Ec 2014 07 introducing nutritional criteria in potato breeding programs

Page 1

Food Security and Nu00on

The 19th Triennial Conference Brussels, 6 – 11 July 2014

Exploring biodiversity to introduce nutri0onal quality criteria in potato breeding program Piñeros C., Peña C.B., Del Castillo S., Rodríguez E., Restrepo P., Kubow S., Sabally, K., Kushalappa A., Mosquera-Vásquez T.

Project undertaken with the financial support of the Interna7onal Development Research Centre (IDRC), www.idrc.ca, and the Government of Canada, provided through the Department of Foreign Affairs, Trade and Development Canada (DFATD), Canada, www.interna7onal.gc.ca


Content 1.  2.  3.  4.

Introduction Objective General methodology Results 4.1. Nutritional status of the population 4.2. Proximal analysis 4.3. Functional food analysis 4.4. Potato breeding program 5. Conclusions and perspectives


1. Introduction


Where are we working?


Working with Andean communities

Pasto


Why are we working in nutritional quality? v Nariño is the second undernourished province in Colombia. v High percentage of food insecurity. v 21.5% of the population younger than five years suffers malnutrition. v There is not accurate data to design public policies about food security and nutrition. v Breeding programs have not considered nutritional quality criteria for selecting new cultivars. v Nariño is a potato biodiversity center.


Food security is built on three pillars We have worked on:

1. Food use: appropriate use based on knowledge of basic nutrition and care, as well as adequate water and sanitation.

v Nutritional status of the population. v Analysis of potato intake.

2. Food availability: sufficient quantities of food available on a consistent basis.

v  Analysis of nutritional quality for potato tubers. v  Producing tuber seeds of good quality for potato production.

3. Food access: having sufficient resources to obtain appropriate foods for a nutritious diet.

v  Delivering new potato cultivars, with high yield and reducing costs.


2. Objective

Improve food security in indigenous communities by selection of potato cultivars with high yield and nutritional qualities to improve their daily diet.

Photo: Ernesto Rodríguez


3. General methodology Social study Nutritional analysis for population

Advanced technologies Nutritional quality of potatoes

Food consumption pattern

Biochemical analysis

Nutritional status of population

•  Micronutrientes •  Macronutrients •  Polyphenols

Functional analysis Potato breeding program New potato cultivars with better nutritional quality

•  Metabolomics: metabolites •  Biotransformation and bioaccessibility


4. Results


4.1. Nutritional status of the population Percentage of boys and girls under nutritional standards

Nutritional situation by anthropometry Stunting Overweight Obesity Underweight

Boys

Girls

41.6 23.4 18.0 3.1

30.8 44.4 7.0 1.8

Percentage of the population with deficient intake Age groups Under 5 From 5 to 12 years Adults Total

Calories

Proteins

56.6 73.8 88 72.9

15.8 33.2 85.1 41.9

Fe 21.8 26.1 42.7 29.0

Minerals Zn 81.6 91.0 99.8 91.1

Ca 50.8 92.1 91.9 82.7

Vitamins A C 45.7 5.9 72.5 17.3 87.9 42.7 70.4 20.8


Samples preparation for the analysis Planting and harvesting

1 Grind and pulverization

6

Cooking according to the tuber size

Clasification and washing

3

2

Cutting in slides

Lyophilization

4 5


4.2. Proximal analysis: Macronutrients content in Solanum phureja genotypes 25"

Clara%Peña% 20"

CONTENT"mg/100g"

Phureja collec7on Phu$collec)on$ Advanced clones Advanced$ clones$ Commercial Commercial$ clones$ cul7vars

15"

10"

5"

0" Dry"Ma+er""

Protein"

Ash"

Fat"


4.2. Proximal analysis micronutrients: mineral content in Solanum phureja genotypes 25

a

Content mg/kg DW

20

a

b

Phureja collec7on Advanced clones Advanced clones Commercial Commercial clones cul7vars

15

Phu collec7on

a

10

a

5

0

Fe

Mn

Cu

Zn


4.3. Phenolic compounds in Solanum tuberosum group Phureja Clara Piñeros

Chromatograms at 320 nm of two potato extracts ChA

Cripto-­‐ChA Neo-­‐ChA

CaA

Chlorogenic acid (ChA) Crypto-­‐ChA Neo-­‐ChA Caffeic acid (CaA)

Col 23 Col 112

Dionex Ultimate 3000 UHPLC system (Thermo Scientific corp) coupled to diode array detector


Chlorogenic acid content in Solanum tuberosum group Phureja

Genotypes Â


4.3. Functional food analysis Materials and methods Metabolite extraction & LC-MS analysis

ü  Lyophilized potato flesh ü  Eight advanced clones

ground in liquid N

Liyao-­‐Ji Kushalappa A.

60% methanol, 0.1% formic acid

Supernatant filtered and injected

LC-ESI-LTQ Orbitrap, Thermo Fisher, Waltham, MA, USA High resolution hybrid mass spectrometer system


Functional food analysis Materials and methods

v

Identification and classification of metabolites ü  Accurate mass error <5 ppm ü  MS/MS fragmentation pattern ü  Classified based on their chemical groups

v  Statistical analysis ü  ANOVA will be done to identify significant metabolites.

v  Identification of functional foods ü  Metabolites were searched for functional foods properties in the literature.


Preliminary results v 302 metabolites were detected in cooked potatoes in eight genotypes. v 99 were found to have beneficial roles in human health: ü Anti-cancer ü Anti-HIV ü Anti-hypertension ü Anti-inflammatory ü Anti-malarial ü Antimicrobial ü Anti-diabetic


Metabolites chemical groups Important chemical groups Glycerolipids 1% Pesticides 1% Sulfur Acids 2%

Terpenoids 11%

Sterol Lipids 1% Prenol Lipids 3%

Alkaloids Amino acid 6% 4%

Polyketides 5%

Carbohydrates 3% Carboxylic Acids 2% Fatty acids and fatty acyls 20%

Phenylpropanoids 4% Nucleic acids 4% Hydrocarbons 4% Heterocyclic Compounds 9%

Glycerophospholipids 8%

Flavonoids 10%


Relative intensity of some compounds in S. tuberosum group Phureja Relative intensity (* 100,000)

Phenylpropanoids: chlorogenate

Health benefits: antioxiant, anti-diabetic and anti-lipidemic effects. (Bouayed et al., 2007; Shafi and Tabassum, 2013)

Relative intensity (* 100,000)

Flavonoids: Dihydrokaempferol 1. 4 1. 2 1

Health benefits: antimicrobial

0. 8 0. 6

(Malterud et al., 1985)

0. 4 0. 2 0

CA04 CA09 CA50 CA51 CA52 CA59 CA63 CA64


Limitations in identifying the health benefits of polyphenolics Stan Kubow

1. Most of the studies are in vitro studies. Pure polyphenolic compound as found in foods.

Variation in polyphenolic structure and absorption in the human gut.

2.  Lack of fundamental knowledge regarding bioactive polyphenols formed in the human gut.

Saura-Calixto et al., 2007


Pancreatic solution

Base solution

Acid solution

Pump

pH probe

Control panel Water bath

1 Computer

Food

Stomach

2 Small Intestine

3

4

Ascending Transverse colon colon

5 Descending colon

Nitrogen cylinder

Effluent

• 1 g/L arabinolactan • 2 g/L pectin • 1 g/L xylan GI • 3 g/L starch food • 0.4 g/L glucose • 3 g/L yeast extracts • 1 g/L peptone • 4 g/L mucin • 0.5 g/L cystein powders

Step 1: Inoculation Fecal slurry from the fecal samples of five volunteers Step 2: Stabilization period (two weeks)


Pancreatic solution

Base solution

Acid solution

Pump

pH probe

Control panel Water bath

1 Computer

Food

Stomach

GI food + Treatment /Potato

Feeding protocol: 3 times/24 hours

2 Small Intestine

3

4

Ascending Transverse colon colon

Sampling T= 0 hours T= 8 hours T= 24 hours

5 Descending colon

Nitrogen cylinder

Effluent

Analyzed by LC-­‐MS TOF Liquid chromatography coupled to time-of-flight mass spectrometry.


Transformation of ingested polyphenols Sample taken from the stomach analyzed for ferulic acid- (trace)

Samples from the small intestine analyze for 3-(3-hydroxyphenyl)-propionic acid – (x)

Sample from stomach analyzed for chlorogenic acid- (xx)


Transformation of ingested polyphenols Chlorogenic acid conversion to 3-­‐phenylpriopionic acid Colombian Cultivar - 24hr

Small

Compound

m/z (-ve)

Stomach

Intestine

Colon

Colon

Colon

Chlorogenic Acid

353.088

xx

xx

x

TRACE

TRACE

CONVERTED

Neochlorogenic acid

353.088

x

x

xx

x

0

CONVERTED

Cryptochlorogenic acid

353.088

x

x

xx

TRACE

TRACE

CONVERTED

Caffeic acid 179.033 3-Hydroxyphenylpropionic acid (3-HPP) 165.056

x

xx

xx

xx

x

CONVERTED

x

xx

xx

xx

xx

FINAL METABOLITE

Ascending Transverse Descending Possible metabolic fate

3-Hydroxyphenylacetic acid

151.04

TRACE

TRACE

x

xx

xx

FINAL METABOLITE

3-Phenylpropionic acid

149.061

-

-

-

xx

xxx

FINAL METABOLITE


Chlorogenic acids

Pathway of ingested polyphenols

Quinic acid

Caffeic acid

Dihydrocaffeic acid

Syringic acid

Ferulic acid

Vanilic acid

Dihydroferulic acid

Benzoic acid

3-­‐Hydroxyphenylpropionic acid (3-­‐HPP) 3-­‐hydroxy-­‐benzoic acid

Ru7n 3-­‐Phenylpropionic acid Querce7n

3,4-­‐dihydroxyphenylace7c acid

3-­‐hydroxyphenylace7c acid

Protocatechuic acid


4.4. Moving results to field Ernesto Rodríguez

Participative research for selecting new potato cultivars Evaluation was carried out in eight localities during four crop cycles.


Some traits evaluated in advanced potato clones 41.000

Yield Kg/ha

39.000 37.000 35.000 33.000 31.000

Col.

Gal.

4

9

50

51

52

59

63

64

Code of genotypes 4"

Sem."A"

Frying quality

3,5"

Sem."2"

3" 2,5" 2"

Scale for the evalua7on

1,5" 1" 0,5" 0"

Col.

Gal.

4

9

50 51 52 Code of genotypes

59

63

64

1

2

3

4

5


Cultivar code 59

New cultivars for linking agriculture to nutrition Cultivar code 64 Vegetative period: 120 das; Yield: 37.3 ton/ha, Dry matter: 23.8; Frying quality: 2.7

Cultivar code 4

Vegetative period: 120 das; Yield: 32.9 ton/ha, Dry matter 25; Frying quality: 1.7

Vegetative period: 120 das; yield: 37.1 ton/ha, dry matter: 24.7; frying quality: 2.5

das: days after sowing


5. Conclusions and perspectives ü  Variability in the content of macronutrients, micronutrients and functional foods were found in potato. This variability allows making pre-selection and selection of advanced breeding clones for new cultivars. ü  Nutritional quality criteria were introduced in the potato breeding program, in order to strengthen the link agriculture-nutrition-health. ü  To measure the incidence of these new cultivars in iron and zinc assimilation for children under five. ü  To find genetic associations using data from GBS and 2bRAD.


Acknowledges

Contract to access gene7c resources 53


Thanks for your attention


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