Fungicide-Driven Cross-Resistance and AMR Dynamics in Crop-Associated Microbial Systems

Overview and Research Personnel

• Research Title: Fungicide-Driven Cross-Resistance and AMR Dynamics in Crop-Associated Microbial Systems.
• Institutional Affiliation: St Joseph’s Research and Innovation Council (SJRIC).
• Research Scholar: Manisha R Yadav (ID: 26PHDBTY002).
• Supervisor: Dr. Susan Mary Philip, Associate Professor, Department of Biotechnology.

Antimicrobial Resistance (AMR): The Silent Pandemic

• Definition: Antimicrobial resistance is defined by the ability of microorganisms to withstand antimicrobial treatments, thereby rendering standard interventions ineffective.
• Mechanisms of Resistance in Bacteria: Six primary mechanisms allow bacteria to resist antimicrobial agents:
  • (a) Increased efflux: Using efflux systems to pump antibiotics out of the cell.
  • (b) Reduced uptake: Altering membrane permeability to prevent drugs from entering.
  • (c) Enzymatic degradation: Producing enzymes that break down the antimicrobial agent.
  • (d) Target site protection: Preventing the drug from binding to its designated target.
  • (e) Target site modification: Mutating or modifying the target site so the drug no longer recognizes it.
  • (f) Alternative enzymes: Expressing alternative enzymes or off-target sites to bypass the inhibited pathway.

Global Impact and Statistics of AMR

• Annual Mortality:
  • Bacterial infections are associated with approximately $7.7\text{ million}$ deaths every year.
  • $4.95\text{ million}$ of these deaths are linked to AMR.
  • $1.27\text{ million}$ deaths are directly caused by resistant bacterial pathogens (based on $2019$ data).
• Future Projections: OECD projections suggest that resistance to last-line antibiotics in high-income countries could be $2.1$ times higher in $2035$ than it was in $2005$.
• Regional Disparities:
  • Sub-Saharan Africa: Highest AMR-associated mortality rate at $98.9\text{ deaths per 100,000}$.
  • Southeast Asia: Identified as one of the world’s most at-risk regions for AMR emergence and spread.
  • South Asia: Significant burden of $76.8\text{ deaths per 100,000}$, compared to high-income countries at $55.7$.
• Economic Correlation: The impact of AMR is heaviest in low- and middle-income areas.

The One Health Perspective on AMR

• Interconnected Crisis: AMR is not confined to a single sector; it moves across humans, animals, food systems, and the environment.
• Agricultural Drivers: The extensive use of antimicrobials in livestock and aquaculture promotes resistant bacterial strains that spread into the environment and food chain.
• Sector Surveillance Status:
  • Human Health: The strongest and most standardized sector for surveillance.
  • Veterinary Sector: Strong policy pushes have seen antimicrobial use in animals in $80$ reporting countries fall from $111.45\,\text{mg/kg}$ to $96.73\,\text{mg/kg}$ animal biomass between $2017$ and $2019$.
  • Plant/Crop Sector: Lacks standardized datasets and surveillance, making the scale of resistance difficult to measure.

Global Surveillance Frameworks

• GLASS (WHO): Global Antimicrobial Resistance and Use Surveillance System; the human-health anchor for standardized reporting.
• WOAH: World Organisation for Animal Health; tracks antimicrobials in animals via the ANIMUSE platform.
• FAO: Food and Agriculture Organization; focuses on surveillance, stewardship, and legal frameworks in food and agriculture.
• TrACSS (AMR Country Self-Assessment Survey): Monitors national systems for antimicrobial pesticide use, including bactericides and fungicides.

Environmental AMR Surveillance Status (TrACSS 6.2)

• Surveillance Capacity Levels:
  • A: No capacity to generate data or reporting systems (wastewater, soil, air, etc.).
  • B: Local data collection only; lacking coordination.
  • C: National data collection present; national coordination/standardization lacking.
  • D: National surveillance system in place with standardized methods and reference labs.
  • E: Integrated surveillance across humans, animals, plants, and environmental residues.
• Survey Findings (Global vs. SEAR):
  • Global (n=183): $11\%$ (A), $6\%$ (B), $50\%$ (C), $33\%$ (D).
  • South-East Asia Region (SEAR, n=10): $30\%$ (A), $10\%$ (B), $0\%$ (C), $60\%$ (D).

Surveillance Networks in India

• Human Surveillance: ICMR-AMRSN (Indian Council of Medical Research - Antimicrobial Resistance Surveillance Network) and NCDC (National Centre for Disease Control).
• Veterinary Surveillance: ICMR-ICAR integrated One Health AMR surveillance network and ICAR-NIVEDI.
• Food and Agriculture: INFAAR (Indian Network for Fishery and Animal Antimicrobial Resistance) surveillance in livestock and fisheries.
• Northeast India: Sentinel surveillance for food and waterborne pathogens.

AMR in the Plant Protection Space

• Heavy Metal Co-selection:
  • Agricultural use of Copper (Cu) and Zinc (Zn) fertilizers exert selective pressure.
  • This pressure promotes co-selection for antibiotic resistance in environmental bacteria.
• Fungicide Impact (Azoles):
  • Agricultural fungicide exposure drives pan-azole resistance in Aspergillus fumigatus.
  • This leads to treatment failure in clinical settings as cross-resistance develops against medical azole drugs.
• US Trends (1992–2016): Wide-scale use of triazole fungicides across various crop types has increased metric tonnage significantly since the early $1990\text{s}$.

Definitions of Resistance Dynamics

• Cross-Resistance: The ability of a microorganism to resist multiple antimicrobial agents through a single resistance mechanism (e.g., a multidrug efflux pump like MdrL in Listeria monocytogenes).
• Co-Selection: The process where selection pressure from one agent (pesticide, metal, biocides) indirectly selects for resistance to other agents because the traits are genetically linked.
  • Includes cross-resistance, co-resistance, and co-regulation.

Literature Review: Fungicide Effects on Microbiomes

• Microbial Diversity (Zhang et al. 2024): Fungicides reduce soil microbial diversity, network stability, and complexity in wheat fields.
• Metaanalysis (Wang et al. 2025): Quantitative analysis (1975–2024) of $73$ studies found that:
  • Fungicides generally suppress soil microbes.
  • Negative impacts apply to soil basal respiration, microbial biomass carbon, and diversity (Shannon, Simpson, McIntosh indices).
  • Effects are dose- and time-dependent and modulated by soil properties.
• Disease Suppression (Chou et al. 2025): Higher fungicide use intensity is associated with lower soil microbiome-mediated disease suppression in turfgrass.
• Pathogen Cross-Resistance (Yang et al. 2019): Strong positive correlation found between tolerances to mancozeb and difenoconazole in Alternaria alternata isolates without fitness penalties.

Research Proposal: PhD Study Framework

• Research Gap: Lack of integrated microbiome-resistome profiling in crops under fungicide exposure; poor characterization of crop habitats as AMR reservoirs.
• Hypothesis: Fungicide exposure shifts crop-associated microbiome composition and enriches resistance-related genes (ARGs) via co-selection.
• Primary Aim: To study the effect of fungicide selection pressure on the evolution of cross-resistance mechanisms using a microbiome-resistome approach.
• Specific Objectives:
  1. Quantify fungicide exposure gradients across farms.
  2. Characterize microbiome composition and diversity.
  3. Profile resistome composition and abundance via genomic tools.
  4. Assess statistical associations between exposure and ARG enrichment.
  5. Evaluate co-occurrence patterns between microbial taxa and ARGs.

Methodology and Study Design

• Sampling Strategy:
  • Stratification of $3\text{--}5$ farms into High, Moderate, and Low/None fungicide use categories.
  • Compartmental sampling: Rhizosphere soil, bulk soil, and phyllosphere (leaf surface).
  • Establishment of metadata including dose, frequency, and irrigation.
• Genomic Profiling:
  • Bacterial: $16S\text{ rRNA}$ gene amplicon sequencing.
  • Fungal: ITS (Internal Transcribed Spacer) region sequencing.
  • Extraction: DNA isolation using DNeasy kits; verification via Nanodrop and Qubit.
• Resistome Profiling:
  • Shotgun Metagenomics: Unbiased functional potential and discovery of novel variants.
  • Targeted qPCR Panels: High-sensitivity quantification of specific ARGs.
  • Databases: Mapping against CARD (Comprehensive Antibiotic Resistance Database) and ResFinder.
• Analysis:
  • Alpha Diversity: Shannon and Simpson indices.
  • Beta Diversity: Bray-Curtis dissimilarity with PERMANOVA testing.
  • Network Analysis: SPIEC-EASI to identify hub taxa and gene-taxa interactions.

Project Timeline (36 Months)

• Year 1:
  • Months 1–3: Protocol setup and farm selection.
  • Months 4–7: Field sampling (Round 1) and metadata collection.
  • Months 6–10: DNA extraction and $16S/ITS$ sequencing.
  • Months 10–12: QIIME2 microbiome analysis.
• Year 2:
  • Months 13–18: Field sampling (Round 2) and Metagenomics/qPCR.
  • Months 17–22: Resistance gene and statistical analysis (DESeq2, PERMANOVA).
  • Months 21–24: SPIEC-EASI network analysis; writing Chapters 1–3.
• Year 3:
  • Months 25–28: Writing results and discussion.
  • Months 29–31: Thesis compilation.
  • Months 32–34: Submission and examination.
  • Months 35–36: Contingency buffer and post-viva corrections.

Expected Outcomes

• Quantification of how fungicide intensity shapes the soil and phyllosphere resistome.
• Understanding gradient-level patterns in bacterial and fungal diversity.
• Identification of specific ARGs and mobile genetic elements (MGEs) enriched by fungicides.
• Discovery of key microbial taxa acting as hubs for ARGs under chemical stress.