Exploring climate adaptations in chile peppers (Capsicum annuum) of

Southern Mexico

Vivian Bernau6 March 2015

Horticulture and Crop Science Colloquium

MS Proposal

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Outline

• Overview of heat and drought stress

• Capsicum annuum: study site and germplasm

• Objective 1: Estimating drought tolerance with PET modeling

• Objective 2: Assessing phenotypic responses to drought and heat stress – Short-term heat and drought stress in seedlings

– Long-term accumulated drought stress

• Objective 3: Genome Wide Association Study

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Abiotic Stress and Plants

TEMPERATURE STRESS

• Denature proteins

• Disrupt membrane lipids

• Inactivate chloroplast enzymes

• Speed up development

• Increase transpiration

WATER STRESS

• Halt growth

• Stimulate root growth

• Foliage wilts

• Stomata close

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3(Chaves et al. 2001; Wahid et al. 2007)

Drought Resistance

Drought Avoidance

Expressed in the absence of drought

• Enhanced water uptake

• Reduced water loss

• Stomatal structure

Drought Tolerance

Triggered by drought

• Osmotic adjustment

• Antioxidant capacity

• Desiccation tolerance

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Capsicum Center of Diversity

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(Pickersgill 1971)

Capsicum Center of Diversity

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(Kraft et al. 2014)

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CAPS Chile Collections 2013-2014

Collection gradients:

geographic, ethnic, domestication, climatic, altitudinal

Collections by altitude

(masl)

Temperature distribution of collection sitesMean temperatureMaximum monthly temperatureMinimum monthly temperature(Hijmans et al. 2005)

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Tem

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*10

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Distribution of precipitation at collection sites

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(Hijmans et al. 2005)

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Pre

cip

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ion

(m

m)

Bioclimatic Variables

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Bioclimatic Variablesbio1 Annual Mean Temperaturebio2 Mean Diurnal Rangebio4 Temperature Seasonalitybio5 Max Temperature of Warmest Monthbio6 Min Temperature of Coldest Monthbio8 Mean Temperature of Wettest Quarterbio9 Mean Temperature of Driest Quarterbio10 Mean Temperature of Warmest Quarterbio11 Mean Temperature of Coldest Quarterbio12 Annual Precipitationbio13 Precipitation of Wettest Monthbio14 Precipitation of Driest Monthbio15 Precipitation Seasonalitybio16 Precipitation of Wettest Quarterbio17 Precipitation of Driest Quarterbio18 Precipitation of Warmest Quarterbio19 Precipitation of Coldest Quarteralt Altitude

(Hijmans et al. 2005)

Principle Component Analysis

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• Component 1: temperature and altitude (bio1, bio5-11, alt)• Component 2: precipitation seasonality, precipitation of driest month and

driest quarter (bio14, bio15, bio17)

Western Coast

Central Valleys

Coast

Yucatan

OBJECTIVE 1

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Estimating drought tolerance with PET modeling

Objective 1: Predicting drought tolerance

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Assessed faba bean germplasm for drought avoidance traits based on precipitation at point of origin.

Introduced PET modeling to distinguish environmental variability

Objective 1: Predicting drought tolerance

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Calculating Drought Indices (DIs)

Potential Evapotranspiration (PET)• Thornthwaite Method -- f(temperature + radiation)

• Hamon Method -- f(temperature)

Objective 1: Predicting drought tolerance

Hypotheses:

• Significant differences between DIs of populations which clustered together in PCA.

• Thornthwaite DI will correlate with performance in the long-term phenotyping study

• Hamon DI will correlate with performance inthe short-term phenotyping study

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Methodology and Analysis:

• Conducted in R

• Machine learning analysis

OBJECTIVE 2

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Assessing phenotypic responses to drought and heat stress

Seed Increase from Original Collections

1. Seed for phenotyping studies– Maternal environmental effects limited

2. Tissue for DNA extraction

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Lines

Plants in Greenhouse

(194)

Accessions

Collected Seed

(105)

Populations

Landrace

(85)

Farm

(59)

Location

(28)

Añil

Farm 1Costeño

RojoPlant 1 Line 1

Plant 2

Line 1

Line 2

CosteñoAmarillo

Plant 1

Line 1

Line 2

Farm 2

Rep

1

Objective 2: Assessing Phenotypes

Short-term assessment (seedling)

• Conducted in growth chambers

• Split-split plot design

• Temperature x Water x Line (factorial)– 25/23˚C (control), 35/33˚C (stress)

– Well watered, cease watering for 10 days

– Line (105 accessions)

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2 – WW 5 – WW 3 – WW 4 – WW

4 – NoW 1 – NoW 5 – NoW 3 – NoW

3 – WW 3 – NoW 1 – WW 2 – WW

5– NoW 2 – NoW 4 – NoW 5 – WW

1 – WW 4 – WW 2 – NoW 1 – NoW

Objective 2: Assessing Phenotypes

Short-term assessment (seedling)

• Conducted in growth chambers

• Split-split plot design

• Temperature x Water x Line (factorial)– 25/23˚C (control), 35/33˚C (stress)

– Well watered, cease watering for 10 days

– Line (105 accessions)

• Measure before and after stress treatment– Gas exchange

– Stomatal conductance

• Record final plant height, stem diameter, and aboveground dry matter

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Rep

2

Rep

1

Objective 2: Assessing Phenotypes

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2 – WW 5 – WW 3 – WW 4 – WW

4 – Stress 1 – Stress 5 – Stress 3 – Stress

3 – WW 3 – Stress 1 – WW 2 – WW

5– Stress 2 – Stress 4 – Stress 5 – WW

1 – WW 4 – WW 2 – Stress 1 – Stress

Long-term assessment (full life-cycle)• Split-plot design

• Factorial design with two factors– Line (105 accessions)

– Water (applied by drip irrigation)• Field capacity (control)

• 30% of field capacity (preliminary trial underway)

Objective 2: Assessing Phenotypes

Long-term assessment (full life-cycle)• Split-plot design

• Factorial design with two factors– Line (105 accessions)

– Water (applied by drip irrigation)• Field capacity (control)

• 30% of field capacity (preliminary trial underway)

• Measured periodically– Gas exchange

– Stomatal conductance

– Relative leaf water content (RWC%)

• Recorded fitness characteristics– Flowers produced

– Fruit set

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Objective 2: Assessing Phenotypes

• Quantitative data analyzed by ANOVA

Hypotheses:

• Lines originating from areas with higher precipitation and lower temperatures will have higher susceptibility to drought and heat stress

• The combination of heat and drought stress will compound plant responses

• Some lines will be resistant to short-term stress but not long-term stress and vice versa.

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OBJECTIVE 3

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Genotyping-by-Sequencing & Genome-Wide Association Study

Objective 3: GBS & GWAS

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Genotyping-by-Sequencing (GBS)

• Collect tissue samples

• Genomic DNA obtained using Qiagen DNeasy Plant Mini Kits

• Sequencing and SNP Calling at Cornell University

Genome-Wide Association Study (GWAS)

GAPIT (Lipka et al. 2012) Many genes w/ small effects

MLMM (Segura et a. 2012) Fewer genes w/ big effects

Objective 3: GBS & GWAS

Hypotheses:

• Using mixed linear models, we will identify DNA sequence variation(s) associated with:– short-term drought resistance

– short-term heat stress resistance

– long-term drought resistance

• Short-term and long-term stress resistance will be associated with different loci.

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Overarching Implications

• Improved understanding of genome

• Provide a genetic basis for local adaptation

• Insight for future conservation efforts

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Acknowledgements

• Advisors– Dr. Leah McHale

– Dr. Kristin Mercer

• Committee Members– Dr. Peter Curtis

– Dr. Lev Jardón Barbolla

• CAPS PlantDom Team– Nathan Taitano

– Rachel Capouya

• Jim Vent

• Mercer Lab

• McHale Lab

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ReferencesChaves, M M. 2002. “How Plants Cope with Water Stress in the Field? Photosynthesis and Growth.” Annals of Botany 89 (7): 907–16. doi:10.1093/aob/mcf105.

Cortés, Andrés J, Fredy a Monserrate, Julián Ramírez-Villegas, Santiago Madriñán, and Matthew W Blair. 2013. “Drought Tolerance in Wild Plant Populations: The Case of Common Beans (Phaseolus Vulgaris L.).” PloS One 8 (5): e62898–e62898. doi:10.1371/journal.pone.0062898.

Khazaei, Hamid, Kenneth Street, Abdallah Bari, Michael Mackay, and Frederick L. Stoddard. 2013. “The FIGS (Focused Identification of Germplasm Strategy) Approach Identifies Traits Related to Drought Adaptation in Vicia Faba Genetic Resources.” PLoS ONE 8 (5): e63107. doi:10.1371/journal.pone.0063107.

Kraft, Kraig H., Cecil H Brown, Gary Paul Nabhan, Eike Luedeling, José De Jesús Luna Ruiz, Geo Coppensd’Eeckenbrugge, Robert J. Hijmans, and Paul Gepts. 2014. “Multiple Lines of Evidence for the Origin of Domesticated Chili Pepper, Capsicum Annuum, in Mexico.” Proceedings of the National Academy of Sciences 111 (17): 1–6. doi:10.1073/pnas.1308933111.

Lipka, Alexander E., Feng Tian, Qishan Wang, Jason Peiffer, Meng Li, Peter J. Bradbury, Michael A. Gore, Edward S. Buckler, and Zhiwu Zhang. 2012. “GAPIT: Genome Association and Prediction Integrated Tool.” Bioinformatics 28 (18): 2397–99. doi:10.1093/bioinformatics/bts444.

Pickersgill, Barbara. 1971. “Relationships between Weedy and Cultivated Forms in Some Species of Chili Peppers (Genus Capsicum.” Evolution 25 (4): 683–91.

Segura, Vincent, Bjarni J. Vilhjálmsson, Alexander Platt, Arthur Korte, Ümit Seren, Quan Long, and Magnus Nordborg. 2012. “An Efficient Multi-Locus Mixed-Model Approach for Genome-Wide Association Studies in Structured Populations.” Nature Genetics 44 (7): 825–30. doi:10.1038/ng.2314.

Thornthwaite, C W, and J R Mather. 1955. “The Water Balance.” Publications in Climatology 8 (1): 1–104.

Wahid, A., S. Gelani, M. Ashraf, and M. R. Foolad. 2007. “Heat Tolerance in Plants: An Overview.” Environmental and Experimental Botany 61 (3): 199–223. doi:10.1016/j.envexpbot.2007.05.011.

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