Akshara Chandrabalan 2020 NSERC USRA Bioresource Engineering, Supervisor Dr. Prasher 2020 Undergraduate Student Research Awards Predicting Biochar Sorption Capacities Using VIEW THE POSTERS Artificial Neural Networks Poster Presentation Event Breakout Room 5 Daniel Moses 2020 NSERC USRA (ZOOM) SEPTEMBER 17, 2020 @ 4pm Parasitology, Supervisor Dr. Salavati In search of the cryptic motif VI on trypanosomatid RNA editing ligases Richard Boivin 2020 NSERC USRA Antoine Gaudreau 2020 NSERC USRA 1 Natural Resource Sciences, Supervisor Dr. Humphries Bioresource Engineering, Supervisor Dr. Akbarzadeh Muriel Wong Min 2020 AES Brown Martlet USRA Evaluating regional patterns of traditional wildlife 3D Printed Agricultural Wastes for Advanced Food Science & Ag. Chemistry, Supervisor Dr. Karboune harvest in northern Quebec Biocomposites A Database Platform for The Selection of Appropriate Fat Substitutes as "Natural" Katerina Lazaris 2020 NSERC USRA Rowena Groeneveld 2020 NSERC USRA Food Ingredients Animal Science, Supervisor Dr. Bordignon Parasitology, Supervisor Dr. Beech Review: Methods to Improve Oocyte Competence and Modelling of a ligand gated ion channel from embryo Development in Prepubertal Animals to Caenorhabditis elegans experimentally shown to bind Liana Fortin-Hamel 2020 NSERC USRA Accerlerate genetic Gain 6 dopamine Parasitology, Supervisor Dr. Scott 2 Trisha Sackey 2020 NSERC USRA Effects of maternal nematode infection on spatial Food Science & Ag.Chemistry, Supervisor Dr. George Alex Zvezdin 2020 NSERC USRA learning and memory of young mouse pups Natural Resource Sciences, Supervisor Dr. Head Genotypic and Phenotypic Profiling of Staphylo- coccus aureus strains isolated from Canadian Impacts of Agricultural Practices on Yellow Perch Xavier Godin 2020 NSERC USRA Dairy Cattle for Antimicrobial Resistance Spawning Sites in Lac-Saint Pierre Québec Plant Science, Supervisor Dr. Seguin Increasing the production and utilization of alfalfa- based forage mixtures in Canada Sreedurga Cherukamalli 2020 NSERC USRA 7 Catherine Bergeron 2020 NSERC USRA Bioresource Engineering, Supervisor Dr. Adamowski Tatiana Rayvich 2020 NSERC USRA Plant Science, Supervisor Dr. Charron Quantification of participatory sociohydrological Bioresource Engineering, Supervisor Dr. Lefsrud Differentiation of Garlic cultivars Growing in Quebec model of the Lake Atitlan watershed in Guatemala A comparison of cost and yield of lactuca sativa Using Phenotypic and Genotypic Traits grown on porous concrete as a function of concrete Bridget O’Brien 2020 NSERC USRA use and LED lights Lakshitaa Lugun 2020 NSERC USRA Animal Science, Supervisor Dr. Ronholm Bioresource Engineering, Supervisor Dr. Zhiming Qi Keepin' it Fresh: A Review on the Application of A comprehensive literature review on the evaluation Nanopillars for Meat Packaging Joel Harms 2020 NSERC USRA of crop models' performance 5 Bioresource Engineering, Supervisor Dr. Adamowski 3 Jonathan Sangiovanni 2020 NSERC USRA Coupling of socioeconomic system dynamic models with Benjamin Vonniessen 2020 AES Brown Martlet USRA Natural Resource Sciences, Supervisor Dr. Head the SWAT+ hydrological model for accessible Integrated Natural Resource Sciences, Supervisor Dr. Lyle Whyte Water Resource Management model development Transcriptomic Profile of BaP in Japanese Quail and Mining archeal MAGs and SAGs for archaeocin double-creted Cormorant Alexandra Kerasias 2020 NSER USRA production related sequences School of Human Nutrition, Supervisor Dr. Kubow Gut microbial biotransformation and first pass metabolism of anthocyanins from purple potato digests: A 4 metabolomic approach to identify bioavailable REGISTER NOW microbial anthocyanin metabolites https://www.mcgill.ca/macdonald/research Yijiang Liu 2020 AES RUDI DALLENBACH USRA /seminars/undergrads Animal Science, Supervisor Dr. Duggavathi Identification of transcriptional mechanisms of H3K4me3 in the regulation of ovulatory genes Quantification of participatory sociohydrological model of the Lake Atitlán watershed in Guatemala Cherukumalli, S., Malard, J.J., Adamowski, J.F. Department of Bioresource Engineering, McGill University, Sainte-Anne-de-Bellevue, QC, Canada

Abstract A participatory sociohydrological model was built of the Lake Atitlán watershed in Guatemala to address the issue of nutrient pollution and cyanobacterial + 3 Results Expected blooms. Part of the model was quantified using data-based and conjectural Preliminary findings, however, more quantification remains to be done: vegetable approaches. Preliminary findings from the model include: vegetable production forms Expected supplied Agricultural land submodule: balancing loops in land required, quantity, and price; tourism and cyanobacterial price quantity of ▪ Vegetable production forms balancing loop between expected and + blooms form a balancing loop; and lake volume is very sensitive to hydraulic gradient. vegetables actual implementation (Fig. 1). Land required, quantity, and price fluctuate then stabilize over time (Fig. 2). Actual + vegetable Land required for vegetable production Vegetable supplied quantity Vegetable price 1 Background Expected land price 200 3 M 1.5 ▪ Lake Atitlán in Guatemala is a popular tourist destination, source of water and for vegetable livelihood for surrounding indigenous community. - production 150 2.25 M 1.125

▪ Nutrient pollution led to toxic cyanobacterial blooms causing decline in tourism, Actual supplied 100 1.5 M .75 Hectares

+ Kilograms fishing incomes, and locals’ quality of life. quantity of

50 USDkilogram per vegetables 750,000 .375 ▪ System dynamics is approach to understand the complex, nonlinear behaviours1 2 and Actual land required feedback loop structures of water resources management over time. + 0 0 0 for vegetable 0 24 48 72 96 120 0 24 48 72 96 120 0 24 48 72 96 120 ▪ Participatory modelling, involving stakeholder engagement and group model- Deaths production Time (Month) Time (Month) Time (Month) Expected Actual building, allows for combined3 4knowledge of locals, NGOs, government, etc. Expected Actual Expected Actual Figure 2. Ten-year trends for agricultural land submodule variables. See Figure 1. Objective: Quantify sociohydrological model of Lake Atitlán watershed by writing Population + equations to relate variables. Persons on + Tourism and cyanobacteria submodule: Births sewer network ▪ Tourists and bloom occurrence form balancing loop (Fig. 1). Tourism Tourists causes pollution and probability of a bloom to increase, causing tourism 2 Methodology to then decrease (Fig. 3). + - ▪ Literature review to quantify relationships in e.g. Total agricultural land ▪ Hydraulic gradient of lake (influences seepage) is sensitive parameter qualitative model. required (conjectural Inflow Tourist memory affecting lake volume and total nutrients. approach): of P of bloom ▪ Modelling software used: VENSIM PLE 8.0.9. ▪ Assume subsistence ▪ Each variable interaction required different + agriculture, vegetable, and Total phosphorus approach to quantify. Examples are below. Figure 3. Fifty-year coffee production make up in lake Cyanobacterial trends for tourism e.g. Probability of cyanobacterial bloom (data- majority. bloom occurrence and cyanobacteria based approach): submodule ▪ Land priority given to Outflow + variables. See Figure ▪ Bayesian inference calibration of equation subsistence agriculture. of P Probability of 1. based on observed phosphorus and bloom Remaining land distributed cyanobacterial data: + between vegetable and bloom coffee proportional to ퟏ + Probability of cyanobacterial bloom = − ퟑ.ퟗퟐퟒ+ퟒ.ퟏퟏퟗ 풍풐품 푷 market prices. Seepage ퟏ + ⅇ Concentration + ▪ Land for subsistence based + of P in lake Conclusion e.g. Tourist memory of bloom (conjectural Hydraulic approach): on percent population living Two balancing loops (vegetable production land requirement and tourism gradient on subsistence farms. and cyanobacteria) and one unexpected effect of hydraulic gradient found. ▪ Memory based on whether a bloom occurred in last 60 months. SMOOTH function takes time Figure 1. Simplified agricultural land (above) and tourism and cyanobacteria (below) In the future: ▪ Land for coffee and vegetable submodules of main model. Arrows indicate causal relationships; plus or minus indicate averages and represents expectations: production based primarily positive or negative relationships. Both are balancing loops. ▪ Determine how much nutrient runoff to lake is from agricultural land. on intersection of supply and ▪ Finish quantification then validate using data and stakeholder input. Acknowledgement: References: 푇표푢푟푖푠푡 푚푒푚표푟푦 표푓 푏푙표표푚 = 푺푴푶푶푻푯푰(푩풍풐풐풎, ퟔퟎ, ퟎ) demand curves. This research was supported by funding from the 1. Forrester, J. (1995). 3. Malard, J.J., et al. (2015). ▪ Established model can be used as a policy decision-making tool. Natural Sciences and Engineering Research Council 2. Winz, I. et al. (2008). 4. Mostashari & Sussman ▪ Goal is to facilitate local control of policy discourse. of Canada. (2004). Keepin’ it Fresh: A Review on the Application of Nanopillars for Meat Packaging Bridget O’Briena & Jennifer Ronholma a Department of Food Science and Agricultural Chemistry, McGill University

Introduction The Bactericidal Mechanism Bacterial Cell Type and the Effect on the • The physical rupturing of bacterial cells is caused by pillar-shaped Pogodin et al. (2013): The Biophysical Model Bactericidal Mechanism nanostructures, referred to as nanopillars – a structure first idenfied • Many studies show that Gram-negave bacteria are more 1,2 on the surface of insect wings. suscepble to rupture than Gram-posive.2,7,11-14

• The discovery of naturally occurring nanopillars prompted the • Diu et al. (2014) demonstrated that mole bacteria are more fabricaon of surfaces, mimicking ones found in nature, to prevent suscepble to rupture than non-mole bacteria, regardless of bacterial cell growth on meat products and potenal foodborne their cell wall composion. infecons.3

• Microbial contaminaon within food processing has severe economic ramificaons, is a common eological agent in food-borne illness, and Conclusions is a major concern within public health safety and food surveillance.4 Fig 2. Nanopillars do not pierce bacterial cells, but rather they stretch the membrane between them to the point of rupture.2 • Nanopillars have the potential to be used in food packaging to reduce the amount of foodborne infections, extend shelf life, • This review highlights early discoveries of nanopillars on naturally Jenkins et al. (2020): Nanopillars’ anbacterial acvies may be mediated by oxidave stress occurring surfaces, as well as proposed mechanisms describing how and mitigate the economic ramifications associated with food nanopillars rupture bacterial cells upon contact. In addion, we review recall. how the mechanism of acon differs between cell type and physical Fig 3. • A common finding in the literature is the increased properes of the nanopillars used. • The levels of the endogenous reactive oxygen susceptibility of Gram-negative bacteria to the bactericidal species (ROS), H O , in S. aureus and E. coli were 2 2 action of nanopillars. slightly higher (1.6- and 3.8- fold increases, respectively) on nanotextured surfaces compared • The attachment, killing, and dead cell detachment is dictated to flat control surfaces.8 Naturally Occurring Bactericidal Surfaces by the bacterial species of interest and the nanopillar surface topology. Taken together, prior research suggests that the • ROS damage DNA, lipids, and proteins, thus, results will vary when these two parameters are altered. increased H2O2 may induce changes in bacterial cell envelope morphology, potentially resulting in • Other results may need to be further investigated including membrane rupture in cell death.8 the susceptibility of motile cells and how the properties of fabricated nanopillars may affect the bactericidal action. Nanopillar Properes and the Effect on The Bactericidal Mechanism References 1. Ivanova EP, Hasan J, Webb HK, et al. Natural Bactericidal Surfaces: Mechanical Rupture of Pseudomonas aeruginosa Cells by Cicada Wings. Small. 2012;8(16):2489-2494. 2. Pogodin S, Hasan J, Baulin VA, et al. Biophysical model of bacterial cell interacons with nanopaerned cicada wing surfaces. Biophys J. 2013;104(4):835-840. 3. Tripathy A, Sen P, Su B, Briscoe WH. Natural and bioinspired nanostructured bactericidal surfaces. Advances in Colloid and Interface Science. 2017;248:85-104. 4. Rawdkuen S, Punbusayakul N, Lee DS. Chapter 17 - Anmicrobial Packaging for Meat Products. In: Barros-Velázquez J, ed. Anmicrobial Food Packaging. San Diego: Academic Press; 2016:229-241. 5. Bandara, C.D. et al. Bactericidal Effects of Natural Nanotopography of Dragonfly Wing on Escherichia coli. ACS Applied Materials & Interfaces 9, 6746-6760 (2017). 6. Green, D.W. et al. High Quality Bioreplicaon of Intricate Nanostructures from a Fragile Gecko Skin Surface with Bactericidal Properes. Scienfic Reports 7, 41023 (2017). 7. Truong, V.K. et al. The suscepbility of Staphylococcus aureus CIP 65.8 and Pseudomonas aeruginosa ATCC 9721 Fig 1. Scanning electronic microscopy images of natural cells to the bactericidal acon of nanostructured Calopteryx haemorrhoidalis damselfly wing surfaces. Appl Microbiol Biotechnol 101, 4683-4690 (2017). bactericidal nanotopographies: (a) dragonfly wing; (b) 8. Jenkins J, Mantell J, Neal C, et al. Anbacterial effects of nanopillar surfaces are mediated by cell impedance, 2,5,6,7 penetraon and inducon of oxidave stress. Nature Communicaons. 2020;11(1):1626. gecko skin; (c) cicada wing; (d) damselfly wing. 9. Dickson MN, Liang EI, Rodriguez LA, Vollereaux N, Yee AF. Nanopaerned polymer surfaces with bactericidal properes. Biointerphases. 2015;10(2):021010. 10. Michalska M, Gambacorta F, Divan R, et al. Tuning anmicrobial properes of biomimec nanopaerned surfaces. Nanoscale. 2018;10(14):6639-6650. 11. Diu T, Faruqui N, Sjöström T, et al. Cicada-inspired cell-instrucve nanopaerned arrays. Scienfic Reports. 2014;4(1):7122. Acknowledgements Fig 4. Short and dense pillars have greater bactericidal Fig 5. Long and sharp pillars are capable of piercing 12. Elbourne A, Crawford RJ, Ivanova EP. Nano-structured anmicrobial surfaces: From nature to synthec effects against E. coli, in comparison to more widely both types of microbial cells regardless of the analogues. Journal of Colloid and Interface Science. 2017;508:603- 616. This project was supported by Natural Sciences and Engineering Research 13. Xue F, Liu J, Guo L, Zhang L, Li Q. Theorecal study on the bactericidal nature of nanopaerned surfaces. Journal spaced pillars.9 species.10 of Theorecal Biology. 2015;385:1-7. Council of Canada (NSERC-USRA) and supervised by Prof. Jennifer 14. Zahir T, Pesek J, Franke S, et al. Model-Driven Controlled Alteraon of Nanopillar Cap Architecture Reveals its Ronholm of Food Science and Agricultural Chemistry. Effects on Bactericidal Acvity. Microorganisms. 2020;8(2). TRANSCRIPTOMIC PROFILE OF BaP IN JAPANESE QUAIL AND DOUBLE-CRESTED CORMORANT Jonathan Sangiovanni1, Jessica Head1 1Faculty of Agricultural and Environmental Sciences, McGill University, Montreal, Quebec, Canada

INTRODUCTION RESULTS DISCUSSION

BACKGROUND Table 1 Analytically determined levels of BaP in dosing solutions and liver tissue of Japanese quail (JQ) and SUMMARY double-crested cormorant (DCCO) Egg injection results for JQ and DCCO are summarized. DCCO experiment had to • Polycyclic aromatic compounds (PACs) are chemical stressors that be performed anew due to significant mortality rates. JQ in sensitivity (mortality); JQ ED9/ED16 Control 0 0 0

apical outcomes. 1 MD 5 25 HD 57 • Previous studies using related chemicals (dioxin-like compounds) • Using ELS exposure and transcriptomics, useful biomarkers can be predicted DCCO and JQ to be equally sensitive based on similar Figure 1 Volcano plots comparing differentially [6] detected for future use in PAC emission regulation. expressed genes in medium and high dose molecular configurations of the aryl hydrocarbon receptor (AHR). treatments Colours represent genes (green: sig. • Therefore, AHR binding may not be a useful predictor/biomarker of downregulated; red: sig. upregulated). Left: medium dose; right: high dose. y-axis: significance (-log10 p- species sensitivity to all PACs in birds METHODS value); x-axis: magnitude of change (log2FC). Table 2 List of 20 differentially expressed genes for JQ high dose group Genes are are listed by decreasing WHAT COMES NEXT? [3,4] |Log2FC|. Green: potential biomarkers; blue: EXPERIMENTAL DESIGN downregulated genes; red: upregulated genes Gene Symbol Gene Name |LogFC| NEXT STEPS 1) Air Cell Injections 2) Incubation 2a) Mid-Incubation 3) Liver extraction 4) RNA sequencing CD247 CD247 molecule, transcript variant X3 6.3283 JQ: 9 days; DCCO: 14 days ORM2 orosomucoid 2 6.0589 Injected Dose (ug/g egg) • DCCO transcriptome analysis: awaiting completed genome annotation. LOC107325440 KIAA1958 homolog 5.5624 Exposure group JQ DCCO(1) DCCO(2) TMPRSS5 transmembrane protease, serine 5 5.0423 Figure 2 Heatmap of top 50 differentially expressed genes • Determine inter-individual variability in BaP response. Low 0.01 0.005* 0.000027 HOXC8 homeobox C8 4.5616 (by adjusted p-value) for JQ exposure groups Control and Medium 0.05* 0.05* 0.00026 SV2B synaptic vesicle glycoprotein 2B 3.5425 • Evaluate potential candidate biomarker genes using qPCR High 0.83 0.5* 0.0027 CAPS2 calcyphosine 2 3.3738 medium dose groups show more similar gene expression GDF15 growth differentiation factor 15 3.2288 *Nominal concentrations patterns compared to significantly dysregulated genes for and dose-response modelling. 37.5C; 60% humidity MRAP melanocortin 2 receptor accessory protein 3.1733 Note: DCCO experiment was repeated due to high high dose treatment. embryomortality LOC107310340 fatty acid-binding protein, adipocyte-like 3.1384 • 2b) Termination 5) Phenotypic Observations amino acid transport 1.967 2.075 Use the data from this study to inform the development of Genome JQ: 14 days;DCCO: 26 days • Growth/development LOC107319120 partitioning defective 6 homolog alpha-like3.1151 • Chemical analysis LOC107323041 aspartate and glycine-rich protein-like 2.9704 amino acid transmembrane transport 1.536 1.961 [7] • Histology Canada funded EcoToxChip project (www.ecoxtoxchip.ca) HRH2 histamine receptor H2 2.8874 monocarboxylic acid transport 1.487 3.529 MF LOC107316020 cystine/glutamate transporter-like 2.7856 response to hypoxia 1.487 0.932 [5] IGFBP1 insulin like growth factor binding protein 1 2.7103 transmembrane transport 1.349 0.696 DATA ANALYSIS CTGF connective tissue growth factor 2.7089 cell proliferation 1.321 0.68 SLC5A5 solute carrier family 5 member 5 2.6777 symporter activity 3.614 1.525 BHLHE40 basic helix-loop-helix family member e40 2.6241 growth factor activity 2.547 1.241 LOC107315857 cytochrome P450 2H1-like 2.6022 amino acid transmembrane transporter… 1.975 1.887 LOC107318671 cytochrome P450 1A4 1.5708 MF monocarboxylic acid transmembrane… 1.851 3.333

oxidoreductase activity, acting on paired… 1.851 1.571

extracellular region 8.166 0.622 Figure 3 (right) Gene set enrichment analysis (GSEA) extracellular space 6.292 0.625

for JQ high dose group Pathway enrichment analysis 3.569 0.556

CC integral to plasma membrane www.galaxy.ecotoxxplorer.ca www.ecotoxxplorer.ca www.ecotoxxplorer.ca performed using Gene Ontology (GO) (MF: molecular plasma membrane 3.171 0.436 function; BP: biological process; CC: cellular proteinaceous extracellular matrix 2.717 0.771 REFERENCES ACKNOWLEDGMENTS Filtration: Low variance, abundance QC: FastQC Data analysis and visualization: component) and KEGG terms. Terms are colour- Glycolysis / Gluconeogenesis 0.900 1.321 [1] Wallace SJ et al. (2020) Environmental Pollution Funders: NSERC (15;20) Heatmap, volcano plot, Drug metabolism - other enzymes 0.900 1.613 Trimming: Trim Galore coded. Left bars: -log10FDR; right bars: the proportion [2] Albers PH (2006) Avian Poulty Biol. Rev. Normalization: log2-counts per million Gene Set Enrichment Analysis (GSEA) Steroid hormone biosynthesis 0.900 1.515 People: Jessica Head, YeonSeon Jeon, Quantification: Kallisto of DEGs involved in a pathway multiplied by 10. GO KEGG [3] Crump D (2020) in preparation Differential expression analysis: EdgeR; terms with FDR < 0.05 included; KEGG terms with Neuroactive ligand-receptor interaction 0.785 0.605 Emily Boulanger, Doug Crump Caffeine metabolism 0.662 4 [4] Farhat A et al. (2020) Environmental Toxicology (|log2FC >1|, adj. p value < 0.05) FDR < 0.25 included. -LOG10 (FDR) Hits/Total x 10 [5] EcoToxXplorer (2020) https://www.ecotoxxplorer.ca/home.xhtml [6] Head J et al. (2008) Environ. Sci. Technol. [7] Basu et al. (2019) Environmental Toxicology and Chemistry