SYN-View: A Phylogeny-Based Synteny Exploration Tool for the Identification of Gene Clusters Linked to Antibiotic Resistance

Abstract

The development of new antibacterial drugs has become one of the most important tasks of the century in order to overcome the posing threat of drug resistance in pathogenic bacteria. Many antibiotics originate from natural products produced by various microorganisms. Over the last decades, bioinformatical approaches have facilitated the discovery and characterization of these small compounds using genome mining methodologies. A key part of this process is the identification of the most promising biosynthetic gene clusters (BGCs), which encode novel natural products. In 2017, the Antibiotic Resistant Target Seeker (ARTS) was developed in order to enable an automated target-directed genome mining approach. ARTS identifies possible resistant target genes within antibiotic gene clusters, in order to detect promising BGCs encoding antibiotics with novel modes of action. Although ARTS can predict promising targets based on multiple criteria, it provides little information about the cluster structures of possible resistant genes. Here, we present SYN-view. Based on a phylogenetic approach, SYN-view allows for easy comparison of gene clusters of interest and distinguishing genes with regular housekeeping functions from genes functioning as antibiotic resistant targets. Our aim is to implement our proposed method into the ARTS web-server, further improving the target-directed genome mining strategy of the ARTS pipeline.

Current Methods for Recombination Detection in Bacteria

“…Although other synteny-aware utilities do not report HGT events directly, they can indirectly point out candidates to explore. Lots of genomic browsers have been developed to visualize synteny, namely, BAGET for retrieving syntenic information for a certain gene [ 52 ], Synima to juxtapose loci between genomes [ 53 ], and SYN-View to investigate antibiotic resistance gene clusters [ 54 ]. Sibelia can obtain syntenic blocks in analyzed genomes [ 55 ], while SyntTax and SynTracker link them with taxonomical and strain-specific relationships [ 56 , 57 ].…”

Fungal secondary metabolites in food and pharmaceuticals in the era of multi-omics

“…Probabilistic models have integrated network and expression data from gene knockout expression studies to predict cell-signaling cascades (Palazzotto and Weber 2018 ). Many pipelines have been developed earlier for the study of the microbial products like Antibiotic Resistance Target Seeker (ARTS) using deep learning and Fungal Resistance Gene-directed Genome mining (FRIGG), and Crux ( https://www.cruxinformatics.com/ ) is a mass spectrometry analysis toolkit that provides multiple machine learning based tool for tandem mass spectra analysis, statistics, and visualization (McIlwain et al 2014 ; Stahlecker et al 2021 ). These tools represent recent progress in many microbial aspects, including RNA sequencing analysis, comparative proteomics, metagenomics, and developments of new computational workflows in fungal physiology and biology (Xiao et al 2017 ).…”

Genome mining for drug discovery: progress at the front end

“…The downstream steps of the genome mining process are beyond the scope of this perspective. Interested readers are referred to recent reviews on other technical advances relevant to bioinformatics, product expression, and strain engineering (Baltz, 2016 ; Blin et al, 2019 ; Kang & Kim, 2021 ; Kautsar et al, 2021 ; Lee et al, 2020 ; Merwin et al, 2020 ; Navarro-Muñoz et al, 2020 ; Nguyen et al, 2020 ; Palazzotto et al, 2019 ; Ren et al, 2020 ; Schorn et al, 2021 ; Sharma et al, 2020 ; Skinnider et al, 2020 ; Stahlecker et al, 2021 ; van der Heul et al, 2018 ; Wang et al, 2019 ; Whitford et al, 2021 ; Xu & Wright, 2019 ).…”