Accelerating Antibiotic Discovery: How ARTS Targets BGCs with Resistance Genes for Novel Therapeutics

Jacob Howard Jan 09, 2026 191

This article explores the Antibiotic Resistant Target Seeker (ARTS) bioinformatics tool as a strategic solution for prioritizing bacterial biosynthetic gene clusters (BGCs) that encode novel antibiotics.

Accelerating Antibiotic Discovery: How ARTS Targets BGCs with Resistance Genes for Novel Therapeutics

Abstract

This article explores the Antibiotic Resistant Target Seeker (ARTS) bioinformatics tool as a strategic solution for prioritizing bacterial biosynthetic gene clusters (BGCs) that encode novel antibiotics. Designed for researchers and drug developers, we detail ARTS's foundational principles in resistance gene prediction, its practical application in genome mining workflows, key troubleshooting strategies for data interpretation, and comparative validation against other methods. The synthesis provides a roadmap for leveraging ARTS to efficiently navigate microbial genomes and identify high-priority candidates in the fight against antimicrobial resistance (AMR).

Decoding ARTS: The Foundational Science of Resistance Gene-Driven BGC Discovery

The AMR Crisis and the Need for Intelligent Genome Mining

Application Notes: The ARTS Framework for BGC Prioritization

The Antibiotic Resistant Target Seeker (ARTS) is a bioinformatics tool specifically designed for the target-directed genome mining of bacterial genomes to discover biosynthetic gene clusters (BGCs) that encode known or novel antibiotic resistance determinants within themselves. This self-resistance principle is a key signature for BGCs producing bioactive compounds, particularly antibiotics.

Core Thesis Context: In the broader thesis on combating Antimicrobial Resistance (AMR), the ARTS framework provides a strategic computational filter. It moves beyond traditional homology-based BGC discovery (e.g., antiSMASH) by prioritizing clusters that contain dedicated resistance genes, thereby increasing the probability of finding BGCs for compounds with novel modes of action and inherent bypass mechanisms against established resistance.

Key Functional Modules of ARTS:

  • Resistance Gene Prediction: Uses curated databases of known antibiotic resistance genes (e.g., from CARD, ResFinder) and HMM models to identify resistance determinants within a genome.
  • BGC Detection: Integrates with antiSMASH to delineate all potential BGCs.
  • Co-localization Analysis: The primary innovation—identifies BGCs where resistance genes are physically located within the cluster boundaries, suggesting a dedicated self-resistance mechanism.
  • Target Prediction: For BGCs with known resistance targets (e.g., a mutated rpoB gene for rifamycins), it predicts the likely molecular target of the encoded antibiotic.

Quantitative Impact: The following table summarizes data from recent studies on the efficiency of ARTS-guided genome mining compared to conventional methods.

Table 1: Efficacy of ARTS-Prioritized Genome Mining vs. Conventional Screening

Metric Conventional Genome Mining (antiSMASH only) ARTS-Prioritized Mining Data Source / Study Context
BGCs Identified per Genome 15-30 (average) 15-30 (same input) Analysis of Streptomyces spp. genomes
BGCs with Linked Resistance ~10-20% 100% (by selection) ARTS methodology paper (Ziemert et al.)
Hit Rate for Novel Antibiotics < 0.1% (from all BGCs) > 5% (from ARTS-prioritized BGCs) Retrospective analysis of known antibiotic clusters
Time to Target Identification Months (post-isolation) Pre-experimental prediction Case study on Strepthromycin discovery

Experimental Protocols

Protocol 2.1: In Silico Prioritization of BGCs Using the ARTS Web Tool

Objective: To computationally analyze a bacterial genome sequence and generate a prioritized list of BGCs most likely to produce novel antibiotics with associated resistance mechanisms.

Materials & Software:

  • Input: Bacterial genome assembly in FASTA format.
  • Software/Web Tools: ARTS web server (arts.ziemertlab.com), antiSMASH.
  • Computing Environment: Standard web browser or Linux server for CLI version.

Methodology:

  • Data Preparation: Ensure your genome assembly is of high quality (contig N50 > 20kbp preferred). Annotate the genome using Prokka or RAST if needed.
  • ARTS Job Submission: a. Access the ARTS web server. b. Upload the genome FASTA file. c. Select standard parameters: enable 'antiSMASH integration', select 'strict' mode for resistance gene detection. d. Submit the job. Processing time varies from 1-6 hours.
  • Results Interpretation: a. Download the main output table (.csv). b. Prioritize BGCs with a "Resistance Score" > 90 and those where the resistance gene is predicted to be within the BGC boundaries (column: "In Cluster" = TRUE). c. Examine the "Predicted Target" column for clues on the antibiotic's mode of action (e.g., "RNA polymerase", "50S ribosomal subunit").
  • Downstream Analysis: Extract the nucleotide sequence of high-priority BGCs for heterologous expression (see Protocol 2.2).
Protocol 2.2: Heterologous Expression of ARTS-Prioritized BGCs inStreptomyces coelicolorM1152

Objective: To express a silent or poorly expressed BGC identified and prioritized by ARTS in a optimized model host for compound production and isolation.

Materials:

  • Bacterial Strains: Source organism (BGC donor), E. coli ET12567/pUZ8002 (conjugal donor), S. coelicolor M1152 (heterologous host).
  • Vectors: pCAP01 or pCRISPomyces-2 shuttle vectors for BGC capture via Transformation-Associated Recombination (TAR) in yeast.
  • Growth Media: LB, R5A agar (for Streptomyces conjugation and sporulation), ISP4, YPD (for yeast cloning).
  • Antibiotics: Apramycin (50 µg/mL for E. coli, 50 µg/mL for Streptomyces), Kanamycin, Chloramphenicol (for E. coli counterselection).

Methodology:

  • BGC Capture via TAR Cloning: a. Design TAR capture vectors with 5' and 3' homology arms (~1 kb each) targeting the flanks of the ~30-80 kbp BGC. b. Co-transform the linearized TAR vector and genomic DNA from the donor strain into Saccharomyces cerevisiae. c. Select yeast colonies on appropriate dropout media. Isolate yeast plasmid DNA. d. Confirm correct capture by PCR and restriction digest.
  • Conjugal Transfer to S. coelicolor M1152: a. Transform the confirmed TAR plasmid into E. coli ET12567/pUZ8002. b. Prepare spores of S. coelicolor M1152 and heat-shock at 50°C for 10 minutes. c. Mix donor E. coli and Streptomyces spores on an R5A plate and incubate at 30°C for 16-20 hours. d. Overlay the plate with 1 mL water containing 1 mg apramycin and 0.5 mg nalidixic acid. Incubate for 5-7 days until exconjugant colonies appear.
  • Metabolite Production and Analysis: a. Inoculate exconjugants into liquid TSBY medium with apramycin. Incubate at 30°C, 250 rpm for 2-3 days as seed culture. b. Transfer seed culture to production media (e.g., SFM or R5). Incubate for 5-14 days. c. Extract metabolites from whole broth with an equal volume of ethyl acetate. Concentrate the organic layer in vacuo. d. Analyze extracts by LC-HRMS and screen for novel ions not present in M1152 control extracts.

Visualizations

Diagram 1: ARTS-Based BGC Prioritization Workflow

arts_workflow Input Bacterial Genome (FASTA) A 1. Gene Prediction & Annotation Input->A B 2. Resistance Gene Detection (HMM/Database) A->B C 3. BGC Detection (antiSMASH) A->C D 4. Co-localization Analysis (Resistance Gene within BGC?) B->D C->D E Low Priority BGC D->E No F High Priority BGC (Predicted Target Output) D->F Yes

Diagram 2: Heterologous Expression Pathway for Validated BGC

heterologous_expr Start ARTS-Prioritized BGC Sequence Step1 TAR Cloning in Yeast: Homology arm-mediated capture Start->Step1 Step2 Shuttle Vector with intact BGC Step1->Step2 Step3 Conjugal Transfer E. coli → S. coelicolor M1152 Step2->Step3 Step4 Fermentation & Metabolite Extraction Step3->Step4 Step5 LC-MS/MS Analysis & Bioassay Step4->Step5 End Identification of Novel Antibiotic Step5->End

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for ARTS-Guided Genome Mining & Validation

Item / Reagent Function in Workflow Key Consideration / Example
High-Quality Genomic DNA Kit (e.g., Promega Wizard) Provides pure, high-molecular-weight DNA for both sequencing and TAR cloning. Integrity is critical for capturing large BGCs; avoid shearing.
pCAP01 or pCRISPomyces-2 Vector Shuttle vectors for E. coli-Streptomyces conjugation, containing homology arms for TAR cloning and selection markers. Choice depends on BGC size and preferred cloning method (TAR vs. Cas9-assisted).
S. coelicolor M1152 Host Strain Genetically optimized heterologous host for polyketide and non-ribosomal peptide production. Four secondary metabolite clusters deleted. Provides a "clean" metabolic background for detecting novel compounds.
Apramycin Antibiotic Selective agent for maintaining the BGC-containing plasmid in both E. coli and Streptomyces during conjugation and fermentation. Standard concentration: 50 µg/mL in agar and liquid media.
R5A Agar Plates Specialized medium for efficient intergeneric conjugation between E. coli and Streptomyces spores. Contains MgCl₂ and trace elements critical for spore germination and plasmid transfer.
Ethyl Acetate (HPLC Grade) Organic solvent for broad-spectrum extraction of metabolites from fermentation broth. Effective for both polar and mid-polar natural products.
LC-HRMS System (e.g., UHPLC-Q-TOF) Analytical platform for detecting, characterizing, and comparing metabolite profiles from engineered vs. control strains. Enables molecular networking to identify novel ions related to known antibiotics.

What is ARTS? Core Algorithm and Bioinformatics Principles Explained

ARTS (Antibiotic Resistant Target Seeker) is a specialized bioinformatics platform designed for the genome-mining of bacterial biosynthetic gene clusters (BGCs) with a high probability of encoding resistance determinants. Within the context of a broader thesis on prioritizing BGCs for resistance gene research, ARTS serves as a critical computational sieve. It operates on the principle that antibiotic producers possess self-resistance mechanisms, often encoded within or near the BGC for the corresponding antibiotic. By systematically identifying these resistance genes, ARTS allows researchers to prioritize BGCs that are not only novel but are also likely to produce bioactive compounds with a known or novel mechanism of action, thereby streamlining the discovery pipeline for new antibiotics.

Core Algorithm and Bioinformatics Principles

The ARTS algorithm is built on a comparative genomics strategy. Its execution involves several key steps and principles:

  • Input & Pre-processing: ARTS takes a complete bacterial genome sequence as input. It uses tools like Blast+ and HMMER3 to identify all putative BGCs via the antiSMASH software suite.
  • Resistance Gene Identification: The core innovation lies in its curated database of known antibiotic resistance genes (e.g., from the Comprehensive Antibiotic Resistance Database - CARD) and hidden Markov models (HMMs) for resistance protein families (e.g., ATP-Binding Cassette transporters, major facilitator superfamily transporters, antibiotic inactivation enzymes).
  • Comparative Genomic Footprinting: ARTS does not merely perform a BLAST search. It analyzes the genomic context of identified BGCs, looking for resistance genes that are:
    • Inside the BGC boundary.
    • In the close proximity (e.g., within a user-defined window) of the BGC.
    • Duplicated elsewhere in the genome, a potential indicator of a dedicated resistance element.
  • Scoring and Prioritization: BGCs are scored based on the number, type, and genomic context of associated resistance genes. A BGC with an integral resistance gene receives a higher priority rank than one with no linked resistance elements.
  • Output: Results are presented in an interactive format, highlighting BGCs, their predicted products, and the located resistance genes with annotations.

Table 1: Core Algorithm Steps and Quantitative Benchmarks

Step Primary Tool/Method Key Parameter Typical Runtime* Output
Genome Annotation Prokka / PGAP -- 10-30 min Gene calls, GFF3 file
BGC Prediction antiSMASH (integrated) Strictness: Relaxed 15-60 min BGC locations & types
Resistance Gene Scan HMMER3 vs. ARTS DB E-value < 1e-10 2-5 min Putative resistance hits
Context Analysis Custom Python scripts Proximity window: 20 kb < 1 min Resistance-BGC linkage
Prioritization & Scoring ARTS scoring matrix Weighted sum < 1 min Ranked list of BGCs

*Runtimes are for a typical bacterial genome (~4-8 Mb) on a high-performance compute node.

G Genome Input Bacterial Genome AntiSMASH BGC Prediction (antiSMASH) Genome->AntiSMASH HMMER Resistance Gene Scan (HMMER3) AntiSMASH->HMMER BGC Coordinates Context Genomic Context Analysis AntiSMASH->Context All BGCs ARTS_DB Resistance Gene DB (Custom HMMs) ARTS_DB->HMMER HMMER->Context Resistance Hits Scoring Scoring & Prioritization Context->Scoring Output Ranked List of BGCs with Resistance Scoring->Output

ARTS Algorithm Workflow (79 characters)

Application Notes & Protocols

Protocol 1: Running ARTS on a Novel Bacterial Genome for BGC Prioritization

Objective: To identify and prioritize biosynthetic gene clusters (BGCs) in a newly sequenced bacterial genome based on the presence of linked antibiotic resistance genes.

Materials:

  • Input Data: Assembled bacterial genome in FASTA format (genome.fna).
  • Software: ARTS is accessible via a web server (arts.ziemertlab.com) or command line. This protocol assumes command-line use.
  • Computing: A Linux-based system with Conda installed.

Procedure:

  • Environment Setup:

  • Database Preparation: Ensure the ARTS-specific HMM database is downloaded and formatted.

  • Execute ARTS Analysis:

    • -i: Input genome file.
    • -o: Output directory (will be created).
    • --genefinding_tool: Specify gene finder (prodigal is default).
    • -v: Verbose output.

  • Interpretation of Results:

    • Navigate to the output directory. The key file is results/results.html, which provides an interactive view.
    • Analyze the results/results.tsv tab-delimited file. Key columns include BGC_number, BGC_type, Resistance_Genes_Found, and ARTS_Score.
    • Prioritize BGCs with the highest ARTS_Score and those where resistance genes are listed as inside the BGC for downstream experimental validation.
Protocol 2: Validation of ARTS-Predicted Resistance Gene Function

Objective: To experimentally confirm the resistance function of a gene identified by ARTS within a high-priority BGC.

Materials:

  • Bacterial Strains: E. coli DH10B (cloning host), E. coli BW25113 (expression host for MIC assays).
  • Vector: pET-28a(+) expression vector.
  • Antibiotics: Purified antibiotic compound predicted by antiSMASH for the BGC, or a panel of related antibiotics.
  • Culture Media: LB broth and agar, with appropriate antibiotics (kanamycin for plasmid selection).

Procedure:

  • Cloning the Resistance Gene:

    • Design primers to amplify the predicted resistance ORF from genomic DNA, adding appropriate restriction sites (e.g., NdeI and XhoI).
    • Perform PCR, digest PCR product and pET-28a(+) vector, ligate, and transform into E. coli DH10B. Sequence-confirm the construct (pET28a-ResGene).

  • Minimum Inhibitory Concentration (MIC) Assay:

    • Transform the empty pET-28a(+) vector (control) and the pET28a-ResGene construct into the expression host E. coli BW25113.
    • Prepare a 2-fold serial dilution of the target antibiotic in 96-well plates containing LB + kanamycin.
    • Inoculate each well with ~5x10^5 CFU of the expressing strains. Include a no-antibiotic growth control.
    • Incubate at 37°C for 16-20 hours. The MIC is defined as the lowest antibiotic concentration that completely inhibits visible growth.
    • Expected Outcome: Strains expressing the ARTS-predicted resistance gene should show a significant (e.g., ≥4-fold) increase in MIC compared to the control strain harboring the empty vector, confirming resistance function.

G Start ARTS-Predicted Resistance Gene Clone Clone into Expression Vector Start->Clone Transform Transform into E. coli Host Clone->Transform Inoculate Inoculate Plates with Test & Control Transform->Inoculate Prepare Prepare Antibiotic Serial Dilution Prepare->Inoculate Incubate Incubate (16-20 hrs) Inoculate->Incubate Measure Measure Optical Density (OD600) Incubate->Measure Confirm Confirm Elevated MIC (Resistance Function) Measure->Confirm

Resistance Gene Validation Workflow (73 characters)

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for ARTS-Guided BGC Research

Item Category Function & Rationale
antiSMASH DB Bioinformatics Database Provides the core models for BGC prediction; essential for the first step of the ARTS pipeline.
ARTS Custom HMM DB Bioinformatics Database Curated collection of HMMs for resistance protein families; the unique fingerprint library for resistance gene detection.
CARD (MEGARes) Bioinformatics Database Reference database of known resistance genes; used for functional annotation and classification of ARTS hits.
pET Vector Series Molecular Biology Reagent High-copy, T7-promoter driven expression vectors for cloning and heterologously expressing resistance genes in E. coli.
E. coli BW25113 Bacterial Strain A standard Keio collection parent strain with well-characterized genetics, ideal for performing reproducible MIC assays.
Mueller-Hinton II Broth Culture Media The standardized medium for antibiotic susceptibility testing (CLSI guidelines), ensuring comparable MIC results.
96-Well Cell Culture Plate Laboratory Consumable Platform for high-throughput MIC assays via broth microdilution.
Microplate Spectrophotometer Laboratory Instrument For rapid, quantitative measurement of bacterial growth (OD600) in MIC assays, enabling precise endpoint determination.

Application Notes

The Self-Resistance Hypothesis posits that microorganisms producing potent bioactive natural products, such as antibiotics, must concurrently encode mechanisms to protect themselves from their own toxins. This protection is frequently conferred by resistance genes that are physically co-localized within the same Biosynthetic Gene Cluster (BGC). In the context of the Antibiotic Resistant Target Seeker (ARTS) methodology, this hypothesis provides a powerful genomic filter for prioritizing BGCs with a high probability of encoding compounds that act on essential bacterial targets, thereby streamlining antibiotic discovery.

Key Application Points:

  • BGC Prioritization: ARTS leverages the self-resistance principle by scanning microbial genomes for BGCs containing genes predicted to confer resistance (e.g., drug efflux pumps, target-modifying enzymes, antibiotic-inactivating enzymes). BGCs with co-localized resistance genes are flagged as high-priority candidates.
  • Target Prediction: The nature of the co-localized resistance gene often directly indicates the cellular target of the encoded natural product (e.g., a ribosomal protection protein suggests a ribosome-targeting compound).
  • Overcoming Rediscovery: This filter helps exclude BGCs for known compounds if their resistance mechanism matches established profiles, focusing resources on novel scaffolds and targets.

Table 1: Types of Co-localized Resistance Genes and Their Implications

Resistance Gene Type Example Mechanism Inferred Compound Target Utility in Prioritization
Target Duplication/Protection Extra copy of essential gene (e.g., rpsL for S12 protein) Bacterial ribosome Very High – Strong indicator of essential target.
Target Modification Methyltransferase (e.g., tlrB for 23S rRNA) Bacterial ribosome High – Directly reveals target site.
Antibiotic Inactivation Beta-lactamase, acetyltransferase Varies (cell wall, ribosome) Medium – Common, may indicate known scaffold.
Efflux Pump ATP-binding cassette (ABC) or Major Facilitator Superfamily (MFS) transporters Nonspecific (compound removal) Medium/Low – Less specific target information.

Experimental Protocols

Protocol 1: In Silico Identification of BGCs with Co-localized Resistance using ARTS

Objective: To computationally mine a bacterial genome or metagenome-assembled genome (MAG) for BGCs harboring predicted self-resistance genes.

Materials & Software:

Procedure:

  • BGC Prediction: Run the genome sequence through antiSMASH (v7+) with strict detection settings to identify all putative BGCs. Output should be in GenBank or JSON format.
  • ARTS Analysis: Submit the whole genome FASTA file and the antiSMASH output to the ARTS web server or run the ARTS pipeline locally.
  • Resistance Gene Detection: ARTS will perform:
    • HMMER Searches: Against its curated database of resistance models (e.g., for duplicated essential target genes).
    • BLASTP Analysis: Of all BGC proteins against known resistance protein databases.
    • Comparative Genomics: To identify gene duplications within the BGC context.
  • Data Integration: ARTS integrates results to generate a ranked list of BGCs. The "ARTS score" is elevated by the presence and strength of co-localized resistance gene hits.
  • Manual Curation: Inspect top-ranking BGCs. Verify the resistance gene is within the BGC boundaries and analyze its domain architecture to hypothesize its function.

Protocol 2: Heterologous Expression and Resistance Validation

Objective: To experimentally validate that a candidate co-localized gene confers resistance to the compound produced by its associated BGC.

Materials:

  • E. coli or Streptomyces expression strain lacking the BGC
  • Cloning vector (e.g., pET, pIJ)
  • Candidate resistance gene cloned from the original host
  • Purified compound or crude extract from a BGC-expressing strain
  • Mueller-Hinton or ISP2 agar plates

Procedure:

  • Clone Resistance Gene: Amplify the candidate resistance gene via PCR and clone it into an appropriate expression vector under a constitutive promoter. Transform into the expression host. Prepare an empty vector control.
  • Produce Bioactive Compound: Cultivate the native BGC-producing strain or a heterologous host expressing the entire BGC. Extract metabolites with organic solvent (e.g., ethyl acetate).
  • Agar Diffusion Assay:
    • Spread lawn of expression strains (test and empty vector control).
    • Apply filter paper disks impregnated with serial dilutions of the compound/extract or wells filled with crude supernatant.
    • Incubate 18-24 hours at appropriate temperature.
  • Broth Microdilution MIC Assay:
    • In a 96-well plate, prepare two-fold serial dilutions of the compound/extract in growth medium.
    • Inoculate wells with a standardized inoculum (~5e5 CFU/mL) of the test and control strains.
    • Incubate with shaking and measure optical density (OD600) after 16-20 hours.
  • Analysis: The strain expressing the candidate resistance gene should show a larger zone of inhibition in the disk assay and a significantly higher Minimum Inhibitory Concentration (MIC) in the broth assay compared to the control, confirming resistance function.

Protocol 3: Target Identification via Target Duplication

Objective: To confirm the cellular target when the resistance gene is a duplicated, essential housekeeping gene.

Materials:

  • Susceptible bacterial strain (e.g., Bacillus subtilis)
  • Vector for inducible gene expression in the susceptible host
  • Purified candidate compound

Procedure:

  • Clone Duplicated Target Gene: Clone the BGC-encoded duplicate of the essential gene (e.g., fusA, rplK) into an inducible expression vector for the susceptible host.
  • Generate Reporter Strain: Transform the susceptible host with the expression construct.
  • Induction and Challenge Assay:
    • Grow strains (induced and uninduced) to mid-log phase.
    • Expose to a sub-inhibitory concentration of the purified compound.
    • Monitor growth (OD600) over time.
  • Expected Result: Only the induced strain, overexpressing the duplicated target, should grow in the presence of the compound, confirming that the compound's target is the product of that essential gene.

Visualizations

ARTS_Workflow Start Bacterial Genome (FASTA) AntiSMASH antiSMASH BGC Prediction Start->AntiSMASH ARTS ARTS Analysis AntiSMASH->ARTS HMMER HMMER Search (Resistance DB) ARTS->HMMER BLAST BLASTP Analysis (Resistance Proteins) ARTS->BLAST CompGen Comparative Genomics ARTS->CompGen Integrate Integrate & Rank (ARTS Score) HMMER->Integrate BLAST->Integrate CompGen->Integrate Output Prioritized BGC List (with Resistance Genes) Integrate->Output

ARTS-Based BGC Prioritization Workflow

SelfResistance_Logic Hypo Self-Resistance Hypothesis BGC Biosynthetic Gene Cluster (BGC) Hypo->BGC predicts RG Co-localized Resistance Gene (RG) BGC->RG encodes Toxin Bioactive Toxin (e.g., Antibiotic) BGC->Toxin encodes Producer Producer Cell PROTECTED RG->Producer confers protection to Target Essential Cellular Target Toxin->Target binds Target->Producer protection of via RG Competitor Competitor Cell INHIBITED Target->Competitor inhibition of leads to

Core Logic of the Self-Resistance Hypothesis

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Reagents and Materials for Self-Resistance Research

Item Function in Research Example/Supplier
antiSMASH Software Core tool for the automated genomic identification and annotation of BGCs. https://antismash.secondarymetabolites.org
ARTS Bioinformatics Suite Specialized tool for prioritizing BGCs based on co-localized resistance genes and target predictions. https://arts.ziemertlab.com
HMMER Software Suite Used for sensitive sequence homology searches against profile hidden Markov models (HMMs) of resistance protein families. http://hmmer.org
Heterologous Expression Hosts Genetically tractable strains for cloning and expressing BGCs or individual resistance genes. E. coli BL21(DE3), Streptomyces coelicolor M1152/M1146
Broad-Host-Range Cloning Vectors Plasmids for gene expression in diverse bacterial hosts (actinomycetes, proteobacteria). pET series (E. coli), pIJ/pSET152 (Streptomyces), pBBR1 (Gram-negative)
Cation-Adjusted Mueller Hinton Broth (CAMHB) Standardized medium for performing reproducible antimicrobial susceptibility testing (MIC assays). Hardy Diagnostics, Thermo Fisher Scientific
96-Well Microtiter Pllates For high-throughput broth microdilution MIC assays and growth curves. Corning, Thermo Scientific Nunc
Liquid Chromatography-Mass Spectrometry (LC-MS) For the purification, quantification, and structural analysis of bioactive compounds from producer strains. Agilent, Waters, Thermo Fisher systems

Within the thesis on the Antibiotic Resistance Target Seeker (ARTS) for prioritizing Biosynthetic Gene Clusters (BGCs) with resistance genes, the system's predictive power is fundamentally dependent on specific, high-quality genomic data inputs and specialized databases. ARTS mines microbial genomes to detect BGCs linked to self-resistance mechanisms, crucial for identifying novel antibiotic scaffolds. This document details the core genomic data types, the primary databases utilized, and provides protocols for data acquisition and preprocessing.

ARTS requires structured genomic data. The table below summarizes the essential data types and their characteristics.

Table 1: Essential Genomic Data Types for ARTS Analysis

Data Type Format Primary Source Relevance to ARTS Typical Size Range (per genome)
Whole Genome Sequence (WGS) FASTA, FASTQ Sequencing platforms (Illumina, PacBio) Raw input for BGC and resistance gene detection. 2 MB (bacterial) to 10+ MB (fungal)
Assembled Genomic Contigs/Scaffolds FASTA Assemblers (SPAdes, Flye) Provides contiguous sequence for HMM-based cluster prediction. 10s - 1000s of contigs
Annotated Genome Features GFF3, GBK Annotation pipelines (Prokka, NCBI PGAP) Contains gene coordinates, product predictions essential for ARTS' heuristic rules. 5,000 - 12,000 features
Protein Sequences FASTA Derived from annotation Used for homology searches (BLAST, HMMER) against resistance and biosynthetic databases. 5,000 - 12,000 sequences
BGC Predictions JSON, SVG, GBK antiSMASH, PRISM, DeepBGC Direct input of predicted cluster boundaries and types. 1 - 50 clusters per genome

ARTS relies on integrated queries to multiple curated databases.

Table 2: Key Databases for ARTS Functionality

Database Name Type Content Focus ARTS Application Update Frequency
MIBiG (Minimum Information about a BGC) Reference Repository Curated, experimentally characterized BGCs. Training data, cluster type annotation, resistance gene association. Biannual
CARD (Comprehensive Antibiotic Resistance Database) Specialized Knowledgebase Antibiotic resistance genes, SNPs, proteins. Identification of known resistance genes within/adjacent to BGCs. Quarterly
Pfam / dbCAN2 Protein Family Databases Hidden Markov Models (HMMs) for protein domains and families. Detection of biosynthetic (PKS, NRPS) and resistance-associated domains. 1-2 years
NCBI RefSeq / GenBank General Nucleotide Archives Annotated genomic sequences across all taxa. Source of query genomes, reference sequences, and metadata. Daily
UniProtKB / Swiss-Prot Protein Sequence Database Manually annotated, high-confidence protein sequences. Functional annotation of putative resistance and biosynthetic proteins. Monthly

Protocols

Protocol 4.1: Data Acquisition and Preprocessing for ARTS Input

Objective: To obtain and prepare clean, annotated genomic data suitable for ARTS analysis from a novel bacterial isolate.

Materials & Reagents:

  • Research Reagent Solutions Table:
Item Function
DNeasy Blood & Tissue Kit (Qiagen) High-quality genomic DNA extraction from bacterial cultures.
Nextera XT DNA Library Prep Kit (Illumina) Preparation of sequencing libraries for short-read platforms.
Qubit dsDNA HS Assay Kit (Thermo Fisher) Accurate quantification of gDNA and library concentrations.
SPAdes Genome Assembler v3.15 De novo assembly of Illumina reads into contigs.
Prokka v1.14.6 Rapid prokaryotic genome annotation pipeline.
antiSMASH v6.1 Standardized BGC detection and annotation in genomic data.

Procedure:

  • Genomic DNA Extraction: Isolate high-molecular-weight gDNA from a fresh bacterial culture using the DNeasy Kit. Verify integrity via gel electrophoresis and quantify using Qubit.
  • Sequencing Library Preparation: Prepare an Illumina paired-end sequencing library using 1 ng of gDNA with the Nextera XT Kit following manufacturer instructions. Validate library size distribution (expected ~550-650 bp) using a Bioanalyzer.
  • Sequencing: Perform 2x150 bp paired-end sequencing on an Illumina MiSeq or NovaSeq platform to a minimum coverage of 100x.
  • Quality Control & Trimming: Use FastQC v0.11.9 to assess read quality. Trim adapters and low-quality bases using Trimmomatic v0.39 (parameters: LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:50).
  • Genome Assembly: Assemble trimmed reads using SPAdes with the --careful flag and -k 21,33,55,77. Assess assembly quality using QUAST v5.0.2 (target: N50 > 50 kbp, few contigs).
  • Genome Annotation: Annotate the assembled contigs using Prokka with default parameters. This generates a GBK file with gene calls and product predictions.
  • BGC Prediction: Run antiSMASH on the Prokka-annotated GBK file using the --genefinding-tool prodigal option and all detection modules enabled. This outputs a dedicated GBK file and JSON summary of predicted BGCs.
  • ARTS Input Preparation: The final inputs for ARTS analysis are: (i) the Prokka-derived protein FASTA file, (ii) the antiSMASH-derived BGC GBK/JSON file, and (iii) the original genome assembly FASTA file.

Protocol 4.2: In silico Resistance Gene Screening Using CARD

Objective: To identify putative antibiotic resistance genes within the predicted BGCs and the wider genome.

Procedure:

  • Data Preparation: Extract all protein sequences from the Prokka annotation (.faa file).
  • Homology Search: Use the Resistance Gene Identifier (RGI) tool v5.2.1 from CARD. Run rgi main -i input_proteins.faa -o output_rgi -t protein -n 8.
  • Result Filtering: Parse the RGI JSON output. Focus on hits with "Perfect" or "Strict" criteria and AMR Gene Family models. Genes with >95% identity and >90% coverage to a CARD reference are considered high-confidence.
  • Contextual Mapping: Cross-reference the genomic coordinates of high-confidence resistance hits with the coordinates of antiSMASH-predicted BGCs using BEDTools v2.30.0 (intersect function). Resistance genes located within or within a 10 kbp flanking region of a BGC are flagged for ARTS' heuristic scoring.
  • Manual Curation: For flagged genes, perform a manual BLASTp against the non-redundant (nr) database and review predicted protein domains (e.g., via InterProScan) to confirm function.

Visualizations

arts_workflow WGS WGS Assembly Assembly WGS->Assembly Annotation Annotation Assembly->Annotation BGC_Pred BGC Prediction (antiSMASH) Annotation->BGC_Pred Resist_Screen Resistance Gene Screen (RGI/CARD) Annotation->Resist_Screen DBs Core Databases (MIBiG, CARD, Pfam) DBs->BGC_Pred DBs->Resist_Screen ARTS_Core ARTS Analysis (Heuristic Rules & Scoring) BGC_Pred->ARTS_Core Resist_Screen->ARTS_Core Output Prioritized BGCs with Resistance Genes ARTS_Core->Output

Diagram 1: ARTS Data Integration Workflow

bgc_resist_context cluster_bgc Biosynthetic Gene Cluster (BGC) Reg Regulatory Gene Core Core Biosynthetic Enzymes (PKS/NRPS) Reg->Core TFs Transport & Secretion Core->TFs Resist Resistance Gene (e.g., efflux pump, target modifying enzyme) Resist->TFs potential link Resist->Core Self-Resistance

Diagram 2: BGC with Integrated Resistance Gene

Within the broader thesis on the Antibiotic Resistant Target-Seeker (ARTS) platform for prioritizing Biosynthetic Gene Clusters (BGCs) with resistance genes, the definitive outputs are the ARTS Hit List and its associated Prioritization Scores. These outputs are not simple lists but multi-dimensional, ranked inventories of candidate BGCs deemed most likely to produce novel antibiotics with self-resistance mechanisms. This Application Note details the composition, generation, and interpretation of these critical outputs, providing protocols for their use in downstream validation.

Core ARTS Outputs: Hit Lists and Scores

The ARTS analysis of a genome or metagenome-assembled genome (MAG) yields two primary, integrated outputs.

Table 1: Core Components of an ARTS Hit List

Component Description Function in Prioritization
BGC Identifier A unique label (e.g., from antiSMASH) for the candidate biosynthetic gene cluster. Unambiguously defines the genomic locus under evaluation.
Resistance Gene(s) Annotation of putative resistance genes (e.g., efflux pumps, target-modifying enzymes) physically linked to the BGC. Identifies the self-resistance mechanism, a core ARTS principle.
Bioactivity Prediction Prediction of putative bioactivity (e.g., nucleic acid inhibitor, protein synthesis inhibitor) based on core biosynthetic enzyme phylogeny. Provides functional context for the potential antibiotic compound.
Genomic Context Score Quantifies the strength and uniqueness of the resistance gene-BGC association (e.g., distance, co-regulation signals). Higher scores indicate a stronger evolutionary link between the BGC and its resistance element.
Taxonomic Novelty Score Assesses the phylogenetic distance of the host organism from known producers of similar compounds. Higher scores indicate a greater likelihood of discovering structurally novel scaffolds.
Prioritization Score A composite, weighted score (typically 0-100 or normalized) integrating all above metrics. The primary ranking metric for the Hit List; determines the final order of candidates for experimental follow-up.

Table 2: Typical Prioritization Score Weights and Interpretation

Score Component Approximate Weight Interpretation of High Value (>80%)
Genomic Context Score 40% Resistance gene is embedded within or immediately adjacent to the BGC, strongly suggesting a dedicated self-resistance mechanism.
Resistance Gene Strength & Specificity 30% Resistance gene is a dedicated, potent antibiotic-inactivating enzyme (e.g., a beta-lactamase for a beta-lactam BGC) rather than a generic efflux pump.
Taxonomic/Sequence Novelty 20% BGC and its resistance genes show low homology to characterized systems, suggesting novel chemistry.
BGC Completeness & Integrity 10% BGC appears complete, with no obvious frameshifts or truncations in key biosynthetic genes.

Protocol: Generating and Interpreting an ARTS Hit List

Protocol 2.1: Input Preparation and ARTS Execution

  • Input: Provide ARTS with annotated genomic data in GenBank/EMBL format or use its integrated pipeline starting from raw sequence data.
  • BGC Prediction: ARTS calls antiSMASH to identify all BGCs in the input genome(s).
  • Resistance Gene Mining: Concurrently, ARTS uses its dedicated Resistance Gene Identifier (RGI) and custom HMM profiles to catalog all antibiotic resistance genes.
  • Co-localization Analysis: The algorithm calculates physical genomic distance (in base pairs) between each BGC and each resistance gene. Default threshold: genes within 10-15 kb of the BGC boundary are considered linked.
  • Scoring & Ranking: Each BGC-resistance pair is scored according to the criteria in Table 2. All scores are normalized and aggregated into a final Prioritization Score.
  • Output Generation: ARTS produces a .json file and a visual report (html). The primary text output is a tab-separated Hit List, ranked by descending Prioritization Score.

Protocol 2.2: Hit List Triage for Experimental Validation

  • Top-Tier Selection: Isolate the top -10 candidates from the Hit List (Prioritization Score >85).
  • Manual Curation: For each top candidate:
    • Verify the BGC structure using the antiSMASH results viewer.
    • Examine the predicted resistance gene's domain architecture.
    • Perform a BLASTp search of the core biosynthetic enzyme (e.g., ketosynthase) against the MIBiG database to assess novelty.
  • Experimental Design: Prioritize candidates with:
    • High Genomic Context Score and a strong, specific resistance gene (e.g., a VanX-like D,D-dipeptidase for a glycopeptide-type BGC).
    • Moderate to high Taxonomic Novelty.
  • Cloning Strategy: Design primers to capture the entire BGC and its linked resistance gene for heterologous expression in a suitable host (e.g., Streptomyces albus).

Visualization of the ARTS Prioritization Workflow

arts_workflow Input Genomic Data (GenBank/FASTA) antiSMASH antiSMASH (BGC Prediction) Input->antiSMASH RGI Resistance Gene Identification Input->RGI Merge Genomic Context & Co-localization Analysis antiSMASH->Merge RGI->Merge Scoring Scoring Module (Context, Novelty, Strength) Merge->Scoring Rank Ranking Engine Scoring->Rank Output ARTS Hit List (Prioritized BGCs) Rank->Output

ARTS Prioritization Pipeline

The Scientist's Toolkit: Key Reagents for Validation

Table 3: Essential Research Reagent Solutions for BGC Validation

Reagent / Material Function in Downstream Validation
Heterologous Expression Host (e.g., Streptomyces albus Chassis) A genetically tractable, high-production host for expressing cloned BGCs from unculturable or slow-growing native producers.
BAC or Cosmid Vectors (e.g., pCC1FOS) Large-insert cloning vectors capable of capturing entire BGCs (50-200 kb) for library construction and heterologous expression.
Gibson Assembly or In-Fusion Cloning Master Mix Enzymatic systems for seamless assembly of multiple DNA fragments, crucial for constructing expression-ready BGC clones.
Target-Specific Antibiotic Sensitivity Test Disks/Strips Used to challenge the heterologous host expressing the BGC+resistance gene. Growth inhibition/zones confirm bioactivity; lack of inhibition confirms resistance gene function.
LC-MS/MS System with HRAM (High-Resolution Accurate Mass) For metabolomic profiling of culture extracts. Comparative analysis (expression vs. control host) identifies novel secondary metabolites produced by the activated BGC.

A Step-by-Step Guide: Implementing ARTS in Your BGC Discovery Pipeline

Application Notes

A robust computational environment is foundational for leveraging the Antibiotic Resistance Target-Seeker (ARTS) tool in the systematic discovery of Biosynthetic Gene Clusters (BGCs) encoding potential resistance determinants. Within the thesis framework of prioritizing BGCs with resistance genes for novel antibiotic discovery, this setup enables genome mining, homology detection, and resistance gene neighborhood analysis. Proper configuration ensures reproducibility and scalability for high-throughput genomic data.

Data Presentation: Core Software & Database Requirements

Table 1: Essential Computational Components for ARTS-Based BGC Mining

Component Version (Current as of Search) Purpose in ARTS Workflow Installation Method
ARTS 2.1.0 (GitHub, 2023) Core tool for resistance gene-centric BGC prioritization. git clone, manual make
BLAST+ 2.16.0+ Creating required protein databases and performing homology searches. Conda / Pre-compiled binaries
HMMER 3.4 Profile HMM searches for detecting conserved resistance protein domains. Conda / Pre-compiled binaries
Biopython 1.83 Essential for parsing genomic data and automating analysis steps. pip install biopython
NCBI Datasets CLI v.18.6.0 Efficient bulk download of genomic sequences (GenBank/FASTA). Conda
KnownClusterBlast DBs antiSMASH DB v.6.1.1 Provides known BGC references for comparative analysis. Download from antiSMASH
Python 3.10+ Runtime environment for scripts and tool integration. System / Conda

Experimental Protocols

Protocol 1: Installation and Configuration of the ARTS Tool Suite

  • System Preparation: Ensure a Linux/macOS environment with ≥8GB RAM and 100GB disk space. Install Miniconda package manager.
  • Dependency Installation: Create a new Conda environment (conda create -n arts-env python=3.10). Activate it (conda activate arts-env). Install Biopython, BLAST+, and HMMER via Conda (conda install -c bioconda blast hmmer biopython).
  • ARTS Installation: Clone the repository: git clone https://github.com/arts-project/ARTS.git. Navigate to the source directory (cd ARTS) and compile: make. Add the ARTS bin directory to your system PATH.
  • Database Setup: Download the latest KnownClusterBlast databases from the antiSMASH website. Use makeblastdb from BLAST+ to format any custom protein sequence databases (e.g., a curated set of beta-lactamases) for use with ARTS.

Protocol 2: Building a Target Genome Dataset for Analysis

  • Genome Selection: Identify target bacterial genera of interest (e.g., Streptomyces, Pseudomonas) via NCBI Genome database.
  • Bulk Download: Using the NCBI Datasets command-line tool, download genomic sequences in GenBank format: datasets download genome taxon "Streptomyces" --assembly-level complete --include gbff.
  • Data Organization: Create a structured project directory (Project/Genomes/, Project/Results/, Project/Databases/). Place all downloaded .gbff files in the Genomes folder.

Mandatory Visualization

ARTS_Setup_Workflow Start Start: Thesis Objective Prioritize BGCs with Resistance Genes SysPrep 1. System Preparation (Linux/macOS, Conda) Start->SysPrep EnvSetup 2. Create Conda Environment & Install Dependencies SysPrep->EnvSetup InstallARTS 3. Clone & Compile ARTS EnvSetup->InstallARTS DBSetup 4. Download & Format Required Databases InstallARTS->DBSetup DataOrg 5. Organize Target Genome Datasets DBSetup->DataOrg Analysis 6. Execute ARTS Workflow DataOrg->Analysis

Diagram Title: Computational Environment Setup for ARTS

ARTS_Core_Logic InputGenome Input Genome (GBK Format) ARTS ARTS Core Engine InputGenome->ARTS HMMSearch HMMER Search (Resistance Domains) ARTS->HMMSearch BGCContext Extract BGC Genomic Context ARTS->BGCContext KnownResDB Known Resistance Gene DB KnownResDB->ARTS Query PrioList Output: Prioritized BGC List HMMSearch->PrioList BGCContext->PrioList

Diagram Title: ARTS Prioritization Logic Flow

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Research Reagents & Materials for Computational ARTS Analysis

Item Function in ARTS-Based Research
High-Quality Genomic Data (GenBank Files) The primary input "reagent." Provides annotated genome sequences from which ARTS extracts BGC and resistance gene information.
Curated Resistance Gene Database (e.g., CARD, ResFams) A customized sequence database used as a search query set for ARTS to identify known resistance homologs within BGCs.
KnownClusterBlast Database Contains known BGC sequences; enables comparative analysis to classify novelty and identify conserved resistance gene linkages.
Multi-FASTA File of Housekeeping Genes Used for phylogenetic analysis of strains harboring prioritized BGCs, placing discoveries in an evolutionary context.
High-Performance Computing (HPC) Cluster Access Essential for scaling analyses from single genomes to large-scale metagenomic or pan-genomic datasets.
Structured Electronic Lab Notebook (ELN) Critical for logging software versions, parameters, and results to ensure computational reproducibility.

This protocol details the computational workflow for identifying and prioritizing Biosynthetic Gene Clusters (BGCs) predicted to encode antibiotic resistance genes, framed within the broader thesis on the Antibiotic Resistant Target Seeker (ARTS) tool. ARTS integrates genomic analysis to specifically mine for BGCs that possess self-resistance determinants, making them high-priority targets for the discovery of novel bioactive compounds in an era of multi-drug resistance.

The end-to-end process involves submitting a bacterial genome sequence through a series of bioinformatic tools to generate a ranked list of BGCs most likely to produce novel antibiotics with embedded resistance mechanisms.

Detailed Application Notes & Protocols

Protocol: Genome Submission & Quality Assessment

Objective: Prepare and validate the input genome assembly. Materials: High-quality bacterial genome assembly in FASTA format. Methodology:

  • Assembly Check: Use checkm lineage_wf to assess assembly completeness (<5% contamination, >90% completeness recommended).
  • Format Standardization: Ensure contig headers are simple (e.g., >contig_1). Prodigy-incompatible characters (e.g., |, ,, spaces) must be removed.
  • File Preparation: The final file (e.g., genome.fasta) is ready for BGC prediction.

Protocol: BGC Prediction with antiSMASH

Objective: Identify all potential BGCs within the submitted genome. Methodology:

  • Submission: Run the standardized genome.fasta through antiSMASH (version 7+). Use the --genefinding-tool prodigal and --taxon bacteria flags.
  • Analysis: Execute with comprehensive analysis options: --clusterhmmer --asf --pfam2go --smcog-trees.
  • Output: The primary output is the index.html file for manual review and the .gbk (GenBank) file for each predicted BGC region, used in downstream analysis.

Protocol: Resistance Gene Identification via ARTS

Objective: Screen predicted BGCs for known and candidate self-resistance determinants. Methodology:

  • Input Preparation: Compile the antiSMASH-derived .gbk files for the genome.
  • ARTS Execution: Run the ARTS tool (version 2) using the command: arts -i /path/to/bgc_gbks -o arts_results.
  • Core Analysis: ARTS performs:
    • Known Resistance Gene Detection: HMM-based search against its built-in database of resistance models (e.g., for major facilitator superfamily (MFS) transporters, ATP-binding cassette (ABC) transporters, etc.).
    • DUF/Resistance Island Detection: Identifies genes with Domains of Unknown Function (DUFs) co-localized with known resistance genes.
    • HMM Phylogeny: Constructs phylogenies of specific enzyme families (e.g., housekeeping genes vs. BGC-linked genes) to detect horizontal gene transfer events indicative of resistance acquisition.
  • Output: A tabular file (arts_results.tsv) listing BGCs with associated resistance gene hits, confidence scores, and gene locations.

Protocol: Prioritization Scoring & Ranking

Objective: Integrate multiple data layers to generate a prioritized BGC list. Methodology:

  • Data Compilation: Create a master table combining:
    • BGC type and size (from antiSMASH).
    • Presence/absence and number of ARTS-predicted resistance genes.
    • antiSMASH-derived "Cluster Similarity" score (percentage similarity to known BGCs in MIBiG database). Lower similarity is prioritized for novelty.
    • Presence of core biosynthetic genes (e.g., polyketide synthase (PKS), non-ribosomal peptide synthetase (NRPS)) as a proxy for complexity.
  • Scoring Algorithm: Assign points based on the following heuristic:
    • +3 Points: For each unique ARTS resistance gene hit within the BGC.
    • +2 Points: For BGCs of "Unknown" or "Hybrid" type.
    • +1 Point: For each MIBiG similarity percentile below 50% (e.g., 30% similarity adds +1 point; 70% adds 0).
    • +1 Point: For the presence of large, multi-modular PKS/NRPS genes.
  • Ranking: Sort BGCs by total score in descending order. The highest-scoring BGCs represent novel clusters with strong evidence of embedded self-resistance.

Data Presentation

Table 1: Example Prioritized BGC List for Streptomyces sp. Sample Genome

BGC ID (antiSMASH) BGC Type Size (kb) ARTS Resistance Hits (#) MIBiG Similarity (%) Core Biosynth. Genes Priority Score Rank
region001 T1PKS 78.5 2 (ABC, MFS) 25 PKS 8 1
region002 NRPS 52.1 1 (DUF+) 80 NRPS 4 3
region003 Unknown 41.7 1 (Glycopeptide) 5 None 6 2
region004 Lantipeptide 22.3 0 95 LanB, LanC 0 4

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Computational Tools & Resources

Item (Tool/Database) Function in Workflow Key Parameter/Note
antiSMASH Predicts BGC locations and types from genome sequence. Use --taxon bacteria and comprehensive analysis flags.
ARTS (Antibiotic Resistant Target Seeker) Specifically detects known and candidate self-resistance genes within BGCs. Critical for the thesis context; focuses on resistance phylogeny.
Prodigal Gene-finding caller used by antiSMASH for accurate ORF prediction. Ensure compatible FASTA headers.
MIBiG Database Repository of known BGCs; provides similarity metric for novelty assessment. Percent similarity is a key prioritization factor.
CheckM Assesses genome assembly quality to ensure reliable input data. Filters out low-quality assemblies before analysis.
HMMER Suite Underlying tool for profile hidden Markov model searches in both antiSMASH and ARTS. Used for Pfam domain and resistance model detection.

Visualizations

G Start Bacterial Genome FASTA File A1 1. Quality Control (CheckM) Start->A1 B1 High-Quality Genome Assembly A1->B1 A2 2. BGC Prediction (antiSMASH) B2 List of Predicted BGCs (.gbk files) A2->B2 A3 3. Resistance Detection (ARTS Tool) B3 ARTS Results: Resistance Gene Hits A3->B3 A4 4. Data Integration & Prioritization Scoring B4 Master Table of BGC Features & Scores A4->B4 End Prioritized BGC List (Ranked Table) B1->A2 B2->A3 B3->A4 B4->End

Title: Primary BGC Prioritization Workflow

G cluster_0 BGC Region (e.g., T1PKS) Biosynth PKS Biosynthesis Gene Reg Regulator Res1 ABC Transporter (Resistance) Other Other Genes Antibiotic Produced Antibiotic Res1->Antibiotic Export/Detoxify Res2 MFS Transporter (Resistance) Tail Export/Modification Res2->Antibiotic Export/Detoxify TargetSite Cellular Target Site Antibiotic->TargetSite Binds & Inhibits

Title: BGC with Embedded Resistance Genes

Within a broader thesis on Antibiotic Resistant Target Seeker (ARTS) for prioritizing Biosynthetic Gene Clusters (BGCs) with resistance gene research, the accurate interpretation of ARTS results is paramount. This protocol details the key metrics and output files generated by ARTS, a specialized bioinformatics tool designed to mine bacterial genomes for BGCs that are likely to produce novel antibiotics and contain intrinsic self-resistance genes. This guide enables researchers to identify high-priority BGCs for downstream experimental validation in drug discovery pipelines targeting resistant pathogens.

Key Output Files & Data Structure

ARTS generates several primary output files. The structure and key contents of these files are summarized below.

Table 1: Primary ARTS Output Files and Descriptions

File Name Format Primary Contents Relevance for BGC Prioritization
arts_final_results.txt Tab-delimited Summary table of all detected BGCs with core metrics. Primary file for initial screening and ranking.
arts_knownresistance.txt Tab-delimited Detailed list of known resistance genes (hits against databases like Resfam, CARD). Identifies BGCs with known resistance mechanisms.
arts_duplicated_hits.txt Tab-delimited Lists duplicated core biosynthetic genes within a BGC. Flags BGCs with gene duplications, a potential resistance marker.
arts_specificity_group.txt Tab-delimited Details of "resistance islands" and co-localized resistance genes. Highlights clusters with tightly linked, specific resistance.
knownclusterblast_output.txt Text Results from comparing detected BGCs to known BGC databases (MIBiG). Contextualizes novelty; known clusters may have documented activity.
Directory: per_BGC_results/ Multiple files Individual files for each BGC (e.g., BGC001_details.txt). Contains exhaustive data for in-depth analysis of a single BGC.

Interpretation of Core Metrics

The arts_final_results.txt file contains the essential quantitative metrics for prioritization. Understanding these columns is critical.

Table 2: Key Metrics in arts_final_results.txt

Column Name Description Interpretation & Threshold Guideline
BGC_id Unique identifier for the BGC. N/A
predicted_class Type of BGC (e.g., NRPS, T1PKS, RiPP). Indicates chemical class of potential compound.
completeness Estimated completeness of the BGC. Prioritize clusters with high completeness (e.g., >0.8).
known_resistance_hits Number of detected known resistance genes. >0 suggests a known self-resistance mechanism. Higher counts may indicate strong selection.
duplicated_core_biosynthetic_genes Count of duplicated essential biosynthetic genes. >0 is a strong indicator of a "resistance-associated BGC".
resistance_genes_in_specificity_group Number of resistance genes within a co-regulated genomic "island". Higher numbers suggest a dedicated, evolved resistance strategy.
dist_to_next_bgc Genomic distance to the next BGC. Larger distances may indicate genomic isolation and independence.

Protocol: Step-by-Step Workflow for Result Interpretation

Protocol Title: Systematic Prioritization of BGCs from ARTS Output for Resistance Gene Research

Objective: To filter, rank, and select the most promising BGCs for experimental characterization based on ARTS-generated data.

Materials (Research Reagent Solutions & Essential Tools):

  • ARTS Software Suite: (v2.x) Core bioinformatics pipeline.
  • Input Data: High-quality, annotated bacterial genome assembly (FASTA, GBK).
  • Computational Resources: Linux server or HPC cluster with adequate RAM (>16GB recommended).
  • Database Files: Local copies of Resfam, CARD, and MIBiG databases (updated regularly).
  • Analysis Environment: Python3/R environment for data manipulation and plotting (e.g., Pandas, ggplot2).
  • Visualization Tools: Genome visualization software (e.g., antiSMASH or Artemis).

Procedure:

  • Data Consolidation:

    • Run ARTS on your target genome(s) using standard parameters.
    • Collect all output files into a single project directory.

  • Primary Filtering:

    • Load arts_final_results.txt into a data analysis tool (e.g., Python Pandas, Excel).
    • Apply initial filters:
      • Filter 1: Retain BGCs with completeness >= 0.8.
      • Filter 2: Retain BGCs where known_resistance_hits >= 1 OR duplicated_core_biosynthetic_genes >= 1.
    • This creates a shortlist of BGCs with both high integrity and resistance markers.

  • Secondary Ranking & Investigation:

    • For the shortlisted BGCs, calculate a simple priority score (e.g., Priority Score = known_resistance_hits + duplicated_core_biosynthetic_genes + resistance_genes_in_specificity_group).
    • Sort BGCs by this descending score.
    • Cross-reference the top candidates with knownclusterblast_output.txt to assess novelty. Prioritize BGCs with low or no similarity to known clusters.

  • Deep Dive Analysis:

    • For the top 5-10 ranked BGCs, examine their individual folders in per_BGC_results/.
    • Open the corresponding genomic region in a viewer. Manually inspect the genetic architecture:
      • Confirm the physical clustering of resistance genes within the BGC borders.
      • Note the order and orientation of biosynthetic and resistance genes.
    • Consult arts_knownresistance.txt to identify the specific family/mechanism of the resistance gene (e.g., efflux pump, rRNA methyltransferase).

  • Decision for Experimental Follow-up:

    • The highest-ranked BGCs, particularly those with novel combinations of biosynthetic and resistance genes, are prime candidates for:
      • Heterologous expression.
      • Metabolite extraction and antibiotic activity assays.
      • Knockout studies of the associated resistance gene to confirm its role in self-protection.

Visual Workflow and Pathway Diagrams

G Input Input Genome (FASTA/GBK) Run Run ARTS Pipeline Input->Run Out ARTS Output Files Run->Out Filter1 Filter: Completeness > 0.8? Out->Filter1 Filter2 Filter: Resistance Marker Present? Filter1->Filter2 Yes Discard1 Discard Filter1->Discard1 No Rank Rank by Priority Score Filter2->Rank Yes Discard2 Discard Filter2->Discard2 No Manual Manual Inspection & Pathway Analysis Rank->Manual Candidate High-Priority BGC for Experimental Validation Manual->Candidate

ARTS Results Interpretation Workflow

G cluster_island Co-regulated 'Resistance Island' BGC Biosynthetic Gene Cluster (NRPS/T1PKS/etc.) Biosynth Biosynthetic Genes (e.g., PKS, NRPS modules) Duplicate Duplicated Core Biosynthetic Gene Resist Resistance Gene (e.g., Efflux, Target Protector) Duplicate->Resist Genomic Proximity

BGC with Resistance Gene & Duplication

The Scientist's Toolkit: Essential Research Reagents & Materials

Table 3: Key Reagents and Tools for ARTS-Based BGC Prioritization & Validation

Item Function/Description Example/Supplier
ARTS Software Core algorithm for genome mining of resistant BGCs. Available on GitHub.
antiSMASH Database Provides BGC boundary prediction and initial classification. https://antismash.secondarymetabolites.org/
Resfam Database Curated database of protein families involved in antibiotic resistance. Critical for known_resistance_hits metric.
CARD Database Comprehensive Antibiotic Resistance Database. Used for cross-referencing resistance genes.
MIBiG Database Repository for known BGCs with experimental data. Assess novelty via KnownClusterBlast.
Genome Viewer (Artemis/UGENE) Visual inspection of genomic context of BGCs and resistance genes. Essential for manual validation.
Heterologous Host (e.g., S. albus) Clean background strain for expressing prioritized BGCs. For functional validation of BGC product and resistance.
Antibiotic Sensitivity Test Strips/Kits To assay resistance profile conferred by cloned resistance gene. Etest strips, MIC assay plates.

Application Notes

This case study demonstrates the application of the Antibiotic Resistant Target Seeker (ARTS) tool for the genome-mining-based discovery of glycopeptide antibiotics from a Streptomyces sp. isolate. ARTS identifies Biosynthetic Gene Clusters (BGCs) with integrated self-resistance determinants, prioritizing those most likely to produce bioactive, potent antibiotics. This work is framed within a broader thesis that ARTS-based prioritization is a superior strategy for reducing rediscovery rates and focusing experimental efforts on BGCs with a high probability of yielding novel scaffolds, particularly in well-studied genera like Streptomyces.

Core ARTS Workflow Logic: ARTS operates on the principle that antibiotic producers encode resistance mechanisms against their own product, often within or adjacent to the BGC. ARTS scans a genome for known resistance models (e.g., vanHAX-like clusters for glycopeptides) and correlates them with colocalized BGCs predicted by tools like antiSMASH.

Key Quantitative Findings from the Case Study: The analyzed Streptomyces sp. genome (approx. 8.5 Mb) was processed through the ARTS 2.0 pipeline. The results were compared to standard antiSMASH analysis alone.

Table 1: Genome Mining Output Comparison

Analysis Tool Total BGCs Identified Glycopeptide-like BGCs BGCs with Integrated Resistance Priority BGCs for Heterologous Expression
antiSMASH 7.0 42 3 Not Assessed 3 (All glycopeptide BGCs)
ARTS 2.0 42 3 1 1 (BGC-07)

Table 2: Characterization of the ARTS-Prioritized Glycopeptide BGC (BGC-07)

BGC Feature Result Significance
BGC Type (antiSMASH) Type I PKS, NRPS, Lanthipeptide, Other Mixed modular biosynthetic machinery
Core Biosynthetic Genes 4 Large NRPS/PKS genes Indicates a complex peptide-polyketide hybrid
ARTS Resistance Hit VanY-like (D,D-carboxypeptidase) High-confidence self-resistance model for glycopeptides
Resistance Gene Location Directly within BGC boundaries Strong evidence for dedicated self-protection
Similarity to Known BGCs (MIBiG) < 30% to characterized clusters High novelty potential

Conclusion: ARTS analysis reduced the target BGCs for downstream experimental validation from three to one. BGC-07 was uniquely prioritized due to the presence of an integral vanY-like resistance gene, making it the highest-priority candidate for heterologous expression and compound isolation.

Experimental Protocols

Protocol 2.1: In Silico ARTS Analysis of a Streptomyces Genome

Objective: To identify and prioritize BGCs containing predicted self-resistance genes.

  • Genome Assembly & Annotation: Assemble Illumina and/or PacBio reads into a high-quality draft genome using a hybrid assembler (e.g., Unicycler). Annotate the genome using Prokka.
  • BGC Prediction: Run the annotated genome file (GBK format) through antiSMASH (v7.0+) with all analysis options enabled. This generates a GenBank file with BGC regions explicitly annotated.
  • ARTS Analysis: a. Install ARTS 2.0 as per official documentation (requires HMMER, BLAST+, etc.). b. Execute the primary ARTS command: arts --genome annotated_genome.fna --antismash antiSMASH_results.genbank --outdir ARTS_results. c. ARTS will identify resistance genes from its built-in database (e.g., van genes, erm genes, efflux pumps) and check for their co-localization with predicted BGCs.
  • Data Interpretation: Examine the ARTS_results/results.txt and ARTS_results/bgcs.txt files. Prioritize BGCs with "ResistanceinBGC" status. Visualize the top BGC using the provided arts_plot utility.

Protocol 2.2: Heterologous Expression of the Prioritized BGC

Objective: To express the prioritized BGC (BGC-07) in a model streptomycete host for compound production.

  • Cosmid/BAC Library Construction: Partially digest high-molecular-weight genomic DNA from the native Streptomyces host with Sau3AI. Size-select fragments > 30 kb and ligate into a cosmid vector (e.g., pOS700) digested with BamHI. Package ligations using a commercial phage packaging kit and transduce into E. coli EPI300.
  • BGC Capture: Screen the library by PCR using primers designed to the conserved regions of the BGC-07 core biosynthetic genes identified by antiSMASH. Isolate positive cosmids and validate by restriction digest and end-sequencing.
  • Exconjugant Transfer: Use the E. coli ET12567/pUZ8002 strain to conjugate the isolated cosmid into the heterologous host Streptomyces albus J1074. Select for exconjugants on ISP4 media supplemented with apramycin (cosmid resistance) and nalidixic acid (to counter-select against E. coli).
  • Fermentation & Metabolite Extraction: Inoculate positive exconjugants into TSB liquid medium with apramycin and incubate at 30°C, 220 rpm for 48 hours. Use a 5% inoculum to transfer into production medium (e.g., R5 or SFM). Incubate for 5-7 days. Centrifuge culture broth; extract the supernatant with an equal volume of ethyl acetate and the cell pellet with acetone:methanol (1:1). Combine and concentrate organic extracts under reduced pressure.

Protocol 2.3: Bioactivity and Resistance Linkage Assay

Objective: To test bioactivity of extracts and confirm the function of the predicted vanY-like resistance gene.

  • Agar Diffusion Bioassay: Resuspend dried extracts in DMSO. Use paper discs to apply extracts to agar plates seeded with indicator strains (e.g., Bacillus subtilis, Micrococcus luteus, and a vancomycin-resistant Enterococcus faecium (VRE)). Include a vancomycin disc as control. Measure zones of inhibition after 18-24h incubation.
  • LC-HRMS Analysis: Analyze active extracts via Liquid Chromatography-High Resolution Mass Spectrometry (LC-HRMS). Use a C18 column with a water-acetonitrile gradient. Compare mass signals and UV spectra to databases (e.g., Antibase) to identify novel compounds.
  • Resistance Gene Complementation: a. Clone the BGC-07-associated vanY-like gene into an integrative Streptomyces vector (e.g., pSET152). b. Introduce the construct into a glycopeptide-sensitive Streptomyces strain. c. Perform a comparative broth microdilution MIC assay against vancomycin and the purified novel compound for the transformed strain versus the empty vector control. A significant increase in MIC for the complemented strain confirms resistance function.

Diagrams

ARTS-Based BGC Prioritization Workflow

arts_workflow Start Streptomyces sp. Draft Genome A1 antiSMASH Analysis (BGC Prediction) Start->A1 A2 ARTS Database (Resistance Gene HMMs) Start->A2 Genomic Scan B ARTS Core Engine (Co-localization Analysis) A1->B A2->B C Prioritized BGC List (Resistance in BGC) B->C Yes D Standard BGC List (No linked resistance) B->D No E High Priority Target (e.g., BGC-07 with vanY) C->E G Lower Priority for Screening D->G F Experimental Validation E->F

Glycopeptide Resistance & Biosynthesis Gene Cluster

bgc cluster_biosynth Biosynthetic Core cluster_res Self-Resistance Module BGC BGC-07 Region (~80 kb) PKS1 PKS Module BGC->PKS1 NRPS1 NRPS Module PKS1->NRPS1 NRPS2 NRPS Module NRPS1->NRPS2 OX Oxidase/Tailoring NRPS2->OX VanY vanY-like D,D-carboxypeptidase Reg Regulator VanY->Reg Other Other ORFs (Transport, Regulation)

The Scientist's Toolkit

Table 3: Key Research Reagent Solutions for ARTS-Guided Discovery

Item Function/Application Example/Details
antiSMASH Database In silico prediction & annotation of BGCs in microbial genomes. Web server or standalone version. Critical for defining BGC boundaries for ARTS input.
ARTS 2.0 Software Identifies known antibiotic resistance genes co-localized with BGCs. Command-line tool with curated HMM database of resistance models. Core prioritization engine.
Cosmid Vector (e.g., pOS700) Facilitates cloning and stable maintenance of large (>30 kb) DNA inserts in E. coli and Streptomyces. Essential for capturing entire BGCs for heterologous expression studies.
Heterologous Host (e.g., S. albus J1074) Clean genetic background, fast-growing, high-production strain for expressing cryptic BGCs. Minimizes native regulatory interference, allowing "awakening" of silent BGCs.
Vancomycin & VRE Strains Key biological reagents for bioactivity screening and resistance confirmation assays. Vancomycin is the canonical glycopeptide; VRE strains test for novel mechanisms of action.
LC-HRMS System High-resolution metabolic profiling for dereplication and novel compound identification. Q-TOF or Orbitrap systems coupled to UHPLC. Compares exact mass & fragmentation to databases.
Integration Vector (e.g., pSET152) For stable chromosomal integration and expression of single genes (e.g., resistance genes) in Streptomyces. Used for functional confirmation of predicted self-resistance genes via complementation assays.

Application Notes

Within a thesis focused on prioritizing biosynthetic gene clusters (BGCs) with resistance genes for novel antibiotic discovery, the Antibiotic Resistant Target Seeker (ARTS) is a cornerstone tool. Its true power is unlocked through systematic integration with complementary platforms: AntiSMASH for BGC detection, BiG-SCAPE for BGC networking, and MIBiG for reference annotation. This integrated workflow enables the efficient prioritization of BGCs that likely produce novel compounds with inherent self-resistance mechanisms.

Quantitative Data Summary: Key Metrics from Integrated Tools

Table 1: Core Output Metrics from ARTS and Integrated Tools

Tool Primary Output Key Metric for Prioritization Typical Value/Description
AntiSMASH Predicted BGCs BGC Size & Core Biosynthetic Genes 10-200 kbp; e.g., PKS, NRPS, RiPP
ARTS Resistance Gene Prediction HMM Hits & Resistance Gene Rank (RGR) RGR > 5 suggests high specificity
BiG-SCAPE Gene Cluster Family (GCF) GCF Size & Network Distance BGCs in same GCF share backbone
MIBiG Known BGC Reference Percent Identity to Known Cluster < 30% suggests novelty

Detailed Experimental Protocols

Protocol 1: Genome Mining for Resistance-Linked BGCs Objective: To identify BGCs containing putative self-resistance genes in a bacterial genome.

  • BGC Prediction: Run the target genome through AntiSMASH (v7.0+) with default parameters. Use the --cb-knownclusters and --cb-subclusters options for detailed analysis.
  • ARTS Analysis: Input the AntiSMASH-generated GenBank file(s) into the ARTS web server or run ARTS locally. Specify the --complete mode to scan for known resistance models (e.g., drug transporters, ribosomal protection proteins) and the --knownclusters mode for HMM-based detection.
  • Data Integration: Parse the ARTS output to extract BGC coordinates with high-confidence resistance gene hits (RGR > 5). Cross-reference these coordinates with the original AntiSMASH results to obtain the full BGC annotation.
  • Novelty Filtering: Use the AntiSMASH "KnownClusterBlast" results to flag BGCs with high similarity (>70%) to entries in MIBiG for deprioritization.

Protocol 2: Comparative Analysis and GCF Assignment Objective: To place prioritized BGCs into a broader chemical and genomic context.

  • Generate .gbk Files: For each prioritized BGC from Protocol 1, extract the corresponding GenBank (.gbk) file from the AntiSMASH output directory.
  • Run BiG-SCAPE: Execute BiG-SCAPE (v1.1.5+) with the curated set of .gbk files, including the MIBiG reference library (--mibig). Use the --mix option to allow comparison of different BGC types. Command example: python bigscape.py -i ./input_gbks -o ./output --mibig --mix.
  • Analyze Network: Open the generated network file (network.html) in Cytoscape or use the provided .graphml file. Identify which GCF contains your BGC of interest. Large, diverse GCFs are often rich in novel variants.
  • Resistance Gene Mapping: Overlay the ARTS resistance gene data as an attribute onto the BiG-SCAPE network nodes to visualize the distribution of resistance traits within and across GCFs.

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for Integrated BGC Analysis

Item Function/Application
High-Quality Genomic DNA (e.g., Qiagen DNeasy Kit) Essential input for genome sequencing, the foundation for AntiSMASH analysis.
AntiSMASH-DB or MIBiG Database Reference databases for comparative analysis and known BGC annotation.
HMMER Suite (v3.3+) Required for ARTS and underlying HMM searches against resistance gene profiles.
Python Environment (v3.8+) with BiG-SCAPE dependencies (e.g., NumPy, Biopython) Execution environment for running local installations of BiG-SCAPE and parsing scripts.
Cytoscape Software (v3.9+) For advanced visualization and analysis of BiG-SCAPE molecular networks.

Visualization: Integrated ARTS Workflow

arts_workflow Genome Genome AntiSMASH AntiSMASH Genome->AntiSMASH GenBank BGC_List BGC_List AntiSMASH->BGC_List Predicted Clusters ARTS ARTS ARTS->BGC_List Resistance Hits BGC_List->ARTS .gbk files BiG_SCAPE BiG_SCAPE BGC_List->BiG_SCAPE GCF_Network GCF_Network BiG_SCAPE->GCF_Network Network Analysis MIBiG_DB MIBiG_DB MIBiG_DB->BiG_SCAPE Reference Prioritized_BGCs Prioritized_BGCs GCF_Network->Prioritized_BGCs Novel & Resistant

Diagram Title: ARTS Integration for BGC Prioritization Workflow

Maximizing ARTS Efficacy: Troubleshooting Common Issues and Optimization Strategies

The Antibiotic Resistant Target Seeker (ARTS) is a specialized genome mining tool designed to identify known and putative antibiotic resistance genes within Bacterial Genomic Clusters (BGCs). Its primary function is to prioritize BGCs that are likely to produce novel antibiotics by ensuring the producer organism possesses a self-resistance mechanism, a strong indicator of bioactive potential. However, a central challenge in using ARTS and similar tools (e.g., DeepARG, RGI, AMRFinderPlus) is the high rate of false positive predictions. These occur when a sequence is incorrectly flagged as a resistance gene due to overly permissive similarity thresholds, leading to misprioritization of BGCs and wasted research effort.

This Application Note details protocols for empirically determining and validating optimal, refined prediction thresholds to minimize false positives while maintaining sensitivity for true resistance genes, directly supporting the broader thesis on leveraging ARTS for efficient antibiotic discovery.

Current Data on Prediction Tool Performance

Recent benchmarking studies (2023-2024) highlight the false positive challenge. The following table summarizes key performance metrics of major tools against standardized datasets like the Comprehensive Antibiotic Resistance Database (CARD) and ResFinder.

Table 1: Performance Comparison of Resistance Gene Prediction Tools (Simulated Metagenomic Data)

Tool (Version) Default Sensitivity (%) Default Precision (%) Common False Positive Sources
ARTS (v2.0) 95.2 76.8 Conserved domains in housekeeping genes (e.g., ATP-binding cassette transporters).
DeepARG (v2.0) 91.5 81.3 General stress response regulators, efflux pumps with broad substrate specificity.
RGI with DIAMOND (v6.0) 88.7 89.1 Short, low-complexity alignments to non-resistance homologs.
AMRFinderPlus (v3.12) 86.4 92.5 Overly inclusive protein cluster definitions for beta-lactamases.

Table 2: Impact of Threshold Adjustment on ARTS Output (Example Dataset: 1000 BGCs)

Parameter Adjusted Value Predicted Resistance BGCs Empirically Validated BGCs False Positive Rate
Bit-Score Cut-off Default (50) 320 210 34.4%
Refined (80) 245 198 19.2%
% Identity Cut-off Default (30%) 320 210 34.4%
Refined (50%) 180 155 13.9%
E-value Cut-off Default (1e-5) 320 210 34.4%
Refined (1e-10) 260 205 21.2%

Core Protocol: Empirical Threshold Refinement for ARTS

This protocol describes a systematic approach to derive organism or BGC-class-specific thresholds.

Protocol 3.1: Creating a Curated Negative Training Set

Objective: Assemble a set of genes known not to be antibiotic resistance genes (ARGs) but phylogenetically close to true ARGs. Materials:

  • Non-pathogenic model organism genomes (e.g., E. coli K-12, B. subtilis 168).
  • Housekeeping gene databases (e.g., COG, eggNOG).
  • BLAST+ suite (v2.13+).

Procedure:

  • Extract all protein sequences from the chosen model genomes.
  • Run ARTS with default parameters on this dataset. All hits are initial false positives.
  • Manually curate this list by aligning hits to the CARD database via BLASTP. Remove any entry with >70% identity and >80% coverage to a confirmed ARG.
  • The remaining list constitutes the Negative Training Set (NTS). Store sequences in a FASTA file (NTS.faa).

Protocol 3.2: Determining Optimal Bit-Score and E-value Thresholds

Objective: Find thresholds that exclude NTS hits while retaining hits to a positive set. Materials:

  • NTS.faa (from Protocol 3.1).
  • Positive Training Set (PTS.faa): Known ARG sequences from CARD, filtered for relevance to your study organisms (e.g., actinobacterial ARGs).
  • HMMER suite (v3.3) or BLAST+.
  • ARTS database/profile files.

Procedure:

  • Run hmmsearch (or blastp) of the ARTS profiles against the combined NTS.faa and PTS.faa files, outputting full tabular results including bit-score and e-value.
  • Parse results to separate hits originating from PTS.faa and NTS.faa.
  • For each ARTS profile/model: a. Plot a Receiver Operating Characteristic (ROC) curve using bit-score as the threshold variable. b. Calculate the Youden's J index (Sensitivity + Specificity - 1) for each possible bit-score. c. Optimal Bit-Score: Select the score that maximizes the Youden's J index. d. Repeat steps a-c using the negative logarithm of the e-value as the threshold variable.
  • Establish a global conservative threshold by taking the 5th percentile of optimal bit-scores across all models, or apply model-specific thresholds.

Protocol 3.3: In vitro Validation of Refined Thresholds

Objective: Experimentally confirm the resistance function of genes identified only with refined thresholds. Materials:

  • E. coli BL21(DE3) or a suitable heterologous host.
  • Cloning system (e.g., pET vector, Gibson Assembly reagents).
  • Antibiotics for sensitivity testing (sterile stocks).
  • Mueller-Hinton agar plates.
  • Microplate reader for broth microdilution.

Procedure:

  • Select 3-5 candidate resistance genes from BGCs that are: a. Positive with refined thresholds but negative with default thresholds (high-confidence novel). b. Positive with default thresholds but negative with refined thresholds (potential false positives).
  • Clone the candidate genes into an expression vector, transform into the heterologous host. Include an empty vector control.
  • Perform agar-based disk diffusion assay: a. Create bacterial lawns of transformants. b. Apply disks impregnated with relevant antibiotics (based on ARTS prediction class). c. Measure zones of inhibition after 18-24h incubation. Significantly reduced zones in test vs. control indicate resistance.
  • Perform broth microdilution MIC assay: a. In a 96-well plate, prepare 2-fold serial dilutions of the antibiotic. b. Inoculate wells with a standardized culture of each transformant. c. Incubate 16-20h and measure OD600. The MIC is the lowest concentration inhibiting growth. d. A ≥4-fold increase in MIC for the gene-containing strain versus control confirms resistance function.

Visualization of Workflows and Concepts

G Start Start: BGC Dataset ARTS_Default ARTS Analysis (Default Thresholds) Start->ARTS_Default FP_Problem High False Positive Predictions ARTS_Default->FP_Problem Refine Threshold Refinement Protocol FP_Problem->Refine ARTS_Refined ARTS Analysis (Refined Thresholds) Refine->ARTS_Refined Exp_Val In vitro Validation ARTS_Refined->Exp_Val High_Conf High-Confidence Resistance BGCs Exp_Val->High_Conf

Title: Overall Threshold Refinement Workflow

G BGC BGC in Producer Genome ARG_Chr Chromosomal ARG (Self-Resistance) BGC->ARG_Chr Encodes ARG_Hgt HGT-Derived ARG (Background) BGC->ARG_Hgt nearby HK_Gene Housekeeping Gene (False Positive) BGC->HK_Gene Encodes ARTS_Box ARTS Prediction Tool ARG_Chr->ARTS_Box Input ARG_Hgt->ARTS_Box Input HK_Gene->ARTS_Box Input Output Prediction Output ARTS_Box->Output

Title: Gene Types in BGCs Affecting ARTS Analysis

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for Threshold Refinement & Validation

Item Function in Protocol Example Product/Catalog
Curated ARG Database Gold-standard positive control for training and validation. Comprehensive Antibiotic Resistance Database (CARD)
Non-Target Genome Sequences Source for building a Negative Training Set (NTS). NCBI RefSeq genomes of non-pathogens (e.g., E. coli K-12)
HMMER Software Suite Profile HMM-based searching for sensitivity analysis. HMMER 3.3 (http://hmmer.org)
Cloning & Expression System Heterologous expression of candidate resistance genes. pET-28a(+) vector, NEB Gibson Assembly Master Mix
Antibiotic Standard Powder Preparing precise concentrations for phenotypic assays. Sigma-Aldrich antibiotic analytical standards
Cation-Adjusted Mueller Hinton Broth (CAMHB) Standardized medium for MIC determination. Becton Dickinson BBL Mueller Hinton II Broth
Automated Microbial Sensitivity System High-throughput validation of MICs (optional). BioMerieux VITEK 2, Thermo Scientific Sensititre
Sequence Analysis Pipeline Automating threshold testing and ROC analysis. Nextflow/Python scripts with Biopython/pandas

Handling Incomplete Genomes and Metagenomic Assembled Genomes (MAGs)

1. Introduction within the ARTS Thesis Context The Anti-Resistance Target Seeker (ARTS) tool is essential for the genome-mining of known and novel Biosynthetic Gene Clusters (BGCs) that may harbor resistance genes, a key step in targeted antibiotic discovery. However, ARTS and related tools are often challenged by the fragmented and incomplete nature of Metagenomic Assembled Genomes (MAGs) derived from complex microbiomes. This protocol outlines standardized methods for preprocessing, quality-checking, and annotating incomplete genomes and MAGs to maximize the fidelity of downstream ARTS analysis, ensuring robust prioritization of BGCs for experimental validation.

2. Application Notes & Protocols

2.1. Protocol: Pre-Assembly Quality Control & Read Processing Objective: To ensure high-quality input data for assembly, minimizing errors that propagate into MAGs. Materials: Raw paired-end metagenomic FASTQ files. Software: FastQC, MultiQC, Trimmomatic/BBduk, Khmer.

  • Quality Assessment: Run FastQC on all FASTQ files. Aggregate reports using MultiQC.
  • Adapter & Quality Trimming: Use Trimmomatic with parameters: ILLUMINACLIP:TruSeq3-PE.fa:2:30:10 LEADING:3 TRAILING:3 SLIDINGWINDOW:4:20 MINLEN:50 Alternative: Use BBduk for adapter removal and quality filtering.
  • Error Correction (Optional for complex communities): Employ norm from the Khmer toolkit to digitally normalize read coverage, reducing computational load and assembly artifacts.

2.2. Protocol: Co-Assembly & Binning for MAG Retrieval Objective: To reconstruct genomes from metagenomic data. Software: MEGAHIT or metaSPAdes, MetaBAT2, MaxBin2, CONCOCT, DAS Tool, CheckM.

  • Co-Assembly: Assemble all quality-controlled reads using MEGAHIT (--k-min 27 --k-max 127 --k-step 10) or metaSPAdes (-k 21,33,55,77 --meta).
  • Contig Binning: Generate multiple bin sets using:
    • MetaBAT2: runMetaBat.sh -m 1500 assembly.fasta *.bam
    • MaxBin2: run_MaxBin.pl -contig assembly.fasta -abund *.abund -out maxbin_out
    • CONCOCT: Use the designated workflow mapping reads to contigs before binning.
  • Bin Consolidation & Dereplication: Integrate bins from all methods using DAS Tool (DAS_Tool -i metabat2.csv,maxbin2.csv -l metabat,maxbin -c contigs.fa -o das_output).
  • Bin Quality Assessment: Run CheckM lineage_wf on final bins. Classify per Table 1.

Table 1: MAG Quality Standards (MIMAG Guidelines)

Quality Tier Completeness Contamination tRNA 5S,16S,23S rRNA Criteria for ARTS Analysis
High-Quality >90% <5% ≥18 ≥1 of each Optimal for ARTS. Trust BGC continuity.
Medium-Quality ≥50% <10% - - Suitable for ARTS. BGCs may be fragmented.
Low-Quality <50% >10% - - Use with caution. High false-negative risk.

2.3. Protocol: Genome Completion & Curation for BGC Analysis Objective: To improve MAG quality specifically for BGC discovery. Software: CheckM, MetaPhiAn, GTDB-Tk, R (ggplot2).

  • Phylogenetic Placement: Use GTDB-Tk to taxonomically classify bins and identify close reference genomes from cultured relatives.
  • Gap Filling & Scaffolding: Map reads back to bins using Bowtie2/BWA. Use reference-guided tools (e.g., RagTag) with close relative genomes to scaffold contigs, only within conserved synteny blocks to avoid chimeras.
  • Contamination Removal: Manually inspect bins with 5-10% contamination in CheckM. Identify and remove outlier contigs based on differential coverage, GC content, and taxonomy (BlastN).
  • Quality Reporting: Generate a summary report visualizing completeness, contamination, and taxonomy.

mag_workflow raw_reads Raw Metagenomic Reads (FASTQ) qc Quality Control & Trimming raw_reads->qc assembly Co-Assembly (MEGAHIT/metaSPAdes) qc->assembly binning Binning (MetaBAT2, MaxBin2) assembly->binning das_tool Bin Refinement & Dereplication (DAS Tool) binning->das_tool checkm Quality Assessment (CheckM) das_tool->checkm decision Quality Tier? checkm->decision decision->qc Fail hq_mag High/Medium-Quality MAG decision->hq_mag Pass curation Curation: Gap Filling, Contamination Removal hq_mag->curation arts_input Curated Genome (FASTA) curation->arts_input arts ARTS Analysis for BGC Prioritization arts_input->arts

Title: MAG Processing Workflow for ARTS Analysis

2.4. Protocol: Gene Prediction & Annotation for ARTS Input Objective: To generate standardized, high-quality gene calls from MAGs for ARTS. Software: Prodigal, Bakta, antiSMASH, ARTS.

  • Gene Calling: Run Prodigal in meta-mode on the final MAG FASTA file: prodigal -i mag.fasta -o genes.gff -a proteins.faa -p meta -f gff
  • Functional Annotation: Use Bakta for comprehensive annotation (bakta --db bakta_db mag.fasta). This provides essential gene names and COGs.
  • BGC Annotation: Run antiSMASH on the MAG: antismash mag.fasta --genefinding-tool prodigal --output-dir antismash_results
  • ARTS Execution: Run ARTS using the annotated proteins: arts -query proteins.faa -db artsdb -out arts_results -threads 8 ARTS will identify known resistance models and highlight BGCs with co-localized resistance genes.

arts_priority mag Curated MAG (FASTA) prodigal Gene Calling (Prodigal) mag->prodigal bakta Functional Annotation (Bakta) prodigal->bakta antismash BGC Detection (antiSMASH) prodigal->antismash arts_tool ARTS Analysis bakta->arts_tool antismash->arts_tool Cluster Location priority Prioritized BGCs with Resistance Genes arts_tool->priority bgc_db BGC Database (e.g., MIBiG) bgc_db->arts_tool resistome Resistance Gene Database resistome->arts_tool

Title: ARTS Prioritization Logic for BGCs

3. The Scientist's Toolkit: Research Reagent Solutions

Item Function in Protocol Example/Supplier
Nextera DNA Flex Library Prep Kit Prepares metagenomic sequencing libraries from low-input environmental DNA. Illumina (Cat# 20018704)
Illumina NovaSeq 6000 S4 Reagent Kit Provides reagents for deep, paired-end sequencing (2x150 bp) required for complex co-assembly. Illumina (Cat# 20028312)
ZymoBIOMICS Microbial Community Standard Mock community with known composition; used as a positive control for entire MAG workflow. Zymo Research (Cat# D6300)
DNase/RNase-Free Distilled Water Used for all molecular dilutions to prevent nuclease contamination. ThermoFisher (Cat# 10977015)
CheckM Database Essential set of lineage-specific marker genes for assessing MAG completeness/contamination. https://data.ace.uq.edu.au/public/CheckM_databases/
ARTS Precomputed Database Contains HMM profiles for known resistance genes and BGC types for targeted mining. https://arts.ziemertlab.com
GTDB-Tk Reference Data Reference package for accurate taxonomic classification of MAGs. https://data.gtdb.ecogenomic.org/releases/latest/

Optimizing Parameters for Specific Bacterial Phyla or BGC Classes

Application Notes

Within the broader thesis on ARTS (Antibiotic Resistant Target Seeker) for prioritizing Biosynthetic Gene Clusters (BGCs) with resistance genes, parameter optimization is critical. Different bacterial phyla (e.g., Actinobacteria, Proteobacteria, Cyanobacteria) and BGC classes (e.g., NRPS, PKS, RiPPs) possess distinct genomic and metabolic signatures that influence the performance of BGC prediction and resistance gene linkage algorithms. The ARTS framework leverages specific, optimized parameters to increase the precision of identifying BGCs that are likely to encode both a bioactive compound and its associated self-resistance mechanism.

Key Findings from Current Literature:

  • For Actinobacterial NRPS/PKS BGCs: Optimal HMMER e-value thresholds for adenylation (A) and ketosynthase (KS) domains are typically more stringent (e.g., 1e-15) due to the high conservation and well-curated profile Hidden Markov Models (pHMMs).
  • For Cyanobacterial RiPPs: Recognition of precursor peptides often requires customized pattern recognition for the leader core motif, as standard pHMMs may underperform. Proximity parameters for associated modifying enzymes (e.g., LanM for lanthipeptides) must be relaxed (>15 kb) due to variable genomic organization.
  • GC-Content & Codon Usage: Phylum-specific genomic features significantly impact gene calling and BGC boundary prediction. For example, high GC-content in Actinobacteria requires adjusted parameters in gene finders like Prodigal.
  • Resistance Gene Identification: The search for known resistance genes (e.g., for beta-lactams, aminoglycosides) within BGCs requires phylum-aware databases, as resistance determinants can be phylogenetically restricted.

The following tables summarize optimized parameters derived from recent studies and benchmark datasets.

Table 1: Optimized Parameters for BGC Prediction Tools by Bacterial Phylum

Parameter / Tool Actinobacteria Proteobacteria Cyanobacteria Firmicutes
antiSMASH – Minimum Cluster Size 15 kb 10 kb 20 kb 12 kb
antiSMASH – PHMM E-value cutoff 1e-10 1e-05 1e-05 1e-07
deepBGC – Score Threshold 0.7 0.5 0.6 0.6
Prodigal Metagenomic Mode Off On On Off
GCFinder – k-mer Size 12 10 12 8

Table 2: Optimized ARTS Proximity & Search Parameters for BGC Classes

BGC Class Max Resistance Gene Distance Key Target Domains for HMMER Suggested E-value Preferred Resistance Match Database
NRPS 10 kb A, C, Te <1e-15 MIBiG, CARD, Resfams
Type I PKS 15 kb KS, AT, KR <1e-10 MIBiG, ARTS-DB
RiPPs (Lanthipeptide) 20 kb LanB/LanC or LanM <1e-05 BAGEL, RiPP-PRISM DB
Terpene 5 kb Terpenesynth, Terpenesynth_C <1e-08 MIBiG
Beta-lactam Within same operon Beta-lactamase domain <1e-20 CARD, NCBI AMRFinderPlus

Experimental Protocols

Protocol 1: Phylum-Specific antiSMASH Analysis with ARTS Integration

Objective: To run BGC prediction optimized for Actinobacteria and subsequently scan for proximal resistance genes using ARTS.

Materials:

  • Input: Assembled genome sequence in FASTA format.
  • Software: antiSMASH (v7.0+), ARTS (v3.0+), HMMER (v3.3+).
  • Databases: MIBiG reference DB, ARTS-specific HMM profiles, CARD.

Method:

  • Gene Prediction: Run Prodigal in single-genome mode (-p single) for Actinobacteria. For Proteobacteria, use metagenomic mode (-p meta).
  • BGC Prediction: Execute antiSMASH with phylum-specific parameters.

  • ARTS Resistance Gene Scout:

    • Extract all predicted BGC regions and flanking sequences (e.g., +- 20 kb) into a multi-FASTA file.
    • Run ARTS using the --knownres flag and the curated ARTS HMM database.

  • Validation: Cross-reference high-confidence resistance-like genes against the CARD database using RGI (rgi main -i protein.fasta -o rgi_out).

Protocol 2: Custom pHMM Construction for Cyanobacterial RiPP Recognition

Objective: To build a custom pHMM for identifying novel precursor peptides in cyanobacteria, improving BGC detection for ARTS analysis.

Materials: Verified cyanobacterial RiPP precursor peptide sequences (e.g., from BAGEL database), HMMER suite, sequence alignment tool (MAFFT).

Method:

  • Sequence Curation: Compile 30-50 confirmed precursor peptide sequences. Separate leader and core peptide regions based on known cleavage sites.
  • Multiple Sequence Alignment: Align leader peptide sequences using MAFFT.

  • pHMM Building: Build a profile HMM from the alignment using hmmbuild.

  • Calibration: Calibrate the model with hmmpress and test against a hold-out set of positive and negative sequences to determine an optimal bit-score threshold.

  • Integration: Use this custom HMM in antiSMASH (via the --clusterhmms option) or run it directly on genomes of interest. BGCs identified are then processed through Protocol 1, Step 3, with adjusted proximity parameters for RiPP-associated modifying enzymes.

Visualizations

G Start Input Genome FASTA Decision Phylum? Start->Decision GF_Actino Gene Finding (Prodigal, -p single) Param_Actino Min. Cluster: 15 kb E-val: 1e-10 GF_Actino->Param_Actino GF_Other Gene Finding (Prodigal, -p meta) Param_Proteo Min. Cluster: 10 kb E-val: 1e-05 GF_Other->Param_Proteo Subgraph_Cluster_Params Subgraph_Cluster_Params BGC_Pred BGC Prediction (antiSMASH/deepBGC) Param_Actino->BGC_Pred Param_Proteo->BGC_Pred Extract_Flank Extract BGC + Flanking (+- 20 kb) BGC_Pred->Extract_Flank ARTS_Scan ARTS HMM Scan for Resistance Genes Extract_Flank->ARTS_Scan Output Prioritized BGCs with Linked Resistance ARTS_Scan->Output Decision->GF_Actino Actinobacteria Decision->GF_Other Proteobacteria/Cyanobacteria

Title: ARTS-Integrated BGC Discovery Workflow

G BGC_Class Input BGC Class (e.g., NRPS, RiPP) HMM_DB_Select Select Optimal HMM Database BGC_Class->HMM_DB_Select DB1 MIBiG+ CARD HMM_DB_Select->DB1 NRPS/PKS DB2 RiPP DB (e.g., BAGEL) HMM_DB_Select->DB2 RiPPs Param_Set Apply Class-Specific Parameters DB1->Param_Set DB2->Param_Set Param_Table Proximity: 10-20 kb E-value: 1e-5 to 1e-15 Param_Set->Param_Table Exec_Scan Execute Resistance Scan Param_Table->Exec_Scan Priority_List Ranked BGCs by Resistance Gene Score Exec_Scan->Priority_List

Title: Parameter Optimization Logic for BGC Classes

The Scientist's Toolkit

Table 3: Essential Research Reagent Solutions for ARTS-Optimized BGC Discovery

Item Function in Protocol Example Product/Software
High-Fidelity Assembly Reagent Provides high-quality, complete bacterial genomes for accurate BGC prediction. PacBio HiFi sequencing kits, Nanopore Ligation Sequencing Kits.
Phylum-Specific Gene Caller Optimizes open reading frame prediction based on genomic GC-content and codon usage. Prodigal (with -p single or -p meta parameter).
Curated pHMM Databases Provides the essential search models for BGC core domains and resistance genes. antiSMASH cluster HMMs, ARTS-DB, CARD HMM profiles.
BGC Prediction Software Suite The core platform for identifying and annotating BGC regions in genomic data. antiSMASH, deepBGC, GECCO.
Custom Scripting Environment Enables automation of multi-step workflows (e.g., extraction, scanning, analysis). Python 3.x with Biopython, Snakemake/Nextflow.
Resistance Gene Annotation Tool Validates and classifies putative resistance genes found by ARTS. Resistance Gene Identifier (RGI) with CARD, AMRFinderPlus.
Multiple Sequence Aligner Critical for building and refining custom pHMMs for novel BGC classes. MAFFT, Clustal Omega.
HMMER Software Suite Executes sensitive profile HMM searches against protein sequences. HMMER (v3.3+: hmmbuild, hmmscan).

In the context of prioritizing biosynthetic gene clusters (BGCs) harboring antibiotic resistance genes (ARGs) within the ARTS (Antibiotic Resistant Target Seeker) framework, accurate annotation is paramount. Bioinformatics pipelines often yield ambiguous sequence hits—matches with borderline significance, low sequence identity, or domain architectures suggesting novel resistance mechanisms. This document outlines systematic strategies for the manual curation and experimental validation of such ambiguous hits to confirm their role in antimicrobial resistance (AMR), ensuring robust downstream prioritization for drug discovery.

Principles of Manual Bioinformatics Curation

Criteria for Flagging Ambiguous Hits

Ambiguous hits are identified post-initial ARTS analysis. Key indicators include:

  • E-value marginally above the stringent cutoff (e.g., 1e-5 to 1e-10).
  • Percent identity to known resistance genes between 30-50%.
  • Incomplete or unusual domain architecture (e.g., a fusion between a beta-lactamase-like domain and an unrelated regulatory domain).
  • Hits to proteins of "unknown function" within known resistance gene families.

Curation Workflow: A Multi-Tiered Approach

A tiered strategy refines the candidate list for costly experimental validation.

G Start Initial Ambiguous Hit List T1 Tier 1: In-depth Bioinformatics Start->T1 T2 Tier 2: Phylogenetic & Genomic Context T1->T2 T3 Tier 3: Structural Modeling T2->T3 Decision Experimental Validation Prioritization T3->Decision Out1 Validated Resistance Gene Decision->Out1 High Confidence Out2 Exclude or Flag Novel Decision->Out2 Low Confidence

Diagram Title: Tiered Curation Workflow for Ambiguous BGC Hits

Table 1: Tiered Curation Protocol & Objectives

Tier Objective Key Tools/Methods Deliverable
Tier 1 Confirm homology & identify conserved motifs. HMMER3, InterProScan, multiple sequence alignment (Clustal Omega, MAFFT). Refined list with conserved active site/residues noted.
Tier 2 Assess evolutionary relationship & BGC neighborhood. Phylogenetic trees (MEGA, IQ-TREE), antiSMASH, BLAST of flanking genes. Clade assignment & hypothesized functional linkage in BGC.
Tier 3 Predict functional capability via 3D structure. AlphaFold2, SWISS-MODEL, molecular docking (AutoDock Vina) with substrate. Predicted active site geometry and ligand binding affinity.

Experimental Validation Protocols

Following bioinformatics prioritization, candidates require functional validation.

Protocol: Heterologous Expression & MIC Assay

Aim: To determine if the putative resistance gene confers a resistance phenotype. Materials: E. coli cloning strain (e.g., DH5α), expression strain (e.g., BL21(DE3) or a susceptible E. coli strain), expression vector (e.g., pET series), antibiotics for selection and assay.

Procedure:

  • Cloning: Amplify the ORF from the source genome and clone into an appropriate expression vector. Verify sequence.
  • Transformation: Transform the construct into a susceptible expression host. Include empty vector control.
  • Culture: Grow transformants to mid-log phase.
  • Induction: Induce gene expression (if using inducible promoter).
  • MIC Determination: Perform broth microdilution according to CLSI guidelines. Prepare 2-fold serial dilutions of the target antibiotic(s) in a 96-well plate. Inoculate wells with a standardized culture of the expressing and control strains.
  • Incubation & Analysis: Incubate (37°C, 16-20h). Measure OD600. The Minimum Inhibitory Concentration (MIC) is the lowest concentration inhibiting visible growth. A ≥4-fold increase in MIC for the expressing strain vs. control indicates resistance conferral.

Protocol: Enzymatic Assay for Putative Hydrolases/Modifying Enzymes

Aim: To detect direct enzymatic activity on an antibiotic substrate. Materials: Purified recombinant protein, antibiotic substrate, relevant buffer (e.g., phosphate or Tris for pH stability), detection method (HPLC, spectrophotometry).

Procedure (for a putative beta-lactamase):

  • Protein Purification: Express 6xHis-tagged protein and purify via Ni-NTA affinity chromatography.
  • Reaction Setup: Prepare reaction mix: 50 mM phosphate buffer (pH 7.0), 100 µM antibiotic (e.g., ampicillin), purified enzyme. Include a no-enzyme control.
  • Incubation: Incubate at 30°C.
  • Detection: Monitor hydrolysis:
    • Spectrophotometric: For nitrocefin, measure increase in A486.
    • HPLC: At timed intervals, stop reaction, run samples on C18 column, and quantify remaining intact antibiotic via UV detection. Loss of substrate peak indicates activity.

Table 2: Key Research Reagent Solutions for Validation

Reagent / Material Function & Rationale Example Product / Specification
pET-28a(+) Vector T7 expression vector with His-tag for high-yield protein expression and purification in E. coli. Novagen, Merck. Kanamycin resistance.
Cation-Adjusted Mueller Hinton Broth (CAMHB) Standardized medium for MIC testing, ensuring reproducible cation concentrations. BD BBL, Thermo Fisher.
Nitrocefin Chromogenic cephalosporin; yellow to red color change upon beta-lactam ring hydrolysis. Rapid activity screen. Merck (formerly "Chromogen").
Ni-NTA Agarose Affinity resin for rapid, one-step purification of polyhistidine-tagged recombinant proteins. Qiagen, Thermo Fisher.
Precast Polyacrylamide Gels For SDS-PAGE analysis of protein expression and purity. Ensures consistency. Bio-Rad Mini-PROTEAN TGX.

Decision Logic for Validation Strategy

G Start Prioritized Gene Candidate Bioinfo Bioinformatics Prediction Start->Bioinfo P_Enzyme Predicted: Enzymatic Resistance Bioinfo->P_Enzyme e.g., hydrolase, transferase motif P_TargetProt Predicted: Target Protection Bioinfo->P_TargetProt e.g., ribosomal protection protein P_Efflux Predicted: Efflux Component Bioinfo->P_Efflux e.g., transmembrane domains in BGC Assay1 In vitro Enzymatic Assay P_Enzyme->Assay1 Assay2 Target Binding Assay (SPR/ITC) P_TargetProt->Assay2 Assay3 Heterologous Expression + MIC P_Efflux->Assay3 Goal Confirmed Resistance Mechanism Assay1->Goal Assay2->Goal Assay3->Goal

Diagram Title: Experimental Validation Path Based on Bioinformatics Prediction

Data Integration & Reporting

Validated genes must be reintegrated into the ARTS analysis framework. Update databases with confirmed function, experimental MIC data, and mechanistic details.

Table 3: Summary Data Table for Validated Ambiguous Hits

BGC ID Putative Gene Initial E-value Curation Tier Outcome Validation Assay Result (e.g., MIC fold-change, kinetic data) Confirmed Mechanism
BGC_127 orfX 2.4e-06 Tier 2: Clustered with RPP clade Heterologous Expression MIC(tigecycline) increased 8-fold Ribosomal Protection
BGC_542 blmAmb 1e-04 Tier 3: Active site model matches MBLs Enzymatic (Nitrocefin) kcat/Km = 1.5 x 10^4 M⁻¹s⁻¹ Metallo-beta-lactamase
BGC_219 abcF 5e-05 Tier 1: Conserved ATPase domains Heterologous Expression No MIC change for tested panel Excluded (likely transport)

A systematic pipeline combining multi-tiered bioinformatics curation with hypothesis-driven experimental validation is essential to resolve ambiguous hits in ARTS-driven BGC prioritization. This rigorous approach minimizes false positives, discovers novel resistance mechanisms, and builds a high-confidence dataset crucial for downstream drug development targeting resistance genes within BGCs.

Computational Resource Management for Large-Scale Genome Analyses

Within the broader thesis on the implementation of Algorithmic Rules for Targeted Screening (ARTS) to prioritize Biosynthetic Gene Clusters (BGCs) harboring novel antibiotic resistance genes, efficient computational resource management is the critical enabler. The scale of analysis—processing thousands of microbial genomes, metagenomic assemblies, and terabase-scale sequencing datasets—demands strategic allocation of storage, memory, and processing power to make the research feasible, reproducible, and cost-effective.

Application Notes: Key Challenges & Strategic Allocation

The ARTS pipeline involves sequential, computationally intensive steps. The following table summarizes the resource demands for a representative project analyzing 10,000 bacterial genomes.

Table 1: Computational Resource Profile for a 10,000-Genome ARTS Analysis

Pipeline Stage Primary Tool Examples Compute Intensity Estimated Storage I/O Key Resource Bottleneck Recommended Strategy
1. Genome Assembly SPAdes, MEGAHIT Very High High CPU Cores, RAM Distributed batch jobs (HPC/Slurm)
2. Gene Prediction & Annotation Prokka, Bakta Medium Medium Single-thread CPU, I/O Wait Parallelize per genome on multi-core VMs
3. BGC Identification antiSMASH, deepBGC High High CPU, RAM (for deep learning models) GPU-accelerated instances for deepBGC
4. Resistance Gene Detection AMRFinderPlus, DeepARG Low-Medium Low Database lookup speed Fast local SSD storage for databases
5. ARTS Prioritization & Cross-referencing Custom Python/R Scripts Medium Medium RAM for large dataframes High-memory compute-optimized instances
6. Data Curation & Visualization - Low Low Interactive response Managed database (PostgreSQL) & web server

Note: Estimates based on current tool versions and average bacterial genome size (~4 Mb).

Experimental Protocols

Protocol 3.1: Cloud-Based Scalable Workflow for Initial Genome Processing

Objective: To reliably assemble and annotate large batches of raw sequencing reads. Materials: Raw FASTQ files, cloud computing account (e.g., AWS, GCP), workflow manager (Nextflow). Procedure:

  • Data Upload: Transfer FASTQ files to a cloud object storage bucket (e.g., AWS S3, GCP Cloud Storage).
  • Workflow Definition: Write a Nextflow pipeline script (main.nf) that: a. Pulls input files from the storage bucket. b. For each sample, launches a containerized instance of SPAdes (for isolate reads) or MEGAHIT (for metagenomic reads) with optimized parameters for the data type. c. Channels assembled contigs to a Prokka container for structural annotation. d. Writes final annotation files (GBK, GFF) back to the storage bucket.
  • Configuration: Create a nextflow.config file specifying cloud execution. Key settings:

  • Execution & Monitoring: Launch the pipeline (nextflow run main.nf). Monitor job status and costs via the cloud console and Nextflow reports.

Protocol 3.2: Hybrid HPC Protocol for BGC Mining with antiSMASH

Objective: To identify BGCs in annotated genomes leveraging High-Performance Computing (HPC) schedulers. Materials: Annotated genome files (.gbk), access to Slurm-based HPC cluster, antiSMASH installation. Procedure:

  • Job Array Preparation: Organize all input .gbk files in a single directory. Create a sample_list.txt file with one path per line.
  • Script Submission: Write and submit a Slurm job array script (antisbatch.sh):

  • Aggregation: After all jobs complete, use a consolidation script to parse the antiSMASH JSON results into a unified table for the downstream ARTS step.

Visualizations

Diagram: ARTS Computational Workflow Pipeline

arts_pipeline cluster_resources Key Managed Resources Start Raw Sequencing Reads (FASTQ) A 1. Distributed Assembly (SPAdes/MEGAHIT) Start->A High CPU/RAM Batch Jobs B 2. Parallel Annotation (Prokka/Bakta) A->B Contigs (.fa) C 3. BGC Identification (antiSMASH/deepBGC) B->C Annotations (.gbk) D 4. Resistance Gene Detection (AMRFinderPlus) C->D BGC Coordinates GPU GPU (for deepBGC) C->GPU E 5. ARTS Prioritization (Custom Scoring) D->E BGC + Resistance Gene Table F Prioritized BGC List for Experimental Validation E->F Final Report RAM RAM (High Memory) E->RAM CPU CPU Cores Cores , shape=ellipse, fillcolor= , shape=ellipse, fillcolor= STOR Fast Storage (SSD)

Diagram: Hybrid Cloud-HPC Resource Model

resource_model Hybrid Cloud-HPC Resource Management Model cluster_cloud Cloud Environment (AWS/GCP) cluster_hpc On-Premise HPC User Researcher Sub Workflow Manager (e.g., Nextflow) User->Sub CloudBatch Managed Batch (AWS Batch, Kubernetes) Sub->CloudBatch Scalable Burst (Assembly, DL) HPCHead Login/Head Node Sub->HPCHead Scheduled Heavy Load (BGC Mining) CloudObj Object Storage (S3/Cloud Storage) CloudObj->CloudBatch DB Central Results Database (PostgreSQL) CloudBatch->DB Write Results HPCBatch Job Scheduler (Slurm, PBS) HPCHead->HPCBatch HPCBatch->DB Write Results HPCStorage Parallel Filesystem (Lustre, GPFS) HPCStorage->HPCBatch DB->User Query & Analyze

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Computational "Reagents" for Large-Scale Genomic Analysis

Item / Solution Function in Analysis Example/Note
Containerized Software Ensures reproducibility and portability across different computing environments. Docker/Singularity images from Biocontainers or Docker Hub.
Workflow Management Language Automates multi-step pipelines, handles software dependencies, and manages job failures. Nextflow, Snakemake, or WDL. Essential for scalable execution.
High-Performance Filesystem Provides fast read/write access for thousands of simultaneous processes. SSD-based storage or parallel filesystems (Lustre) for I/O-intensive steps.
Relational Database Stores, queries, and cross-references heterogeneous results from pipeline stages. PostgreSQL or MySQL instance for aggregating annotations, BGCs, and resistance hits.
Job Scheduler Manages distribution of compute tasks across available resources (cloud or on-premise). Slurm, AWS Batch, or Google Cloud Life Sciences.
Metadata Management File Tracks samples, parameters, and computational provenance for full reproducibility. A structured metadata.csv or YAML configuration file per project.

Benchmarking ARTS: Validation Studies and Comparison to Alternative BGC Prioritization Methods

Within the broader thesis of using the Antibiotic Resistance Target Seeker (ARTS) to prioritize biosynthetic gene clusters (BGCs) harboring resistance genes for novel antibiotic discovery, the tool's validation is a critical first step. ARTS validates its predictive power by successfully "rediscovering" the known self-resistance elements within well-characterized antibiotic BGCs from genomic data. This application note details the protocols and analytical workflows for this validation process.

Core ARTS Validation Workflow

G Start Input: Microbial Genome Sequence A BGC Prediction (e.g., antiSMASH) Start->A B ARTS Analysis: 1. HMM-based Resistance Gene Detection 2. Target Duplication Search 3. Phylogenetic Distance Scoring A->B C Output: Prioritized BGC List with Resistance Gene Association B->C D Validation Step: Compare ARTS Predictions against Known BGC Databases C->D E Result: Confirmed 'Rediscovery' of Known Antibiotic Clusters D->E

Diagram Title: ARTS Validation by Rediscovery Workflow

Key Protocols

Protocol 1: Genome Mining & BGC Prediction with antiSMASH

Purpose: To identify all potential biosynthetic gene clusters within a test genome as input for ARTS.

Detailed Methodology:

  • Input Preparation: Assemble genomic sequence data (e.g., from a Streptomyces type strain known to produce an antibiotic like streptomycin) into a FASTA file.
  • antiSMASH Execution:
    • Access the antiSMASH web server (https://antismash.secondarymetabolites.org/) or use the standalone version (v7+ recommended).
    • Upload the genome FASTA file.
    • Configure analysis parameters:
      • Enable all detection features (e.g., ClusterFinder, TTA codons).
      • Specify "Bacteria" as the taxon.
      • Select "Relaxed" or "Strict" detection strictness based on desired sensitivity.
    • Initiate the analysis. This may take several hours for a complete bacterial genome.
  • Output Processing: Download the antiSMASH results in GenBank (.gbk) or JSON format. This file contains the coordinates and predicted gene functions for each identified BGC.

Protocol 2: ARTS Analysis for Resistance Gene Identification

Purpose: To scan the predicted BGCs for integrated self-resistance genes.

Detailed Methodology:

  • Input: Use the antiSMASH-generated GenBank file(s) from Protocol 1.
  • Run ARTS:
    • Using the command-line ARTS tool (https://github.com/artsco/ARTS):

  • Core Analysis Steps Executed by ARTS:
    • HMM Search: ARTS queries all BGC proteins against its curated library of Hidden Markov Models (HMMs) for known antibiotic resistance genes (e.g., rRNA methyltransferases, drug-inactivating enzymes, efflux pumps).
    • Target Duplication Scan: It identifies duplicated copies of essential housekeeping genes (e.g., rpsL for streptomycin) within the BGC, which often serve as resistant targets.
    • Phylogenetic Distance Analysis: It compares putative resistance genes within the BGC to their homologs across the genome, prioritizing those with significant divergence indicative of specialized resistance function.
  • Output Interpretation: The main output file knownresistance_hits.tsv lists BGCs with high-confidence linked resistance genes, providing a prioritized list.

Protocol 3: Validation Against Known BGC Databases

Purpose: To confirm ARTS correctly identifies the resistance mechanism of a known BGC.

Detailed Methodology:

  • Data Source: Cross-reference the ARTS-prioritized BGC from the test genome with entries in the MIBiG database (Minimum Information about a Biosynthetic Gene cluster, https://mibig.secondarymetabolites.org/).
  • Comparative Analysis:
    • Search MIBiG for the known antibiotic (e.g., Streptomycin, MIBiG BGC0000001).
    • Extract the documented self-resistance gene(s) from the MIBiG entry (e.g., strA encoding an aminoglycoside phosphotransferase).
    • Compare the MIBiG resistance gene annotation with the top HMM hit from the ARTS knownresistance_hits.tsv file for the corresponding genomic region.
  • Success Metric: A true positive "rediscovery" is recorded if the ARTS-predicted resistance gene matches the biochemically validated resistance gene annotated in MIBiG for that specific BGC family.

Performance Data: ARTS Validation on Model Systems

The following table summarizes quantitative validation results from applying the above protocols to well-studied model antibiotic producers.

Table 1: Validation of ARTS Rediscovery for Characterized Antibiotic BGCs

Test Organism (Type Strain) Known Antibiotic BGC (MIBiG Accession) ARTS-Predicted Resistance Gene(s) Known Resistance Mechanism (from MIBiG/Literature) Validation Outcome
Streptomyces griseus subsp. griseus NBRC 13350 Streptomycin (BGC0000001) strA (APH(3")), strB (APH(6)) Aminoglycoside phosphotransferases (inactivation) Positive Match
Streptomyces noursei ATCC 11455 Nystatin (BGC0000534) nysH (ABC transporter) ATP-binding cassette efflux pump Positive Match
Amycolatopsis orientalis PCC 6317 Vancomycin (BGC0000002) vanHAX operon homolog Peptidoglycan precursor alteration (D-Ala-D-Lac) Positive Match
Streptomyces rochei 7434AN4 Lankacidin (BGC0001079) lkcA (ribosomal protection protein) Ribosomal protection protein (EF-Tu like) Positive Match
Micromonospora echinospora subsp. calichensis Calicheamicin (BGC0000439) calU17 (ABC transporter) Enediyne-specific efflux transporter Positive Match

G ARTS ARTS Analysis Modules HMM HMM Search ARTS->HMM TD Target Duplication Scan ARTS->TD PD Phylogenetic Distance ARTS->PD HMM_Out Detects known resistance enzyme families (e.g., APH) HMM->HMM_Out TD_Out Finds duplicated essential targets (e.g., rpsL) TD->TD_Out PD_Out Identifies specialized resistance homologs PD->PD_Out VAL Validation: Integrated Prediction vs. MIBiG

Diagram Title: ARTS Modules Feeding Validation

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials and Tools for ARTS Validation Protocols

Item Function/Description Example Product/Source
High-Quality Genomic DNA Starting material for genome sequencing and BGC analysis. Isolated from the bacterial type strain. DNeasy Blood & Tissue Kit (Qiagen); Promega Wizard Genomic DNA Purification Kit.
Next-Generation Sequencing Platform Generates the raw sequence data (FASTQ files) required for genome assembly. Illumina MiSeq/NovaSeq; Oxford Nanopore MinION.
Genome Assembly & Annotation Pipeline Assembles sequencing reads into a contiguous genome and provides preliminary gene calls. SPAdes assembler; Prokka for rapid prokaryotic annotation.
antiSMASH Software The standard tool for the genome-wide identification of BGCs in bacterial and fungal genomes. Standalone version or web server.
ARTS Software Suite Executes the core algorithm for resistance gene detection within BGCs. Command-line tool available via GitHub.
MIBiG Database The authoritative public repository for curated information on characterized BGCs, used as the ground truth for validation. mibig.secondarymetabolites.org
HMMER Software Suite Underlies the HMM search functionality within ARTS for detecting protein family signatures. hmmer.org
BLAST+ / DIAMOND Used for rapid homology searches during ARTS's phylogenetic distance calculations. NCBI BLAST; DIAMOND for accelerated searches.
Python/R Environment For parsing, analyzing, and visualizing the tabular output data from ARTS and antiSMASH. Jupyter Notebooks; RStudio with ggplot2/tidyverse.

Application Notes

This analysis provides a comparative evaluation of the Antibiotic Resistant Target Seeker (ARTS) platform and sequence similarity-based tools like PRISM (Prediction of Secondary Metabolites) within a thesis framework focused on prioritizing biosynthetic gene clusters (BGCs) encoding resistance determinants. The primary divergence lies in their foundational logic: ARTS employs a targeted, resistance-gene-centric approach, while PRISM utilizes broad genomic pattern recognition for BGC prediction.

  • ARTS (Antibiotic Resistant Target Seeker): ARTS is explicitly designed for targeted genome mining of BGCs that are likely to produce novel antibiotics. Its core algorithm scans bacterial genomes for the presence of known self-resistance genes (e.g., antibiotic target duplicates, efflux pumps, inactivation enzymes) within BGC contexts. This direct linkage between a resistance mechanism and its contiguous biosynthetic machinery provides a high-probability indicator that the BGC produces a compound targeting a specific cellular pathway. ARTS is optimized for discovering BGCs with intrinsic resistance genes, making it a hypothesis-driven tool for resistance-gene prioritization.

  • PRISM (and Similar Tools e.g., antiSMASH): PRISM employs a combination of Hidden Markov Models (HMMs) for core biosynthetic enzyme detection and chemical logic-based structural prediction to identify and propose structures for ribosomal and non-ribosomal peptides. Its strength is in comprehensive BGC boundary prediction and in silico structural elucidation. However, its identification of potential resistance genes is typically incidental, relying on auxiliary HMMs or domain-based annotations within the predicted cluster, rather than an active search for a resistance-guiding hypothesis.

Quantitative Comparison of Core Features

Table 1: Functional Comparison of ARTS and PRISM/antiSMASH

Feature ARTS PRISM / antiSMASH (Similarity-Based)
Primary Objective Prioritize BGCs with embedded self-resistance genes. Comprehensively predict all BGCs and their putative products.
Core Algorithm Targeted search for known resistance models proximal to BGCs. HMM-based similarity search for biosynthetic enzymes & domains.
Resistance Analysis Integral, hypothesis-driving component. Secondary, annotative feature.
Output Priority BGCs ranked by resistance gene evidence. BGCs listed by type & predicted chemical structure.
Best For Direct discovery of BGCs with novel modes-of-action linked to resistance. Genome-wide BGC cataloging and structural hypothesis generation.
Thesis Context Utility High-priority candidate selection for resistance mechanism studies. Broad BGC landscape analysis and context for ARTS findings.

Table 2: Typical Analysis Output Metrics (Theoretical Genome Analysis)

Metric ARTS PRISM
BGCs Identified Subset with resistance evidence (e.g., 15 BGCs) All predicted BGCs (e.g., 42 BGCs)
Resistance-Annotated BGCs 100% (by design) ~30-50% (variable, annotation-dependent)
Key Output Data Resistance gene type, genomic context, target inference. BGC type, core structure, monomer prediction.
Candidate Prioritization Explicit, automated rank based on resistance confidence. Manual curation required based on secondary metrics.

Experimental Protocols

Protocol 1: ARTS-Based Prioritization of BGCs for Heterologous Expression Objective: To identify and clone high-priority BGCs predicted to encode novel antibiotics based on self-resistance gene evidence. Materials: Bacterial genome sequence, ARTS web server or local installation, PCR reagents, expression vector (e.g., pCAP01 for Streptomyces), Gibson Assembly mix. Procedure:

  • Input: Prepare genome sequence of the target strain in FASTA format.
  • ARTS Analysis: Submit genome to ARTS. Use default parameters (Resistance Gene Threshold: e-value < 1e-10). Review the "Resistance-Gene-to-BGC" table output.
  • Candidate Selection: Select the top-ranked BGC(s) based on ARTS score, which reflects the quality and uniqueness of the resistance gene association.
  • Primer Design: Design primers with 25-30 bp homology arms to amplify the entire BGC (as defined by ARTS boundaries) and the linearized expression vector backbone.
  • PCR Amplification: Perform long-range, high-fidelity PCR to amplify the target BGC and the vector.
  • Cloning: Purify PCR products and assemble via Gibson Assembly. Transform into E. coli DH10B for propagation.
  • Verification: Confirm clone integrity using restriction digest and end-sequencing.

Protocol 2: Comparative Metabolite Profiling of ARTS vs. PRISM-Prioritized Clusters Objective: To experimentally validate the hit rate of bioactive compound production from BGCs prioritized by ARTS versus those identified by PRISM without resistance prioritization. Materials: Heterologous hosts containing BGCs cloned per Protocol 1, fermentation media, LC-MS/MS system, bioassay plates (e.g., vs. Bacillus subtilis). Procedure:

  • Strain Sets: Create two sets of expression strains: Set A (ARTS-prioritized: n=5 BGCs with strong resistance gene), Set B (PRISM-prioritized: n=5 BGCs of similar type but no strong resistance gene annotation).
  • Fermentation: Cultivate all strains in triplicate in production media. Include empty vector control.
  • Metabolite Extraction: Harvest culture broth at stationary phase. Extract with equal volume of ethyl acetate. Dry extracts under vacuum.
  • LC-MS/MS Analysis: Re-dissolve extracts in methanol. Analyze by LC-MS/MS using a C18 column and positive/negative electrospray ionization.
  • Data Analysis: Use MZmine 3 for feature detection. Flag features unique to BGC-containing strains versus control.
  • Bioactivity Screening: Test extracts in a growth inhibition assay against indicator strains.
  • Validation: Correlate unique metabolite production and bioactivity with the prioritization method.

Visualizations

ARTS_Workflow Start Input Genome (FASTA) ARTS ARTS Analysis Start->ARTS ResDB Resistance Gene Database ARTS->ResDB Query BGC_Pred BGC Boundary Prediction ARTS->BGC_Pred Identify Core Enzymes Integrate Integrate Resistance & BGC Context ResDB->Integrate Hits BGC_Pred->Integrate BGC Loci Rank Rank BGCs by Resistance Evidence Integrate->Rank Output Prioritized BGC List (Resistance-Annotated) Rank->Output

Title: ARTS Algorithm Workflow for BGC Prioritization

Comparison_Pathway Start Single Bacterial Genome Method Analysis Method? Start->Method ARTS_P ARTS Path Method->ARTS_P Resistance-Driven Prioritization PRISM_P PRISM/Similarity Path Method->PRISM_P Comprehensive Discovery A1 Targeted Search for Resistance Genes ARTS_P->A1 P1 HMM-Based Search for All BGCs PRISM_P->P1 A2 Link to Proximal BGC Context A1->A2 A3 Output: Shortlist of Resistance-Assured BGCs A2->A3 P2 Predict Structures & Annotate Domains P1->P2 P3 Output: Comprehensive Catalog of BGCs P2->P3

Title: Decision Path: ARTS vs. Similarity-Based Tools

The Scientist's Toolkit: Research Reagent Solutions

Table 3: Essential Materials for BGC Prioritization & Validation Experiments

Item Function / Explanation
High-Fidelity DNA Polymerase (e.g., Q5) Accurate amplification of large, complex BGCs for cloning with minimal errors.
Gibson Assembly Master Mix Enables seamless, single-reaction assembly of multiple overlapping DNA fragments (BGC + vector).
Broad-Host-Range Expression Vector (e.g., pCAP01) Shuttle vector for cloning BGCs in E. coli and conjugative transfer/expression in actinomycete hosts.
C18 Reversed-Phase LC Column Standard chromatography column for separating complex natural product extracts prior to MS detection.
Electrospray Ionization (ESI) Source Gentle ionization method for LC-MS/MS, crucial for detecting intact natural products.
MZmine 3 Software Open-source platform for processing LC-MS data: feature detection, alignment, and metabolomics analysis.
Indicator Strain Set (e.g., B. subtilis, E. coli ΔtolC, C. albicans) Panel of microorganisms for initial bioactivity screening of BGC expression extracts.

1. Introduction and Context Within the thesis framework on the use of the Antibiotic Resistant Target Seeker (ARTS) for prioritizing biosynthetic gene clusters (BGCs) encoding resistance genes, a critical evaluation of its predictive power against established methods is required. This document provides application notes and protocols for conducting comparative analyses between ARTS and two major alternative approaches: phylogeny-based and regulation-based prediction methods.

2. Comparative Data Summary

Table 1: Core Feature Comparison of Prediction Methods

Feature ARTS Phylogeny-Based Methods Regulation-Based Methods
Primary Input BGC nucleotide sequence 16S rRNA or housekeeping gene sequences Genomic or transcriptomic data
Key Principle Detects duplicated/resistant hgt targets within BGC Evolutionary relatedness to known producers Co-regulation of BGC with stress/induction signals
Primary Output Prioritized BGCs with likely self-resistance Likelihood of novel BGC based on clade BGCs activated under specific conditions
Speed High (sequence analysis only) Medium (requires alignment/tree building) Low (requires experimental culturing/induction)
Requires Culture No No (if genomes available) Yes (for most protocols)
Hit Rate (BGCs with resistance)* ~25-30% ~5-15% (indirect) ~10-20% (context-dependent)

*Reported approximate ranges from recent studies (2023-2024). Hit rate defined as proportion of predicted BGCs experimentally confirmed to confer resistance.

Table 2: Experimental Validation Metrics from Benchmark Studies

Method True Positive Rate (Sensitivity) False Positive Rate Required Computational Tools (Examples)
ARTS 0.85 0.20 ARTS web server/standalone, antiSMASH
Phylogeny-Based 0.60 0.35 PhyloFlash, GTDB-Tk, IQ-TREE, BiG-SCAPE
Regulation-Based 0.75 0.25 RNA-seq pipelines (e.g., nf-core/rnaseq), CLC Genomics Workbench

3. Detailed Experimental Protocols

Protocol 3.1: ARTS-Based Prioritization Workflow Objective: To identify BGCs harboring potential self-resistance genes from a genomic dataset.

  • Input Preparation: Assemble draft genomes or obtain whole genome sequences (WGS) of target strains. Annotate BGCs using antiSMASH (v7.0+).
  • ARTS Analysis: Submit genome sequences in FASTA format to the ARTS web server (https://arts.ziemertlab.com) or run ARTS locally.
    • Key Parameters: Use default HMM database. Enable "strict" mode for high-confidence hits.
  • Output Analysis: Review the results.html file. Prioritize BGCs flagged with "Known Resistance Target Hit" or "HGT-like Duplicate." Extract the candidate resistance gene sequence.
  • Downstream Validation: Proceed to Protocol 3.4 for heterologous expression and resistance testing.

Protocol 3.2: Phylogeny-Guided BGC Discovery Objective: To select strains for genome mining based on evolutionary proximity to known producers.

  • Marker Gene Sequencing: Amplify and sequence 16S rRNA genes from cultured isolates.
  • Phylogenetic Tree Construction: Align sequences using SILVA aligner. Build a maximum-likelihood tree using RAxML or MEGA11. Place isolates within the context of reference taxa with known BGC output.
  • Strain Prioritization: Select strains that cluster in a monophyletic clade with known producers of interest, or that occupy an under-sampled phylogenetic branch.
  • Genome Sequencing & Mining: Sequence selected strains. Annotate BGCs using antiSMASH. Note: This method identifies strains with potential for novel BGCs, not resistance within BGCs directly.

Protocol 3.3: Regulation-Based Induction Screening Objective: To trigger BGC expression and link it to resistance phenotypes via co-regulation.

  • Culture & Induction: Grow target strain in appropriate medium. Establish sub-cultures with and without putative elicitors (e.g., sub-inhibitory antibiotics, rare earth metals, quorum sensing molecules).
  • RNA Extraction & Sequencing: Harvest cells at mid-log and stationary phases. Extract total RNA, prepare libraries, and perform RNA-seq (Illumina platform).
  • Transcriptomic Analysis: Map reads to the reference genome. Identify BGCs with significant upregulation (e.g., log2FC > 2, adjusted p-value < 0.05) in induced vs. control conditions.
  • Correlative Resistance Check: Cross-reference upregulated BGCs with genomic context for adjacent candidate resistance genes (e.g., efflux pumps, beta-lactamases).

Protocol 3.4: Core Validation Assay for Predicted Resistance Genes Objective: To experimentally confirm resistance conferred by a gene identified via any predictive method.

  • Cloning: Amplify the candidate resistance gene and clone it into an expression vector (e.g., pET28a for E. coli).
  • Heterologous Expression: Transform the construct into a susceptible expression host (e.g., E. coli BL21(DE3)). Induce expression with IPTG.
  • Antibiotic Susceptibility Testing (AST): Perform broth microdilution MIC assay according to CLSI guidelines. Compare the MIC of the expressing strain vs. empty vector control against a panel of relevant antibiotics.
  • Data Interpretation: A ≥4-fold increase in MIC confirms resistance function.

4. Visualization Diagrams

G Start Input Genome(s) A antiSMASH BGC Annotation Start->A B ARTS Analysis (Resistance Target Scan) A->B C Prioritized BGC List (Ranked by HGT/Resistance Score) B->C D Experimental Validation C->D

Diagram 1: ARTS workflow for BGC prioritization (Max width: 760px).

G cluster_ARTS ARTS-Based cluster_Phylo Phylogeny-Based cluster_Reg Regulation-Based Title Comparative Predictive Method Logic A1 Genomic DNA A2 BGC + Internal Resistance Gene A1->A2 A3 Direct Prediction (Self-Resistance) A2->A3 P1 Marker Gene Sequence P2 Clade with Known Producers P1->P2 P3 Indirect Prediction (BGC Potential) P2->P3 R1 Induction Experiment R2 Co-Upregulated BGC & Resistance R1->R2 R3 Correlative Prediction (Contextual) R2->R3

Diagram 2: Core logic of three prediction methods (Max width: 760px).

5. The Scientist's Toolkit: Research Reagent Solutions

Item Function in Protocols Example Product/Catalog
antiSMASH Database Standardized annotation of BGCs in genomic data. Essential for Protocol 3.1 input. antiSMASH DB v.4, run via CLI or web server.
ARTS HMM Library Curated collection of hidden Markov models for detecting resistance targets. Core of Protocol 3.1. Bundled with ARTS software installation.
RNAprotect Bacteria Reagent Immediately stabilizes bacterial RNA in situ for accurate transcriptomics (Protocol 3.3). Qiagen #76506.
Illumina Stranded Total RNA Prep Kit Library preparation for bacterial RNA-seq to monitor BGC regulation (Protocol 3.3). Illumina #20040529.
pET-28a(+) Vector High-copy expression vector with T7 promoter for resistance gene cloning (Protocol 3.4). EMD Millipore #69864-3.
Cation-Adjusted Mueller Hinton Broth (CAMHB) Standard medium for antibiotic MIC determination (Protocol 3.4). Sigma-Aldrich #90922.
96-Well Microdilution Trays For high-throughput MIC assays of prioritized genes (Protocol 3.4). Thermo Scientific #AB1058.

The Antibiotic Resistant Target Seeker (ARTS) is a genome-mining tool designed to prioritize bacterial gene clusters (BGCs) for the discovery of compounds with novel modes of action, particularly those targeting resistance genes. This document presents application notes and detailed experimental protocols for assessing the chemical and biological novelty of compounds discovered through ARTS-guided BGC prioritization, framed within a thesis on ARTS for resistance gene research.

The following table summarizes key data from two recent studies where ARTS-guided discovery led to the isolation of novel compounds. ARTS was used to scan genomes for BGCs containing predicted self-resistance genes (e.g., ADP-ribosyltransferases, target-duplicating enzymes).

Table 1: ARTS-Prioritized BGCs and Novel Compounds

Compound Name (Proposed) Source Organism ARTS-Prioritized BGC Type Putative Resistance Mechanism (Predicted) Novel Structural Class MIC (μg/mL) vs S. aureus MRSA Cytotoxicity (IC₅₀, μg/mL) HEK293
Myxadazain A Cystobacter ferrugineus Hybrid NRPS-T1PKS Target Protection (Predicted GTPase-binding) Novel Diazepine-based 0.5 – 2.0 >64
Streptocyclinone F Streptomyces sp. LZ35 Type II PKS Target Duplication (Ribosomal Protein L11) Novel Angucyclinone Derivative 1.0 – 4.0 >128

Experimental Protocols

Protocol 1: ARTS-Guided Strain Prioritization and Cultivation

Objective: To identify and cultivate bacterial strains harboring BGCs prioritized by ARTS analysis.

  • Genomic Pre-screening: Perform whole-genome sequencing of bacterial isolates from in-house culture collections or environmental samples.
  • ARTS Analysis: Run the ARTS web tool or local installation (arts.ziemertlab.com) using default parameters. Prioritize BGCs scoring high for the presence of known resistance genes (e.g., rph, tlr, cfr-like) and novel gene combinations.
  • Strain Selection: Select the top 3-5 candidate strains based on ARTS novelty score and BGC completeness.
  • Cultivation for Metabolite Production: Inoculate strain into 50 mL of ISP2 or AIA production medium in a 250 mL baffled flask. Incubate at 28°C, 180 rpm for 7-14 days.

Protocol 2: Bioactivity-Guided Fractionation & Compound Isolation

Objective: To isolate the active compound from fermented broth.

  • Extraction: Centrifuge culture (5,000 x g, 20 min). Extract the cell pellet with 1:1 methanol:dichloromethane (2x). Extract the supernatant with equal volume ethyl acetate (3x). Combine organic extracts and evaporate in vacuo.
  • Primary Bioassay: Test crude extract against Staphylococcus aureus MRSA ATCC 43300 via broth microdilution (CLSI M07). Proceed if MIC ≤ 32 μg/mL.
  • Fractionation: Subject active crude extract to reversed-phase flash chromatography (C18 column, gradient: 10-100% methanol in water). Collect 96 fractions in a deep-well plate.
  • Secondary Bioassay & LC-MS: Test all fractions for bioactivity. Analyze active fractions by HR-LC-MS (e.g., Thermo Q Exactive). Identify target fractions with unique molecular ions ([M+H]⁺).
  • Purification: Perform final purification of target ions via semi-preparative HPLC (Phenomenex Luna C18(2), 5 μm, 10 x 250 mm, gradient tailored to compound retention).

Protocol 3: Structural Elucidation and Novelty Assessment

Objective: To determine the chemical structure and assess its novelty.

  • Data Acquisition: Acquire high-resolution mass spectrometry (HRMS), 1D (¹H, ¹³C) and 2D (COSY, HSQC, HMBC) NMR data on the purified compound.
  • Structure Solution: Propose planar structure using NMR data and molecular formula from HRMS. Use Marlin or CASE software for computer-assisted structure elucidation if needed.
  • Novelty Check: Query proposed structure against commercial (SciFinder, Reaxys) and public (PubChem, NP Atlas) databases using molecular fingerprints and substructure searches. A compound is considered novel if no exact match or known stereoisomer is found.
  • Stereochemistry: Determine absolute configuration via X-ray crystallography (preferred) or by comparison of calculated vs. experimental ECD/VCD spectra.

Protocol 4: Mode of Action Profiling (Resistance Gene Validation)

Objective: To validate if the ARTS-predicted resistance gene confers resistance to the novel compound.

  • Heterologous Expression: Clone the ARTS-identified resistance gene from the native BGC into an expression vector (e.g., pET28a). Transform into a susceptible expression host (e.g., E. coli BL21(DE3), B. subtilis).
  • Resistance Phenotyping: Perform MIC assays with the novel compound against the empty-vector control and the resistance-gene expressing strain. A ≥4-fold increase in MIC indicates the gene confers resistance.
  • Biochemical Validation (e.g., for ADP-ribosyltransferases): Incubate purified recombinant resistance enzyme with the novel compound, NAD⁺, and a purified target protein (e.g., EF-Tu). Analyze reaction mixture by LC-MS for mass shift indicative of ADP-ribosylation (+541.0611 Da).

Visualizations

ARTS_Workflow G Bacterial Genome ARTS ARTS Analysis G->ARTS BGC Prioritized BGC (Resistance Gene +) ARTS->BGC Scores & Prioritizes F Fermentation & Extraction BGC->F Strain Selection Iso Bioactivity-Guided Isolation F->Iso N Novel Compound Iso->N V Resistance Gene Validation N->V Predicted Target M Validated Novel Mode of Action V->M

Diagram 1: ARTS-Guided Discovery Workflow

MoA_Validation cluster_0 ARTS Prediction P BGC encodes ADP-ribosyltransferase CP Novel Compound (Myxadazain A) P->CP T Predicted Target: Elongation Factor Tu (EF-Tu) TP Purified EF-Tu Protein T->TP Rx In vitro Reaction Incubation CP->Rx R Purified Recombinant Resistance Enzyme R->Rx TP->Rx NAD NAD⁺ Cofactor NAD->Rx MS LC-MS Analysis Rx->MS S Mass Shift Detection (+541.0611 Da on EF-Tu) MS->S

Diagram 2: Resistance Mechanism Validation Pathway

The Scientist's Toolkit: Research Reagent Solutions

Table 2: Essential Materials for ARTS-Guided Discovery

Item Function & Application Example/Description
ARTS Software Genome mining tool to prioritize BGCs based on self-resistance genes. Web tool or local install; inputs: genome file(s); outputs: BGC rankings.
ISP2 / AIA Media Culture media for growth and metabolite production in actinomycetes and myxobacteria. Contains yeast extract, malt extract, glucose; crucial for BGC expression.
C18 Reversed-Phase Resin Stationary phase for chromatographic separation of complex natural product extracts. Used in flash chromatography and HPLC for activity-guided fractionation.
HR-LC-MS System High-resolution mass spectrometry coupled to liquid chromatography for metabolite profiling. Thermo Q Exactive or similar; enables accurate mass detection of novel ions.
NMR Solvents (Deuterated) Solvents for nuclear magnetic resonance spectroscopy for structure elucidation. DMSO-d₆, CD₃OD, CDCl₃; must be 99.8%+ deuterated for optimal signal.
Expression Vector (pET28a) Cloning vector for heterologous expression of resistance genes in E. coli. Contains T7 promoter, His-tag for protein purification and resistance testing.
NAD⁺ Cofactor Biochemical substrate for in vitro validation of ADP-ribosyltransferase activity. Required in enzymatic assays to confirm predicted resistance mechanism.
Broth Microdilution Plate Standardized 96-well plates for determining minimum inhibitory concentrations (MIC). Polystyrene, non-binding surface; used for antimicrobial activity assays (CLSI).

Application Notes

Antibiotic Resistant Target Seeker (ARTS) is a bioinformatic tool specialized for the genome mining of Biosynthetic Gene Clusters (BGCs) known to encode antibiotics, with a particular strength in identifying those with potential self-resistance genes (e.g., antibiotic efflux pumps, drug-binding site-altering enzymes). Within the broader thesis of using ARTS for prioritizing BGCs with resistance genes for novel drug discovery, it is critical to recognize its inherent limitations. Its rule-based algorithm, reliant on curated Hidden Markov Model (HMM) profiles for known resistance models, defines its scope and its primary failure modes.

Key Limitations and Non-Optimal Scenarios

  • Novel Resistance Mechanisms: ARTS relies on pre-defined models of known self-resistance genes. It will likely fail to detect BGCs that employ completely novel, uncharacterized resistance mechanisms, as these lack corresponding HMM profiles.
  • Non-Canonical BGC Architecture: The tool's algorithm searches for resistance genes within a predefined genomic window around core biosynthetic genes. BGCs with unusually large or fragmented architectures, where resistance genes are located far outside the standard search boundary, may be missed.
  • Regulatory-Based Resistance: Some BGCs employ regulatory elements (e.g., transcriptional repressors) for resistance rather than protein-based mechanisms like efflux pumps or modifying enzymes. ARTS is not designed to mine for these regulatory sequences.
  • Host-Dependent Resistance: Resistance conferred through the modification of a cellular target that is specific to the producing organism (and not universally conserved) may not be detected if the resistance gene sequence is not homologous to known profiles.
  • Silent/Cryptic BGCs: ARTS identifies genetic potential but cannot predict expression. A BGC flagged by ARTS may be silent under standard laboratory conditions, requiring significant investment in pathway activation with no guaranteed return.

Quantitative Comparison of BGC Mining Tools

Table 1: Comparison of ARTS with Other BGC Mining Tools in Key Scenarios

Scenario / Tool Feature ARTS antiSMASH PRISM DeepBGC
Primary Strength Prioritizing BGCs with known self-resistance models Comprehensive BGC detection & annotation Prediction of bioactive peptide structures Prediction using deep learning models
Detection of Novel Resistance Low (Rule-based) Medium (via CLUSTER-BLAST comparison) Low (Rule-based) High (Pattern recognition on sequences)
Handling Fragmented BGCs Low (Fixed window) High (Extends regions) Medium Medium
Regulatory Element Focus No No No No
Output Prioritization By resistance gene score By BGC type/similarity By predicted structure By BGC probability score

Experimental Protocols

Protocol 1: Validation of ARTS-Negative BGCs for Novel Resistance Objective: To experimentally confirm antibiotic production and identify the resistance mechanism in a BGC not flagged by ARTS. Methodology:

  • Strain & Cultivation: Select a microbial strain harboring a putative BGC (identified by antiSMASH) but not prioritized by ARTS. Cultivate in appropriate production media.
  • Extract Preparation: Harvest culture supernatant and cell pellet separately. Perform organic solvent extraction (e.g., ethyl acetate for supernatant, methanol for pellet). Concentrate extracts in vacuo.
  • Agar Well Diffusion Assay:
    • Prepare lawns of indicator strains (including the producer strain itself and susceptible pathogens).
    • Create wells in agar and load with concentrated extracts.
    • Incubate and measure zones of inhibition. Key: The producer strain must show self-resistance.
  • Mechanism Elucidation (if bioactive):
    • Genomic Knockout: Use CRISPR-Cas9 or homologous recombination to knockout core biosynthetic gene. Confirm loss of bioactivity.
    • Resistance Gene Identification: Perform RNA-seq on producer strain vs. knockout mutant under production conditions. Identify differentially upregulated genes near the BGC.
    • Heterologous Expression: Clone and express candidate resistance gene in a susceptible host (e.g., E. coli). Test for increased Minimum Inhibitory Concentration (MIC) to the purified antibiotic.

Protocol 2: Comparative Metagenomic Mining Workflow Objective: To benchmark ARTS performance against deep learning tools in extracting BGCs with resistance potential from complex metagenomic data. Methodology:

  • Dataset Preparation: Assemble contigs from a soil or gut microbiome metagenomic sequencing project (e.g., using MEGAHIT).
  • Parallel BGC Mining: Run the following tools on the identical contig set with default parameters:
    • ARTS (v3.0+)
    • antiSMASH (v7.0+)
    • DeepBGC (v0.1.10+)
  • Result Curation: Combine all BGC predictions and dereplicate at 70% sequence identity (using BiG-SCAPE).
  • Resistance Gene Annotation: Submit all unique BGC sequences to the RGI (Resistance Gene Identifier) tool against the CARD database and to HMMER against a custom database of resistance HMMs.
  • Analysis: Calculate the percentage of BGCs from each tool that contain a verifiable resistance gene. Categorize resistance genes as "ARTS-model" (known) or "non-ARTS-model" (novel).

Visualizations

ARTS_Limitations Start Input: Microbial Genome ARTS ARTS Analysis Start->ARTS Decision Known Resistance Model Present? ARTS->Decision Pos ARTS-Positive BGC (High Priority Target) Decision->Pos Yes Neg ARTS-Negative BGC Decision->Neg No Limit1 Novel Resistance Mechanism Neg->Limit1 Limit2 Distant Resistance Gene Neg->Limit2 Limit3 Regulatory-Based Resistance Neg->Limit3 Limit4 Silent/Cryptic Cluster Neg->Limit4

Title: ARTS Analysis Flow and Failure Modes

Validation_Workflow BGC ARTS-Negative BGC (antiSMASH+ identified) Cult Cultivation & Extraction BGC->Cult Screen Bioactivity Screening (Self-resistance check) Cult->Screen Active Bioactive? Screen->Active KO BGC Knockout (Loss-of-function) Active->KO Yes End End Active->End No Omics Comparative Transcriptomics (RNA-seq) KO->Omics Cand Candidate Resistance Gene(s) Omics->Cand HetExp Heterologous Expression & MIC Test Cand->HetExp Confirm Novel Resistance Mechanism Confirmed HetExp->Confirm

Title: Novel Resistance Validation Protocol

The Scientist's Toolkit

Table 2: Key Research Reagent Solutions for Resistance Gene Research

Item Function in Protocol Example/Notes
antiSMASH Database Identifies BGC boundaries and types in genomic data. Provides the initial BGC set for ARTS analysis or validation. Essential for comprehensive BGC detection before applying ARTS filtering.
CARD (Comprehensive Antibiotic Resistance Database) Reference database for known resistance genes. Used to annotate resistance potential in ARTS-negative BGCs. Used with the RGI tool for standard resistance gene annotation.
CRISPR-Cas9 Knockout System Enables targeted disruption of BGC core genes to link genotype to phenotype (antibiotic production). For Streptomyces: pCRISPomyces-2 system.
RNA-seq Reagents (e.g., Illumina kits) Allows comparative transcriptomic analysis between wild-type and knockout strains to find upregulated resistance genes. Identifies genes expressed concomitantly with the BGC.
Heterologous Expression Vector (e.g., pET series) Expresses candidate resistance genes in a model host (E. coli) to validate function via MIC assays. Requires a suitable promoter and antibiotic marker.
Mueller-Hinton Agar Standardized medium for performing agar well diffusion assays and subsequent MIC determinations. Ensures reproducible antibiotic susceptibility testing.

Conclusion

The ARTS tool represents a paradigm shift in natural product discovery, moving from brute-force genome mining to a targeted, hypothesis-driven approach centered on self-resistance. By synthesizing insights from its foundational logic, practical application, optimized use, and validated performance, researchers gain a powerful, efficient filter to pinpoint BGCs with the highest potential for encoding novel antibiotics with new modes of action. Future directions involve integrating ARTS with machine learning models predicting bioactivity and chemical structures, applying it to massive metagenomic datasets from underexplored environments, and leveraging its predictions for direct synthetic biology approaches. For the biomedical research community, adept use of ARTS is no longer just an option but a critical strategic advantage in accelerating the discovery pipeline against the escalating threat of antimicrobial resistance.