Evaluation of a Yeast Two-Hybrid Library by High-Throughput Sequencing.

Yeast two-hybridization (Y2H) is a classical method to study protein-protein interactions in organisms, and the top limitation of Y2H is the high ratio of false negatives and false positives. The most efficient way to reduce this error is to improve the quality of the library. The traditional library quality evaluation method can only inform us of the capacity of the library and a very limited number of library insert lengths. Therefore, we developed a new method to evaluate the library by using only a 10 ng library, amplifying the inserted fragments through 15 cycles of PCR, and then carrying out high-throughput sequencing. This method can eliminate the randomness and one-sidedness of the traditional method and can be used to obtain key indicators, such as the number of inserted genes and gene abundance, to effectively evaluate the quality of the library. In addition, the new library quality assessment method can also reveal the gene sequences of species. This method is expected to greatly accelerate PPI research on nonmodel species.

Machine-learning-guided Directed Evolution for AAV Capsid Engineering.

Target gene delivery is crucial to gene therapy. Adeno-associated virus (AAV) has emerged as a primary gene therapy vector due to its broad host range, long-term expression, and low pathogenicity. However, AAV vectors have some limitations, such as immunogenicity and insufficient targeting. Designing or modifying capsids is a potential method of improving the efficacy of gene delivery, but hindered by weak biological basis of AAV, complexity of the capsids, and limitations of current screening methods. Artificial intelligence (AI), especially machine learning (ML), has great potential to accelerate and improve the optimization of capsid properties as well as decrease their development time and manufacturing costs. This review introduces the traditional methods of designing AAV capsids and the general steps of building a sequence-function ML model, highlights the applications of ML in the development workflow, and summarizes its advantages and challenges.

CmWRKY41 activates CmHMGR2 and CmFPPS2 to positively regulate sesquiterpenes synthesis in Chrysanthemum morifolium.

Chrysanthemum morifolium is one of the most significant multipurpose crops with ornamental, medicinal, and edible value. Terpenoids, an essentials component of volatile oils, are abundant in chrysanthemum. However, the transcriptional regulation of terpenoid biosynthesis in chrysanthemums remains unclear. In the present investigation, we identified CmWRKY41, whose expression pattern is similar to that of terpenoid content in chrysanthemum floral scent, as a candidate gene that may promote terpenoid biosynthesis in chrysanthemum. Two structural genes 3-hydroxy-3-methylglutaryl-CoA reductase 2 (CmHMGR2) and farnesyl pyrophosphate synthase 2 (CmFPPS2), play key role in terpene biosynthesis in chrysanthemum. CmWRKY41 can directly bind to the promoters of CmHMGR2 or CmFPPS2 through GTGACA or CTGACG elements and activate its expression to promote sesquiterpene biosynthesis. In summary, these results indicate that CmWRKY41 targets CmHMGR2 and CmFPPS2 to positively regulate sesquiterpene biosynthesis in chrysanthemums. This study preliminarily revealed the molecular mechanism of terpenoid biosynthesis in chrysanthemum while enriching the secondary metabolism regulatory network.

Labour-saving construction of a target protein interaction network by selective culture and high-throughput sequencing.

Yeast two-hybrid (Y2H) is a classic method of screening for protein-protein interactions. However, the operation process is labor intensive and time consuming, and there is a high possibility of false positives and false negatives. Combined with wet lab operation and bioinformatics analysis, we developed a novel method of Y2H library screening using Chrysanthemum morifolium CmMPK3 as an example. The protocol can not only greatly simplify the steps of traditional Y2H library screening but also identify as many interacting proteins as possible. Furthermore, this protocol is applicable to any species, even if no genomic information is available yet.