International Center for Tropical Agriculture Advances in breeding and genetics to improve carotenoids content in cassava roots. International Center for Tropical Agriculture (CIAT). Cali, Colombia

Why Cassava A basic staple where both poverty and malnutrition are widespread 70 million people rely on cassava for basic sustenance. Cassava grows in poor marginal soils, where other crops will fail. 2

Why CIAT Located in the center of diversity for the crop: access to in situ germplasm. Holds the largest ex-situ cassava germplasm collection >6,000 accessions. Well-established cassava breeding and genetics programs, and supporting lab facilities. 3

 Sampling and processing protocols Pre-selection based on NIRS Genetics of carotenoids Genetic progress Next steps: the pathway to impact

Some key background work on protocols: Two challenges of root sampling and processing procedures for carotenoids: Variation among roots from the same plant and/or roots from different plants within the same genotype (Ortiz et al., 2011). b) In some cases a drastic variation in levels of pigmentation within a root (following slides). This section describes solutions that have been implemented to overcome some of the problems listed above, and alternative approaches to quantify carotenoids in fresh roots of cassava.

Variation of levels of pigmentation within the root

Variation of levels of pigmentation within the root Source: Peter Kulakow (IITA)

Addressing the issue of root to root variation Former standard procedure: only one root per plant was taken Beginning in 2011: three roots per plant used in carotenoids quantification. Uniform homogenized subsamples taken as follows: Two capsules for Near Infrared Spectroscopy (NIRs) screening Two samples (≈ 80g and 30g) for dry matter content estimation One sample (≈ 100g) for chromameter reading One sample (5g) for carotenoid extraction and quantification in spectrophotometer and HPLC

Food processor used to grind the roots rather than chopping them

Examples of the texture in ground root samples

Minolta Chromameter CR 410 Root sample ground, not chopped

 Sampling and processing protocols Analysis & pre-selection based on NIRS Genetics of carotenoids Genetic progress Next steps: the pathway to impact 

A B C Phytoene = 13.69 ug/g DM GM3736-54 Phytofluene= 7.44 ug/g DM 12 1 = Violaxanthin (5.5) 2 = Antheraxanthin? (7.0) 3 = Unknown (7.2) 4 = Unknown (7.4) 5 = Lutein (8.4) 6 = Phytoene (12.6) 7 = Phytofluene (13.5) 8 = β-cryptoxanthin? (13.8) 9 = Unknown (14.6) 10 = 15-cis-β-carotene? (15.2) 11 = 13-cis-β-carotene (15.7) 12 = All-trans- β-carotene (17.8) 13 = 9-cis-β-carotene (18.6) Phytoene = 13.69 ug/g DM GM3736-54 6 11 λ:286 nm 5 13 B 7 12 Phytofluene= 7.44 ug/g DM 11 5 13 λ:348 nm C 12 All-trans-beta carotenes= 29.47 ug/g DM λ:450 nm 1 2 4 9 8 11 13 3 5 10 2 4 6 8 10 12 Minutes 14 16 18 20 22 24

A B C GM3739-13 Phytoene = 2.15 ug/g DM Phytofluene= 1.39 ug/g DM 1 = Violaxanthin (5.5) 2 = Antheraxanthin? (7.0) 3 = Unknown (7.2) 4 = Unknown (7.4) 5 = Lutein (8.4) 6 = Phytoene (12.6) 7 = Phytofluene (13.5) 8 = β-cryptoxanthin? (13.8) 9 = Unknown (14.6) 10 = 15-cis-β-carotene? (15.2) 11 = 13-cis-β-carotene (15.7) 12 = All-trans- β-carotene (17.8) 13 = 9-cis-β-carotene (18.6) 12 GM3739-13 Phytoene = 2.15 ug/g DM 6 11 13 5 λ:286 nm B 11 12 Phytofluene= 1.39 ug/g DM 7 5 13 λ:348 nm C 12 All-trans-BC= 20.53 ug/g DM 2 11 1 13 4 5 8 10 λ:450 nm 3 9 2 4 6 8 10 12 Minutes 14 16 18 20 22 24

Wet chemistry descriptive statistics (2009-2012 data; N=3419) Missing values Min Max Mean Dry matter content (%) 36 9.8 52 34 HCN Total(ppm) 2621 23.3 3927 792 Total carotenoids (ug/g fresh weight) By spectrophotometer 24 0.24 25 10 By HPLC 271 0.11 26 β-carotene (ug/g FW) 270 0.0 17 6.0 Anteroxanthins (ug/g FW) 279 3.0 0.3 Violaxanthins (ug/g FW) 707 1.6 Luteins (ug/g FW) 277 3.9 0.4 β-Criptoxantihns (ug/g FW) 854 1.1 0.1 15-cis-β carotene (ug/g FW) 705 1.5 13-cis-β carotene (ug/g FW) 3.4 0.9 9-cis- βcarotene (ug/g FW) Phytoen(ug/g FW) 986 18.6 3.6 Phytofluen(ug/g FW) 1564 8.8 1.8 Parameters from Minolta Chromameter l* 1985 53 104 73 a* -3.8 b* 44 127 97

Calibration curves (2009-2011) Dry Matter R2: 0.947 SECV: 1.604 R2 SECV: 0.937 RPD: 4.0 Total HCN R2: 0.812 SECV: 309.0 R2 SECV: 0.766 RPD: 2.1

Total Carotenoids Colorimetry SECV: 1.191 R2 SECV: 0.906 RPD: 3.3 Total Carotenoids HPLC R2: 0.897 SECV: 1.500 R2 SECV: 0.887 RPD: 3.0

Beta Carotene HPLC Provitamin A R2: 0.928 SECV: 0.837 R2 SECV: 0.922 RPD: 3.6 Provitamin A R2: 0.910 SECV: 1.099 R2 SECV: 0.901 RPD: 3.2

Scatter plot of Dry Matter Content NIRS predicted values (from equations developed using 2009-2011 data) vs. laboratory values (from 2012 nursery)

Scatter plot of total carotenoids NIRS predicted values vs Scatter plot of total carotenoids NIRS predicted values vs. laboratory values (HPLC and spectrophotometer).

Scatter plot of β-carotene NIRS predicted values (based on equations developed from 2009-2011 data) vs. laboratory values (from 2012 nursery)

 Sampling and processing protocols Analysis & pre-selection based on NIRS Genetics of carotenoids Genetic progress Next steps: the pathway to impact 

The Molecular Genetics Approach -- Requires: Understanding of the factors affecting high β-carotene accumulation. Understanding of the β-carotene biosynthetic pathway. A good segregating mapping population (F1 vs. Fn). Large number of molecular markers (e.g. SNPs) Good field design. A high-fidelity phenotyping system. 23

Carotene biosynthetic pathway isopentenyl diphosphate 24

Segregation for beta carotene and its building blocks in an F2 population 25

Carotenoid distribution in GM373X Potential of intermediate products that are being retained in the pathway (not converted to beta carotene) Carotenoid Potential Carotenoid Gained 26

Carotenoid distribution in GM373X Potential contributions of carotenoids not being converted to pro-vitamin A (degraded) Carotenoid Potential Carotenoid Gained 27

Carotenoid distribution in GM373X Carotenoid Potential Carotenoid Gained Unknown Carotenoids 28

Identification of unknown carotenoids 29

Molecular Genetics Generate high density markers (SSRs and SNPs) Build a consensus genetic map. Conduct statistical association between trait values and the genotypes of marker loci Evaluate changes at the DNA sequence level on genes involved in the carotenoid that may explain high β-carotene varietal improvement (MePSY2 SNP-AC)1. 1 Welsch, R. et al. Provitamin A Accumulation in Cassava (Manihot esculenta) Roots Driven by a Single Nucleotide Polymorphism in a Phytoene Synthase Gene. The Plant Cell Online 22, 3348-3356, doi:10.1105/tpc.110.077560 (2010). 30

Conclusions from genetics studies Potential both to promote synthesis of beta carotene and stop the degradation Fully characterizing beta carotene biosynthesis pathway to maximize genetic gains Explored one mutation associated with the color trait. Yellow color determined by more than one gene.

 Sampling and processing protocols Analysis & pre-selection based on NIRS Genetics of carotenoids Genetic progress Next steps: the pathway to impact 

RAPID CYCLING RECURRENT SELECTION (3-year cycle) Planting of a new crossing nursery high-carotenoids progenitors crossed RAPID CYCLING RECURRENT SELECTION (3-year cycle) Visual selection in the field. Pre-selection by NIRS. Selection based on total carot. content and total beta carotene Seed germinated, F1 plants evaluated at 11 MAP Clonal evaluation trial Preliminary yield trial Advanced yield trial Regional trial TO ACID SOIL SAVANNAS FOR SED AND CBB IN PALMIRA

Nutritional traits across years and plant ages Dry Matter % Total carot. (spectroph.) Total carot. (HPLC) Total β-carotene Total carot. (DW basis) Year of original nursery 2004 35 7.8 8.2 5.3 25 2005 38 9.2 9.6 5.5 26 2006 36 7.5 7.3 4.7 21 2007 37 10.3 10.9 7.2 30 2008 10.4 6.2 2009A 11.8 12.4 32 2009B 39 13.0 13.8 8.6 Age of plants sampled (MAP) 8 9.5 10.0 6.5 9 11.1 7.1 29 10 10.6 10.7 6.7 28 11 10.1 10.5 5.8

Human consumption: Results of evaluation nurseries for high-carotenoids roots 45 Evolution of dry matter content 40 Dry matter content (%) 35 30 Total carotenoids content (μg/g FW) Evolution of total carotenoids content Year 35

Regression for carotenes and carotenoids on year of selection (Independent variable is year of original nursery) DM Content Total carot. (spectro.) Total carot. (HPLC) Total β-carotene Total carot. (DW basis) Across Locs 0.49 0.82 0.90 0.54 2.03 Uni. Nac. 0.13 0.72 0.94 0.64 2.47 CIAT 0.66 0.84 0.86 0.48 1.76 Results indicate that an indirect effect of selection for high-carotenoids content was an increase in DMC !!!

Clonal Evaluation Trial Results, 2011/12 (First step in the selection for agronomic performance; based on single row plots with 6-8 plants, no replications) Fresh root yield Harvest Index Dry matter content Dry matter yield Plant type score (t/ha) (0-1) (%) (1-5) Data from 46 selected genotypes Max 64 0.74 42 24 4.0 Min 23 0.37 32 9 1.0 Mean 40 0.51 36 14 2.8 Data from 170 genotypes evaluated 5.0 2.2 0.12 21 1 26 0.43 35 3.3

Evaluation Trials, Palmira, 2012 Clonal Evaluation Trials Planted May 2012 426 entries Planted August 2012 452 entries Preliminary Yield Trials Planted August 2012 60 entries Advanced Yield Trials Planted August 2012 30 entries

(Materials in preliminary and advanced trials, Palmira) Results of evaluations for disease resistance in the acid soil savannas (Materials in preliminary and advanced trials, Palmira) Average Minimum Maximum Super Elongation Disease (SED) = 2.1 1.0 5.0 Cassava Bacterial Blight (CBB) = 1.0 1.0 3.0 Disease score, 1= Excellent; 5= Very poor

 Sampling and processing protocols Analysis & pre-selection based on NIRS Genetics of carotenoids Genetic progress Next steps: the pathway to impact 

Select high performance materials in multi-location yield trials Tentative Partners: Corpoica (Colombia) Prepare pathogen-free in-vitro materials for international shipment CIAT GRU Plant Health Laboratory Send selected clones to Haiti for recovery from in vitro culture and multiplication, and testing Tentative partners: Catholic Relief Services Processing trials and acceptability studies; studies on gender differentiation Tentative partners: Catholic Relief Services; CRP 3.4 (Roots, Tubers and Bananas)

Recent publications: Ortiz, D., T. Sánchez, N. Morante, H. Ceballos, H. Pachón, M.C. Duque, A.L. Chávez, and A.F. Escobar (2011). Sampling strategies for proper quantification of carotenoids content in cassava breeding. Journal of Plant Breeding and Crop Science 3(1):14-23. Morillo-C., A. C., Y. Morillo-C., M. Fregene, H. Ramirez, A.L. Chávez, T. Sánchez, N. Morante and H. Ceballos-L. (2011). Diversidad genética y contenido de carotenos totales en accesiones del germoplasma de yuca (Manihot esculenta Crantz). Acta Agronómica 60(2): 97-107. Ceballos, H., J. Luna, A.F. Escobar, J.C. Pérez, D. Ortiz, T. Sánchez, H. Pachón and D. Dufour (2012). Spatial distribution of dry matter in yellow fleshed cassava roots and its influence on carotenoids retention upon boiling. Food Research International (45:52-59). Morillo-C., Y., T. Sánchez, N. Morante, A.L. Chávez, A.C. Morillo-C., A. Bolaños, and H. Ceballos (2012). Estudio preliminar de herencia del contenido de carotenoides en raíces de poblaciones segregantes de yuca (Manihot esculenta Crantz). Acta Agronómica 61(3):253-264.

Contributors CIAT’s breeding team (Hernan Ceballos, Fernando Calle and Nelson Morante) CIAT’s starch quality lab (Dominique Dufour, Teresa Sanchez, Monica Pizarro) CIAT’s nutritional lab (Darwin Ortiz, Moralba Dominguez) CIAT’s cassava genetics lab (Luis Augusto Bececerra, Tatiana Ovalle, Adriana Alzate) CIARAD collaboration (Fabricio Davrieux ) 43