Integration of Transcriptome Analysis and Consensus Qtl in the Identification of Candidate Genes Associated with Zinc Concentration in Common Bean Phaseolus Vulgaris

INTEGRATION OF TRANSCRIPTOME ANALYSIS AND CONSENSUS QTL IN THE IDENTIFICATION OF CANDIDATE GENES ASSOCIATED WITH ZINC CONCENTRATION IN COMMON BEAN PHASEOLUS VULGARIS

Carolina Astudillo-Reyes

A THESIS

Submitted to Michigan State University in partial fulfillment of the requirements for the degree of

Crop and Soil Sciences – Master of Science

2014

ABSTRACT

INTEGRATION OF TRANSCRIPTOME ANALYSIS AND CONSENSUS QTL IN THE IDENTIFICATION OF CANDIDATE GENES ASSOCIATED WITH ZINC CONCENTRATION IN COMMON BEAN PHASEOLUS VULGARIS

Carolina Astudillo-Reyes

Dry bean, Phaseolus vulgaris, is an important legume for human consumption. Common bean is produced on almost 20 million hectares of land worldwide, with the highest production and consumption occurring in Latin America, Africa and US. It is well-recognized for its nutritional qualities such as high levels of protein, fiber, zinc and iron. Micronutrients are essential elements for human well-being and adequate supply of zinc will help to prevent or alleviate human zinc deficiency. The main objective of this study was to identify and characterize genes responsible for zinc transport to the seed of P. vulgaris. The first study, transcriptome analysis identified members of mineral transporter gene families expressed during bean pod development as potential candidate genes for seed mineral biofortification. Based on transcriptome analysis and proximity to QTL associated with zinc accumulation from previous studies, expression analysis of seven transporter genes of ZIP (ZRT, IRT like protein) family and three transcription factors of the bZIP family showed a diverse expression profile. High expression was found in leaves with some members being expressed in pods under low Zn fertilization. Genes potentially related to zinc transport were identifed within consensus QTL regions on chromosomes 2, 6 and 11. Genes including PvZIP, Pv bZIP, PvHMA, and PvVIT were found to be aligned to a zinc QTL both in the reference map and the consensus QTL. These results provide a useful resource for more detailed and analysis of candidate genes associated with zinc seed accumulation to develop efficient marker-assisted breeding strategies.

I dedicate my thesis to my family…my husband Giovanni and my kids Juan and Alejandro. A special feeling of gratitude to my loving parents, Harvey, Mercedes and my Aleja whose words of encouragement and endless love pushed me for tenacity. Papi y mami nunca van a existir suficientes palabras para expresar el gran amor y orgullo que siento por ustedes. Doy principalmente gracias a Dios por haberme los mejores padres…Esto es para ustedes

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ACKNOWLEDGMENTS

I would like to express my gratitude to my supervisor, Dr. Karen A. Cichy, whose humanity,

expertise, understanding, and patience have been a personal and professional motivation.

I would like to thank the other members of my committee, Dr. James Kelly, and Dr.

Hideki Takahashi for the assistance in my research.

I would also like to thank my family for the support they provided me through my entire life and in particular, I must acknowledge my husband and my lovely kids Juan y Alejo without whose love, encouragement and editing assistance, I would not have finished this thesis. I also thank to my friends: Carrasco family, Norma, Weija… I will always appreciate all they have done.

This research would not have been possible without the assistantship from the Plant

Breeding, Genetic and Biotechnology and the Food Legume Genetics Laboratory at MSU and

USDA_ARS.

TABLE OF CONTENTS

LIST OF TABLES………………..………...…….………………………..………………….....vii LIST OF FIGURES………………………………………..……………………..…………..…..ix LITERATURE REVIEW ...... 1 Agricultural importance ...... 1 Genetic and genomic resources ...... 1 Nutritional quality of dry bean ...... 3 Status of iron and zinc deficiencies in the world and the impact of biofortified crops ...... 4 Consensus QTL ...... 7 Marker assisted selection…………………………………………………...…………………...9 Genes related to Zn and Fe uptake and movement……………………..……………………...10 Transcriptome analysis…………………………...…………………………………………….14

CHAPTER 1: TRANSCRIPTOME CHARACTERIZATION OF DEVELOPING BEAN (PHASEOLUS VULGARIS L.) PODS FROM TWO GENOTYPES WITH CONTRASTING SEED ZINC CONCENTRATIONS...... 18 ABSTRACT ...... 18 INTRODUCTION ...... 19 MATERIALS AND METHODS ...... 21 Plant material...... 21 Plant zinc uptake experiment ...... 21 Greenhouse RNA-seq experiment ...... 22 RNA sequencing and pre-processing ...... 23 Functional annotation and classification ...... 24 Differential expression analysis ...... 24 SNP discovery and validation ...... 25 RESULTS AND DISCUSSION ...... 26 Growth chamber experiment ...... 26 Pod transcriptome characterization ...... 28 Differential expression analysis ...... 32

CHAPTER 2: THE PHASEOLUS VULGARIS ZIP GENE FAMILY: IDENTIFICATION, CHARACTERIZATION, MAPPING AND GENE EXPRESSION...... 41 ABSTRACT ...... 41 INTRODUCTION ...... 42 MATERIALS AND METHODS ...... 45 Plant material and phenotypic data ...... 45 Identification of PvZIP and Pv bZIP genes and phylogenetic analysis ...... 46 In silico mapping of PvZIP and Pv bZIP genes ...... 47 Genetic mapping of select members of the PvZIP and Pv bZIP family genes...... 47 QTL data and analysis ...... 48

Expression analysis of select Pv ZIP and Pv bZIP...... 48 RNA extraction and Real-time quantitative PCR ...... 49 Quantification of Zn concentrations in tissue ...... 51 RESULTS ...... 51 Identification of ZIP family members and comparison with homologs in other species ...... 51 Mapping of PvZIP genes and QTL for seed Fe and Zn concentration ...... 55 Expression analysis of PvZIP genes ...... 60 Expression analysis of three transcription factors bZIP ...... 63 Tissue Zinc concentration ...... 64 DISCUSSION ...... 65

CHAPTER 3: IDENTIFICATION OF PRECISE AND CONSISTENT QTL REGIONS ASSOCIATED WITH IRON AND ZINC ACROSS DIFFERENT GENETIC BACKGROUNDS USING QTL META-ANALYSIS APPROACH...... 70 INTRODUCTION ...... 70 MATERIALS AND METHODS ...... 72 Construction of consensus map and meta-QTL analysis ...... 72 Gene content analysis ...... 73 RESULTS AND DISCUSSION ...... 74 Meta-analysis ...... 74 Gene content analysis and identification of candidate genes ...... 76

CONCLUSIONS...... 83

APPENDIX ...... 84

BIBLIOGRAPHY……………………………………………………………………………..….91

LIST OF TABLES

Table 1. Bean production and consumption in USA, Latin America, and Africa...... 2

Table 2. Nutritional content in 100 grams of seed of common bean and total content of calories, proteins, carbohydrates, fiber, fat, and vitamins ...... 4

Table 3. Meta-QTL identified by meta-analysis for wheat, rice, soybean, and maize…….……...9

Table 4. QTL studies associated to seed iron and zinc concentration in P. vulgaris……………10

Table 5. Mean concentration of zinc, iron, and nitrogen in pods and seeds of Albion and Voyager plants from which RNA samples for sequencing were taken ...... 28

Table 6. Genes and function of the most highly differential expressed in Albion and Voyager ..33

Table 7. Gene families involved in Zn and/or Fe transport and expression analysis in the developing pods of Albion and Voyager ...... 37

Table 8. Identification of SNPs in genes that are members of Zn and/or Fe transport-related families, followed by the length of the CDS, genomic length, number of SNPs between Albion and Voyager, whether those SNPs validated via PCR and if the SNPs resulted in an amino acid change ...... 39

Table 9. Primer list for gene expression analysis via RT-qPCR and genetic mapping of ZIP genes…………………………..………………………………………………………………....49

Table 10. The Zrt and Irt -like protein (ZIP) family genes and bZIP genes identified in the P. vulgaris genome. Chromosome and position in base pairs indicate the location of each gene. Their respective homologs in A. thaliana and M. truncatula are shown. The program tBlastn was used to compare the A. thaliana ZIP genes against the bean genome. Homology was based on E- 10 ...... 53

Table 11. Quantitative trait loci (QTL) for iron and zinc concentration identified with composite interval mapping in the DOR364 x G19833 population ...... 60

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Table 12. Details of the Zn QTLs from different studies include in the QTL meta-analysis…...72

Table 13. Summary of QTLs used in the meta-QTL analysis…………………………………..73

Table 14. Characteristics of meta-QTL identified for Zn concentration in common bean……..76

Table 15. Candidate genes reported in the identified meta-QTL regions……………………….81

Table 16. Forward and reverse sequence for all primer pairs used to validate putative SNPs in genotypes Albion and Voyager……………………………………………………………..……85

Table 17. Genotypes scored taking QTL donor allele as a base………………………………...87

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LIST OF FIGURES

Figure 1. Andean and Mesoamerican genepools and races result of the domestication (modified from Beebe et al., (2000) and Gepts, (1998))…………………………………………………..…3

Figure 2. Genes involved in the uptake, transport and translocation of zinc and iron in plants. Zinc and iron are taken up into the symplast by ZIP and IRT transporters in the epidermis. Reduction of Fe is achieved by FRO2 and acidification of the soil by an AHA in order to increased metal uptake. Members of the family could be responsible for transport of minerals into the xylem. In the xylem they are unloaded into the shoot by a member of the YSL family which translocate metals to the phloem for delivered to the seed by member of the ZIP family..11

Figure 3. Zinc concentration of roots, leaves, pods and seed of two bean genotypes grown under normal Zn and no Zn fertilization………………………………………………………………..27

Figure 4. Number of expressed transcripts on eleven common bean chromosomes (in base pairs)……………………………………………………………………………………………. 29

Figure 5. Characterization of genes in the bean pods into biological processes, cellular components and metabolic function……………………………………………………………..31

Figure 6. Expression analysis of gene families involved in Zn and/or Fe transport identified in pod in developing transcriptome. Vertical box in colors corresponde to each member of gene family…………………………………………………………………………………………….34

Figure 7. Phylogenetic tree of homologs ZRT, IRT –like protein family in Phaseolus vulgaris, Arabidopsis. thaliana and Medicago truncatula. Analysis was based on alignment of amino acid sequences using Geneious program v. 6.0.3 and N-J trees were generated. Arabidopsis genes are indicated with the ZIP and IRT number used on TAIR database. ZIP1 to ZIP7 names used in Medicago were according to Lopez-Millan et al. (2004). ZIP8 in front were assigned with a consecutive number……………………………………………………………………………...54

Figure 8. Alignment of the predicted ZRT, IRT –like protein using CLUSTAL W. Identical amino acids are indicated with dark shading and similar amino acids are indicated with light shading. The histidine-rich sequence located in the variable region between transmembrane domains III and IV and fully conserved histidine motifs are indicated by grey lines. The eight domains are shown as a red line above the sequences…………………………………….……..55

Figure 9. Genetic mapping, chromosomal location of PvZIP genes and QTLs associated with iron and zinc. Nineteen ZIP genes and four IRT genes were localized to 9 of 11 chromosomes in P. vulgaris on the DOR364 x G19833 genetic map and G19833 sequenced genome. They were aligned for identification of gene position and the coincidence in locations to QTLs with the PvZIP genes. Blue boxes highlight genes mapped in silico and green boxes those mapped genetically………………………………………………………………………………………..56

Figure 10. Relative expression level of PvZIP gene transporters and three bZIP transcription factors in genotypes Dor364 and G19833 in different tissues and two Zn treatment: (i) roots at vegetative stage (V_ROOT- and V_ROOT+), (ii) roots at flowering stage (F_ROOT- and F_ROOT+); (iii) leaves at vegetative stage (V_LEAF- AND V_LEAF+) stage; (iv) leaves at flowering stage (F_LEAF- and F_LEAF+); and (v) pods (POD- and POD+) of plants under Zn (- ) and Zn (+) treatment……………………………………………………………………………62

Figure 11. Zinc concentration in DOR364 and G19833. Zn concentration (ppm) in (i) roots at vegetative stage (V_ROOT- and V_ROOT+), (ii) roots at flowering stage (F_ROOT+); (iii) leaves at vegetative stage (V_LEAF- AND V_LEAF+) stage; (iv) leaves at flowering stage (F_LEAF- and F_LEAF+); (v) pods (POD- and POD+) and seeds (SEED- and SEED+) of plants under Zn (-) and Zn (+) treatment. Different letters above the bars show significant difference between tissues (P <0.05)………………………………………………………………………...... 64

Figure 12. Meta-QTLs analysis on chromosomes a) Chr 2, b) Chr 6 and c) Chr 11 defining cluster of QTLs coming from individual analysis for Zn concentration in seed………………...78

Figure 13. Primer design for the five SNPs found on PvHMA2 gene…………………………..88

LITERATURE REVIEW

Agricultural importance

Dry bean, Phaseolus vulgaris, is the most important grain legume among the twenty that are commonly consumed in human diets. It can be consumed as a grain and also as a vegetable

(Myers and Baggett, 1999). Common beans have been cultivated for millennia. Fossil records dating from 7,000 years B.C. show that natives from Middle America grew and used common beans in their diets (Salinas, 1988). Currently beans are an important staple crop for small farmers in many Latin American and African countries (Broughton et al., 2003). Beans are produced on more than 20 million hectares of land worldwide, with the highest production and consumption occurring in Latin America (6.5 M ha) and Africa (3.9 M ha). In the U.S, it is an important specialty crop grown on 1.7 million acres in 19 states. Approximately 90% of the production is localized in the major production states of North Dakota, Michigan, Nebraska and

Minnesota http://faostat.fao.org/site/339/default.aspx (Data from 2012) (Table 1). Dry Beans are a diverse crop in terms of cultivation methods, type of environments, morphological variability and consumer preferences which have determined its adaptation to many different niches

(Broughton et al., 2003).

Genetic and genomic resources

The Phaseolus genus is made up of 75 species, five of which are domesticated, including P. vulgaris; P. coccineus: runner bean; P. acutifolius: tepary bean; P. dumosus: year bean (Gepts et al., 2008) and P. lunatus: lima bean (Kuboyama et al., 1991 and Leonard et al., 1987).

Phaseolus vulgaris, whose domestication center has been postulated to be in Mesoamerica,

Table 1. Bean production and consumption in USA, Latin America, and Africa. Country/region Production (MT) Average Annual per capita consumption (kg) USA 1,442,470 3.0 Brazil 3,202,150 18.7 Mexico 1,156,250 16+ Central America 337 12.3 South America 3,557,289 Central America 1,649,937 12.6 Caribbean (Cuba, Haiti, Dominican Republic) 225,093 Africa 39.0 Eastern Africa 3,961 ,679 Southern Africa 1,290 Western Africa 135 Lowlands-winter season 200 Source: http://faostat.fao.org/site/339/default.aspx (Data from 2012) MT: metric tones possesses a wide diversity represented in two gene pools, Andean and Mesoamerican (Bitocchi et al., 2012). The Middle American gene pool is comprised of four races Durango, Jalisco, Meso

America and Guatemala whereas that the Andean gene pool has been divided into three races:

Nueva Granada, Chile and Peru (Singh et al., 1991) (Figure 1). These genetic and geographic differences are represented in dissimilarities in seed size, growth habit, photoperiod responses and partial reproductive isolation (Gepts, 1998). In terms of genetic differences based on proportion of nucleotide polymorphisms the Middle American gene pool is more diverse than the

Andean gene pool (Schmutz et al., 2014).

Common bean is a true diploid with a small genome (≈587 Mb) distributed among 11 chromosomes (Arumuganatham and Earle, 1991). Despite the nutritional and economic importance of common bean in developed and developing countries, genomic resources have been limited until recently (Kalavacharla et al., 2011). The Andean landrace G19833 has been sequenced (Schmutz et al., 2014) and is available at http://www.phytozome.net/commonbean.php.

Figure 1. Andean and Mesoamerican genepools and races result of the domestication (modified from Beebe et al., (2000) and Gepts, (1998)).

Assembly was conductedand genomic and genetic information was combined from different sources. Some of the P. vulgaris genomic resources used in the assembly include: sequenced libraries from Roche 454 Platform and 24.1 Gb of Illumina-sequenced fragment libraries, three fosmid and two BAC libraries and 26,906 unique sequences identified from different cDNA libraries such as nitrogen-fixing root nodules, phosphorus-deficient roots, developing pods, and leaves. A total coverage of 21.0x was obtained and 472.5 Mb of the total genome size of 587 Mb was organized into 11 chromosomes. Gene annotation of combined EST resources and RNA sequencing reads from 11 tissues and developmental stages was achieved by finding the homology genes from different database and de novo gene prediction which showed

27,197 protein coding loci (Schmutz et al 2014).

Nutritional quality of dry bean

Common bean has been described as a “nearly perfect food” (Broughton et al., 2003) because of its many well-recognized nutritional qualities (Welch and Graham, 2004). The seed has high amounts of protein (19-33%) and complex carbohydrates (approx. 40%); it is low in fat, high in dietary fiber and it is a good source of iron, zinc, calcium, thiamine, folic acid, and niacin

(Shellie and Hosfield, 1991). For example, a cup of cooked beans can provide 29% of dietary iron in women and 55% for men and 20% potassium and copper and 10% of calcium and zinc

(Bazel et al. 1994).

In terms of nutritional value, the Andean and Middle American gene pools show a wide range of minerals in the seed. The Andean gene pool and inter gene pool hybrids tend to have higher concentrations of minerals (Blair, et al., 2013; Beebe et al., 2000). Germplasm screening has been the starting point for biofortification. A core collection of 1,400 bean genotypes has been characterized for mineral accumulation where iron ranged between 34 – 91 mg/kg and zinc ranged from 20 - 59 mg/kg (Islam et al., 2002). Additionally, an Andean diversity panel has been analyzed for mineral concentration, cooking time, and phytic acid. This collection of ≈400 different seed types and market classes from 28 countries on the six continents represent landraces, breeding lines, and cultivars. Mineral analysis from cooked beans showed a wide range for iron concentration of 48 to 100 μg g-1 and Zn of 21 to 45 μg g-1 which were higher than the Harvest Plus breeding targets (Katuuramu and Cichy et al 2014) for raw seed.

Table 2. Nutritional content in 100 grams of seed of common bean and total content of calories, proteins, carbohydrates, fiber, fat, and vitamins. Total seed Content in 100g

content seed Calories 110 a 143 Kcal

Proteins 21 – 25% 8 g Carbohydrates 60-65% 19-24 g Fiber 3 a 7% 6-9 g Fat 0.8 – 1.5% 0.1-0.6 g Folate 65-183 µg

Thiamine 25%

Pyridoxin 10-12% Niacina y riboflavin 10% Calcium 23-63 mg Iron 2-3 mg Zinc 0.9-1 mg Sources: Bazel et al. (1994), Islam et al. 2002.

Status of iron and zinc deficiencies in the world and the impact of biofortified crops

Micronutrient deficiencies have increased over recent decades due to a general decrease in the quality of people’s diet both in developed and developing countries and, even in areas

where food is not limited (Graham et al., 2012; Miller and Welch, 2013). Zinc in humans helps to regulate metabolic rates, metabolizing carbohydrates, proteins and fat. Human zinc deficiency is known as a “hidden disease” common in children and people with vegetarian diets. Zinc deficiency is called hypozincemia, associated with intestinal malabsorption causing respiratory infections (Nriagu 2006), cognitive and motor function impairment (Sanstead et al., 2000), diarrhea, pneumonia (Penny et al., 2004), reduction of production of testosterone and esophageal cancer (Kmet and Mahboubi 1972). It is estimated that 48% of the human population is at risk for inadequate zinc in their diet (Brown et al., 2001). Its detection is difficult, given the lack of specific biochemical markers in the blood associated with deficiency. Although recently a potential biomarker (dematin) has been identified (Ryu et al., 2012).

There are several strategies to address mineral deficiency, such as mineral supplementation, and biofortification. Biofortification, is defined as the development of crop plants with higher micronutrient and bioavailable content using the best traditional breeding practices and biotechnology tools (Welch, 2002). Different strategies have been successful in increasing zinc and iron. For instance, foliar application of zinc fertilizer and targeted breeding has been successful in wheat (Velu et al., 2014). Intercropping systems have been successful in peanut/maize, wheat/chickpea and guava/sorghum or maize combinations which have increased zinc and iron content in seeds (Zuo and Zhang 2008).

Iron has several vital functions in the human body. It is an essential component of hemoglobin that transfers oxygen from the lungs to the tissues (Seo and Wessling-Resnick,

2014). Iron is directly related to growth, development, normal cellular functioning, and synthesis of some hormones (Aggett, 2012). Iron deficiency is probably the most frequent nutritional deficiency in the world. In U.S, six percent of toddlers aged 1 to 3 years are iron

deficient (Brotanek et al., 2007). Even though approximately 14% to 18% of Americans use a supplement containing iron, rates of use of supplements containing iron vary by age and gender

(Bailey et al., 2011).

In dry beans, iron and zinc biofortified lines NUA 35 and NUA 56 have been released as varieties in Uganda and Malawi (Blair et al., 2010 and Blair, 2013). Mineral average concentration for these genotypes ranged in 81 mg kg−1 and 76 mg kg−1 for iron and 34 mg kg−1 and 33 mg kg−1 for zinc. In Rwanda, nine varieties have been released showing up 90 mg kg−1 for iron with good yield and farmer acceptance (Beebe and Andersson, 2014). They corresponded to the first strategy applied for biofortification breeding of Andean beans in the

International Center for Tropical Agriculture (CIAT) in Colombia. These red mottled bean lines were developed backcrossed and selected in the BC1F1 and the BC1F3 generations. Two pedigrees were used in this stage: CAL 96 x (CAL 96 x G14519) and CAL 143 x (CAL 143 x

G14519).

Inter-specific crosses to introgress high zinc and iron from related species in Middle

American gene pool have been achieved. The P. dumosus accession, G 35575 was crossed to the

P. vulgaris line FEB 226, followed by a single backcross to FEB 226 at CIAT in Colombia. At the F5 generation, lines were selected for carioca seed type, short growing cycle, and indeterminate upright growth habit and exceeding the average of 25 and 65 mg Kg-1 for seed Zn and Fe levels respectively (Beebe et al., 2007 and Islam et al. 2002). These results provided evidence that the high seed zinc and iron trait was introgressed from secondary gene pool.

Consensus QTL

Integration of genomics and quantitative trait loci information allows for the identification of genes involved in the variation of specific traits. This is a first step in the understanding of biological processes underlying the expression of these traits. Consensus QTL or meta-QTLs (MQTL) was proposed by Goffinet and Gerber (2000). It is based on co-location of genomic regions statistically related to loci from several individual maps (Mott and Flint,

2001). The process consists on merged genetic maps by homothetic projection based on bridging of common loci between two or more genetic maps.

Analysis of four different dry bean populations of different gene pools were generated as recombinant inbred lines (Cichy et al., 2009, Blair et al., 2009, 2010a, 2010b, and 2011). These analyses determined that inheritance of iron and zinc accumulation is polygenic. In total, 47

QTLs were associated with seed zinc levels and 46 for seed iron levels, explaining 15 to 40% of the variability in both iron and zinc concentration in seed. In Blair et al., (2009, 2010a) showed that iron and zinc were positively correlated (r=0.63; P<0.001). The implication of these correlations, together with QTLs overlapping at least on three linkage groups for iron and zinc concentration is that some genetic factors for different minerals co-segregate and that selection for iron will also result in an increase in zinc in a breeding process (Beebe et al., 2000). The numerous QTL studies conducted for seed mineral analysis in common bean and the use of common markers across different maps make it possible to integrate such QTLs in order to improve the accuracy of position and smaller confidence interval using QTL meta-analysis approach.

Meta-QTLs analysis has been used in the integration of traits in rice, maize, wheat, cotton, potato, soybean and cacao (Table 2). In rice, MQTL analysis has been used in the

improving resolution of QTLs position involved in drought avoidance (Khowaja et al., 2009 and

Courtois et al., 2009) and yield (Swamy et al., 2011). A database of 675 root QTLs coming from

12 populations was constructed revealing six or more true QTLs allowing researchers to concentrate on only a few genes related with this trait. In maize, 53 yield QTLs reported in 15 studies were integrated resulting in fourteen meta-QTLs. Meta-analysis has made it possible to integrate maps and determine accurate co-location of major genes related with grain yield components (Li et al., 2011), silage quality (Truntzler et al., 2010), and drought tolerance (Hao et al., 2010).

For zinc and iron, only one study in maize by Jin et al., (2013) has been reported. MQTL analysis for zinc and iron was done in order to estimate the number and positions of consensus

QTLs. In that study, 218 F2:3 families of the population and four previous QTL studies were used to conduct meta-analysis. As result, 10 Meta QTLs (MQTLs) involved in zinc and/or iron accumulation were detected on six chromosomes at interval confidence of 95% and phenotypic variation more than 10%.

Identification of candidate genes related with Zn and Fe concentration in seed increases the success rate identifying genotypes in early generations. Together with plant breeding, new technologies have potential to accelerate the development of gene-based markers, efficient quantitative trait loci (QTL) mapping procedures, and lower cost genotyping and phenotyping systems (Xu and Crouch, 2008). An understanding of genes involved in uptake, transport and accumulation in the seed will be essential for making progress in biofortification for ultimate consumption by humans.

Table 3. Meta-QTL identified by meta-analysis for wheat, rice, soybean, and maize.

Specie Meta-QTL Reference Loffler et al., 2009; Liu et al., 2009; Miedaner e al., 2011; Wheat Fusarium head blight resistance Mao et al., 2010 Ear emergence Griffiths et al., 2010 Height Griffiths et al., 2009 Earliness Hanocq et al., 2007 Grain size and shape variation Gegas et al., 2010 Grain dietary fiber content Quraishi et al., 2011 Yield Zhang et al., 2010

Courtois et al., 2009; Coudert et al., 2010; Norton et al., Rice Root genetic architecture 2008 Drought Swamy et al., 2011; Khowaja et al., 2009 Blast resistance Ballini et al., 2008 Yield Swamy et al., 2011

Soybean Oil content Qi et al., 2011 Cyst nematode Guo et al., 2006 100-seed weight ZhaoMing et al., 2009 Seed protein concentration Zhao-Ming et al., 2009

Maize Grain yield components Li et al., 2011 Silage quality (digestibility and cell wall composition) Truntzler et al., 2010 Drought tolerance Hao et al., 2010 Nitrogen use efficiency Liu et al., 2012 Plant height Wang et al., 2006

Marker assisted selection

Iron and zinc concentration are traits which are invisible to breeders unlike disease resistance, growth habit, and seed color. For this reason, selecting genotypes with high mineral concentration by conventional breeding requires secondary techniques that allow a breeder to make decisions to discard undesirable genotypes. As a part of breeding for high mineral concentration, techniques such as Inductive Coupling Plasma (ICP) and Atomic Absorption

Spectrophotometric have been used to quantify minerals (Salt et al., 2008) and make selections in each generation. However, these methods are costly and time consuming, making breeding

unsustainable in terms of time and budget. In comparison, marker assisted selection makes it possible to analyze large numbers of lines quickly and accurately to select those that will be advanced to the next generation.

Table 4. QTL studies associated to seed iron and zinc concentration in P. vulgaris.

Population Genepool Population QTL QTL Environments Map distance Total size Zinc Iron (cM) Markers

Dor364 x G198331 M x A 87 13 13 2 1,703 236 G21242 x G210782 A x A 100 3 6 3 720 118 G14519 x G48253 M xM 110 9 8 3 915 114 AND696 x G198334 A x A 77 11 12 2 1,105 167 G10022 x Cerinza5 A x A 138 6 1 1 1,992 142 Bat93 x Jalo EEP6 A x M 72 3 4 1 1,364 217 Black Magic x Shiny Crow6 M x M 100 2 2 2 1,644 1,500 Total 47 45 9,443 References: 1Blair et al 2009; Blair et al.2010a; 2Blair et al., 2011; 3Blair et al 2010b; 4Cichy et al., 2009; 5Blair et al., 2013; 6unpublished M x A: Mesoamerican x Andean A x A: Andean x Andean M xM: Mesoamerican x Mesoamerican

Genes related to Zn and Fe uptake and movement

The molecular mechanism by which Fe and Zn move within plants starting from uptake by roots to ultimate movement into seed is controlled by many genes. This process involves uptake, binding, transportation, and storage (Baxter, 2009 and Roschzttardtz et al., 2010). Iron uptake in higher plants excluding graminaceous plants follow strategy I (Eide et al. 1996;

Robinson et al. 1999) while graminaceous plants use strategy II (Takagi 1976; Takagi et al.1986).

Source: Palmer and Guerinot (2009) modified Figure 2. Genes involved in the uptake, transport and translocation of zinc and iron in plants. Zinc and iron are taken up into the symplast by ZIP and IRT transporters in the epidermis. Reduction of Fe is achieved by FRO2 and acidification of the soil by an AHA in order to increased metal uptake. Members of the family could be responsible for transport of minerals into the xylem. In the xylem they are unloaded into the shoot by a member of the YSL family which translocate metals to the phloem for delivered to the seed by member of the ZIP family.

Plants using strategy I respond to iron deficiency by inducing root ferric chelate reductase (FRO) in the plasma membrane, releasing protons to acidify the rhizosphere soil and secreting organic acids or reductants such as phenolic compounds (Zheng 2010). Strategy II of metal transport includes release of low molecular weight phytosiderophores from roots and then reabsorption of the metal-phytosiderophore complex by root membrane transporter proteins (Takagi 1976;

Takagi et al., 1986). Phytosiderophores are organic compounds that are released into the rhizosphere to bind ferric iron. Although phytosiderophores do not chelate zinc directly, under

low levels of zinc plants use iron deficiency-induced phytosiderophores strategy to acquire zinc being transported across the root plasma membrane (Wiren et al., 1996). Once minerals are absorbed, they are either available for local nutritional needs of root cells or are transported to leaves and other plant parts.

Uptake of Zn from the soil is less well defined than Fe uptake, but in Arabidopsis it is likely carried out by zinc transporters in the ZIP family, some of which are regulated by Zn levels in roots (Grotz et al., 1998). In grasses, zinc uptake by the root through the symplasm has been proposed to occur in the form of the free Zn+2 ion and in the form of a Zn-complex, similar to strategy II for iron (Halvorson and Lindsay, 1977; Takagi et al., 1984). Once minerals are absorbed, they are either available for local nutritional needs of root cells or are transported to leaves and other plant parts.

Translocation to seed, embryo, endosperm, and seed coat is probably carried out via phloem trough chelates or ligand-bound with ITP (iron transport protein), Nicotianamina (NA),

Yellow stripe-like (YSL) and ZIP genes (Waters and Sankaran 2011;Waters Sankaran, 2011).

A large number of cation transporters potentially involved in metal ion uptake and transport have been identified in the model plant Arabidopsis thaliana. Many gene families have been identified in plants which function as cation transporters are potentially involved in Zn and

Fe transport. These families include the zinc induced facilitator (ZIF), zinc related transporter and iron related transporter (ZIP), beta ZIP transcription factor (bZIP), yellow stripe like (YSL), natural resistance associated macrophage protein (NRAMP), Nicotianamine (NA), heavy metal associated (HMA), dehydrin, and metal tolerance protein (MTP). Those involved in iron transport are ferritin, ferric reductase (FRO), iron transport protein (ITP), oligopeptide transporter family (OPT), and vacuolar iron transport (VIT). Transporter genes have been

characterized in Arabidopsis thaliana (Bauer et al., 2004), Medicago truncatula (Lopez de

Millan et al 2007), Oryza sativa (Ishimaru et al., 2012; Schroeder et al., 2013; and Menguer et al., 2013), and P. vulgaris (Blair et al., 2010).

The zinc induced facilitator (ZIF) family is involved in zinc transport to the vacuole

(Haydon, Kawachi et al., 2012). The loss-of-function Atzif1 mutant changed zinc distribution to the vacuoles (Haydon and Cobbet, 2007). The ZIP family has been implicated in Zn uptake, transport to leaves and translocation to seeds, embryo, endosperm, and seed coat ( Guerinot et al.

1998). In addition, transcription factors regulating ZIP genes include members of the bZIP family. bZIP19 and bZIP23 contain two DNA binding domains, leucine zipper dimerization and histidine-rich motifs, which are needed to respond to low Zn supply in Arabidopsis (Bookum et al., 2003 and Assuncao et al., 2009). HMA (heavy metal associated) proteins are involved with

ATP dependent heavy metal transport across membranes. Some members of this family are involved in root to shoot long distance transport and others with sequestration of heavy metals in vacuoles (Morel, Crouzet et al. 2009). Another family involved in long-distance transport from leaves to seed is the YSL gene family (yellow stripe like). This gene family is well characterized in Arabidopsis and AtYLS2 is involved in metal uptake transport of minerals such as Mn, Zn, Cu and Fe from leaves and for loading of the Fe-NA complex into the seed (Curie, Cassin et al.

2009, Zheng et al., 2011).

The NRAMP family (natural resistance associated macrophage protein) is involved in transport of metals out of vacuoles (Thomine et al., 2003). In Arabidopsis, AtNRAMP3 and

AtNRAMP4 are required for iron mobilization in germinating seeds (Lanquar et al., 2010).

Nicotianamine (NA) a non proteinogenic amino acid, chelates Fe and Zn in phloem movement to

sink tissue (Schuler et al., 2012). Four NA genes have been characterized (Bauer et al., 2004) and are related to seed Fe loading (Waters et al., 2006).

Genes in the ferritin family have important roles in iron storage. They have been found in cotyledons, roots, shoot apices, and young nodules of soybean (Ragland and Theil, 1993).

Dehydrin is an iron binding protein identified in the phloem sap of 7 d. old castor bean shoots

(Morrissey and Geurinot, 2010). Ferric reductase encodes an iron-deficiency inducible iron reductase responsible for reducing iron at the root surface (Yi and Guerinot, 1996). Transport families have shown enhanced expression in developing pods in P. vulgaris and are expected to play a role in the transport of Zn, Fe and other metals in specific tissues or in the whole plant.

Some members of the Oligopeptide transporter family (OPT), are important for transport of mineral micronutrients to the seeds (Wintz et al., 2003). OPT complete knockout mutants were lethal in embryos (Stayce et al., 2002). Iron is loaded into vacuoles by the vacuolar iron transport protein (VIT) during embryo development (Jeong and Guerinot, 2009).

There are many genes and regulation points involved in moving Zn and Fe to the seed for ultimate consumption by humans. Therefore, an understanding of genes involved in uptake, transport and accumulation in the seed is essential for making progress in biofortification. QTL analysis and RNA sequencing are powerful tools to relate gene expression patterns and sequence polymorphisms to biological functions.

Transcriptome analysis

Before high-throughput technology reached common bean projects, expressed sequence tags (EST) was the first instrument in gene discovery and gene sequence determination.

However, quality and fragment length was limited by current Sanger sequencing technologies.

A transcriptome is the set of all RNA molecules including mRNA, rRNA, tRNA, and other non- coding RNA produced in one or a population of cells. The transcriptome considers all genes that are being actively expressed at any specific time. This analysis shows the expression profile in a given cell population using high-throughput approaches based on DNA microarray and RNA-seq

(Wang et al., 2009).

Ramirez et al. (2005) sequenced a total of 21,026 ESTs, identifying 7,969 different transcripts from different cDNA libraries (nitrogen-fixing root nodules, phosphorus-deficient roots, developing pods, and leaves). They constructed cDNA libraries from the Mesoamerican genotype Negro Jamapa 81, and leaves from the Andean genotype G19833. Libraries from the common bean breeding line SEL 1308 were constructed from 19-day old trifoliate leaves, 10-day old shoots, and 13-day old shoots inoculated with Colletotrichum lindemuthianum. A total of

3,126 genes were identified of which just 314 showed similarity to sequences from the existing database (Melotto et al., 2005). Two suppression subtractive cDNA libraries were constructed from the genotypes G19833 and cultivar Early Gallatin. From the rust resistant cultivar Early

Gallatin, 6,202 new EST were identified. Libraries from the genotype G19833 identified genes differentially expressed involved in response to phosphorus starvation when plants were exposed to low and high phosphorus (Tian et al., 2007).

In recent years, advances have been achieved in transcriptome analysis in order to understand biological processes in common bean. In Kalavacharla et al., (2011), transcriptomes from four tissue (leaves, flowers, and roots) from the cultivar Sierra and pods from the genotype

BAT93 were sequenced by 454 GS FLX. A total of 2,516 transcription factors were identified based on the Arabidopsis data base representing about half discovered in soybean (Mochida et al., 2010). Identification of simple sequence repeats were also done in this study. From 22.93

Mbp of sequences and additional 64.67 Mbp common bean genomic sequences, a total of 6,033

SSRs were detected. The closeness of SSRs in expressed regions for mapping will allow the identification of variability of important agronomic traits and for integration of genetic and physical maps in common bean. In Hernandez et al., (2009) global gene expression and metabolome approaches showed how nodulation and nitrogenase activity were reduced when plants were inoculated with Rhizobium tropici CIAT899 grown under deficient phosphorus conditions. A total of 459 genes showed significant differential expression in response to phosphorus.

Transcriptomics has been useful in functional genomics and has the potential to be a useful tool to identify candidate genes for biofortification, but, progress in common bean is limited compared to model species including L. esculentum, Arabidopsis, Glycine max (Severin et al., 2010; Wilson and Grant 2010; and Woody et al., 2011) and Medicago (Cannon et al.,

2005; Bell et al., 2001). For instance, 1) genome-wide transcriptional analysis in tomato roots, identified genes potentially involved in Fe starvation and root response to nutrient deficiency

(Zamboni et al., 2012); 2) microRNA (miRNA) survey of genes related to Fe deficiency in

Arabidopsis, found 24 miRNA genes containing Fe deficiency responsive cis-Element in their promoter regions (Kong Yang, 2010); 3) In tomato roots, genome-wide transcriptional analysis identified genes potentially responsible in Fe starvation and root response to nutrient deficiency

(Zamboni et al., 2012). MicroRNAs have been identified using high-throughput sequencing in

Arabidopsis, M. truncatula, and G. max. MicroRNAs genes were involved in Fe deficiency and stress response (Kong and Yang 2010, Szittya et al., 2008 and Li et al., 2011). Transcriptome analysis of roots through RNA-seq was performed in P. vulgaris, M. truncatula and G. max. Fe

deficiency chlorosis-related genes were detected being up-regulated in the three species being annotated as metal ligands, transferases, zinc ion binding and metal ion binding genes.

Once genes are identified by throughput sequencing or RT-qPCR a downstream analysis is yeast functional complementation analysis. In this system, any gene on a yeast chromosome is deleted through homologous recombination. Then, the desired gene is cloned in the yeast and expressed episomally. This is a powerful tool for obtaining information about eukaryotic genes through mutational analysis. Yeast is a simple free-living cell, convenient for studying fundamental processes and can be applied to candidate genes for Zn and Fe biofortification. For example, the mutant ZHY3 ((MATα ade6 can1 his3 leu2 trp1 ura3 zrt1::LEU2 zrt2::HIS3) was derived from its parent strain DY1457 (MATα ade6 can1 his3 leu2 trp1 ura3) by mutating zrt1 and zrt2 genes. Therefore, this mutant lacks both high and low affinity zinc uptake systems and is highly sensitive to zinc limitation. Many zinc transporters have been assessed in Arabidopsis and Medicago using functional complementation analysis. ZIP genes were isolated by functional expression using the ZHY3 mutant. The expression of these genes in yeast restored zinc-limited growth in both species (Grotz et al., 1998 and Lopez-Millan et al., 2004).

CHAPTER 1: TRANSCRIPTOME CHARACTERIZATION OF DEVELOPING BEAN (PHASEOLUS VULGARIS L.) PODS FROM TWO GENOTYPES WITH CONTRASTING SEED ZINC CONCENTRATIONS.

ABSTRACT

Dry bean (Phaseolus vulgaris L.) seeds are a rich source of dietary zinc, especially for people consuming plant-based diets. Within P. vulgaris there is at least two-fold variation in seed Zn concentration. Genetic studies have revealed seed Zn differences to be controlled by a single gene in two closely related navy bean genotypes, Albion and Voyager. In this study, these two genotypes were grown under controlled fertilization conditions and the Zn concentration of various plant parts was determined. The two genotypes had similar levels of Zn in their leaves and pods but Voyager had 52% more Zn in its seeds than Albion. RNA was sequenced from developing pods of both genotypes. Transcriptome analysis of these genotypes identified 27,198 genes in the developing bean pods, representing 86% of the genes in the P. vulgaris genome (v

1.0 DOE-JGI and USDA-NIFA). Expression was detected in 18,438 genes. A relatively small number of genes (380) were differentially expressed between Albion and Voyager.

Differentially expressed genes included three genes potentially involved in Zn transport, including zinc-regulated transporter, iron regulated transporter like (ZIP), zinc-induced facilitator

(ZIF) and heavy metal associated (HMA) family genes. In addition 12,118 SNPs were identified between the two genotypes. Of the gene families related to Zn and/or Fe transport, eleven genes were found to contain SNPs between Albion and Voyager.

INTRODUCTION

Zinc is essential for human health and nutrition. Zinc is an important enzyme cofactor and components of proteins, and is needed for DNA synthesis, RNA transcription, and cell division (Chasapis et al., 2012). Human Zn deficiency symptoms are quite varied, including reduced immune function, fetal brain cell development, reproductive and cognitive development

(Hambidge et al., 2000). Mild to moderate Zn deficiency is common, especially in populations consuming vegetarian diets rich in unrefined cereals (Sandstead, 1991). Biofortification of staple foods such as wheat and dry beans with Zn is one agricultural science based approach being developed and applied to combat micronutrient malnutrition (Bouis et al., 2011).

Dry beans (Phaseolus vulgaris L.) are a nutrient dense food crop and a dietary staple in

East Africa and Latin America. Genotypic variability for seed Zn levels is relatively high within the species and Zn seed levels from 20 to 59 µg g-1 have been observed (Blair et al., 2010 ;

Islam et al., 2002). Understanding the genetic control of seed Zn content has the potential to improve the breeding process for this important nutritional trait by identifying candidate genes for marker assisted selection and also increase the overall Zn content levels achievable through breeding.

Numerous genes involved in Zn transport have been characterized in model plant species including Arabidopsis and Medicago (Waters and Sankaran, 2011). Major gene families shown to play a role in transport of Zn include ZIF, ZIP, YSL NRAMP, NAS, and HMA. Zinc induced facilitator (ZIF1) protein contributes to Zn and NA sequestration into the vacuoles thus removing the opportunity for both to be transported symplastically (Haydon et al., 2012). The ZIP family is made up of ZRT (zinc related transporter) and IRT (iron related transporter) like proteins. The common feature of members of this family is eight transmembrane domains and a metal binding

domain (Guerinot, 2000). In addition, transcription factors that regulate ZIP genes include members of the basic region leucine zipper (bZIP) gene family and bZIP19 and bZIP23 have been shown to interact with ZIP genes in Arabidopsis (Assuncao et al., 2010). YSL (yellow stripe like) are a gene family that transport metal-NA complexes long distance. In Arabidopsis

AtYLS2 is responsible for mobilization of micronutrients such as Mn, Zn, Cu and Fe from leaves and for loading of Fe-NA complex into seed (Curie et al., 2009). NRAMP (natural resistance associated macrophage protein) are involved in transport of metals out of vacuoles (Thomine et al., 2003). Six members have been identified in Arabidopsis and AtNRAMP3 and AtNRAMP4 are required for iron mobilization in germinating seeds (Languar et al., 2010). Nicotianamine

(NA) a non proteinogenic amino acid chelates Fe and Zn phloem movement to sink tissue

(Schuler et al., 2012). Four NA synthase genes have been characterized (Bauer et al., 2004) and are related in reproduction and seed Fe loading (Waters et al., 2006). HMA (heavy metal associated) proteins are involved with ATP dependent heavy metal transport across membranes.

Some members of this family involved with root to shoot long distance transport and others with sequestration of heavy metals into vacuoles (Morel et al., 2009). These gene families involved in mineral transport and sequestration represent some of the most obvious candidate genes for increased Zn seed levels in crops such as P. vulgaris. Very little is known about how Zn is transported from leaf xylem to phloem of developing seeds and ultimately unloaded into seeds

(Olsen and Palmgren, 2014).

Here we characterize the transcriptome of developing pod of two bean genotypes, the genotype Voyager had more Zn in the seed than Albion. These genotypes were shown to have similar Zn concentration in roots sampled during vegetative growth, leaves, and pods, but different levels of Zn in the seed. A total of 380 genes were differentially expressed including

four genes that may play a role in Zn or other mineral transport to seeds including zinc-regulated transporter, iron regulated transporter like (ZIP), bZip transcription factor, zinc-induced facilitator (ZIF) and natural resistance associated macrophage protein (NRAMP) family genes.

A total of eleven genes in the ZIF, NRAMP, YSL, and ferritin gene families contained SNPs between the two genotypes.

MATERIALS AND METHODS

Plant material

The two common bean genotypes used for this study both are small white seeded beans from the Mesoamerican gene pool. Albion is a navy bean variety released by Asgrow in 1987,

Voyager, is a navy bean released by Rogers Brothers Seed Company in 1995. These genotypes were selected based on their contrasting seed Zn concentration. Voyager has higher levels of seed Zn than Albion in diverse growing conditions (Cichy et al., 2005). In addition, in contrast to

Voyager, Albion exhibits foliar Zn deficiency symptoms in low Zn and/or calcareous soils

(Moraghan and Grafton, 1999).

Plant zinc uptake experiment

Seeds of Albion and Voyager were individually planted in 500 ml pots with 3:1 Sunshine

Brand premium grade vermiculate and horticultural perlite grade (P.V.P. Industries, Inc.).

Treatments consisted of 0.5X strength modified Hoagland solution (Duarte et al., 2009) with zinc added and without zinc (3 mMKNO3, 2 mM Ca (NO3)2 x 4H2O, sequestrene DTPA 10% Fe,

1.0 mM MgSO4 x 7H2O, 23.1 mM H3BO3, 0.38 mM ZnSO4 x 7H2O, 0.16 mM CuSO4 x

5H2O, 4.6 mM MoO4 x 2H2O, 1M KH2PO4 (pH to 6.0) was used as a fertilization treatment at a rate of 400 ml three times per week. Plants were grown in a growth chamber with a

photoperiod of 16 hours light and 8 hours dark. Two replicates per plant were harvested as follows: roots and leaf tissue samples of vegetative plants were collected when the third trifoliate leaf had unfolded. Tissue of roots and leaves during flowering was collected when 30% of flowers were opened. Flowering was monitored daily and pods were collected 20 d after flowering. Seed was collected at physiologic maturity. Tissue was collected in liquid nitrogen and stored at -80 oC. All samples were lyophilized and ground to powder with a Geno/Grinder

2000 (SpexCertiPrep, Metuchen, NJ) and zircon grinding balls. Plant tissue samples sent to A L

Laboratories (Fort Wayne, IN) for mineral analysis using induced coupled plasma spectroscopy.

Mineral concentration was measured on 48 beans samples as follows: six tissue types, two Zn fertilization treatments, two genotypes, and two replications of each. Statistical significance was determined using proc glm and Tukey tests for pairwise comparisons in SAS for Windows v.9.2

(SAS Institute Inc., Cary, NC, USA).

Greenhouse RNA-seq experiment

Seed of Voyager and Albion were planted in a greenhouse at Michigan State University.

Two seeds were planted in 22 cm clay pots filled with SureMix potting soil (Michigan Peat

Company). Three pots were planted of each genotype and each pot was treated as a replication.

Plants were watered as needed and fertilized with 0.5X Hoaglands solution (3 mM KNO3, 2 mM

Ca (NO3)2 × 4H2O, sequestrene DTPA 10% Fe, 1.0 mM MgSO4 × 7H2O, 23.1 mM H3BO3,

0.38 mM ZnSO4 × 7H2O, 0.16 mM CuSO4 × 5H2O, 4.6 mM MoO4 × 2H2O, 1M KH2PO4 (pH to 6.0) biweekly starting at 20 d after germination. At anthesis, flowers were marked with a tag.

At 12 days after anthesis individual pods were removed from plants and flash frozen in liquid nitrogen. Two pods per replication were ground to a fine powder with a mortar and pestle while completely frozen. Liquid nitrogen was continuously added to ensure tissue remained frozen

throughout the grinding process. Total RNA was extracted from the samples using an RNA easy

Plant kit (Qiagen). Following extraction, RNA samples were treated with RNase free DNase I

(Qiagen). RNA integrity and concentration was assessed for each of the samples using an

Aligent 2100 Bioanalyzer (Agilent Technologies, Inc.). A subsample of the pod tissue was retained for mineral analysis. Following pod sampling, plants were grown to maturity and mature seeds were also analyzed for mineral concentration as described above and nitrogen concentration according to the Dumas method. Statistical significance was determined based on

Tukey tests in SAS for Windows v.9.2 (SAS Institute Inc., Cary, NC, USA)

RNA sequencing and pre-processing

Six RNA samples in total (3 replicates each of Albion and Voyager) were sequenced at the Michigan State University Research Technology Support Facility (RTSF) using an Illumina

Genome Analyzer II (GA II). The library and flow cell preparation using kits and protocols from

Illumina was conducted by the MSU RTSF. The sequencing was conducted as 75-bp paired-end reads. The RNA sequence was received from RTSF in FASTQ formatted files containing 75-bp paired-end reads. The file contained sequences and quality information about each sequence.

The data were filtered using scripts from FASTX-Toolkit (FASTQ Quality Trimmer and FASTQ

Quality Filter http://hannonlab.cshl.edu/fastx_toolkit/). FASTQ Quality Trimmer clipped the low quality ends with a quality threshold of 20 and removed the reads shorter than 64 bp.

Subsequently, FASTQ Quality Filter script was used to remove low quality sequences with quality scores of 20 or less.

The clean reads from Albion and Voyager were processed separately and aligned to the

P. vulgaris reference genome sequence v. 1.0 (DOE-JGI and USDA-NIFA http://www.phytozome.net). A P. vulgaris genome index was built using Bowtie v. 0.12.7

(Langmead et al., 2009). Splice junctions were automatically determined by TopHat v 1.4.1

(Trapnell et al., 2009) with the provided guidance of annotated gene models (GTF file) obtained from www.phytozome.org. TopHat incorporates the Bowtie algorithm to perform the alignment and builds a database of potential exons and splice junctions. The aligned mapped reads were then used to identify potential exons. Reads from Albion and Voyager were de novo assembled into the contigs using SOAPdenovo-63mer (Li et al., 2010). The output was reassembled by

Cap3 which is used in computation of overlaps between reads, construction of multiple sequence alignments of reads, and generation of consensus sequences (Huang and Madan, 1999).

Functional annotation and classification

The gene.diff file obtained from Cuffdiff was used to identify the start site and end site for every gene in each chromosome. Next package extraction in Python programing language was used to extract nucleotide sequences in FASTA format from the common bean genome.

Transcripts were evaluated for homology and annotated using Blast2GO software

(http://www.blast2go.com). This process included three steps: 1) BLAST to find homologous sequences, with the following options, e-value threshold of E-10, non-redundant protein database

(nr), high-scoring segment pairs (HSP) length cutoff 33. 2) MAPPING to retrieve (Gene ontology) GO terms and 3) ANNOTATION to select reliable functions, with e-value hit filter of

1E-6, cutoff 55, GO weight 5, Hsp-Hit coverage cutoff 0BLASTx sequence translation tool.

Differential expression analysis

The transcript profile and abundance estimation was carried out using Cufflinks v1.3.0 with default parameters (Trapnell et al., 2010). The resulting alignment data from TopHat were then fed to Cufflinks to assemble aligned RNA-seq reads into transcripts (Trapnell et al., 2010).

Normalization, estimated abundance, and tests for differential expression between tissue samples were performed using the program Cuffdiff. Transcript abundance was measured in fragments per kilo base of transcript per million mapped reads (FPKM) (Trapnell et al., 2013). FPKM greater than zero was considered as the threshold to consider a gene as being expressed. The

FDR-adjusted p-value of the test statistic was used to infer differential expression of transcripts

(FDR<0.05). Validation of the expression profiles obtained by RNA-seq was done by RT-qPCR on eight genes belonging to a ZIP gene family as described by Astudillo et al., (2013).

SNP discovery and validation

Single nucleotide polymorphisms (SNPs) between the assembled transcriptomes of

Voyager and Albion pods were discovered with bcftools and samtools mpileup (Li et al., 2009).

The options selected were –D (Output per-sample read depth), –u (Compute genotype likelihoods), and –f (The faidx-indexed reference file in the FASTA format). To validate SNPs called from the transcriptome sequence analysis, three genes YSL, HMA and ZIF were selected based on their role in Zn transport. Primers were designed to amplify a template approximately

700 bp long that contained three to four SNPs (Table 16). DNA was extracted from primary leaf tissue of Voyager and Albion using centrifugal filter “DNeasy Plant Kit” (QIAGEN) and quantified with Quant-iTPico Green dsDNA Assay kit (Invitrogen) following the manufacturer’s instructions. The mixture for each gene was optimized to contain 30 ng of DNA extract, 30 pmol of the primers and 0.5 U of Taq polymerase (AccuPrimePfxSuperMix, Invitrogen). After initial denaturation (95°C 5 min) 35 cycles (95°C 30 sec, 63°C 30 sec, 72°C 30 sec) of amplification were performed, followed by a final extension of 72°C for 5 min. Amplification products of both parents Albion and Voyager were visualized by electrophoresis on 2% agarose gel, stained with ethidium bromide and detected by ultraviolet transillumination. PCR products

with a single band were purified by ethanol precipitation, and directly sequenced via Sanger sequencing with the same primers used for PCR. Sequencing was conducted at the MSU RTSF.

Sequenced products were compared and aligned with the reference common bean genome using

Geneious (v. 5.6.2) (Biomatters).

RESULTS AND DISCUSSION

Growth chamber experiment

Voyager and Albion are two small seeded white bean genotypes from Middle American gene pool. Voyager has been shown to contain higher seed Zn than Albion and these differences have been noted in both field and controlled environment experiments (Cichy et al., 2005;

Moragham and Grafton, 1999). In order to determine if the difference in Zn levels between

Voyager and Albion is limited to the seed, Zn levels were measured in roots, leaves, pods, and seeds of each genotype under two Zn fertilization treatments. Plants grown under the low Zn fertilization treatment had reduced vegetative root, vegetative leaf, flowering leaf and pod Zn concentrations in both genotypes. Zinc levels between Albion and Voyager were not significantly different for each tissue evaluated, except for seeds, where Voyager had 1.8 to 2.3 fold higher seed Zn levels (Figure 3). Zn content on a per seed basis was also calculated to determine if the seed Zn differences between the genotypes were due to seed size differences.

On a per seed basis, Voyager had 3 times and 1.6 times more Zn than Albion under normal and no Zn fertilization respectively. The higher seed Zn content in Voyager indicate that the differences in seed Zn concentration observed are not due to a dilution effect because of seed size. The importance of seed size in influencing seed micronutrient concentrations has been observed in genetic studies with Medicago truncatula and P. vulgaris (Sankaran et al., 2009;

Astudillo et al., 2013). Previous physiological and fertilization studies with Albion and Voyager

showed that Albion accumulated higher Zn levels in stems, leaves and pod walls than Voyager.

Voyager also had higher seed yield than Albion under low Zn fertilization and similar seed yield under normal and high Zn fertilization treatments (Moraghan and Grafton, 1999). Based on these findings, it appears that Voyager is better able to remobilize Zn within the plant and transport Zn to seeds.

Figure 3. Zinc concentration of roots, leaves, pods and seed of two bean genotypes grown under normal Zn and no Zn fertilization.

Since very little is known about genes involved in Zn transport into developing seeds, and it appears that root and leaf Zn levels are similar between the two genotypes, we decided to study the transcriptome of developing pods for potential clues on genes involved in Zn remobilization to the seeds. The seed Zn concentration differences between Voyager and Albion have been

shown to be controlled by a single gene (Cichy et al., 2005) thereby making these two genotypes excellent candidates for transcriptome analysis to identify genes responsible for the seed Zn differences.

Pod transcriptome characterization

Voyager and Albion were grown in a replicated greenhouse experiment. Under these growing conditions Voyager had 1.4 times more seed Zn than Albion (Table 1). These values observed in greenhouse grown plants are similar to what has been observed for these genotypes in some field studies in Michigan (Cichy, unpublished). Seed Fe levels were variable in the greenhouse grown plants and therefore no significant differences were detected. However other studies have shown Voyager to have higher seed Fe than Albion (Gelin et al., 2007). Voyager also had 12.5% more N in the seed than Albion (Table 5). Positive correlations between seed Zn and N have been found in a number of crops including wheat and beans (Cakmak et al., 2010;

Pinheiro et al., 2010).

Table 5. Mean concentration of zinc, iron, and nitrogen in pods and seeds of Albion and Voyager plants from which RNA samples for sequencing were taken. Pod Seed Zn (µg g-1) Fe (µg g-1) Zn (µg g-1) Fe (µg g-1) N (%) Voyager 36 a 92 a 42 a 101a 3.19a Albion b b b a b 28 75 29 88 2.79 Means followed by the same letter in a column are not significantly different at P = 0.05

Developing pods were collected from the greenhouse grown plants at 12 days after flowering. At this developmental stage, 84% of pod weight was the pod wall and 15% was the developing seed in Voyager and 66% was the pod wall and 34% was the developing seed in

Albion. This developmental stage has been characterized as the time prior to seed filling and when nitrogen is accumulating in the pods (Oliker et al., 1978).

Voyager and Albion pod RNA was sequenced as 75 bp paired end reads. RNA reads were mapped to the P. vulgaris genome sequence, v 1.0 DOE-JGI and USDA-NIFA http://www.phytozome.net). There was an average of 31,505,836 high quality reads per sample which were mapped to the P. vulgaris genome. The transcriptome of the developing bean pods at 12 days after anthesis was comprised of 27,197 unique transcripts of which 24,311 were annotated. Analysis of the number of transcripts per chromosome showed that chromosomes 1,

2, 3, 7, 8, and 9 contained the highest number of transcripts. Chromosome 10 had the lowest number of transcripts. In dry bean mapping populations, few quantitative trait loci have been found on chromosome 10 and it is often difficult to identify polymorphic markers on this chromosome, indicating a low rate of recombination. Positive correlation (r=0.5; P<0.05) between size of the chromosome and number of transcript was determined. Chromosomes 2, 3,

7, and 11 had the highest number of highly expressed genes in the pods (Figure 4).

Figure 4. Number of expressed transcripts on eleven common bean chromosomes (in base pairs).

Gene annotation was achieved using Blast2Go (using BLASTx and E-value -6 as parameters). Using gene ontology, graphs were developed which classify gene expression in the

bean pods. Genes expressed during this pod developmental stage were most related to oxidation reduction, auxin biosynthesis and amino acid phosphorylation (Figure 5a). Genes functioning in the nucleus and plasma membrane were the most represented cell types in the developing bean pods (Figure 5b). Genes related to ATP binding and protein binding were the most highly abundant gene types (Figure 5c).

Genes highly expressed in developing pods included several seed maturation proteins, acid phosphatase and lipid synthesis related genes. The most highly expressed genes in the transcriptome of developing pods of dry beans were HAD IIIB acid phosphatase, LTP3 (lipid transfer protein 3), PAP85 cupin protein, PRXR1 peroxidase protein, and phaseolin. The HAD

IIIB acid phosphatase belong to a family of plant phosphatases. Some members of this family have been annotated as vegetative storage proteins (VSP) highly expressed in soybean leaves and also abundant in A. thaliana flowers (Kim et al., 2006).

Figure 5. Characterization of genes in the bean pods into biological processes, cellular components and metabolic function.

LTP3 (lipid transfer protein 3) transfers several different phospholipids, bind fatty acids and could play a major role in membrane biogenesis (Dubbs and Grimes, 2000). PAP85 cupin family are metalloenzymes with two motif conserved sequences which act as ligands for the binding of an active-site metal ion, such as Fe, Mn, or Zn (Anand et al., 2002). Additionally, they encode seed storage proteins and are involved in the regulation of nitrogen utilization

(Chinoy et al., 2011). Phaseolin is the seed storage protein most abundant in P. vulgaris seeds

(Chappell and Chrispeels, 1986). It is a glycoprotein formed by two genes, the α and β phaseolin genes with a relative electrophoretic diversity useful for discriminating geographical origin and wild and domesticated beans (Gepts et al., 1986; Debouck et al., 1993). These results

demonstrated that in P. vulgaris, storage protein transcripts were the most abundant process beginning at R5 and R6 growth stage which involve storage product accumulation, phases of cell expansion and synthesis of reserve metabolites (Bobb et al., 1995).

Differential expression analysis

Using all of the sequence reads, the expression levels of genes in developmental pods were estimated. Expression levels were measured in fragments per kilobase of exon model per million mapped reads (FPKM). Using this criterion, in developing pods 19,510 expressed genes were detected in Albion and 19,527 expressed genes in Voyager.

The distribution of gene expression values in log10 was left-skewed, the median and mean FPKM values are 11.11 and 45.43 respectively. There were 380 genes differentially expressed genes between the developing pods, of which 130 were more highly expressed in

Albion and 215 more highly expressed in Voyager. Genes with the highest differential expression patterns between the two genotypes included cysteine proteinases and MLP-like protein-43 more highly expressed in Albion pods (table 6). These genes are related to growth and mobilization and accumulation of storage proteins in seeds during development (Sheokand et al., 2005). In Voyager, the genes most highly differentially expressed as compared to Albion were cinnamoyl-CoA reductase (CCR-like) and 2Fe-2S ferredoxin-like. Both of these genes are related to metal ion transport in addition to abscisic acid biosynthesis and the electron transport chain. Differential expression analysis showed that the cinnamoyl-CoA reductase (CCR-like) and 2Fe-2S ferredoxin-like genes were1.65 and 1.42 respectively more expressed in Voyager pods than Albion pods.

Table 6. Genes and function of the most highly differential expressed in Albion and Voyager.

Expression level log2 (Fold Genotype Function Albion Voyager Change) Albion Cysteine proteinases Extracellular proteinase probably having a crucial role 791 203 -1.96 during rapid cell growth and leaf expansion

Associated with fruit and flower development and MLP-like protein 43 732 98 -2.91 pathogen defense responses SCR-like 11 S locus cysteine-rich protein 392 66 -2.57

Serine Serine-type carboxypeptidase activity involved in 237 81 -1.55 carboxypeptidase-like proteolysis

Encodes an aspartic proteinase that forms a heterodimer Aspartic proteinase 231 76 -1.60 and is stable over a broad pH range

Low-molecular- Predicted to encode a PR (pathogenesis-related) protein weight cysteine-rich 215 31 -2.78

Voyager Cellular cation homeostasis, divalent metal ion transport. CCR-like 146 460 1.65 Expressed in embryo axis, cotyledons.

Abscisic acid biosynthetic process, electron transport 2Fe-2S ferredoxin 110 295 1.42 chain, pentose-phosphate shunt.

DNA recognition, RNA packaging, transcriptional Zinc-binding activation, regulation of apoptosis, protein folding and 74 169 1.20 ribosomal protein assembly, and lipid binding. Function unknown. Involved in response to stress. Adenine nucleotide Expressed during petal differentiation and expansion 65 162 1.32 alpha hydrolases stage. Function in lipid binding. Involved in lipid transport. seed storage 2S Located in endomembrane system. Expressed in shoot albumin superfamily 35 161 2.18 apex, embryo, flower, leaf, and seed. Expressed during protein petal differentiation and cotyledon expansion stage. Basic chitinase Defense response after wounding or pathogenic attack 10 150 3.90

Gene families known to be involved in Zn transport were identified and their expression in the developing pods of Albion and Voyager was quantified (Figure 6). These families included ZRT and IRT –like protein (ZIP), basic region/leucine zipper motif (bZIP) transcription

factors, vacuolar iron transport (VIT), natural resistance-associated macrophage protein

(NRAMP), zinc induced facilitator (ZIF), yellow stripe (YSL), heavy metal ATPase (HMA), nicotianamine synthase (NAS), dehydrin, and metallothionein. The ZRT and IRT –like protein

Figure 6. Expression analysis of gene families involved in Zn and/or Fe transport identified in pod in developing transcriptome. Vertical box in colors corresponde to each member of gene family.

(ZIP) family is involved in uptake, transport to leaves and translocation to seeds, embryo, endosperm, and seed coat of zinc (Grotz et al., 1998). It was the largest family and 20 out of 23 members (Astudillo et al., 2013) were found in the developing pod transcriptome of which fifteen were expressed (Table 7). Expression analysis of this family in dry bean showed that some members were highly expressed in leaves and pods under two Zn treatments (Astudillo et al., 2013). Additionally, in Arabidopsis and maize, some members of this family are

preferentially expressed in the embryo and endosperm (Li et al., 2013). These results emphasize the importance of these genes in Zn transport into sink organs. bZIP transcription factors bZIP19 and bZIP23 in Arabidopsis were associated with promoter regions of the zinc deficiency-induced

ZIP4 gene of ZIP family (Assuncao et al., 2010). These bZIPs belonging to group F, have been described containing two DNA binding domains needed to respond to low zinc supply in

Arabidopsis (Assuncao et al., 2010). Two bZIP basic leucine-zipper transcription factor genes homologous to bZIP23 in Arabidopsis were identified and both were expressed in pods.

We identified fifteen vacuolar iron transport (VIT) genes and nine members were expressed in developmental pods. Relative low levels of expression (0 to 38 FPKM) was determined, unlike in Arabidopsis, where VIT1 has been found highly expressed in the developing seeds and mediating iron storage in the embryo (Kim et al., 2006). Nicotianamine synthase (NAS) is a metal chelator that produces a nonproteinogenic amino acid which binds to a variety of transition metals (Stephan and Scholz, 1993). Although it has been found to be expressed in roots, leaves, and seeds in this study no evidence of expression was observed in developing pods. Metallothionein proteins bind transition metals and play a role in the homeostasis and detoxification of non-essential minerals (Guo et al., 2008). Four genes were identified in the developing pods and 3 of them showed a high expression (246 to 3,989 FPKM) but none were differentially expressed. In Arabidopsis, AtMT4a and AtMT4b have been suggested to be involved in Zn storage in seeds (Ren et al., 2012). The remarkably high expression may suggest a role in Zn homeostasis in seeds.

Very few of the Zn and/or Fe transport related genes were differentially expressed.

Those that were differentially expressed included one gene each from NRAMP, ZIP and ZIF families (Table 7). Of those differentially expressed genes a member of Nramp was expressed in

Albion and not expressed in Voyager (2.3 fold change). PvZIF1and PvZIP12 were more expressed in Voyager than Albion in 4.6 and 2.2 fold change respectively. ZIP and ZIF family genes have been shown to be involved in transportation of minerals to the vacuole and transport to seeds in Arabidopsis (Haydon et al., 2012; Morel et al., 2009) and rice (Ricachenevsky et al.,

2011). This suggests that one possible reason why Albion has lower seed Zn that Voyager is because it is being moved to the vacuoles and not transported to the seed. For each Zn/Fe related family analyzed transcripts were from 1 to 72 of FPKM value which was relatively low as compared to those genes related to lipid synthesis and storage protein.

The transcript sequences from Voyager and Albion were also analyzed for SNPs. A total of 12,118 SNPs were identified between the two genotypes. On average there were 3.6 SNPs per gene and 3, 401 genes contained SNPs. Sanger sequencing was used to validate SNPs in nine genes (Table 16). Of the gene families related to Zn and/or Fe transport, eleven genes were found to have SNPs with a total of 47 SNPs average of four SNPs per gene. In total, 15 of the

SNPs result in an amino acid change (Table 8). Of the genes with SNPs, the same ZIF gene

(Phvul.002G108300) which was more highly expressed in Albion than Voyager contained 10

SNPs in the coding region. This gene maps to chromosome 11. It is interesting to note that the

Table 7. Gene families involved in Zn and/or Fe transport and expression analysis in the developing pods of Albion and Voyager.

Shaded line represent members of the family differentially expressed

P. vulgaris Gene Position Albion Voyager fold Homologous A. thaliana Chromosome Genome_Id family (bp) (FPKM) (FPKM) change

Phvul.005G034400 PvbZIP1 bZIP23 basic-leucine zipper Chr05 3,212,438 22 23 0.0 Phvul.011G035700 PvbZIP2 bZIP23 basic-leucine zipper Chr11 3,134,439 41 46 0.2 Phvul.001G177500 PvNRAMP1 ATNRAMP, metal ion transporter family protein Chr01 44,116,444 26 31 0.3 ATNRAMP3, metal ion transporter family protein Phvul.002G014300 PvNRAMP2 3 Chr02 1,609,575 61 62 0.0 ATNRAMP3, metal ion transporter family protein Phvul.003G238600 PvNRAMP3 3 Chr03 46,129,963 12 11 -0.2 Phvul.005G182000 PvNRAMP4 ATNRAMP6, metal ion transporter 6 Chr05 40,351,734 17 17 0.0 Phvul.007G150600 PvNRAMP5 ATNRAMP, metal ion transporter family protein Chr07 37,134,084 30 34 0.2 Phvul.009G069700 PvNRAMP6 ATNRAMP2, metal ion transporter 2 Chr09 11,751,007 19 23 0.3 Phvul.009G127900 PvNRAMP7 ATNRAMP6, metal ion transporter 6 Chr09 18,914,511 14 12 -0.3 Phvul.010G110500 PvNRAMP8 ATNRAMP2, metal ion transporter 2 Chr10 37,315,780 0 0 0.0 Phvul.010G160800 PvNRAMP9 ATNRAMP6, metal ion transporter 6 Chr10 42,893,083 2 0 -2.3 * Phvul.002G108300 PvZIF1 ZIFL2, zinc induced facilitator-like 2 Chr02 21,890,013 1 19 4.6 * Phvul.005G012400 PvZIF2 ZIFL1, zinc induced facilitator-like 1 Chr05 1,050,386 3 6 0.9 Phvul.011G173100 PvZIF3 ZIFL1, zinc induced facilitator-like 1 Chr11 44,602,386 0 0 0.0 Phvul.011G173300 PvZIF4 ZIFL1, zinc induced facilitator-like 1 Chr11 44,656,239 0 0 0.0 Phvul.011G173400 PvZIF5 ZIFL1, zinc induced facilitator-like 1 Chr11 44,662,432 0 0 -0.4 Phvul.011G189500 PvZIF6 ZIFL1, zinc induced facilitator-like 1 Chr11 46,613,123 9 11 0.2 Phvul.011G189600 PvZIF7 ZIFL1, zinc induced facilitator-like 1 Chr11 46,625,565 0 0 0.0 Phvul.011G189700 PvZIF8 ZIFL1, zinc induced facilitator-like 1 Chr11 46,638,452 0 0 0.0 Phvul.011G189800 PvZIF9 ZIFL1, zinc induced facilitator-like 1 Chr11 46,652,668 4 3 -0.4 Phvul.011G189900 PvZIF10 ZIFL1, zinc induced facilitator-like 1 Chr11 46,667,766 18 8 -1.1

Phvul.001G035800 PvZIP1 ATZIP4, zinc transporter 4 precursor Chr01 3,438,922 3 3 0.1 Phvul.002G099700 PvZIP2 ZIP10, zinc transporter 10 precursor Chr02 19,642,778 0 0 0.1 Phvul.002G184200 PvZIP3 ZIP metal ion transporter family Chr02 33,721,809 19 20 0.0 Phvul.003G262400 PvZIP4 ZIP10, zinc transporter 10 precursor Chr03 49,001,484 0 0 0.0 Phvul.003G262500 PvZIP5 ZIP10, zinc transporter 10 precursor Chr03Table 49,013,792 0 0 0.0 Phvul.005G145900 PvZIP6 ZIP11, zinc transporter 11 precursor Chr05 37,425,474 0 0 0.0 Phvul.005G146000 PvZIP7 ZIP11, zinc transporter 11 precursor Chr05 37,429,894 1 2 1.2 Phvul.005G048900 PvZIP8 ZIP1, zinc transporter 1 precursor Chr05 5,642,976 6 8 0.4 Phvul.005G149800 PvZIP9 ZTP29, ZIP metal ion transporter family Chr05 37,714,954 18 17 -0.1 Phvul.006G055800 PvZIP10 ZIP11, zinc transporter 11 precursor Chr06 17,173,381 12 21 0.8 Phvul.006G001000 PvZIP11 ZIP1, zinc transporter 1 precursor Chr06 199,508 1 1 -0.2 Phvul.006G003300 PvZIP12 ZIP5, zinc transporter 5 precursor Chr06 1,040,877 2 8 2.2 * Phvul.006G070200 PvZIP13 ATZIP6, metal ion transporter family Chr06 18,953,200 7 8 0.2 Phvul.008G079500 PvZIP14 ZIP metal ion transporter family Chr08 7,633,778 11 12 0.1 Phvul.008G290500 PvZIP15 ZIP5, zinc transporter 5 precursor Chr08 59,348,008 8 15 0.9 Phvul.008G259200 PvZIP16 ATZIP6, metal ion transporter family Chr08 57,181,379 21 24 0.2 Phvul.009G077700 PvZIP17 ATIRT3, iron regulated transporter 3 Chr09 12,668,955 31 36 0.0 Phvul.010G059200 PvZIP18 ZIP metal ion transporter family Chr10 9,814,851 2 2 0.1 Phvul.011G058500 PvZIP19 ZTP29, ZIP metal ion transporter family Chr11 5,068,287 1 1 0.0

Phvul.L002700 PvZIP20 ZIP10, zinc transporter 10 precursor scaff 1,071 0 1 0.2 Phvul.002G322800 PvVIT1 ATVIT1, vacuolar iron transporter 1 Chr02 48,170,585 2 1 -0.5 Phvul.002G322900 PvVIT2 ATVIT1, vacuolar iron transporter 1 Chr02 48,175,491 14 18 0.4

Phvul.002G323700 PvVIT3 ATVIT1, vacuolar iron transporter 1 Chr02 48,252,436 12 14 0.2 Phvul.002G113500 PvVIT4 Vacuolar iron transporter (VIT) family protein Chr02 23,134,245 0 0 0.0 Phvul.002G205000 PvVIT5 Vacuolar iron transporter (VIT) family protein Chr02 36,507,752 0 0 -1.5 Phvul.002G205100 PvVIT6 Vacuolar iron transporter (VIT) family protein Chr02 36,521,460 0 0 -0.8 Phvul.002G205200 PvVIT7 Vacuolar iron transporter (VIT) family protein Chr02 36,533,751 0 0 0.4 Phvul.002G205300 PvVIT8 Vacuolar iron transporter (VIT) family protein Chr02 36,541,077 0 0 0.0 Phvul.004G096500 PvVIT9 Vacuolar iron transporter (VIT) family protein Chr04 27,416,262 10 19 0.9 Phvul.007G079100 PvVIT10 Vacuolar iron transporter (VIT) family protein Chr07 7,508,398 28 38 0.4 Phvul.008G070000 PvVIT11 ATVIT1, vacuolar iron transporter 1 Chr08 6,284,802 6 7 0.4

Table 7 (cont’d)

Phvul.008G187600 PvVIT12 Vacuolar iron transporter (VIT) family protein Chr08 49,131,247 6 6 0.0 Phvul.009G040800 PvVIT13 vacuolar iron transporter (VIT) family protein Chr09 8,164,280 8 7 0.0 Phvul.010G021600 PvVIT14 Vacuolar iron transporter (VIT) family protein Chr10 3,221,741 1 1 0.1 Phvul.010G021700 PvVIT15 vacuolar iron transporter (VIT) family protein Chr10 3,229,195 0 0 1.0 Phvul.001G081600 PvYSL1 ATYSL1, YELLOW STRIPE like 1 Chr01 13,421,083 18 28 0.0 Phvul.001G088900 PvYSL2 YSL6, YELLOW STRIPE like 6 Chr01 16,152,062 60 65 0.1 Phvul.003G006400 PvYSL3 YSL7, YELLOW STRIPE like 7 Chr03 626,298 16 18 0.1 Phvul.003G006500 PvYSL4 YSL7, YELLOW STRIPE like 7 Chr03 631,299 2 2 0.0 Phvul.004G090100 PvYSL5 ATYSL1, YELLOW STRIPE like 1 Chr04 21,588,269 3 4 0.4 Phvul.004G138900 PvYSL6 YSL7, YELLOW STRIPE like 7 Chr04 41,773,229 17 15 -0.1 Phvul.006G083800 PvYSL7 YSL7, YELLOW STRIPE like 7 Chr06 20,249,225 0 0 0.0 Phvul.008G157800 PvYSL8 ATYSL3, YELLOW STRIPE like 3 Chr08 40,137,873 4 9 0.0 Phvul.009G048800 PvYSL9 ATYSL3, YELLOW STRIPE like 3 Chr09 9,292,230 62 72 0.0 Phvul.002G156800 PvHMA1 HMA5heavy metal atpase 5 Chr02 29,860,709 11 5 -1.1 Phvul.002G156900 PvHMA2 HMA5heavy metal atpase 5 Chr02 29,878,666 1 0 -0.6 Phvul.002G288300 PvHMA3 HMA5heavy metal atpase 5 Chr02 45,175,820 0 0 0.0 Phvul.002G288400 PvHMA4 HMA5heavy metal atpase 5 Chr02 45,187,687 0 0 0.3 Phvul.002G208800 PvHMA5 HMA6,PAA1P-type ATP-ase 1 Chr02 36,870,747 5 6 0.3 Phvul.002G190000 PvHMA6 HMA7, copper-transporting ATPase (RAN1) Chr02 34,600,085 13 13 0.0 Phvul.003G047300 PvHMA7 ATHMA1, heavy metal atpase 1 Chr03 5,628,284 30 26 -0.2 Phvul.003G240100 PvHMA8 ATHMA1, heavy metal atpase 1 Chr03 46,285,474 26 25 -0.1 Phvul.003G142700 PvHMA9 ATHMA2, heavy metal atpase 2 Chr03 33,726,455 20 19 -0.1 Phvul.009G240000 PvHMA10 ATHMA4, heavy metal atpase 4 Chr09 35,288,969 1 1 -0.5 Phvul.009G082400 PvHMA11 ATHMA8, type ATPase of Arabidopsis 2 Chr09 13,120,413 8 7 -0.2 Phvul.009G241800 PvHMA12 HMA7, copper-transporting ATPase (RAN1) Chr09 35,544,425 33 35 0.1 Phvul.010G023900 PvHMA13 HMA5 heavy metal atpase 5 Chr10 3,512,059 3 3 0.1 Phvul.001G225000 PvNAS1 ATNAS2, nicotianamine synthase 2 Chr01 48,680,147 0 0 -0.2 Phvul.005G052500 PvNAS2 ATNAS4, nicotianamine synthase 4 Chr05 6,792,803 0 0 0.0 Phvul.006G117300 PvNAS3 ATNAS4, nicotianamine synthase 4 Chr06 23,217,021 0 0 -0.1 Phvul.004G158800 PvDehydrin Dehydrin Chr04 44,048,043 2 1 -1.8 Phvul.009G005300 PvDehydrin Dehydrin Chr09 921,414 24 14 -0.8 Phvul.011G210300 PvDehydrin Dehydrin Chr11 49,334,383 0 0 0.3 Phvul.008G101800 PvMT metallothionein 2A Chr08 11,131,986 3262 3989 0.3 Phvul.010G009500 PvMT metallothionein 2A Chr10 1,509,086 669 847 0.3 Phvul.010G012300 PvMT metallothionein 2A Chr10 1,905,781 310 246 -0.3 Phvul.010G012200 PvMT metallothionein 2A Chr10 1,900,737 0 1 1.1

HMA gene (Phvul002G288300 and Phvul002G19000 ) maps to chromosome 2 in a region where a major QTL for seed Zn concentration has been identified in bean RIL populations from both

Mesoamerican and Andean intra gene pool crosses (Blair et al., 2011; Blair et al., 2011). The seed Zn differences in Albion and Voyager with single marker genetic analysis indicated that this trait associated with SSR markers BM154 and BM184 found on chromosome 9 (Gelin et al.,

2007). Based on the physical position of these markers on chromosome 9 (1,856,660

Family CDS Genomic SNPs in Chr SNPs AA change Length Length CDS confirmed1

HMA 2,982 5,648 5 2 Syn, Syn, Syn, Syn, Phe/Ser

HMA 2,958 4,150 6 2 * Syn, Syn, Syn, Syn, Syn, Syn

HMA 2,835 24,712 1 2 Ala/Thr

HMA 3,564 9,174 4 3 Syn, Syn, Syn, Syn

NAAT 1,392 3,339 6 2 Gln/Arg, Syn, Syn, Syn, Syn, Syn

Nramp 1,524 2,952 4 2 Val/Leu, Syn, Syn, Syn

YSL-OPT 1,908 2,751 3 8 * Met/Ile, Syn, Syn

YSL-OPT 2,031 6,538 2 1 Gly/Ser, Syn

ZIF 1,470 5,330 10 11 * Gln/Leu, Asp/Glu, Arg/Pro, Ala/Val, Syn, Syn, Syn, Thr/Ile, Val/Ile, Gln/His

ZIF 1,470 4,785 5 11 Syn, Ile/Val, Syn, Syn, Syn

Ferritin 891 2,550 1 8 Glu/Lys

1: * indicates SNPs were confirmed by PCR amplification and sequencing.

1,718,891bp respectively) genes such as dehydrin and bZIP44 were found in the surrounding region (922,386 and 1,006,283). Dehydrins are responsible for osmotic stress from drought, cold, and high salinity but also binds metals reducing metal toxicity in plant cells under water- stressed conditions (Hara et al., 2005).

Accumulation of minerals in the seed involved several complex and still unknown mechanisms. The ability to uptake and accumulate minerals, how much mineral is absorbed by roots, transfer into the shoots and leaves via the xylem, and translocation to seeds via the phloem are all potentially important genetic regulation points. It still unclear which mechanism is the most important step in terms of uptake, transport, remobilization and accumulation to determine where our effort to increase concentration of Zn in seed should focus. In Pisum sativum, Zn

remobilization from vegetative tissues to the seeds has been measured and 75-95% of mineral content in pods was remobilized to the seed tissue (Sankaran and Grusak, 2014). In this study we reported the main genes that likely are related to Zn remobilization during the seed filling period.

This research will guide follow up genetic studies with specific candidate genes for seed

Zn accumulation and analysis of partitioning of minerals in different tissues. The gene expression and SNP information gathered in this study has the potential to be useful beyond its relevance to seed Zn levels. It can be applied to elucidate the genetic control of other phenotypic differences between the genotypes, including differences in disease resistance and growth habit types.

RNA sequencing was used to identify members of mineral transporter gene families expressed during bean pod development. The comparative analysis of two closely related bean genotypes with different levels of seed Zn indicate which genes are differentially expressed and which contain SNPs. This information is useful to identify candidate genes for seed mineral biofortification and the most promising candidate from this study is the ZIF gene

(Phvul.002G108300).

CHAPTER 2: THE PHASEOLUS VULGARIS ZIP GENE FAMILY: IDENTIFICATION, CHARACTERIZATION, MAPPING AND GENE EXPRESSION.

ABSTRACT

Zinc is an essential mineral for humans and plants and is involved in many physiological and biochemical processes. In humans, Zn deficiency has been associated with retarded growth and reduction of immune response. In plants, Zn is an essential component of more than 300 enzymes including RNA polymerase, alkaline phosphatase, alcohol dehydrogenase, Cu/Zn superoxidase dismutase, and carbonic anhydrase. The accumulation of Zn in plants involves many genes and characterization of the role of these genes will be useful in biofortification.

Here we report the identification and phlyogenetic and sequence characterization of the twenty three members of the ZIP (ZRT, IRT like protein) family of metal transporters and three transcription factors of the bZIP family in Phaseolus vulgaris L. Expression patterns of seven of these genes were characterized in two bean genotypes (G19833 and DOR364) grown under two

Zn treatments. Tissue analyzed included roots and leaves at vegetative and flowering stages, and pods at 20 days after flowering. In general ZIP gene expression was upregulated in the Zn (-) treatment. G19833 had higher expression levels than DOR364 and was more responsive to Zn deficiency. PvZIP12, PvZIP13, PvZIP16 and Pv bZIP1 were expressed in leaves (at vegetative and flowering stage) and early pods and expression in some cases was higher under Zn (-) treatment. PvIRT3 was slightly expressed in vegetative leaves and it was not expressed in pods.

Five PvZIP genes were mapped genetically in the Dor364 x G19833 mapping population.

PvZIP2 was located in chromosome Pv01, PvZIP7 and PvZIP8 were located on chromosome

Pv05, PvZIP13 was found on chromosome Pv06 and PvIRT3 on chromosome Pv09. The remaining 18 PvZIP genes and three bZIP genes were mapped in silico. PvZIP12, PvZIP13 and

PvZIP18, Pv bZIP2, and Pv bZIP3 were located near QTLs for zinc accumulation in seed on

chromosomes Pv06 and Pv011, respectively. These results increase understanding of the role of

ZIP genes in metal uptake, distribution and homeostasis in P. vulgaris and their potential importance in seed Zn accumulation.

INTRODUCTION

Dry beans (Phaseolus vulgaris L) are the most highly consumed whole food legume in the world. Beans are a food security crop for small farmers and urban poor in many African and

Latin American countries (Siddiq and Uebersax, 2012). In contrast to many other staple crops, beans are rich in a variety of nutrients, including protein, fiber, folate, and minerals (Juliano,

1999). Beans are also a good source of dietary iron and zinc. According to the USDA Nutrient

Database, a 100 g of cooked beans provides an average of 2 mg Fe and 1 mg Zn and the

Estimated Average Requirement for Fe ranges from 3-23 mg per day and 2.5-10.9 mg per day per Zn depending on age and gender (USDA-ARS, 2012). Meeting the Fe and Zn dietary requirements is a challenge for many people. An estimated two billion people suffer from iron deficiency, which is a major cause of anemia (Rastogi and Mathers, 2002; Balarajan et al., 2011).

Zinc deficiency is also widespread, with an estimated 48% of humans at risk, especially populations consuming vegetarian diets rich in unrefined cereals (Sandstead, 1991). In humans,

Zn deficiency can be expressed through diverse symptoms including reduced immune function, fetal brain cell development and child’s growth, reproductive and cognitive development

(Hambidge, 2000). Biofortification of staple foods, including dry beans, with Fe and Zn is one agricultural based approach being developed and applied to combat micronutrient malnutrition

(Bouis et al., 2011). While average dry bean Fe and Zn levels are 55 mg kg-1 and 34 mg kg-1 respectively, three fold genotypic variation in both Fe and Zn levels exist within the species

(Blair et al., 2009 and Islam et al., 2002).

This existing variation makes breeding common beans a viable biofortification approach.

Significant progress has been achieved in Fe biofortification of beans through conventional breeding as illustrated in the recent release of five high Fe bean varieties in Rwanda (Saltzman et al., 2013). Zinc biofortification has lagged behind that of Fe-biofortification perhaps because of lower quantities of Zn in the seeds but also perhaps less incentive because of the difficulty in assessing Zn nutritional status in humans. While there are biomarkers to asses Fe deficiency readily in humans, no such biomarkers are yet available for Zn, although recently a potential biomarker (dematin) has been identified (Ryu et al., 2012).

In addition to relying solely on phenotypic selection to increase seed Fe and Zn levels, there has been an effort to understand the genetic control of seed Zn and Fe accumulation. Since

2009, at least five QTL studies have been published for seed micronutrient levels. In total, 38

QTLs were associated with zinc accumulation, explaining 15 to 40% of the variability. These studies have been in inter gene pool populations (Blair et al., 2009; Blair et al., 2010c), Andean populations (Cichy et al., 2009 and Blair et al., 2011) and Mesoamerican populations (Blair et al., 2012). QTL studies have yet to be applied to marker assisted selection. There has also been limited effort in identifying genes underlying QTL for Fe and Zn. Discovery of genes involved in increased seed Fe and Zn levels would be useful for biofortification efforts in beans and possibly also as targets for transgenic biofortification approach in other crops.

The Zrt and Irt-like Protein (ZIP) family is well characterized for its role in Zn transport and to a lesser extent it role in Fe transport (Eide et al., 1996). The ZIP family is well conserved among bacteria, fungi, protozoa, animals, and plants (Chen et al., 2008, Grotz et al., 1998). ZIP proteins are predicted to have eight trans membrane domains with a histidine motif which may be part of an intramembranous heavy metal binding site that plays a role in the transport pathway

for the minerals that are transferred (Eng et al., 1998). ZIP transporters have been implicated in

Zn uptake, transport of Zn in leaves and translocation to seeds, embryo, endosperm, and seed coat (Waters Sankaran, 2011). Previous information on the role of ZIP genes in Zn movement throughout the plant come from expression analysis, yeast complementation and Zn hyper accumulator mutants. In A. thaliana fifteen members have been identified and characterized, revealing a wide variety of localization and function (Milner et al., 2012). AtZIPs have been detected mainly in the roots, shoots (Milner et al., 2012). In rice, seventeen ZIP coding sequences were identified. They have been evaluated in roots, shoot, and panicles of efficient and inefficient genotypes (Chen et al., 2008, Grotz et al., 1998, Milner et al., 2012, Connolly et al., 2002, Guerinot., 2000, Shanmugam et al., 2011, Weber et al., 2004). In Medicago truncatula, six genes were identified in roots and leaves which were upregulated under Zn deficiency and three of them restored yeast growth on Zn-limited media (Lopez-Millan et al.,

2004). In Glycine max, GmZIP1 has been detected in nodules and was highly selective for Zn in a functional complement in yeast (Moreau et al., 2001). In Vitis vinifera, VvZIP3 was expressed in developing flowers and its expression was correlated with high Zn accumulation in this tissue

(Gainza-Cortes et al., 2012 and Afoufa-Bastien et al., 2010). Analysis of this family in different species demonstrates the importance of these genes in Zn transport.

Another important gene family related with Zn transport is the bZIP family. This family has been well characterized in Arabidopsis with 75 members divided in ten groups based on conserved motifs that reflect functional similarities (Jakoby et al., 2002). Group F includes bZIP19, bZIP23 and bZIP24. These transcription factors contain a DNA binding domain, a leucine zipper dimerization motif and histidine-rich motif, essentials for responding to low Zn supply in Arabidopsis (Assuncao et al., 2003 and Assuncao et al., 2010).

With the recent release of the P. vulgaris genome sequence (Phaseolus vulgaris v1.0,

DOE-JGI and USDA-NIFA, http://www.phytozome.net/commonbean), it is possible to identify candidate genes for seed Fe and Zn levels. Characterization of genes related to Zn homeostasis in

P. vulgaris will provide useful information on specific target genes in the biofortification breeding effort. This research has identified and characterized of 23 members of the PvZIP gene family. Three members of a second family of genes, bZIP transcription factors, were also characterized similarly. The relative expression of genes from both the ZIP and bZIP families were characterized in various tissues and stages of development in two P. vulgaris genotypes,

DOR 364 and G19833 under two Zn treatments is described. Selected ZIP and bZIP genes were also located on a linkage map overlaid with QTL locations for Zn accumulation in seed.

MATERIALS AND METHODS

Plant material and phenotypic data

Two bean genotypes were evaluated in this study, DOR364, a small seeded, high yielding improved cultivar from the Middle American genepool and G19833, a large seeded landrace from the Andean genepool known for its tolerance to low P soils (Beebe et al., 2006). These genotypes also exhibit contrasting seed mineral levels as shown in field trials in Darien,

Colombia. DOR364 had 49 mg kg -1 Fe while G19833 had 75.5 mg kg -1, and DOR364 had 21.7 mg kg -1 Zn while G19833 had 29.9 mg kg -1 (Blair et al., 2009). DOR364 and G19833 were specifically chosen for this study because valuable genetic information exists for the lines. A recombinant inbred line (RIL) between these parents was developed by single seed descent at the

International Center for Tropical Agriculture (CIAT), Colombia. It consists of 87 individuals and has a linkage map of 499 single copy markers with a coverage of 2,306 cM (Galeano et al.,

2011). This population has been used by different research groups for map saturation and QTL

identification associated to biotic and abiotic traits (Lopez and Blair, 2009) and QTL positions for seed minerals (Blair et al., 2003, Beebe et al., 2006, Blair et al., 2009, Galeano et al., 2011).

Identification of PvZIP and Pv bZIP genes and phylogenetic analysis

ZIP genes in P. vulgaris were identified using the sequences of eighteen Arabidopsis thaliana ZIP genes (http://www.arabidopsis.org/). The program tBlastn was used to compare the

Arabidopsis ZIP genes against the bean genome (Phaseolus vulgaris v1.0, DOE-JGI and USDA-

NIFA, http://www.phytozome.net/commonbean). These sequence data were produced by the US

Department of Energy Joint Genome Institute. Conserved domains in each predicted transcript was verified using Pfam 26.0 protein database (http://pfam.sanger.ac.uk/) to confirm the reliability of the match with the ZIP family. The coding sequence (CDS) for each gene was aligned with genomic DNA sequence to confirm splice signals in boundaries between introns and exons. The P. vulgaris ZIP genes were assigned unique names from PvZIP1 to PvZIP19 and

PvIRT1 to PvIRT4. These names do not relate to naming of ZIP genes in others species. Since this gene family characterization is based on an incomplete genome sequence, the existence of additional ZIP genes in the bean genome is a possibility.

Three Pv bZIP genes were identified in the dry bean genome based on sequences of bZIP19, bZIP23, and bZIP24 reported by Assuncao et al., (2010) in Arabidopsis. Identification of the new bZIP genes was based on the homology with the Basic Leucine Zipper Domain (bZIP domain).

Sequence alignments, phylogenetic analysis, tree estimation using bootstrapping and graphs of each gene were performed using ClustalW (Larking et al., 2007) using the program

Geneious® 6.0.3, created by Biomatters (build 2012-11-06 10:52).

In silico mapping of PvZIP and Pv bZIP genes

Each of the 23 putative ZIP transport protein genes and 3 putative bZIP transcription factor genes were mapped in silico to a location on the DOR364 x G19833 linkage map based on sequence homology with the P. vulgaris genome. This alignment was conducted with an MS

Excel based program MapSynteny (Fernandez et al., 2011).

Genetic mapping of select members of the PvZIP and Pv bZIP family genes.

Five ZIP genes were mapped genetically based on QTLs for seed Fe and Zn concentration in the DOR364 x G19833 population (Blair et al., 2009). QTL for seed Zn have been located on chromosomes Pv01, Pv03, Pv06, and Pv08 (Blair et al., 2009). The ZIP genes located in silico in these regions were mapped genetically in the full set of RILs of the DOR364 x G19833 population. These include PvZIP2, PvZIP6, PvZIP8, PvZIP13, and PvIRT3. Primers were designed to flank ZIP gene intron sequence (Table 9). PCR was conducted on DOR364 and G19833 parents as a first step to test for polymorphisms. The mix for the reactions were Mg

2.0 mM, dNTP’s 0.2 µM, primer 0.3 µM. PCR reactions were carried out for 3 min at 95 °C, followed by 35 cycles of 30 s at 95 °C, 30 s at 55 or 60 °C (based on the annealing temperature of each primer), and a final period of 5 min at 72 °C. Products were visualized on agarose gels to verify amplification and identify insertion/deletions that had potential to serve as a molecular marker. To increase the possibility of finding polymorphisms for those monomorphic products, the SSCP technique (from single strand conformational polymorphism) was used, which is based on detection of conformational differences of single stranded DNA fragments due mobility shifts in non-denaturing polyacrylamide gel electrophoresis (Orita et al., 1989) such as MDE acrylamide gels (MDE Gel Solution 250ML Lonza NJ, USA) as described in Galeano et al.

(2009). For genetic mapping, Mapdisto software version 1.7 Beta 132 (Lorieux, 2012) was used

to locate the position of the new ZIP genes on the DOR364 x G19833 genetic map reported by

Galeano et al. (2011). The command place locus was used to located the ZIP genes, using as criteria the highest LOD value and lowest recombination rate. The position of each ZIP gene was confirmed using the Ripple order command.

QTL data and analysis

Phenotypic data for Fe and Zn concentration from Popayan and Darien Colombia in 1998 and 2003 were reported for this population in Blair et al. (2009) and additionally, Fe and Zn concentration from the same locations in 2006 (not previously reported) were used for QTL analysis with the linkage map reported in Galeano et al. (2011). QTL cartographer v. 2.5 (Wang et al., 2012) was used to find QTLs following the same parameters described in Blair et al.

(2009).

Expression analysis of select Pv ZIP and Pv bZIP

Plant growing conditions

Seeds of DOR364 and G19833 were surface sterilized and planted in 500 ml clay pots with 3:1 Sunshine Brand premium grade vermiculite (Sunshine Brand, Texas, USA) and horticultural grade perlite (Industries, Inc MA, USA). Half strength Hoagland solution (3 mM

KNO3, 2 mM Ca (NO3)2 x 4H2O, sequestrene DTPA 10% Fe, 1.0 mM MgSO4 x 7H2O, 23.1 mM

H3BO3, 0.38 mM ZnSO4 x 7H2O, 0.16 mM CuSO4 x 5H2O, 4.6 mM MoO4 x 2H2O, 1M

KH2PO4 (pH to 6.0) was applied to pots a rate of 400 ml three times per week. Two Hoagland solution treatments were employed 1) Zn (+), Zn was added as ZnSO4 x 7H2O and 2) Zn (-). A total of three pots per genotype were planted and each one was designated as a biological replicate. The experiment was a randomized complete block design. Plants were grown in a

growth chamber (1.86 m2) with a photoperiod of 16 hours light and 8 hours dark and an average of temperature of 29 oC /20 oC (day/night). For the vegetative samples, roots and leaves were collected from the vegetative 3 stage (V3), when the third trifoliate leaf was unfolded at node 5.

Leaf and root samples collected at flowering were harvested at the R2 stage when 30% of the flowers were opened. Pod samples were collected at 20 days after flowering. Plant tissue was collected in labeled sterilized tubes of 50 ml in liquid nitrogen and stored at -80oC.

Table 9. Primer list for gene expression analysis via RT-qPCR and genetic mapping of ZIP genes. Gene Sequence Approach PvZIP12 GGGCAGAGGCAAGTGCAGGG GGGCGTGATGGAGATGCAGGA RT qPCR PvZIP13 CGCGCTCTTCGATTGCCAGGT CCACCGGCGTGTAGTGCGTA RT qPCR PvZIP13 GCGGTGGCTCGTTGAGTATT TGCTATGAGGTCAACAAGAGCC Mapping PvZIP16 TGCACGGTTGATGGCGACGG ACGGAACTCCTTCGCCATCGT RT qPCR PvIRT3 AGAATAACACCATCCCCAAAATTA AGTCACTATGGGAATGTCACAGAA RT qPCR PvIRT3 AATGCACATCGTGGGGATGC GGCTTTAAACTGCGCTTGGG Mapping bZIP1 ATGCAACCCACCTGGCCCTGATGCT TGCCTGCCCTTGTAGTTTCCTCGCT RT qPCR bZIP2 ATCGGGAGAAGAAGAAGGCTCGCGC TCCGGCCCCTTATGTCCACCAGCAA RT qPCR bZIP3 GCAGCAGTTCTTGAGCGTGGAGGCT TGAAGGTGGTGTTGCCGAAACCTGCA RT qPCR PvactinII TGCCATCCAGGCCGTTCTTTCA GGGGACTGTGTGGCTGACACC RT qPCR

RNA extraction and Real-time quantitative PCR

About 2 g of tissue from each sample collected was ground in liquid N2. Total RNA from root and leaf tissue of two developmental stages was extracted by RNeasy Plant Mini Kit

(Qiagen). Pods were extracted following the Li and Trick et al., (2005) protocol optimized for high starch samples. Total RNA was stored in aliquots at -80oC. The concentration of RNA was quantified through Quant-iT™ RiboGreen (Invitrogen). Two µg of RNA of each sample were treated with DNase I and purified by 0.1 vol of 3M sodium acetate (pH 5.2) and 3 vol of 100% ethanol. cDNA synthesis was carried out by High Capacity cDNA Reverse Transcription Kits

(Applied Biosystem), using 1 ug of RNA. cDNA concentration was measured by Quant-iT™

PicoGreen (Invitrogen).

The relative expression levels of eight ZIP genes; PvZIP2, PvZIP7, PvZIP6, PvZIP12,

PvZIP13, PvZIP16, PvZIP18 and PvIRT3 and three transcription factors belong to the bZIP family, bZIP1, bZIP2, bZIP3 were measured using RT-qPCR. Primers for RT-qPCR were designed for each gene in such a way that they spanned one or two exons in genes with intronic regions to detect genomic DNA contamination (Table 1). Quantiﬁcation of all transcripts was performed using the SuperScript III Platinum SYBR Green One-Step qRT-PCR Kit (Invitrogen,

Carlsbad, CA) according to the manufacturer’s instructions. In total 50 ng total of cDNA in triplicate as technical repeats for each biological replicate of all tissues were used as template.

Ten-fold serial dilutions were used to determine the efficiencies of each primer. RT-qPCR master-mix was prepared as follows: 1 µl of diluted cDNA, 5 µl of 2X SYBR Green Reaction

Mix, 0.5 µl 3 pmol of each primer and nuclease-free water in a final volume of 10 µl. The

StepOnePlus™ Real-Time PCR System (Applied Biosystems) was used for amplification and fluorescence measurement of each transcript at each temperature step and cycle during the reaction. Thermal cycling conditions consisted of 10 min at 95°C followed by 40 cycles of 15 s at 95°C and 45 s at 60°C. The identity and purity of the amplified product was checked through analysis of the melting curve carried out at the end of amplification. Relative gene expression was calculated using the comparative CT method (Livak Schmittgen, 2001). bActin was used as a reference gene and root in vegetative stage Zn (-) treatment as a calibrator (Wen et al., 2005).

Fold change of greater than 2 was used as criteria to determine if genes were differentially expressed. Statistical analysis was performed using SAS V 9.3 (SAS Institute Inc. NC, USA). A repeated measurement analysis (Proc Mixed) was performed. Main effects were tested by

ANOVA and a probability of P<0,05 was chosen as the level of significance for the statistical test.

Quantification of Zn concentrations in tissue

Plant tissue from two biological replicates of DOR364 and G19833 under two zinc treatments was quantified for Zn concentration. Tissue was freeze dried and ground to powder using a Geno Grinder 2000 (Spex CertiPrep, Metuchen, NJ) and zircon grinding balls. Two grams were sent to AL Great Lakes Labs, Inc. Fort Wayne, IN, for mineral analysis by induced coupled plasma spectroscopy.

RESULTS

Identification of ZIP family members and comparison with homologs in other species

Twenty three sequences, including nineteen ZIP and four IRT genes were identified in the P. vulgaris genome sequence based on similarity to ZIP genes in A. thaliana and/or

Medicago truncatula. All new genes have full-length coding sequences containing open reading frames (ORF) ranging from 153 to 655 amino acids in length. Sequences identified were confirmed in the PFAM database based on ZIP transmembrane domain and had E-values higher than -10. Peptide sequences of all new ZIP genes identified in common bean were aligned with eighteen ZIP genes reported in the A. thaliana, and M. truncatula (Table 10). A phylogenetic neighbor joining tree shows the relationship among ZIP genes in P. vulgaris, A. thaliana, and M. truncatula (Fig. 7). Alignments at the amino acid level predicted eight highly conserved transmembrane domains (Fig.8) and a potential metal binding motif containing histidine residues implicated in metal transport which are highly conserved throughout the entire family. (Lopez-

Millan., 2004 and Guerinot., 2000). All ZIP genes contained a histidine motif between

transmembrane domain III and IV except PvZIP6, PvZIP7, and PvZIP18. The ZIP gene family members in P. vulgaris shared 3 to 81.4% homology to each other. Of all ZIP genes found

PvIRT3 was the most closely related to Arabidopsis, sharing 59 and 57.3% similarity with genes

AtIRT3_AT1G60960.1 and AtZIP4_AT1G10970 respectively. PvZIP14 also showed high similarity with AtZIP6_AT3G30080.1 at 53.8%.

Sequence ID Gene Chrom Position Homology to A. thaliana Homology to M. truncatula

Phvulv091010812m PvZIP1 Pv01 3,442,406 ATZIP4_Zinc transporter 4 3E-63 ZIP-like zinc transporter -Medtr1g016120.1 4E-145 Phvulv091015745m PvZIP2 Pv01 49,770,995 ATZIP4_Zinc transporter 4 0 Zinc transporter 5 -Medtr3g082050.1 4E-21 Phvulv091015614m PvZIP3 Pv01 49,839,431 ZIP3_Zinc transporter 3 4E-37 Zinc transporter 5 -Medtr3g082050.4 2E-22 Phvulv091019402m PvZIP4 Pv02 33,735,220 IAR1_ZIP metal ion transporter 9.5E-42 Zinc transporter 5 Phvulv091012034m PvZIP5 Pv05 5,645,010 ZIP1_Zinc transporter 1 3E-156 Zinc transporter - Medtr3g082050.3 2E-109 Phvulv091029608m PvZIP6 Pv05 37,426,497 ZIP2_ZRT/IRT-like protein 2 2.1E-46 Iron regulated transporter -Medtr2g097580.1 5E-99 Phvulv091029689m PvZIP7 Pv05 37,431,839 ZIP2_ZRT/IRT-like protein 2 2E-43 Iron regulated transporter -Medtr2g097580.1 2E-161 Phvulv091029664m PvZIP8 Pv05 37,715,863 ZTP29_ZIP metal ion transporter 6.5E-23 Zinc transporter 5 -Medtr4g065640.1 2E-130 Phvulv091026664m PvZIP9 Pv06 200,959 ZIP5_Zinc transporter 5 1E-132 Zinc transporter -Medtr3g082050.1 1E-150 Phvulv091009317m PvZIP10 Pv06 1,033,953 ZIP5_Zinc transporter 5 4E-130 Zinc transporter -Medtr3g082050.1 4E-44 Phvulv091009315m PvZIP11 Pv06 1,040,964 ZIP5_Zinc transporter 5 1E-129 Zinc transporter zupT -Medtr3g082050.1 7E-147 Phvulv091018095m PvZIP12 Pv06 17,174,396 ZIP11_Zinc transporter 11 5.7E-52 Zinc transporter 5 -Medtr2g097580.1 3E-113 Phvulv091002113m PvZIP13 Pv06 18,954,219 ATZIP6_ZIP metal ion transporter 1.5E-56 Zinc transporter 5 -Medtr5g071990.1 7E-141 Phvulv091007436m PvZIP14 Pv08 7,634,926 ZIP metal ion transporter family 1.2E-14 Zinc transporter 5 -Medtr7g074060.1 0 Phvulv091022274m PvZIP15 Pv08 57,181,509 ATZIP6_ZIP metal ion transporter 2E-158 Zinc transporter -Medtr5g071990.1 2E-139 Phvulv091004709m PvZIP16 Pv08 59,351,699 ZIP1_Zinc transporter 1 3.2E-33 Zinc transporter 6-Medtr3g082050.3 7E-91 Phvulv091010505m PvZIP17 Pv10 9,817,594 ZIP metal ion transporter family 0 ZIP transporter -Medtr7g074060.1 0 Phvulv091003125m PvZIP18 Pv11 5,071,268 ZTP29_ZIP metal ion transporter 1.3E-20 Zinc transporter 6, -Medtr4g065640.1 3E-124 Phvulv091030363m PvZIP19 Pv02 19,642,824 ZIP10_Zinc transporter 10 1E-102 Zinc transporter 3E-174 Phvulv091011372m PvIRT1 Pv03 49,001,506 IRT1_Iron-regulated transporter 1 4E-126 Zinc transporter 3E-169 Phvulv091011626m PvIRT2 Pv03 49,013,793 IRT1_Iron-regulated transporter 1 5E-112 Zinc/iron permease-Medtr8g105030.1 6E-129 Phvulv091000876m PvIRT3 Pv09 12,670,278 ATIRT3_Iron regulated transporter 3 2.2E-68 ZIP transporter - Medtr3g104400.1 2E-131 Phvulv091000875m PvIRT4 Pv09 12,670,315 ATIRT3_Iron regulated transporter 3 9.5E-42 Zinc transporter 4 - Medtr4g083570.1 3E-174 Phvulv091018638m bZIP1 Pv05 3,213,447 bZIP23 - transcription factor family 7E-49 Basic leucine zipper - Medtr4g073100.1 Phvulv091015330m bZIP2 Pv11 3,134,797 bZIP19 - transcription factor family 1E-109 Basic leucine zipper - Medtr4g073100.1 5E-57 Phvulv091015414m bZIP3 Pv11 3,709,270 bZIP44 | basic leucine-zipper 44 2E-52 bZIP transcription factor - Medtr4g070860.1 4E-71 Gene structure analysis of ZIP genes in P. vulgaris revealed that the twenty three genes have different intron-exon structures with a wide range of lengths. PvZIP2, PvZIP6, PvZIP7,

PvZIP15, and PvIRT1, were composed by three exons and two introns. PvZIP3, PvZIP5,

PvZIP9, PvZIP10, PvZIP11, PvZIP13, PvZIP16, PvZIP19, and PvIRT2 each have four exons and three introns. PvZIP17 and PvIRT3 have five exons and four introns. Seven exons were identified in PvZIP1 and PvIRT4. Many exons (ten to fourteen) were present in PvZIP4, PvZIP8,

PvZIP12, PvZIP14, and PvZIP18.

Given the importance of some members of the bZIP gene family in the regulation of ZIP genes and in turn plant Zn homeostasis, their sequences were also characterized in the P. vulgaris genome. The common bean genes bZIP1, bZIP2 and bZIP3 were 261, 266 and 154 amino acids long respectively. None of the bZIP genes contained introns. The three amino acids

sequences encoding the bZIP genes shared 4.0 to 38.5% similarity among each other and 15 to

55.4% similarity with bZIP19, bZIP23, and bZIP24 genes described in A. thaliana (Assuncao et al., 2010).

Mapping of PvZIP genes and QTL for seed Fe and Zn concentration

ZIP and bZIP were mapped in silico on the DOR364 x G19833 genetic map by aligning

ZIP gene sequences and molecular marker sequence in DOR 364 x G19833 against the P. vulgaris genome sequence. The results of the in silico mapping indicate that ZIP genes are distributed on all P. vulgaris chromosomes except Pv04 and Pv07. There was a tendency for ZIP genes to cluster together, most notably on chromosome Pv05 and Pv06 (Fig. 9).

Figure 9 (cont’d)

Through in silico mapping, bZIP1 was located on chromosome Pv05 and bZIP2 and bZIP3 were located near each other on chromosome Pv11. The bZIP3 gene location was expected based on the position and sequence of the SNP marker Pv g785 which contains the bZIP domain (McClean et al., 2010). Selected ZIP genes were also mapped genetically via DNA polymorphisms in the DOR364 x G19833 population using the P. vulgaris reference genetic map published by Galeano et al. (2011) of 499 single copy markers and 2,306 cM of coverage (Fig.

3). PvZIP2, PvZIP8 and PvZIP13 mapped to chromosomes 1, 5 and 6 respectively.

Once gene markers were mapped, QTLs for seed Fe and Zn were also identified on this map. These QTLs included previously published data for two sites (Blair at al., 2009) as well as

QTL identified in whole and cotyledon seed mineral evaluation from a 2006 planting of the same population in Darien, Colombia. QTL analysis in the 2006 evaluation identified new QTLs for zinc concentration on chromosomes 1 and 2 and also confirmed the QTLs identified by Blair et al. (2009) (Table 11). For seed Fe concentration, thirteen QTLs were found on chromosomes 2,

3, 6, 8 and 11. For seed Zn concentration eleven QTLs were found on 1, 2, 3, 6, 9 and 11 (Table

3). ZIP genes mapped on chromosomes 3, 6, 8, and 11 mapped within the region of a QTL for

Fe and/or Zn. On chromosome 11 two bZIP genes (with genomic position 3,134,797 and

3,709,270 bp) were mapped in silico within the region of two QTLs for seed Fe and one QTL for seed Zn (Fig. 9). Two PvIRT genes are present on chromosome 3 (at 49,001,506 and 49,013,793 bp) and three QTLs for seed Fe concentration mapped between the QTLs (Fig. 9). Table 11 shows specifically which ZIP genes are located within or nearby QTL for seed mineral concentration.

Table 11. Quantitative trait loci (QTL) for iron and zinc concentration identified with composite interval mapping in the DOR364 x G19833 population.

Additive Trait QTL Tissue Environment Chromosome Marker interval Position (cM) 1 Genomic position (bp) LOD R2 Source ZIP genes nearby effect Iron2.1DG* Cotyledon Darien 2003 2 g680 - BSNP6 361.1 5.7 10.6 3.1 G19833 Iron3.1DG Whole seed Darien 2003 3 BMb1188 - BMb1259 194.8 5.7 11.1 1.8 G19833 Iron3.2 DG Whole seed Darien 2003 3 g1388 - Leg213 226.2 PvIRT1 – PvIRT2 9.4 19.7 2.4 DOR364 Iron3.3 DG Whole seed Darien 2006 3 G1388 – BSNP59 239.5 PvIRT1 – PvIRT2 49,001,506 - 49,013,793 4.5 10.5 1.3 DOR364 Iron 3.4 DG* Cotyledon Darien 2003 3 BSNP59 245.5 PvIRT1 – PvIRT2 3.3 5.9 2.3 DOR364 Iron3.5 DG Whole seed Popayan 1998 3 BSNP56 – BMb590 251.1 4.0 9.4 3.4 G19833 Iron6.1 DG* Whole seed Darien 2006 6 PVBR5 - Bng104 183.4 6.1 15.3 1.6 G19833 Iron Iron8.1 DG Whole seed Popayan 1998 8 BMb266 - BMb196 192.1 5.0 13.4 3.4 G19833 Iron8.2 DG* Cotyledon Darien 2003 8 BMb386 - BSNP43 100.8 PvZIP14 7,634,926 8.2 17.7 4.1 G19833 Iron11.1 DG Whole seed Popayan 1998 11 BSNP82 - BMd27 22.0 Pv bZIP3 3,709,270 9.9 24.5 4.4 G19833 Iron11.2 DG Whole seed Darien 2003 11 BSNPc27 - BMa145 81.4 14.7 34.8 3.0 G19833 Iron11.3 DG* Cotyledon Darien 2003 11 BSNP39 - BMd27 30.8 Pv bZIP2-Pv bZIP3 3,134,797 - 3,709,270 7.6 15.0 3.6 G19833 Iron11.4 DG* Whole seed Darien 2006 11 BSNPc27 - BMa6 79.4 7.2 20.1 1.8 G19833

Zinc1.1 DG* Whole seed Darien 2003 1 g510 - BMb356 0.0 PvZIP3 49,839,431 4.6 8.8 0.6 G19833 Zinc2.1 DG* Cotyledon Darien 2003 2 BMb1286 - Leg188 302.5 PvZIP4 33,735,220 6.2 14.1 1.7 DOR364 Zinc3.1 DG Whole seed Popayan 1998 3 IAC34 - BSNP28 150.1 4.6 10.1 1.2 G19833 Zinc3.2 DG Whole seed Darien 2003 3 g1830 - Bng012 154.7 4.5 8.5 0.6 G19833 Zinc6.1 DG* Whole seed Darien 2006 6 BMc238 - Bng009 139.3 4.4 13.1 0.7 G19833 Zinc6.2 DG Whole seed Popayan 1998 6 BMb182 78.8 PvZIP12 - PvZIP13 17,174,396 - 18,954,219 3.6 9.8 1.0 DOR364 Zinc Zinc9.1 DG Whole seed Popayan 1998 9 G1286 0.0 3.2 6.7 1.0 G19833 Zinc11.1 DG Whole seed Popayan 1998 11 BMd27 - BSNPc27 53.4 PvZIP18 5,071,268 6.6 17.7 1.5 G19833 Zinc11.2 DG Whole seed Darien 2003 11 Bng187 - BMa145 92.7 11.5 28.0 1.1 G19833 Zinc11.3 DG* Cotyledon Darien 2003 11 BSNP82 - BN 22.0 Pv bZIP2 3,134,797 7.8 17.5 1.8 G19833 Zinc11.4 DG* Whole seed Darien 2006 11 Bng001 - BMa6 92.7 5.5 16.0 0.8 G19833 1 PvZIP genes and Pv bZIP transcription factor coinciding with QTLs found. *New QTLs found

Expression analysis of PvZIP genes

Studies in Arabidopsis, Glycine, Vitis and Medicago indicate that ZIP genes may be expressed in roots, leaves and reproductive tissue (Grotz et al., 1998; Lopez-Millan et al., 2004).

Many studies so far have focused on expression in roots and shoots (Grotz et al., 1998, Lopez-

Millan et al., 2004, and Milner et al., 2012). From the perspective of biofortification, it is necessary for a bean plant not only to efficiently take up Zn from the soil, but also transport and accumulate it in vegetative tissue, pods and seeds. In order to determine the expression profile of members of ZIP family and their relevance during the development of common bean, relative expression levels were measured by RT qPCR. PvZIP2, PvZIP7, PvZIP6, PvZIP12, PvZIP13,

PvZIP16, PvZIP18, and PvIRT3 genes were selected for this analysis based on their location in the genome in relation to presence of QTLs for Zn and Fe in the DOR364 x G19833 population.

Three tissue types were analyzed for gene expression in DOR364 and G19833, roots, leaves, and pods. Roots and leaves were collected at two time points, one during vegetative growth, and one during flowering. Pods were sampled 20 days after flowering. Each tissue type was selected from plants grown under two Zn treatments. At four weeks after planting, DOR364 and G19833 plants in the Zn (-) treatment exhibited some Zn deficiency symptoms such as interveinal chlorosis, bronzing and shortening of the internode (Brown and Leggett, 1967). In general, the

ZIP genes were expressed in all tissue analyzed (Fig 10). However, PvZIP2, PvZIP6, PvZIP7, and PvZIP18 were undetectable under RT qPCR in all tissue types. This finding is supported by pod transcriptome data which also found low to no expression for these genes (Astudillo et al., in preparation).

Gene expression of PvZIP12and PvZIP16 was induced upon low Zn status in leaf than root tissue. PvZIP12 was most highly expressed in leaves under Zn (-) treatment, especially in

G19833 (Fig. 4). For PvZIP13, G19833 exhibited higher expression in leaves at flowering as compared to vegetative leaves under both Zn treatments. Of each of the ZIP genes studied,

PvZIP16 showed the highest differential expression based on tissue type. It was 139 to 848 fold more expressed in the leaves than the roots for both genotypes and developmental stages under the Zn(-) treatment. PvZIP16 was higher expressed in the pods of G19833 under both Zn treatments than DOR364 grown under the Zn (+) treatment. Significant differences were detected between genotypes, Zn treatments, genes and developmental stages (P<0.05) as is showed in figure 4.

Expression analysis of three transcription factors bZIP

RNA from the same samples described above were also used to determine the relative expression of three transcription factors Pv bZIP1, Pv bZIP2 and Pv bZIP3, which are homologous to Arabidopsis bZIP genes in the zinc homeostasis network (Table 10). The common bean homologue bZIP1 was detected in roots, leaves, (at vegetative stages) and pods but expression pattern did not change based on Zn treatment. This gene was more highly expressed in leaf tissue sampled during flowering than vegetative tissue in both G19833 and

DOR364. Transcripts of Pv bZIP2 were detected in roots, leaves and pods and its expression pattern was not influenced by tissue type, developmental stage or Zn treatment. Pv bZIP3 was expressed in roots and leaves during flowering. It was highly expressed in pods and was upregulated under the Zn (-) treatment.

Tissue Zinc concentration

Zn concentration was determined for DOR364 and G19833 in all tissues, developmental stage, and Zn treatment (Fig. 11). The highest Zn concentration was observed in roots for both genotypes. There was no significant effect of Zn treatment in leaf Zn levels at vegetative and flowering stages. Although significant differences were not found, plants grown under the Zn

(+) treatment tended to have higher levels of Zn in pods, and seed than those grown under the Zn

(-) treatment. Seed Zn levels were 26 and 53% less in the Zn (-) treatment in DOR364 and

G19833 respectively. G19833 had higher seed Zn levels that DOR364 under the Zn (+) treatment but not under the Zn (-) treatment (Fig. 11).

DISCUSSION

Common bean is becoming an alternative to dietary supplements as a way to improve human health in plant based diet. ZIP metal transporters are one of the most important gene families for Zn and Fe cellular uptake and translocation in plants (Adams et al., 2012, Chen et al., 2008, Guerinot, 2000, and Wu et al., 2009). Identification of ZIP members in P. vulgaris and characterization of their expression patterns is useful to increase the understanding of uptake, transportation and storage of Zn. This study is a unique combination of gene family characterization with physical and genetic mapping and functional expression data that has utility in common bean improvement.

Twenty three ZIP genes were identified in the P. vulgaris genome and genes were annotated and characterized based on similarity to other ZIP family members in A. thaliana and

M. truncatula. According to total number ZIP family members across species the family origin may be from a common ancestor that has undergone sequence duplication followed by divergence events (D’Ovidio et al., 2004). PvZIP genes clustered on chromosomes 3, 5, 6 and 9 showed high sequence similarity. The close proximity and sequence similarity of many of the

ZIP gene family members might suggest of gene duplication followed by diversification (Yang et al., 2009). On the other hand, heterogeneity in structure and expression in each PvZIP genes correlated with high diversity in function. Four of eight genes evaluated were not expressed in any of the tissue analyzed. This outcome was confirmed in transcriptome analysis in pods where ten of twenty three genes analyzed were scarce or not detected in this particular tissue (Astudillo et al., in preparation). Loss of function in these proteins might be overcome by compensation by duplicate genes (D’Ovidio et al., 2004).

It is important to consider the link between functional variation and gene structural differences among ZIP family members. In many cases, Zn interacts with cysteine and histidine in proteins and may determine the ionic selectivity of ion transporters (Ramesh et al., 2003 and

Lopez-Millan et al., 2004). The motif of histidine in variable region between transmembrane domain III and IV in many ZIPs has been postulated to serve as a potential metal ion binding site

(Eide et al., 1996, Zhao and Eide, 1996, and Grotz et al., 1998). For PvZIPs identified in this study, all contained this motif except PvZIP6, PvZIP7, and PvZIP18, interestingly these ZIP genes were also not expressed in all tissue analyzed, suggesting without the motif they are not functional.

In Arabidopsis, ZIP genes have been shown to regulate and also contribute to the uptake, transport and accumulation of Zn (Grotz et al., 1998, Weber et al., 2004, Talke et al., 2006, Lin et al., 2009 and Milner et al., 2013). Here we used RT-qPCR approach to obtain a picture of ZIP gene transcription in roots and leaves at vegetative and flowering stages, and pods at 20 days after flowering in P. vulgaris. Some processes such as Zn uptake, have been studied in detail, while others such as remobilization of Zn from vegetative to reproductive tissues are less well understood (Genc et al., 2006). The evaluation of gene expression patterns based on tissue, Zn treatments, and genotype not only provides information on the functionality of the ZIP family genes but also may help explain genotypic differences in seed Zn accumulation. These data indicate differential gene regulation associated to the nutritional requirements and possible mechanism of partitioning of Zn along the plant. According to analysis of ZIP genes in

Arabidopsis approximately half of the genes characterized are induced in response to Zn deficiency (Grotz et al., 1998 and Talke et al., 2006).

ZIP gene expression differences in P. vulgaris were related to Zn treatments, genotype, and tissue type. Genotypic differences in Zn translocation capacity in different organs may be an important factor in Zn accumulation in seeds (Hacisalihoglu et al., 2004). Observed differences between genotypes could also be due to genetic differences and diversity among Andean and

Mesoamerican gene pools (Blair et al., 2009). Similarly to previous studies, G19833 had higher seed Zn level than DOR364 (Blair et al., 2009). However DOR364 had higher Zn in its roots as compared to G19833 suggesting that G19833 can translocate more Zn from roots to seeds.

Zinc plays a specific role in fertilization and pollen grains contain very high concentrations of

Zn (Fageria et al., 2011). At flowering most of the Zn taken up is incorporated into the developed seed (Jiang et al., 2008) so genes highly expressed at flowering and in pods such as

PvZIP12, PvZIP16, and bZIP1 could be directly related to Zn remobilization to seeds.

Although leaves are known as the major source of remobilized micronutrients in common bean (Sekara et al., 2005) in rice stems are the major source of Zn in the seed (Waters and

Sankaran, 2011). With this study it was not possible to determine how much and the source of

Zn remobilization. Future studies with radio labeled Zn would be warranted to asses Zn remobilization.

Based on the relative expression values established via RT-qPCR, the high Zn concentration in roots did not reflect expression values for the ZIP genes evaluated in this tissue.

In Arabidopsis at least ten different members of the ZIP family play a role in zinc uptake in roots, including ZIP1, 2, 3, 4, 5, 9, 10, 11, 12 and IRT3 (van de Mortel et al., 2006b). We evaluated four of their respective homologous in P. vulgaris and found that they were only weakly expressed in roots.

The DOR364 x G19833 RILs mapping population consists of 86 individuals, which are adequate for identifying QTL with moderately large effects based on QTLs previously detected

(Blair et al., 2009, Blair et al., 2011, and Galeano et al., 2011). In silico mapping of ZIP genes was a successful strategy to locate PvZIP genes aligned with QTL for seed Fe and Zn in the bean genome. QTL analysis was carried out in the current reference bean map (Galeano et al., 2011).

It is worth noting where PvZIP4 and PvZIP12 and PvZIP13 are located on chromosomes 2 and

6, there are QTL for seed Zn concentration. For Fe, the IRT genes are considered to be the main transporters for high-affinity iron uptake in roots in Arabidopsis (Lin et al., 2008, Connolly et al.,

2002, and Henriques et al., 2002), In this study, PvIRT1 and 2 were located on chromosome 3 within an important QTL region associated with seed Fe concentration. The Pv bZIP2 and Pv bZIP3 genes were located on chromosome 11 and aligned with the most important QTL for Fe and Zn reported in P. vulgaris. There are no obvious genotypic differences in expression of these genes in G19833 and DOR 364, however. The QTL in this region has been found in at least three mapping populations, including Mesoamerican and Andean intra and inter genepool crosses (Blair et al., 2010; Blair et al., 2009; and Blair et al., 2011). The bZIP transcription factors analyzed correspond to genes in Arabidopsis responsible for response and adaptation to low Zn supply. In general, PvZIP, PvIRT and Pv bZIPs co-localization with QTLs for Fe and Zn levels suggesting that their function is important in Fe and Zn homeostasis in P. vulgaris. In

Arabidopsis, the bZIP transcription factors that interacted with ZIP genes were found directly upstream of the ZIP genes (Assuncao et al., 2010). In the case of P. vulgaris none of the bZIP genes were adjacent to ZIP genes.

This study is the first to characterize the ZIP gene family, report the expression profile in various tissues with two bean genotypes and fertilization treatments. It provides evidence of the

relationship among level of transcripts and QTLs in dry bean seed as was identified in PvZIP12,

PvZIP13 genes and the transcription factor PvZIP3. This contribution will be particularly useful for advancing bean breeding programs. The use of such gene markers encoding proteins associated with transport of Zn and Fe and accumulation could increase the efficiency and accuracy in the selection of bean breeding materials for biofortification.

CHAPTER 3: IDENTIFICATION OF PRECISE AND CONSISTENT QTL REGIONS ASSOCIATED WITH IRON AND ZINC ACROSS DIFFERENT GENETIC BACKGROUNDS USING QTL META-ANALYSIS APPROACH.

INTRODUCTION

In order to explain the genetic variation of complex traits, quantitative trait loci (QTL) analysis allows the identification of genetic actions, interactions and number of regions linked to phenotype on specific regions of chromosomes (Falconer Mackay 1995). The accuracy in detection depends on population size, number and type or molecular markers, and phenotyping

(Erickson et al 2004). A large number of populations have been generated to analyzed QTLs in many crops in a wide variety of traits of agricultural importance , in order to find all possible genetic sources of variation represented in different genetic backgrounds.

The meta-QTL analysis compiles information from multiples studies, improves QTL position comparing individual experiments narrowing down confidence intervals obtained from individual analyses (Goffinet and Gerber 2000). Various statistical methods have been developed for meta-QTLs analysis. The software Biomercator uses the transformed akaike classification criterion (AIC) to determine the best model between one, two, three QTLs etc. until the maximum number of QTLs mapped in the same region (Arcade et al., 2004).

Knowledge of genes controlling accumulation of zinc in seed will enhance breeding programs focusing on biofortification (Jin et al., 2013). Recent QTL analyses have been conducted in legumes to identify regions associated with zinc. In soybean (Diers et al 1992;

Peiffer et al., 2012; King et al., 2013 and Raghuprakash et al., 2014), Medicago (Sankaran et al.,

2009), Lotus japonicus (Klein and Grusak, 2009) and at least seven QTL studies have been published in common bean (Guzman-Maldonado et al., 2003, Blair et al., 2009; 2010a; 2010b;

2011; 2013 and Cichy et al., 2009) (Table 4). The large amount of information available in common beans and the use of common markers across different maps make it possible to integrate such QTLs in order to improve accuracy position and smaller confidence interval using

QTL meta-analysis approach.

To date, meta-QTL including iron and zinc has been reported in maize by Jin et al.,

(2013). This analysis was conducted in order to estimate the number and positions of consensus

QTLs. In that study, 218 F2:3 families of the population and four previous QTL studies were used to conduct meta-analysis. Ten Meta QTLs involved in zinc and/or iron accumulation were detected on six chromosomes at CI of 95% and phenotypic variation more than 10%.

QTL analysis of four dry bean populations from different gene pools were conducted for seed iron and zinc concentration (Cichy et al., 2009, Blair et al., 2009, 2010a, 2010b, and 2011).

These analyses determined that inheritance of their accumulation is polygenic. In total, 41 QTLs were associated with zinc, explaining 7 to 24% of the variability in zinc concentration in seed.

Regarding the interaction with others minerals, zinc showed a positive and significant correlation with iron (r=0.63; P<0.001) (Blair et al., 2009 and 2010a). The implication of these correlations, together with overlapping QTLs at least on three linkage groups for iron and zinc concentration is that some genetic factors for different minerals are co-segregating, and that selection for iron will in fact result in an increase in zinc (Beebe et al., 1999).

In this study, individuals QTL studies published for zinc were projected onto a the bean consensus map on chromosomes 2, 6 and 11 where QTL were clustered into meta-QTL to narrow down confidence intervals of initial individual analysis. The projected QTL information was combined with the physical position on bean genome to determine gene density across chromosomes.

MATERIALS AND METHODS

Construction of consensus map and meta-QTL analysis

In the identification of consensus QTL for zinc concentration in seed data from different common bean populations were compiled, to create a consensus map and projection of linkage maps of individual QTLs studies for meta-analysis of QTL clusters. QTL information was collected from published studies involving QTL mapping for zinc concentration from different populations. Details of the parents used in developing population, size of the mapping population, number of markers, and QTLs identified are given in table 12.

Table 12. Details of the Zn QTLs from different studies include in the QTL meta-analysis.

Population Genepool Population No. Chr Environment Map Total size QT distance Markers (cM) Dor364 x G198331 M x A 87 13 2, 3, 6, 7, 9, 11 2 1,703 236 G21242 x G210782 A x A 100 3 2, 7, 8 3 720 118 G14519 x G48253 M xM 110 9 1, 2, 3, 6, 8, 3 915 114 AND696 x G198394 A x A 77 11 1, 6, 5, 11 2 1,105 167 Bat93 x Jalo EEP5 A x M 72 3 1, 11 1 1,364 217 Total 41 9,443 1 Blair et al., 2009, 2Blair et al., 2010, 3Blair et al., 2010b, 4Cichy et al., 2009, 5 non-published

A consensus genetic map was developed and meta-QTL analysis was performed using

Biomercator v2.1 (Arcade et al., 2004). For the consensus map, the projection function was used and the highly saturated Dor 364 x G19833 map (Galeano et al 2011) was used as reference.

Chromosomes 2, 6 and 11 were chosen for the analysis because three population shared QTLs related with zinc concentration. Locations of QTLs for zinc concentration (Table 13) were extrapolated onto the consensus map on the basis of common genetic marker positions. Co- location of QTLs was determined by the Akaike’s information criterion (AIC) (Hirotogu, 1974), and the lowest value was considered the best fit model for Meta-QTL prediction. In order to

Table 13. Summary of QTLs used in the meta-QTL analysis.

QTL Environment Chromosome LOD R2 Position From To ZnAA2.1 Palmira Pv02 4 18.63 133 125 134 ZnDG2.1 Darien Pv02 3.1 8.47 310.11 300 311 ZnDG2.2 Shelled seed_Darien Pv02 5.31 17.58 267.51 300 311 ZnDG2.3 Shelled seed_Darien Pv02 2.71 7.64 306.11 316 320 ZnAG6.1 Low P_Darien_2000 Pv06 4.71 24.13 76.26 76.26 79.26 ZnAG6.2 High P_Darien_2000 Pv06 2.56 7.39 18.44 18.44 21.44 ZnAG6.3 High P_Darien_2006 Pv06 3.01 10.08 18.01 18.01 21.01 ZnDG6.1 Popayan_1998 Pv06 2.92 9.86 78.81 76 80 ZnDG6.2 Darien_2003 Pv06 2.5 7.59 181.41 180 182 ZnBJ11.1 Darien Pv11 3.65 15.06 26.91 26.91 29.91 ZnDG11.1 Popayan_1998 Pv11 5.35 17.77 53.41 43.3 60 ZnDG11.2 Darien_2003 Pv11 5.08 16.47 96.31 83 106.5 ZnDG11.3 Darien_2003 Pv11 3.56 10.66 18.81 0 22 ZnDG11.4 Shelled seed_Darien Pv11 6.51 22.03 30.91 14.8 43.3 control heterogeneity of confidence intervals across studies, they were re-estimated (Swamy et al., 2011), using the approach described by Darvasi and Soller (1997): CI=530/NR2. Where N is the population size and R2 the proportion of the phenotypic variance explained by the QTL.

Gene content analysis

Meta-QTL regions were analyzed for gene content to determine the presence of genes and gene cluster responsible for Zn concentration in seed. A comparative genomics approach was followed to analyze the genes present in meta-QTL. Gene content was based on annotated data of homologous regions in the common bean genome (www.phytozome.org). We assumed that the genes identified in common bean genome are homologous among bean genotypes and were collinear to those underlying the Zn QTL.

RESULTS AND DISCUSSION

Meta-analysis

Zn transport and accumulation is a complex trait which is governed by genes of small effect. Zn QTL identification and analysis requires different approaches such as molecular mapping, accurate phenotyping, different genetic backgrounds and variability from different environments. Genetic and genomic information generated by the QTL then is used in marker assisted selection. However, due to the large diversity of information of discovered QTL from different studies and populations it is not possible to have a concise region where further genomics analysis can be conducted for identification of candidate genes.

QTL data from five studies related to iron and zinc content were collected and used for meta-analysis. These studies were carried out on five genetic populations; G19833 x DOR364

(DG), BAT 93 x Jalo EEPP58 (BJ), AND696 x G19839 (AG), G21242 x G21078 (AA), G14519 x G4825 (MM). Five populations of common bean have been screened for seed Zn concentration. Their population size ranged from 72 to 110 individuals. The number of markers used ranged from 118 (among single sequenced repeat SSRs, RAPDs, and AFLPs) to 236 SNP markers. The map distance was 720 to 1,703 cM with a marker every 0.5 to 8 cM depending on each population. The number of environments per population where Zn concentration was phenotyped varied from 1 to 3. From the 5 studies, 39 Zn QTLs were reported, which were distributed on all chromosomes except chromosomes 4 and 10. The number of QTLs per population ranged from 1 to 4. The proportion of QTL per chromosome ranged from 2 to 13 and the phenotypic variance of the initial QTL varied from 9 to 39% (Table 2). The map distance for each chromosome was 172.3, 199.9 and 139.1 cM respectively. Three linkage groups and their

QTLs were aligned in order to identify common clusters of regions associated with zinc

accumulation. Thirteen Zn QTLs have been identified on chromosomes 2, 6 and 11 in more than one population. On chromosome 2, three QTLs for zinc accumulated in seed were identified in the population DG. In the Andean population AA, one QTL was reported on chromosome 2. On chromosome 6, two QTLs were identified in DG population and three QTLs were identified in the AG population. On chromosome 11, the population DG and BJ showed 1 and 3 QTLs respectively. QTLs found in the three chromosomes were used for meta-QTL analysis resulting in a short listed based on the Akaike Information Criterion (AIC) (Table 14). The lowest AIC value was the criteria to determine a significant model. In total 5 meta-QTL for Zn seed concentration from 13 individual analysis coming from different experiments were identified at a confidence interval of 95% (Figure 12a, 12b, 12c). Two meta-QTLs were found each on chromosome 2 and 6 and one was identifed on chromosome 11. The phenotypic variance of the meta-QTL varied from 9.2% to 15.9%. The confidence intervals of zinc meta-QTLs ranged from 8 to 37 cM. All meta-QTLs were narrower than their respective original QTL showing that genetic and physical length was significantly reduced regarding the initial length on the genetic map (Table 4).

A QTL analysis for zinc accumulation was performed in a black seeded bean population,

Shiny Crow x Black Magic. This population has been analyzed for canning quality traits related to water uptake, color retention and anthocyanin concentration (Cichy et al., 2014). QTLs related to Zn were identified on chromosomes 2 and 8. It was not possible include this population in the meta-QTL analysis due to lack of common markers with other mapping populations. However, interval of a QTL found on chromosome 2 among 4.5 and 7.5 Megabases spanned the same region that the meta-QTL identified with a Zn binding ion as candidate gene.

Table 14. Characteristics of meta-QTL identified for Zn concentration in common bean Physical No. No of Meta- Flanking AIC Mean QTL CI length MQTL Chr QTL initial QTL CI markers value R2 (cM) MQTL model QTL (cM) (Mb) MQTL2.1 2 BM152 - BMb495 26.3 4 2 18.1 34.0 8 12.0 MQTL2.2 2 BMb97 – g2540 2 7.8 18 12.0 18.0 MQTL6.1 6 BMb182 – PvZIP 371.1 4 3 9.2 96.8 36.4 2.1 MQTL6.2 6 BM170 – AGAC01 2 15.9 58 37.5 3.3 MQTL11.1 11 BMd22 – g1932 36.9 3 4 14.9 46.2 23.2 2.9

A 50% reduction of the genetic and physical interval with a phenotypic variance up to 15.9% was observed. The 5 meta-QTL regions with small genetic and physical intervals are important regions for marker assisted selection in biofortification programs, fine mapping, candidate gene identification, and functional analysis. These QTLs can be introgressed in to different market classes or varieties to develop high zinc lines.

Gene content analysis and identification of candidate genes

The genome sequence within the narrow confidence intervals of the meta-QTLs were screened to identify a short list of candidate genes with possible function in transport of zinc in plants. Using the annotated gene information available in the common bean database, the genes present in the 5 meta-QTL regions were analyzed by comparative genomics. Nine important genes and functions underlying meta-QTL for Zn concentration were identified (Table 15). Zinc ion binding is a transcription regulator which interact selectively with Zn ions. A total of 8 members were found, 5 genes on chromosome 2 and 3 members on chromosome 6. The ZIP family have been implicated in Zn uptake and transport to leaves and translocation to seeds, embryo, endosperm, and seed coat (Guerinot et al., 1998) and were common across the meta-

QTL regions on chromosomes 2, 6 and 11. In addition, transcription factors regulating ZIP genes include members of the bZIP family. bZIP19 and bZIP23 contain a two DNA binding

domains, a leucine zipper dimerization and histidine-rich motifs, essential in the response to low

Zn supply in Arabidopsis (Assuncao et al., 2010).

2 6

0 A0102B 7 P0903B 11 H1901A 12 H1902B 20 E0403A 24 L0205B 30 BM167 a) Consensus Chr 2 38 b) BM139 b ) Consensus Chr 6 2 40 BM164 6 51 BM172 0 Bng084 0 A0102B 67 BMb1166 7 P0903B 69 BMb1266 11 H1901A 73 BMb180 H1902B 8 Bng080 12 79 BMb125 20 E0403A 24 L0205B 86 BMb122 30 BM167 93 NFP_2 38 BM139 98 PVBR18 40 BM164 101 g2020 51 BM172 103 Bng011 0 Bng084 67 BMb1166 104 BMb365 69 BMb1266 107 PVBR94 73 BMb180 8 Bng080 79 BMb125 108 BMb1163 86 BMb122 111 BM156 93 NFP_2 116 BM152 98 PVBR18 BMd18 41 g471 101 g2020 118 GATS91 Bng011 103 PVBR243 104 BMb365 107 PVBR94 119 PVBR78 108 BMb1163 121 PVBR15 51 BMd12 111 BM156 124 BMb137 116 BM152 125 BMb80 BMd18 127 NORK_2 41 g471 60 BMb419 118 GATS91 BMb259 PVBR243 129 119 PVBR78 BMb1289 BMe30 121 PVBR15 130 51 BMd12 BMb137 PVBR11 124 71 BMb519 125 BMb80 BMb497 127 NORK_2 BMb527 60 BMb419 129 BMb259 131 BSNP41 77 BMb182 BMb1289 BMe30 133 BMb252 130 BMb712

PVBR11 ZnDG_6.1 71 BMb519 88 BMb341 BMb497 134 BMa133 BMb527 BMb1131 93 NAS2 131 77 BMb182 BSNP41 PVBR25 95 BMb539 133 BMb252 BMb420 97 Leg736 BMb712

ZnDG_6.1 101 CAC1 135 BMb97 88 BMb341 134 BMa133 BMb1108 BMb1131 Bng117 93 NAS2 95 BMb539 BES41H07.r PVBR25 BMd17 Meta-QTL_1 102 Leg736 BMb1279 BMb420 136 BMd47 97 101 CAC1 BM137 135 BMb97 137 g1801 Bng117 BMb1108 OD12 138 CCS52_3 BES41H07.r BMd17 Meta-QTL_1 BMb1061 140 BM143 102 BMb1279 136 BMd47 BMb1158 141 SSR-1AC29 BM137 137 g1801 ZnAG_6.2 104 OD12 BMb342 138 CCS52_3 142 BM142

BMb1061ZnAG_6.1 g2553 140 BM143 CA5 BMb1158 141 SSR-1AC29 YS1 143 BMb1194 ZnAG_6.2 104 BMb342 GCTC03 142 BM142 SSR-1AC13 108 CA5 ZnAG_6.1 g2553 g739 144 BMa269 YS1 143 BMb1194 110 PVBR163 SSR-1AC13 g2581 GCTC03 108 g739 114 PVBR198 144 BMa269 SSR-1AC57 110 PVBR163 116 ZIP10 g2581 ZnAA_2.1 BMb1126 Meta-QTL_1 SSR-1AC57 145 BMd02 114 PVBR198 126 Bng046 116 ZIP10 ZnAA_2.1 BMb1126 131 Leg81 Meta-QTL_1 145 PG02 Bng046 BMd02 ZnDG_2.1 126 133 BMb1105 BMa7 131 Leg81 PG02 136 BMc238 ZnDG_2.1 BMa7 BMa150B 133 BMb1105 BMa180 136 BMc238 142 Bng009 BMa150B 146 BMd76

BMa180 g321 142 Bng009 ZnDG_2.3

g321ZnDG_2.2 BMb1192 146 BMd76 148 BMb1157

Meta-QTL_2 ZnDG_2.3 ZnDG_2.2 BMb1192 146 148 BMb1157 155 BM170 Meta-QTL_2 BSNP85 146 BSNP85 155 BM170 157 FRO1 BMa150 FRO1 BMa150 157 162 Bng027 BMa07 162 Bng027 BMa07 ZnAG_6.3 ZnAG_6.3 166 Leg58 g1148 g1148 166 Leg58 PVBR14 PVBR14 147 BMa16 147 BMa16 170

170 PVBR20 PVBR20 Meta_QTL_2 148 BMb1286 148 BMb1286 Meta_QTL_2 BMd37 BMd37 SSR-1AC46 ZnAG_6.2

149 ZnAG_6.2 149 SSR-1AC46 171 AGAT05171 171 150 BMb495 AGAT05171 150 BMb495 CTTA05 152 Leg188 172 172 CTTA05 Leg188 PVBR5 153 Bng108 152 174 Bng094 PVBR5 154 BMc280 153 Bng108 174 AGAC01 Bng094 160 g2540 154 BMc280 178 g1998 178 AGAC01 162 g680 160 g2540 182 g1757 164 Leg301 185 182 g1998 162 g680 BSNP67 165 g774 189 185 g1757 164 Leg301 Bng104 166 g2427 196 189 BSNP67 165 g774 g1174 169 BSNP6 200 Bng104 166 g2427 g2480 196 172 BSNP4 g1174 169 BSNP6 200 g2480 172 BSNP4 Figure 12. Meta-QTLs analysis on chromosomes a) Chr 2, b) Chr 6 and c) Chr 11 defining cluster of QTLs coming from individual analysis for Zn concentration in seed.

Figure 12 (cont’d) 11 c) Consensus Chr 11

0 Leg183 10 Leg241 Leg100 12 Leg470 17 BMa116 21 BMd22 23 BSNP39 BSNP82 26 g811 27 BM441 29 g735 BN 30 BMd33 32 Leg133 33 g2273 36 BMd27 38 g1731 43 g1415

44 g1932 ZnDG_11.3

ZnBJ_11.1 49 g2307 50 BSNPc27

52 Bng167 ZnDG_11.2

Meta-QTL_1 55 Bng001 56 g1438

ZnDG_11.1 57 Bng025 BMa6 59 BMb185 60 BMa145 61 Bmb2150w g2285 62 Bmb2149w 64 Bmb1344w 66 Bmb32 g835 67 Bmb310 Bmb1228 68 Bmb1093 69 Bmb654 g2527 70 g156 Bmb484 71 g1510 g1489 72 g1168 Bmb653 Bng145 73 BMa241a BM10 BMb588 74 g1598 BMb619 80 DMI3-1 BMa324 81 BMa32 82 BM1074 83 BMb1072 84 Baja 86 Leg43 90 BMy2 91 Leg236 97 Leg449 99 Leg220 106 Leg218 113 Leg208 124 g1215 126 g188 132 g1983 139 g2135

(Bookum et al., 2003 and Assuncao et al., 2009). Two bZIP genes in common bean were found on chromosome 11. Additional bZIP genes were also found underlying meta-QTLs on chromosome 2 and 6. A tandem region with vacuolar iron transport genes was found on chromosome 2. This gene family has been involved in iron-loading during embryo development

(Jeong and Guerinot, 2009). HMA proteins (heavy metal associated) are involved with ATP dependent heavy metal transport across membranes. Members of this family are involved in root

to shoot long distance transport and sequestration of heavy metals in vacuoles (Morel et al.,

2009). Here HMA proteins, were found on chromosomes 6 and 11, with a tandem organization on chromosome 6. The zinc induced facilitator (ZIF) gene, located on chromosome 2 is involved in zinc transport to the vacuole and it has been found being expressed in the tonoplast (Haydon and Kawachi, 2012). In functional studies, the loss-of-function atzif1 mutant affected zinc distribution and its transcription was upregulated by Zn-excess (Haydon and Cobbet, 2007). On chromosome 6 two member of Nicotianamine (NA) and ferric reductase were found.

Nicotianamine is a non proteinogenic amino acid that chelates Fe and Zn in phloem movement to sink tissue (Schuler, Rellán-Álvarez et al. 2012). Four NA genes have been characterized (Bauer et al., 2004) and are related in reproduction and seed Fe loading (Waters et al., 2006). Ferric reductase encodes an iron-deficiency inducible iron reductase responsible for reducing iron at the root surface (Yi and Guerinot, 1996). QTLs for iron reductase acitivity have been mapped on chromosomes 2 and 11 (Blair et al., 2010c). A comparision of regions based on proximity markers, determined that those QTLs are clustered with zinc meta-QTLs in both chromosomes.

Table 15. Candidate genes reported in the identified meta-QTL regions ID Locus Chr Position Gene Function Phvul.002G066000 Pv02 7,686,359 Phvul.002G076800 Pv02 11,395,433 Phvul.002G080500 Pv02 12,067,481 Phvul.002G144100 Pv02 28,039,140 Interacting selectively and non-covalently with zinc Zinc ion binding Phvul.002G206100 Pv02 36,601,189 (Zn) ions Phvul.006G055600 Pv06 17,167,752 Phvul.006G085700 Pv06 20,410,218 Phvul.006G105100 Pv06 22,145,643

Phvul.002G099700 Pv02 19,642,778 Phvul.002G184200 Pv02 33,721,809 ZIP metal ion Zinc transport proteins responsible for zinc uptake Phvul.006G055800 Pv06 17,175,727 transporter family in the plant Phvul.006G070200 Pv06 18,954,796 Phvul.011G058500 Pv11 5,068,287

Zinc induced Transporter involved in Zn homeostasis and its Phvul.002G108300 Pv02 21,890,013 facilitator-like 2 transport to the vacuole

Phvul.002G110600 Pv02 22,492,108 Phvul.002G203300 Pv02 36,344,588 Phvul.006G029200 Pv06 12,406,073 Basic-leucine zipper Transcription factors regulating function for the Phvul.006G071300 Pv06 19,074,132 (bZIP) adaptation of plants to zinc deficiency Phvul.006G101700 Pv06 21,870,121 Phvul.011G038200 Pv11 3,309,633 Phvul.011G042600 Pv11 3,708,498

Phvul.002G156800 Pv02 29,860,709 Heavy metal atpase 5 Some members have been involved in Zn and Cd Phvul.002G156900 Pv02 29,878,666 (HMA) ions loading to the shoots (Hanikenne et al., 2008)

Phvul.002G113500 Pv02 23,134,245 Phvul.002G205000 Pv02 36,507,752 Vacuolar iron Transport and load of Fe into the vacuoles during Phvul.002G205100 Pv02 36,521,460 transporter (VIT) embryo development (Kim et al., 2006) Phvul.002G205200 Pv02 36,533,751 Phvul.002G205300 Pv02 36,541,077

Phvul.006G090100 Pv06 20,862,354 Phvul.006G093700 Pv06 21,160,159 Phvul.006G139700 Pv06 25,474,240 Heavy metal Metallochaperones for safe transport of metallic Phvul.006G139900 Pv06 25,481,346 transport/detoxification ions. They contain a metal binding domain involved (HMA) in heavy metal homeostasis and detoxify Phvul.006G153400 Pv06 26,649,216 Phvul.006G173800 Pv06 28,443,517 Phvul.011G068500 Pv11 5,918,539

Nicotianamine Phvul.006G117300 Pv06 23,218,312 Phloem chelator capacity to bind Cu, Co, Fe(II) and synthase 4 Fe(III), Mn, Ni, and Zn and transport to seed

Ferric reduction Phvul.006G133600 Pv06 24,818,368 oxidase 7 (FRO) Responsible for Fe uptake and regulation

In this study, we carried out the analysis 13 QTLs of zinc accumulation in seed across five studies. The analysis was based on individual projection of maps 2, 6, and 11 onto a consensus map. Then, confidence intervals of QTL location were combined and five meta-QTLs were identified. Consensus map linked to the physical map and sequence-based markers and enable us compare QTL based on genetic (cM) and physical distance (bp). Based on this result, comparative genomics approach showed consistency in location of zinc transporters genes which were co-located within the five meta-QTLs. The meta-QTL analysis reduced genetic and physical intervals. Co-location of meta-QTLs and genes involved in transport or regulation of zinc, identified nine candidate genes useful in the detailed analysis of zinc transport in common bean and development of an efficient marker-assisted breeding strategy.

CONCLUSIONS

In order to increase zinc and iron in human diets, plant breeding and new technologies aim in biofortification have a goal to discover genes involved on zinc uptake, transport and storage in the seed. This study developed three major resources that will improve to understanding of genetic control of Zn concentration in bean seed. The first approach was comparative analysis of RNA sequencing of two navy beans with different levels of seed Zn.

Those genes differentially expressed during bean pod development are likely related to Zn remobilization during the seed filling period. SNPs identified in the transcriptome can be used as a reservoir of markers for saturation of region of interest and could be used by breeders for indirect selection of presence of favorable alleles for Zn accumulation.

Meta-QTL refined and reduced both in the number of QTL and size of their confidence interval helped identify underlying candidate genes by using flanking DNA markers. Based on physical location, gene annotation from bean genome identified nine genes on chromosomes 2,

6, and 11. The likely candidate genes under meta-QTLs were zinc ion biding, ZIP metal ion transport family, zinc induced facilitator (ZIF), basic-leucine zipper (bZIP), heavy metal atpase

(HMA), vacuolar iron transporter (VIT), heavy metal transport, nicotianamine synthase (NA) and ferric reduction oxidase (FRO). Identification of these candidate genes will increase the knowledge of mechanisms of transport and success rate of identifying superior genotypes for seed Zn in early generations stage of breeding programs.

APPENDIX

Table 16. Forward and reverse sequence for all primer pairs used to validate putative SNPs in genotypes Albion and Voyager. Product Name Forward primer Reverse primer Size YSL8080_2F CGCTATGTCGTAACACTTCTGCACC TTTGTGCTTGCTGCCTTAGGTGGG 741 YSL8080_3F GGGAAGGGCAGAAAAGCCTTCGAC TTGCCTTGATCCTCGGTGATGGGT 874 HMA12775_1F AACCTCTCACCGCGACCTCACTAC CCCACACAACAACCCCATCGGAAG 1483 HMA12775_3F AACCTCTCACCGCGACCTCACTAC CATGTTCGCAGATCCACGGCGTAA 1131 HMA12775_4F GACACGGCGGTTTTGCTGACTTTG GCACTCTCTAATGCCTGCCCTCCT 746 HMA12775_5F GTTGGTGCATCTCAGGGTGTGCTC GGCCAATGGATGCTCACTATTCACCT 853 ZIF8636_2F GCCCAGCATTGGGAGGCTATTTGG AAGCCACATTCGGAACATGACCGC 1211 ZIF8636_5F CGTGACGTGTGCAATGATGCCACT CGCACCAACAACACAAAACAGGGA 561 ZIF8636_10F AGGTGGTGCAGTGTGAGTGTTCCT AGGCAAGTTACAGATTGAATTGGTTCCCT 886

Protocol for QTL validation

Candidate genes

The SNP marker g785 which contains the bZIP domain described as

PvMcCleanNDSU2007_11_g785 (http://cmap.comparative-legumes.org) and PvHMA1 and

PvHMA2 member of the heavy metal transport ATPases gene family were selected for QTL validation. PvHMA1 and PvHMA2 mapped on chromosome 2 were located where major QTL for seed Zn concentration has been identified in bean RIL populations from both Mesoamerican and Andean intra gene pool crosses. Additionally, one SNP represented a non-synonymous aminoacid change was identified on the sequence of the PvHMA1 and 6 SNPs were identified on

PvHMA2. Those marker have been located underlying QTLs for zinc in the population DG and were confirmed for the meta-QTL.

Genomic DNA extraction

A group of 20 genotypes from the Common Bean Coordinated Agricultural Project (BeanCAP)

Mesoamerican panel were choosen based on their zinc concentration in seed. These lines were grown in MI in 2010 and evaluated for minerals via ICP (Grusak, unpublished). They were

divided regarding their high and low concentration (Table 17). For DNA extraction, modified protocol from raw seeds were used (Kamiya and Kigochi 2003). Drill bit of one-mm diameter in an electric drill (Con-Torque Eberbach 115 volts) was used to bore out 20 mg of dry bean seed.

Powder was collected directly into a 0.5 ml 96 tubes for DNA extraction. SDS/NaCl buffer (200 mM Tris HCl pH=7.5; 25 mM EDTA; 0.5% SDS; 2.25 M NaCl) was added to the sample and mixed thoroughly for 1min. Samples were incubated at 65 oC for 20 min and centrifuge at 3000 r.p.m a TA for 15 min. Aqueous phase was transfered (300 µl) to a new tube and 2/3 of 100%

Isopropanol and 10% 3 M Sodium acetate pH=5 was added and mixed by inversion 6 times.

They were centrifuged at 3,000 r.p.m at Ta for 20 min and supernatant was eliminated. The pellet was washed with 500 µl 70% ethanol mixed by inversion 5 times and centrifuged at 3,000 r.p.m at RT for 20 min. Then supernatant was discarded and pellet was dried at Ta for 20 min and resuspended in 100 µL of 1x TE and RNAase. Incubation was done at 37 oC for 1 h. DNA samples were quantified in Nanodrop and visualized on 0.8% agarose gel.

PCR amplification and melting analysis

For g785 marker, PCR amplification was done with a 15-µL reaction mixture having 10 ng DNA, 7.5 µL of Go TaqR green master mix (Promega). The PCR profiles started with an initial denaturation of DNA at 95 oC for 5 min, followed by 35 amplification cycles of denaturation at 94 oC and annealing temperature at 62 oC for 30 s and extension at 72 oC and final extension at 72 oC for 5 min. The PCR product was visualized on 1.5 % agarose gel. The bands were scored based on the QTL donor alleles as reference band in order to validate the

QTL.

Table 17. Genotypes scored taking QTL donor allele as a base.

CAP entry number Genotype Type Zn (ug/g) 332 CDC Jet black 68.5 329 CDC Crocus GN 66.8 2 BelMiNeb-RMR-3 GN 66.2 259 Hyden small white 65.9 274 Silver Cloud WK 65.8 296 GN9-4 GN 65.4 331 CDC Expresso black 64.8 6 BelMiNeb-RMR-4 GN 64.4 258 NW-395 small white 62.9 160 UI-537 pink 62.8 159 UI-37 small red 62.5 118 Lassen WK 36.0 128 Ensign navy 36.0 109 Poncho pinto 35.7 83 Beluga WK 35.2 16 Bill Z pinto 35.0 47 F07-004-9-1 navy 34.7 20 Montrose pinto 32.7 123 Sonora pinto 32.4 121 Baja pinto 31.9 114 Agassiz pinto 31.0

For SNPs found on PvHMA1 and PvHMA2 genes, Tm shift primer approach was used

(Wang et al., 2005). Primers were design for each SNP consisting of two forward allelic specific primers with the 3’ base of each primer matching one of the SNP, adding to each primer a 14-bp and 6-bp GC tails to the higher and lower Tm respectively. A common reverse primer was designed for both allelic specific primers (Table 18). They were design using Primer 3 software spanning a 100 bp region to allow good amplification efficiency (Figure 13).

Figure 13. Primer design for the five SNPs found on PvHMA2 gene

For high resolution melting, PCRs were performed with 10 ng genomic DNA in 10 µL of

KAPA SYBR® FAST qPCR Kit, 0.2uM each of the three primers. PCRs for genotyping experiments were ran in The StepOnePlus™ Real-Time PCR System (Applied Biosystems) in the amplification and fluorescence measurement. Thermal cycling conditions consisted of an initial enzyme heat activation step of 2 min at 95°C followed by 40 cycles of 2 s at 95°C, 30 s at

62°C for annealing and 30 s at 72oC for extension. Melting curves measured the fluorescence intensity of the PCR product in a linear denaturation ramp from 70oC – 90oC.

Table 18. Primer list for high-throughput SNP genotyping with Tm-shift primers.

Primer name Forward Primer Common Reverse Primer HMA_1-1-1* GCGGGCAGGGCGGCCACCTTAGTTGGTCTTGGGG TGTGGCCAGCAACTAATTGA HMA_1-1-2 GCGGGCCACCTTAGTTGGTCTTGGGA HMA_2-1-1 GCGGGCAGGGCGGCCACGGAGTCCTCGAAGCT TGGCTTGCAGAATGTCGTTTG HMA_2-1-2 GCGGGCTTCACGGAGTCCTCGAAGCC HMA_2-2-1 GCGGGCCTTACAAACCGGACGTCACT TTCTTTGTCCTTGTTCCGTA HMA_2-2-2 GCGGGCAGGGCGGCCTTACAAACCGGACGTCACA HMA_2-4-1 GCGGGCAGGGCGGCGGCTTGCCTGGTTTTTGGCG CCAAACTGCAAAGCAAGCTC HMA_2-4-2 GCGGGCGGCTTGCCTGGTTTTTGGCT HMA_2-5-1 GCGGGCAGGGCGGCCTGTCTTCTAGGTAAACTGC AATAATTCTGTTCTCACTAT HMA_2-5-2 GCGGGCCTGTCTTCTAGGTAAACTGT *Numbers indicate member gene – SNP - and allele.

Protocol for yeast functional complementation studies

Functional complementation analyses in yeast were performed to determine whether the

PvZIP16 and PvHMA5 and PvHMA6 genes have metal transporting capacity. PvZIP16 showed the highest differential expression based on tissue and PvHMA5 and PvHMA6 carry SNP that resulted in an amino acid change. Additionally, their projection on reference map was under a

QTL associated to zinc concentration. These genes were PCR amplified from leaves cDNA, using specific primers designed with a NotI restriction site. PCR reactions were carried out for 4 min at 95 oC, followed by 35 cycles of 45 s at 95 oC, 45 s at 60 oC, and 1 min at 70 oC, and a final period of 10 min at 70 oC. PCR products were separated in a 1% agarose gel and purified from the gel using the QIAquick Spin protocol (Qiagen Inc., Valencia, CA, USA); they were subsequently ligated into the pTA vector (Clontech Laboratories Inc., Palo Alto, CA, USA) according to the manufacturer’s instructions and transformed into DH5a E. coli competent cells

(Invitrogen, Carlsbad, CA, USA). Transformed colonies were selected on agar plates containing

5-bromo-4-chloro-3-indoyl b-D-galactopyranose (X-gal; 40 mg l-1), isopropyl b-D- thiogalactopyranoside (IPTG; 80 mg l-1), and ampicillin (50 mg l-1). Plasmid DNA were isolated

(Qiaprep; Qiagen Inc., Valencia, CA, USA) and sequenced to ensure that the inserts were in the correct orientation and that no sequence changes had occurred during PCR. Then, plasmid were digested with NotI. To construct yeast expression vectors, inserts were purified from an agarose gel and subsequently ligated into the NotI site in the yeast expression vector pFL61 (Minet et al.,

1992). The yeast strains used in this study will be fet3fet4 1453 (MATa trp1 ura3 Dfet3::LEU2

Dfet4::HIS3; Eide et al., 1996), zrt1zrt2 ZHY3 (MATa ade6 can1 his3 leu2 lys2 trp1 ura3 zrt1::LEU2 zrt2::HIS3 Dfet3::LEU2 Dfet4::HIS3; Zhao and Eide, 1996a) (provided by Dr.

David Eide at University of Wisconsin). Yeast cells were grown in yeast

extract/peptone/glucose or synthetic defined media supplemented with necessary auxotrophic requirements. Yeast transformations were performed by the lithium acetate-based method (Gietz and Schiestl, 1991), and synthetic defined medium will be used to select transformants. For complementation studies in low metal media, complete medium (YPD, pH 6.2) was supplemented with 1mM EDTA (ethylenediaminetetraacetic acid) plus 10 lM FeCl3 (zinc limitation). Control media was prepared by adding 1 mM ZnSO4. Yeast strains were inoculated in a 5 ml culture of complete media and grown overnight. The cultures were adjusted to an

OD600 of 0.1 and three dilutions were made 1:10, 1:50 and 1:100. Dilutions of 5 µl were spotted on plates and grown for 2 days at 30 oC.

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