AI and Knowledge-Based Method for Rational Design of Escherichia coli Sigma70 Promoters

Expanding sigma70 promoter libraries can support the engineering of metabolic pathways and enhance recombinant protein expression. Herein, we developed an artificial intelligence (AI) and knowledge-based method for the rational design of sigma70 promoters. Strong sigma70 promoters were identified by using high-throughput screening (HTS) with enhanced green fluorescent protein (eGFP) as a reporter gene. The features of these strong promoters were adopted to guide promoter design based on our previous reported deep learning model. In the following case study, the obtained strong promoters were used to express collagen and microbial transglutaminase (mTG), resulting in increased expression levels by 81.4% and 33.4%, respectively. Moreover, these constitutive promoters achieved soluble expression of mTG-activating protease and contributed to active mTG expression in Escherichia coli. The results suggested that the combined method may be effective for promoter engineering.

Isolation and Characterization of Pectobacterium Phage vB_PatM_CB7: New Insights into the Genus Certrevirus

To date, Certrevirus is one of two genera of bacteriophage (phage), with phages infecting Pectobacterium atrosepticum, an economically important phytopathogen that causes potato blackleg and soft rot disease. This study provides a detailed description of Pectobacterium phage CB7 (vB_PatM_CB7), which specifically infects P. atrosepticum. Host range, morphology, latent period, burst size and stability at different conditions of temperature and pH were examined. Analysis of its genome (142.8 kbp) shows that the phage forms a new species of Certrevirus, sharing sequence similarity with other members, highlighting conservation within the genus. Conserved elements include a putative early promoter like that of the Escherichia coli sigma70 promoter, which was found to be shared with other genus members. A number of dissimilarities were observed, relating to DNA methylation and nucleotide metabolism. Some members do not have homologues of a cytosine methylase and anaerobic nucleotide reductase subunits NrdD and NrdG, respectively. Furthermore, the genome of CB7 contains one of the largest numbers of homing endonucleases described in a single phage genome in the literature to date, with a total of 23 belonging to the HNH and LAGLIDADG families. Analysis by RT-PCR of the HNH homing endonuclease residing within introns of genes for the large terminase, DNA polymerase, ribonucleotide reductase subunits NrdA and NrdB show that they are splicing competent. Electrospray ionization-tandem mass spectrometry (ESI-MS/MS) was also performed on the virion of CB7, allowing the identification of 26 structural proteins-20 of which were found to be shared with the type phages of the genera of Vequintavirus and Seunavirus. The results of this study provide greater insights into the phages of the Certrevirus genus as well as the subfamily Vequintavirinae.

70ProPred: a predictor for discovering sigma70 promoters based on combining multiple features

BACKGROUND

Promoter is an important sequence regulation element, which is in charge of gene transcription initiation. In prokaryotes, σ70 promoters regulate the transcription of most genes. The promoter recognition has been a crucial part of gene structure recognition. It's also the core issue of constructing gene transcriptional regulation network. With the successfully completion of genome sequencing from an increasing number of microbe species, the accurate identification of σ70 promoter regions in DNA sequence is not easy.

RESULTS

In order to improve the prediction accuracy of sigma70 promoters in prokaryote, a promoter recognition model 70ProPred was established. In this work, two sequence-based features, including position-specific trinucleotide propensity based on single-stranded characteristic (PSTNPss) and electron-ion potential values for trinucleotides (PseEIIP), were assessed to build the best prediction model. It was found that 79 features of PSTNPSS combined with 64 features of PseEIIP obtained the best performance for sigma70 promoter identification, with a promising accuracy and the Matthews correlation coefficient (MCC) at 95.56% and 0.90, respectively.

CONCLUSION

The jackknife tests showed that 70ProPred outperforms the existing sigma70 promoter prediction approaches in terms of accuracy and stability. Additionally, this approach can also be extended to predict promoters of other species. In order to facilitate experimental biologists, an online web server for the proposed method was established, which is freely available at http://server.malab.cn/70ProPred/ .