Pool deconvolution approach for high-throughput gene mining from Bacillus thuringiensis

Area of Science:

• Microbiology
• Genomics
• Biotechnology

Background:

• Bacillus thuringiensis (Bt) genes are crucial for agriculture, human health, and forestry.
• Conventional PCR screening for novel Bt genes is limited by cost and throughput for multiple isolates.
• Next-generation sequencing (NGS) offers advanced gene identification but can be expensive for large-scale screening.

Purpose of the Study:

• To demonstrate the efficacy of a DNA pooling strategy, termed pool deconvolution, for identifying commercially important toxin genes from numerous native Bt isolates.
• To establish a cost-effective and high-throughput method for mining novel genes from bacterial populations.

Main Methods:

• A three-step pool deconvolution strategy was employed: DNA pooling, short-read sequence assembly with gene mining, and host isolate identification.
• Thirty-six native Bt isolates were analyzed using this approach.
• Identified genes were cloned and characterized, with novel cry genes undergoing official nomenclature.

Main Results:

• The pool deconvolution method successfully identified various insecticidal protein (ip) genes, including nine 3D-cry genes, three cyt-type genes, three mtx (mosquitocidal toxin) genes, and one bin and vip-type gene each.
• Three cry-type and three cyt-type genes were cloned.
• Two novel cry genes, ip11 and ip13, were officially named cry4Ca2 and cry52Ca1, respectively.

Conclusions:

• The pool deconvolution strategy is a highly effective and scalable approach for high-throughput gene mining in bacteria.
• This method significantly reduces the cost associated with sequencing multiple isolates for gene discovery.
• The identified Bt genes hold potential for diverse applications in pest control and public health.