Exploring Grape Pomace Extract as a Photoactivated Antifungal Treatment

Yoko Clack

Our Current Food System is Inefficient

  • Current food system is resource-intensive & generates large amounts of waste
    • Around 35% of all food produced is wasted (1)
    • Annual food production produces 20-30% of all greenhouse gase emmisions (2)
    • Annual food production energy foot print is 7.0⋅1016 Kilojoules (2)

B. cineria: A Major Cause of Spoilage

  • Fresh produce is the most vulnerable food category to spoilage
    • Produce losses also waste the land, water, energy, and labor required for cultivation, processing, and transportation
  • Spoilage fungi like Botrytis cineria are major sources spoilage
    • B.cineria is estimated to cause $10 to $100 billion in annual loss worldwide (3)
    • B.cineria has a broad host range of about 500 different plant species (3)
  • B.cineria exhibits rapid fungicide-resistance development
    • Subsets of the population are resistant to 15 different fungicide classes (3)

Synthethic Fungicides have many disadvantages

  • Major disadvantages of Synthethic Fungicides
    • Associated with the development of Multi-drug Resistant Fungi (4)
    • Potential environmental & human health concerns from residual fungicides on produce (4)
    • Consumers prefer clean-label products

Waste by-product are Underutilized

  • Globally 1.0 to 1.6 billion tons of agri-industrial waste produced annually (4)
  • Grape Pomace (GP) from the wine industry is a major residue in Oregon
    • GP contains valuable phenolics
    • Up to 70% total phenolics are retained in GP

Solution: Plant-Phenolic Fungicide

  • Polyphenolics are plant secondary metabolites
    • Confer no direct nutritional value (6)
    • Confer flavor, color and taste properties
    • Antioxidant properties (7)
    • Antimicrobial properties (7)
      • Disrupt microbial cell membranes
      • Inhibit microbial enzymes and metabolism

Photo-Activated Plant-Phenolic Fungicide

  • UV-A photoactivation enhances antimicrobial activity (7)
    • Phenolics absorb UV-A → electron reconfiguration
    • React with O₂ → ROS formation
    • ROS are short-lived but highly damaging to cells

Test GPE as a Photoactivated Fungcide

  • Prepare aqueous grape pomace extract (GPE)
  • Apply GPE treatment to Botrytis cinerea cultures
  • Expose to UV-A light (photoactivation)
  • Monitor fungal growth over time

Growth Kinetics Analysis

  • Measure optical density (OD) over time (UV-spectrophotometry)
  • OD = how much light the sample absorbs and doesnt let pass through to detector
  • Increase Optical density = fungal biomass growth

Growth Curve Basics

Growth Curve Basics

Growth Curve Basics

Growth Curve Basics

Growth Curve Basics

Growth Curve → ΔLPD → Viability Reduction

  1. Fit growth curves to identify ΔLPD

Growth Curve → ΔLPD → Viability Reduction

  1. Fit growth curves to identify ΔLPD
  2. Compare ΔLPD in treatments vs control

Growth Curve → ΔLPD → Viability Reduction

  1. Fit growth curves to identify ΔLPD
  2. Compare ΔLPD in treatments vs control
  3. Use calibration curve to estimate viability reduction

Gompertz model

\[ \text{OD}_{595}(t) = a + (b - a)\,\exp\left(-\exp\left(-c(t - d)\right)\right) \]

(a): Upper asymptote

(b): Lower asymptote

\(G_R\): Maximum growth rate

\(T_{IP}\): Time of \(G_R\) / inflection point

1. Fit growth curves to identify ΔLPD

Data for Rep 1

1. Fit growth curves to identify ΔLPD

Data for Rep 1

1. Fit growth curves to identify ΔLPD

Data for Rep 2

1. Fit growth curves to identify ΔLPD

Data for Rep 1 & 2

1. Fit growth curves to identify ΔLPD

Gompertz Predicted Fit

1. Fit growth curves to identify ΔLPD

95% CI of model fit for n=2

1. Fit growth curves to identify ΔLPD

Inflection point is 33.7 hours

2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]


2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]


2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]


2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]


2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]


2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]

2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]

2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]

2. Calculate ΔLPD

\[ ΔLPD = T_{\text{IP 1}} - T_{\text{IP 2}} \]

2. Calculate ΔLPD

\[ 90\% \text{ viable cell reduction } \approx 11.9 \ \text{hours} \;\Delta \ \text{LPD} \ \]


3. ΔLPD & Cell Reduction Estimation

3. ΔLPD & Cell Reduction Estimation

3. ΔLPD & Cell Reduction Estimation

3. ΔLPD & Cell Reduction Estimation

3. ΔLPD & Cell Reduction Estimation

3. ΔLPD & Cell Reduction Estimation

3. ΔLPD & Cell Reduction Estimation

Conclusion

  • Minimal difference between UV-A and no UV-A treatments
    • UV-A (30 min) and GPE dose insufficient for photoactivation effects

Future Directions

  • Future work:
    • Increase UV-A exposure duration (30 → 60 min)
    • Test alternative photosensitizing co-treatments (e.g., Vitamin B–based formulations)
  • Calibration improvement:
    • Expand viability range (25–90%)
    • Improve ΔLPD model resolution and reduce extrapolation error

Thank you

Refrences 1

  1. Kaipia, R., Dukovska‐Popovska, I., & Loikkanen, L. (2013). Creating sustainable fresh food supply chains through waste reduction. International journal of physical distribution & logistics management, 43(3), 262-276

  2. Reynolds, C., Boulding, A., Pollock, H., Sweet, N., Ruiz, J., & Draeger de Teran, T. (2020). Halving Food Loss and Waste in the EU by 2030: the major steps needed to accelerate progress.

  3. Cheung N, Tian L, Liu X, Li X. The Destructive Fungal Pathogen Botrytis cinerea-Insights from Genes Studied with Mutant Analysis. Pathogens. 2020 Nov 7;9(11):923. doi: 10.3390/pathogens9110923. PMID: 33171745; PMCID: PMC7695001.

Refrences 2

  1. Pintye, A., Bacsó, R., & Kovács, G. M. (2024). Trans-kingdom fungal pathogens infecting both plants and humans, and the problem of azole fungicide resistance. Frontiers in Microbiology, 15, 1354757.

  2. Berenguer, C. V., Andrade, C., Pereira, J. A., Perestrelo, R., & Câmara, J. S. (2022). Current challenges in the sustainable valorisation of agri-food wastes: a review. Processes, 11(1), 20.

Refrences 3

  1. Ganapathy, D., Siddiqui, Y., Ahmad, K., Adzmi, F., & Ling, K. L. (2021). Alterations in mycelial morphology and flow cytometry assessment of membrane integrity of Ganoderma boninense stressed by phenolic compounds. Biology, 10(9), 930

  2. Wang, Q., de Oliveira, E. F., Alborzi, S., Bastarrachea, L. J., & Tikekar, R. V. (2017). On mechanism behind UV-A light enhanced antibacterial activity of gallic acid and propyl gallate against Escherichia coli O157: H7. Scientific Reports, 7(1), 8325.