A generalizable Cas9/sgRNA prediction model using machine transfer learning with small high-quality datasets

Multiplex CRISPR-Cas Genome Editing: Next-Generation Microbial Strain Engineering

Genome editing is a crucial technology for obtaining desired phenotypes in a variety of species, ranging from microbes to plants, animals, and humans. With the advent of CRISPR-Cas technology, it has become possible to edit the intended sequence by modifying the target recognition sequence in guide RNA (gRNA). By expressing multiple gRNAs simultaneously, it is possible to edit multiple targets at the same time, allowing for the simultaneous introduction of various functions into the cell. This can significantly reduce the time and cost of obtaining engineered microbial strains for specific traits. In this review, we investigate the resolution of multiplex genome editing and its application in engineering microorganisms, including bacteria and yeast. Furthermore, we examine how recent advancements in artificial intelligence technology could assist in microbial genome editing and engineering. Based on these insights, we present our perspectives on the future evolution and potential impact of multiplex genome editing technologies in the agriculture and food industry.

Toxicity prediction and classification of Gunqile-7 with small sample based on transfer learning method

Drug-induced diseases are the most important component of iatrogenic disease. It is the duty of doctors to provide a reasonable and safe dose of medication. Gunqile-7 is a Mongolian medicine with analgesic and anti-inflammatory effects. As a foreign substance in the body, even with reasonable medication, it may produce varying degrees of adverse reactions or toxic side effects. Since the cost of collecting Gunqile-7 for pharmacological animal trials is high and the data sample is small, this paper employs transfer learning and data augmentation methods to study the toxicity of Gunqile-7. More specifically, to reduce the necessary number of training samples, the data augmentation approach is employed to extend the data set. Then, the transfer learning method and one-dimensional convolutional neural network are utilized to train the network. In addition, we use the support vector machine-recursive feature elimination method for feature selection to reduce features that have adverse effects on model predictions. Furthermore, due to the important role of the pre-trained model of transfer learning, we select a quantitative toxicity prediction model as the pre-trained model, which is consistent with the purpose of this paper. Lastly, the experimental results demonstrate the efficiency of the proposed method. Our method can improve accuracy by up to 9 percentage points compared to the method without transfer learning on a small sample set.

De Novo Synthesis of a Conjugative System from Human Gut Metagenomic Data for Targeted Delivery of Cas9 Antimicrobials

Metagenomic sequences represent an untapped source of genetic novelty, particularly for conjugative systems that could be used for plasmid-based delivery of Cas9-derived antimicrobial agents. However, unlocking the functional potential of conjugative systems purely from metagenomic sequences requires the identification of suitable candidate systems as starting scaffolds for de novo DNA synthesis. Here, we developed a bioinformatics approach that searches through the metagenomic "trash bin" for genes associated with conjugative systems present on contigs that are typically excluded from common metagenomic analysis pipelines. Using a human metagenomic gut data set representing 2805 taxonomically distinct units, we identified 1598 contigs containing conjugation genes with a differential distribution in human cohorts. We synthesized de novo an entire Citrobacter spp. conjugative system of 54 kb containing at least 47 genes and assembled it into a plasmid, pCitro. We found that pCitro conjugates from Escherichia coli to Citrobacter rodentium with a 30-fold higher frequency than to E. coli, and is compatible with Citrobacter resident plasmids. Mutations in the traV and traY conjugation components of pCitro inhibited conjugation. We showed that pCitro can be repurposed as an antimicrobial delivery agent by programming it with the TevCas9 nuclease and Citrobacter-specific sgRNAs to kill C. rodentium. Our study reveals a trove of uncharacterized conjugative systems in metagenomic data and describes an experimental framework to animate these large genetic systems as novel target-adapted delivery vectors for Cas9-based editing of bacterial genomes.