Discovery of novel bacterial toxins by genomics and computational biology

• Published in: Toxicon (Oxford) 2018-06, Vol.147, p.2-12
• Main Authors: Doxey, Andrew C., Mansfield, Michael J., Montecucco, Cesare
• Format: Article
• Language: English
• Subjects: Bacterial toxins; Bioinformatics; BoNT; Botulinum; Diphtheria; Genomics; Metagenomics; Toxin discovery
• ISSN: 0041-0101; 1879-3150
• DOI: 10.1016/j.toxicon.2018.02.002

Hundreds and hundreds of bacterial protein toxins are presently known. Traditionally, toxin identification begins with pathological studies of bacterial infectious disease. Following identification and cultivation of a bacterial pathogen, the protein toxin is purified from the culture medium and its pathogenic activity is studied using the methods of biochemistry and structural biology, cell biology, tissue and organ biology, and appropriate animal models, supplemented by bioimaging techniques. The ongoing and explosive development of high-throughput DNA sequencing and bioinformatic approaches have set in motion a revolution in many fields of biology, including microbiology. One consequence is that genes encoding novel bacterial toxins can be identified by bioinformatic and computational methods based on previous knowledge accumulated from studies of the biology and pathology of thousands of known bacterial protein toxins. Starting from the paradigmatic cases of diphtheria toxin, tetanus and botulinum neurotoxins, this review discusses traditional experimental approaches as well as bioinformatics and genomics-driven approaches that facilitate the discovery of novel bacterial toxins. We discuss recent work on the identification of novel botulinum-like toxins from genera such as Weissella, Chryseobacterium, and Enteroccocus, and the implications of these computationally identified toxins in the field. Finally, we discuss the promise of metagenomics in the discovery of novel toxins and their ecological niches, and present data suggesting the existence of uncharacterized, botulinum-like toxin genes in insect gut metagenomes. •Traditionally, toxins were discovered based on their isolation from clinical samples and subsequent biochemical and cellular analysis.•Increasingly, researchers are using bioinformatic approaches to discover novel toxins in genomes.•Determining the host, cell type and substrate specificity of computationally predicted toxins is a major challenge.•A goal for bioinformatics is to help prioritize toxin candidates worthy of additional experimental characterization.•Metagenomics presents an exciting future avenue for discovery of toxin genes in their ecological setting.