve the accuracy of gene regulatory network structure prediction with time series gene expression datasets.

Disruption of the 1-deoxy-D-xylulose-5-phosphate reductoisomerase (DXR) gene results in albino, dwarf and defects in trichome initiation and stomata closure in Arabidopsis.

1-Deoxy-D-xylulose-5-phosphate reductoisomerase (DXR) is an important enzyme involved in the 2-C-methyl-D-erythritol-4-phosphate (MEP) pathway which provides the basic five-carbon units for isoprenoid biosynthesis. To investigate the role of the MEP pathway in plant development and metabolism, we carried out detailed analyses on a dxr mutant (GK_215C01) and two DXR transgenic co-suppression lines, OX-DXR-L2 and OX-DXR-L7. We found that the dxr mutant was albino and dwarf. It never bolted, had significantly reduced number of trichomes and most of the stomata could not close normally in the leaves. The two co-suppression lines produced more yellow inflorescences and albino sepals with no trichomes. The transcription levels of genes involved in trichome initiation were found to be strongly affected, including GLABRA1, TRANSPARENT TESTA GLABROUS 1, TRIPTYCHON and SPINDLY, expression of which is regulated by gibberellic acids (GAs). Exogenous application of GA(3) could partially rescue the dwarf phenotype and the trichome initiation of dxr, whereas exogenous application of abscisic acid (ABA) could rescue the stomata closure defect, suggesting that lower levels of both GA and ABA contribute to the phenotype in the dxr mutants. We further found that genes involved in the biosynthetic pathways of GA and ABA were coordinately regulated. These results indicate that disruption of the plastidial MEP pathway leads to biosynthetic deficiency of photosynthetic pigments, GAs and ABA, and thus the developmental abnormalities, and that the flux from the cytoplasmic mevalonate pathway is not sufficient to rescue the deficiency caused by the blockage of the plastidial MEP pathway. These results reveal a critical role for the MEP biosynthetic pathway in controlling the biosynthesis of isoprenoids.