No abstract

Large-scale screening of enzyme libraries is essential for the development of cost-effective biological processes, which will be indispensable for the production of sustainable biobased chemicals. Here, we introduce a genetic circuit termed the Genetic Enzyme Screening System that is highly useful for high-throughput enzyme screening from diverse microbial metagenomes. The circuit consists of two AND logics. The first AND logic, the two inputs of which are the target enzyme and its substrate, is responsible for the accumulation of a phenol compound in cell. Then, the phenol compound and its inducible transcription factor, whose activation turns on the expression of a reporter gene, interact in the other logic gate. We confirmed that an individual cell harboring this genetic circuit can present approximately a 100-fold higher cellular fluorescence than the negative control and can be easily quantified by flow cytometry depending on the amounts of phenolic derivatives. The high sensitivity of the genetic circuit enables the rapid discovery of novel enzymes from metagenomic libraries, even for genes that show marginal activities in a host system. The crucial feature of this approach is that this single system can be used to screen a variety of enzymes that produce a phenol compound from respective synthetic phenyl-substrates, including cellulase, lipase, alkaline phosphatase, tyrosine phenol-lyase, and methyl parathion hydrolase. Consequently, the highly sensitive and quantitative nature of this genetic circuit along with flow cytometry techniques could provide a widely applicable toolkit for discovering and engineering novel enzymes at a single cell level.

The current trend in genome-wide association studies is to identify regions where the true disease-causing genes may lie by evaluating thousands of single-nucleotide polymorphisms (SNPs) across the whole genome. However, many challenges exist in detecting disease-causing genes among the thousands of SNPs. Examples include multicollinearity and multiple testing issues, especially when a large number of correlated SNPs are simultaneously tested. Multicollinearity can often occur when predictor variables in a multiple regression model are highly correlated, and can cause imprecise estimation of association. In this study, we propose a simple stepwise procedure that identifies disease-causing SNPs simultaneously by employing elastic-net regularization, a variable selection method that allows one to address multicollinearity. At Step 1, the single-marker association analysis was conducted to screen SNPs. At Step 2, the multiple-marker association was scanned based on the elastic-net regularization. The proposed approach was applied to the rheumatoid arthritis (RA) case-control data set of Genetic Analysis Workshop 16. While the selected SNPs at the screening step are located mostly on chromosome 6, the elastic-net approach identified putative RA-related SNPs on other chromosomes in an increased proportion. For some of those putative RA-related SNPs, we identified the interactions with sex, a well known factor affecting RA susceptibility.

Clustered regularly interspaced short palindromic repeats interference (CRISPRi) is an emerging technology for artificial gene regulation. Type II CRISPR-Cas endonuclease Cas9 is the most widely used protein for gene regulation with CRISPRi. Here, we present type V-A CRISPR-Cas endonuclease Cpf1-based CRISPRi. We constructed an l-rhamnose-inducible CRISPRi system with DNase-deactivated Cpf1 from Eubacterium eligens (EedCpf1) and compared its performance with catalytically deactivated Cas9 from Streptococcus pyogenes (SpdCas9). In contrast to SpdCas9, EedCpf1 showed stronger gene repression when it was targeted to the template strand than when it was targeted to the nontemplate strand of the 5' untranslated region or coding DNA sequences. EedCpf1 exhibited no strand bias when targeted to the promoter, and preferentially used the 5'-TTTV-3' (V = A, G, or C) protospacer adjacent motif. Multiplex repression of the EedCpf1-based CRISPRi system was demonstrated using episomal and chromosomal gene targets. Our findings will guide an efficient EedCpf1-mediated CRISPRi genetic control.