INTEGRATION OF TRANSCRIPTOME ANALYSIS AND CONSENSUS QTL IN THE IDENTIFICATION OF CANDIDATE GENES ASSOCIATED WITH ZINC CONCENTRATION IN COMMON BEAN PHASEOLUS VULGARIS By 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 By 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 iii 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. iv 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 v 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 vi 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 E10 .....................................................................................................................................................53 Table 11. Quantitative trait loci (QTL) for iron and zinc concentration identified with composite interval mapping in the DOR364 x G19833 population ................................................................60 vii 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 viii 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 ix 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 x 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, 1 Table 1. Bean production and consumption in USA, Latin America, and Africa. Country/region Production (MT) Average Annual per capita consumption (kg) USA Brazil Mexico Central America South America Central America Caribbean (Cuba, Haiti, Dominican Republic) Africa Eastern Africa Southern Africa Western Africa Lowlands-winter season 1,442,470 3,202,150 1,156,250 337 3,557,289 1,649,937 225,093 3.0 18.7 16+ 12.3 12.6 39.0 3,961,679 1,290 135 200 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. 2 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). 3 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 Calories Proteins Carbohydrates Fiber Fat Folate Thiamine Pyridoxin Niacina y riboflavin Calcium Iron Zinc 21 – 25% 60-65% 3 a 7% 0.8 – 1.5% Content in 100g seed 110 a 143 Kcal 8g 19-24 g 6-9 g 0.1-0.6 g 65-183 µg 25% 10-12% 10% 23-63 mg 2-3 mg 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 4 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 5 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. 6 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 7 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. 8 Table 3. Meta-QTL identified by meta-analysis for wheat, rice, soybean, and maize. Specie Meta-QTL Reference Wheat Fusarium head blight resistance Ear emergence Height Earliness Grain size and shape variation Grain dietary fiber content Yield Loffler et al., 2009; Liu et al., 2009; Miedaner e al., 2011; Mao et al., 2010 Griffiths et al., 2010 Griffiths et al., 2009 Hanocq et al., 2007 Gegas et al., 2010 Quraishi et al., 2011 Zhang et al., 2010 Rice Root genetic architecture Drought Blast resistance Yield Courtois et al., 2009; Coudert et al., 2010; Norton et al., 2008 Swamy et al., 2011; Khowaja et al., 2009 Ballini et al., 2008 Swamy et al., 2011 Soybean Oil content Cyst nematode 100-seed weight Seed protein concentration Qi et al., 2011 Guo et al., 2006 ZhaoMing et al., 2009 Zhao-Ming et al., 2009 Maize Grain yield components Silage quality (digestibility and cell wall composition) Drought tolerance Nitrogen use efficiency Plant height Li et al., 2011 Truntzler et al., 2010 Hao et al., 2010 Liu et al., 2012 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 9 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 size QTL Zinc QTL Iron Environments Map distance (cM) Total Markers Dor364 x G198331 MxA 87 13 13 2 1,703 236 G21242 x G210782 AxA 100 3 6 3 720 118 G14519 x G48253 M xM 110 9 8 3 915 114 AND696 x G10022 x Cerinza5 Bat93 x Jalo EEP6 AxA AxA AxM 77 138 72 11 6 3 12 1 4 2 1 1 1,105 1,992 1,364 167 142 217 Black Magic x Shiny Crow6 Total MxM 100 2 47 2 45 2 1,644 9,443 1,500 G198334 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). 10 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 11 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 12 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 13 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. 14 A transcriptome is the set of all RNA molecules including mRNA, rRNA, tRNA, and other noncoding 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 15 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 16 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). 17 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. 18 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 19 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 20 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 21 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 22 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 23 (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). 24 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 25 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 26 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 27 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 Zn (µg g -1 Voyager 36 a Albion 28 b ) Seed Fe (µg g -1 ) Zn (µg g -1 92 a 42 75 b 29 a b ) Fe (µg g-1) a 101 88 a N (%) 3.19a 2.79 b 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). 28 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 29 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). 30 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 31 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. 32 Table 6. Genes and function of the most highly differential expressed in Albion and Voyager. Expression level Albion Voyager log2 (Fold Change) Extracellular proteinase probably having a crucial role during rapid cell growth and leaf expansion 791 203 -1.96 MLP-like protein 43 Associated with fruit and flower development and pathogen defense responses 732 98 -2.91 SCR-like 11 S locus cysteine-rich protein 392 66 -2.57 Serine carboxypeptidase-like Serine-type carboxypeptidase activity involved in proteolysis 237 81 -1.55 Aspartic proteinase Encodes an aspartic proteinase that forms a heterodimer and is stable over a broad pH range 231 76 -1.60 Low-molecularweight cysteine-rich Predicted to encode a PR (pathogenesis-related) protein 215 31 -2.78 Genotype Albion Cysteine proteinases Function Voyager CCR-like Cellular cation homeostasis, divalent metal ion transport. Expressed in embryo axis, cotyledons. 146 460 1.65 2Fe-2S ferredoxin Abscisic acid biosynthetic process, electron transport chain, pentose-phosphate shunt. 110 295 1.42 74 169 1.20 65 162 1.32 35 161 2.18 10 150 3.90 Zinc-binding ribosomal protein Adenine nucleotide alpha hydrolases seed storage 2S albumin superfamily protein Basic chitinase DNA recognition, RNA packaging, transcriptional activation, regulation of apoptosis, protein folding and assembly, and lipid binding. Function unknown. Involved in response to stress. Expressed during petal differentiation and expansion stage. Function in lipid binding. Involved in lipid transport. Located in endomembrane system. Expressed in shoot apex, embryo, flower, leaf, and seed. Expressed during petal differentiation and cotyledon expansion stage. Defense response after wounding or pathogenic attack 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 33 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 34 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 35 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 36 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 Genome_Id Gene family Homologous A. thaliana Chromosome Position (bp) Albion (FPKM) Voyager (FPKM) fold change bZIP23 basic-leucine zipper bZIP23 basic-leucine zipper ATNRAMP, metal ion transporter family protein ATNRAMP3, metal ion transporter family protein 3 ATNRAMP3, metal ion transporter family protein 3 ATNRAMP6, metal ion transporter 6 ATNRAMP, metal ion transporter family protein ATNRAMP2, metal ion transporter 2 ATNRAMP6, metal ion transporter 6 ATNRAMP2, metal ion transporter 2 ATNRAMP6, metal ion transporter 6 ZIFL2, zinc induced facilitator-like 2 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 ZIFL1, zinc induced facilitator-like 1 Chr05 Chr11 Chr01 3,212,438 3,134,439 44,116,444 22 41 26 23 46 31 0.0 0.2 0.3 Chr02 1,609,575 61 62 0.0 Chr03 Chr05 Chr07 Chr09 Chr09 Chr10 Chr10 Chr02 Chr05 Chr11 Chr11 Chr11 Chr11 Chr11 Chr11 Chr11 Chr11 46,129,963 40,351,734 37,134,084 11,751,007 18,914,511 37,315,780 42,893,083 21,890,013 1,050,386 44,602,386 44,656,239 44,662,432 46,613,123 46,625,565 46,638,452 46,652,668 46,667,766 12 17 30 19 14 0 2 1 3 0 0 0 9 0 0 4 18 11 17 34 23 12 0 0 19 6 0 0 0 11 0 0 3 8 -0.2 0.0 0.2 0.3 -0.3 0.0 -2.3 4.6 0.9 0.0 0.0 -0.4 0.2 0.0 0.0 -0.4 -1.1 Chr01 Chr02 Chr02 Chr03 Chr03Table Chr05 Chr05 Chr05 Chr05 Chr06 Chr06 Chr06 Chr06 Chr08 Chr08 Chr08 Chr09 Chr10 Chr11 3,438,922 19,642,778 33,721,809 49,001,484 49,013,792 37,425,474 37,429,894 5,642,976 37,714,954 17,173,381 199,508 1,040,877 18,953,200 7,633,778 59,348,008 57,181,379 12,668,955 9,814,851 5,068,287 3 0 19 0 0 0 1 6 18 12 1 2 7 11 8 21 31 2 1 3 0 20 0 0 0 2 8 17 21 1 8 8 12 15 24 36 2 1 0.1 0.1 0.0 0.0 0.0 0.0 1.2 0.4 -0.1 0.8 -0.2 2.2 0.2 0.1 0.9 0.2 0.0 0.1 0.0 Phvul.005G034400 Phvul.011G035700 Phvul.001G177500 PvbZIP1 PvbZIP2 PvNRAMP1 Phvul.002G014300 PvNRAMP2 Phvul.003G238600 Phvul.005G182000 Phvul.007G150600 Phvul.009G069700 Phvul.009G127900 Phvul.010G110500 Phvul.010G160800 Phvul.002G108300 Phvul.005G012400 Phvul.011G173100 Phvul.011G173300 Phvul.011G173400 Phvul.011G189500 Phvul.011G189600 Phvul.011G189700 Phvul.011G189800 Phvul.011G189900 PvNRAMP3 PvNRAMP4 PvNRAMP5 PvNRAMP6 PvNRAMP7 PvNRAMP8 PvNRAMP9 PvZIF1 PvZIF2 PvZIF3 PvZIF4 PvZIF5 PvZIF6 PvZIF7 PvZIF8 PvZIF9 PvZIF10 Phvul.001G035800 Phvul.002G099700 Phvul.002G184200 Phvul.003G262400 Phvul.003G262500 Phvul.005G145900 Phvul.005G146000 Phvul.005G048900 Phvul.005G149800 Phvul.006G055800 Phvul.006G001000 Phvul.006G003300 Phvul.006G070200 Phvul.008G079500 Phvul.008G290500 Phvul.008G259200 Phvul.009G077700 Phvul.010G059200 Phvul.011G058500 PvZIP1 PvZIP2 PvZIP3 PvZIP4 PvZIP5 PvZIP6 PvZIP7 PvZIP8 PvZIP9 PvZIP10 PvZIP11 PvZIP12 PvZIP13 PvZIP14 PvZIP15 PvZIP16 PvZIP17 PvZIP18 PvZIP19 ATZIP4, zinc transporter 4 precursor ZIP10, zinc transporter 10 precursor ZIP metal ion transporter family ZIP10, zinc transporter 10 precursor ZIP10, zinc transporter 10 precursor ZIP11, zinc transporter 11 precursor ZIP11, zinc transporter 11 precursor ZIP1, zinc transporter 1 precursor ZTP29, ZIP metal ion transporter family ZIP11, zinc transporter 11 precursor ZIP1, zinc transporter 1 precursor ZIP5, zinc transporter 5 precursor ATZIP6, metal ion transporter family ZIP metal ion transporter family ZIP5, zinc transporter 5 precursor ATZIP6, metal ion transporter family ATIRT3, iron regulated transporter 3 ZIP metal ion transporter family ZTP29, ZIP metal ion transporter family Phvul.L002700 Phvul.002G322800 Phvul.002G322900 PvZIP20 PvVIT1 PvVIT2 ZIP10, zinc transporter 10 precursor ATVIT1, vacuolar iron transporter 1 ATVIT1, vacuolar iron transporter 1 scaff Chr02 Chr02 1,071 48,170,585 48,175,491 0 2 14 1 1 18 0.2 -0.5 0.4 Phvul.002G323700 Phvul.002G113500 Phvul.002G205000 Phvul.002G205100 Phvul.002G205200 Phvul.002G205300 Phvul.004G096500 Phvul.007G079100 Phvul.008G070000 PvVIT3 PvVIT4 PvVIT5 PvVIT6 PvVIT7 PvVIT8 PvVIT9 PvVIT10 PvVIT11 ATVIT1, vacuolar iron transporter 1 Vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein ATVIT1, vacuolar iron transporter 1 Chr02 Chr02 Chr02 Chr02 Chr02 Chr02 Chr04 Chr07 Chr08 48,252,436 23,134,245 36,507,752 36,521,460 36,533,751 36,541,077 27,416,262 7,508,398 6,284,802 12 0 0 0 0 0 10 28 6 14 0 0 0 0 0 19 38 7 0.2 0.0 -1.5 -0.8 0.4 0.0 0.9 0.4 0.4 37 * * * Table 7 (cont’d) Phvul.008G187600 Phvul.009G040800 Phvul.010G021600 Phvul.010G021700 Phvul.001G081600 Phvul.001G088900 Phvul.003G006400 Phvul.003G006500 Phvul.004G090100 Phvul.004G138900 Phvul.006G083800 Phvul.008G157800 Phvul.009G048800 Phvul.002G156800 Phvul.002G156900 Phvul.002G288300 Phvul.002G288400 Phvul.002G208800 Phvul.002G190000 Phvul.003G047300 Phvul.003G240100 Phvul.003G142700 Phvul.009G240000 Phvul.009G082400 Phvul.009G241800 Phvul.010G023900 Phvul.001G225000 Phvul.005G052500 Phvul.006G117300 Phvul.004G158800 Phvul.009G005300 Phvul.011G210300 Phvul.008G101800 Phvul.010G009500 Phvul.010G012300 PvVIT12 PvVIT13 PvVIT14 PvVIT15 PvYSL1 PvYSL2 PvYSL3 PvYSL4 PvYSL5 PvYSL6 PvYSL7 PvYSL8 PvYSL9 PvHMA1 PvHMA2 PvHMA3 PvHMA4 PvHMA5 PvHMA6 PvHMA7 PvHMA8 PvHMA9 PvHMA10 PvHMA11 PvHMA12 PvHMA13 PvNAS1 PvNAS2 PvNAS3 PvDehydrin PvDehydrin PvDehydrin PvMT PvMT PvMT Phvul.010G012200 PvMT Vacuolar iron transporter (VIT) family protein vacuolar iron transporter (VIT) family protein Vacuolar iron transporter (VIT) family protein vacuolar iron transporter (VIT) family protein ATYSL1, YELLOW STRIPE like 1 YSL6, YELLOW STRIPE like 6 YSL7, YELLOW STRIPE like 7 YSL7, YELLOW STRIPE like 7 ATYSL1, YELLOW STRIPE like 1 YSL7, YELLOW STRIPE like 7 YSL7, YELLOW STRIPE like 7 ATYSL3, YELLOW STRIPE like 3 ATYSL3, YELLOW STRIPE like 3 HMA5heavy metal atpase 5 HMA5heavy metal atpase 5 HMA5heavy metal atpase 5 HMA5heavy metal atpase 5 HMA6,PAA1P-type ATP-ase 1 HMA7, copper-transporting ATPase (RAN1) ATHMA1, heavy metal atpase 1 ATHMA1, heavy metal atpase 1 ATHMA2, heavy metal atpase 2 ATHMA4, heavy metal atpase 4 ATHMA8, type ATPase of Arabidopsis 2 HMA7, copper-transporting ATPase (RAN1) HMA5 heavy metal atpase 5 ATNAS2, nicotianamine synthase 2 ATNAS4, nicotianamine synthase 4 ATNAS4, nicotianamine synthase 4 Dehydrin Dehydrin Dehydrin metallothionein 2A metallothionein 2A metallothionein 2A Chr08 Chr09 Chr10 Chr10 Chr01 Chr01 Chr03 Chr03 Chr04 Chr04 Chr06 Chr08 Chr09 Chr02 Chr02 Chr02 Chr02 Chr02 Chr02 Chr03 Chr03 Chr03 Chr09 Chr09 Chr09 Chr10 Chr01 Chr05 Chr06 Chr04 Chr09 Chr11 Chr08 Chr10 Chr10 49,131,247 8,164,280 3,221,741 3,229,195 13,421,083 16,152,062 626,298 631,299 21,588,269 41,773,229 20,249,225 40,137,873 9,292,230 29,860,709 29,878,666 45,175,820 45,187,687 36,870,747 34,600,085 5,628,284 46,285,474 33,726,455 35,288,969 13,120,413 35,544,425 3,512,059 48,680,147 6,792,803 23,217,021 44,048,043 921,414 49,334,383 11,131,986 1,509,086 1,905,781 6 8 1 0 18 60 16 2 3 17 0 4 62 11 1 0 0 5 13 30 26 20 1 8 33 3 0 0 0 2 24 0 3262 669 310 6 7 1 0 28 65 18 2 4 15 0 9 72 5 0 0 0 6 13 26 25 19 1 7 35 3 0 0 0 1 14 0 3989 847 246 0.0 0.0 0.1 1.0 0.0 0.1 0.1 0.0 0.4 -0.1 0.0 0.0 0.0 -1.1 -0.6 0.0 0.3 0.3 0.0 -0.2 -0.1 -0.1 -0.5 -0.2 0.1 0.1 -0.2 0.0 -0.1 -1.8 -0.8 0.3 0.3 0.3 -0.3 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 38 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. Family CDS Length Genomic Length SNPs in CDS Chr HMA 2,982 HMA 5,648 5 2 2,958 4,150 6 2 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 YSL-OPT 2,031 6,538 2 1 ZIF 1,470 5,330 10 11 ZIF 1,470 4,785 5 11 Syn, Ile/Val, Syn, Syn, Syn 891 2,550 1 8 Glu/Lys Ferritin SNPs confirmed1 AA change Syn, Syn, Syn, Syn, Phe/Ser * Syn, Syn, Syn, Syn, Syn, Syn * Met/Ile, Syn, Syn Gly/Ser, Syn * Gln/Leu, Asp/Glu, Arg/Pro, Ala/Val, Syn, Syn, Syn, Thr/Ile, Val/Ile, Gln/His 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 waterstressed 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 39 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). 40 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 41 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). 42 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 43 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). 44 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 45 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 USDANIFA, 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). 46 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 47 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 48 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 49 (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 50 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. (LopezMillan., 2004 and Guerinot., 2000). All ZIP genes contained a histidine motif between 51 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%. 52 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 E10 Sequence ID Gene Chrom Position Homology to A. thaliana Phvulv091010812m Phvulv091015745m Phvulv091015614m Phvulv091019402m Phvulv091012034m Phvulv091029608m Phvulv091029689m Phvulv091029664m Phvulv091026664m Phvulv091009317m Phvulv091009315m Phvulv091018095m Phvulv091002113m Phvulv091007436m Phvulv091022274m Phvulv091004709m Phvulv091010505m Phvulv091003125m Phvulv091030363m Phvulv091011372m Phvulv091011626m Phvulv091000876m Phvulv091000875m Phvulv091018638m Phvulv091015330m Phvulv091015414m PvZIP1 PvZIP2 PvZIP3 PvZIP4 PvZIP5 PvZIP6 PvZIP7 PvZIP8 PvZIP9 PvZIP10 PvZIP11 PvZIP12 PvZIP13 PvZIP14 PvZIP15 PvZIP16 PvZIP17 PvZIP18 PvZIP19 PvIRT1 PvIRT2 PvIRT3 PvIRT4 bZIP1 bZIP2 bZIP3 Pv01 Pv01 Pv01 Pv02 Pv05 Pv05 Pv05 Pv05 Pv06 Pv06 Pv06 Pv06 Pv06 Pv08 Pv08 Pv08 Pv10 Pv11 Pv02 Pv03 Pv03 Pv09 Pv09 Pv05 Pv11 Pv11 3,442,406 49,770,995 49,839,431 33,735,220 5,645,010 37,426,497 37,431,839 37,715,863 200,959 1,033,953 1,040,964 17,174,396 18,954,219 7,634,926 57,181,509 59,351,699 9,817,594 5,071,268 19,642,824 49,001,506 49,013,793 12,670,278 12,670,315 3,213,447 3,134,797 3,709,270 ATZIP4_Zinc transporter 4 ATZIP4_Zinc transporter 4 ZIP3_Zinc transporter 3 IAR1_ZIP metal ion transporter ZIP1_Zinc transporter 1 ZIP2_ZRT/IRT-like protein 2 ZIP2_ZRT/IRT-like protein 2 ZTP29_ZIP metal ion transporter ZIP5_Zinc transporter 5 ZIP5_Zinc transporter 5 ZIP5_Zinc transporter 5 ZIP11_Zinc transporter 11 ATZIP6_ZIP metal ion transporter ZIP metal ion transporter family ATZIP6_ZIP metal ion transporter ZIP1_Zinc transporter 1 ZIP metal ion transporter family ZTP29_ZIP metal ion transporter ZIP10_Zinc transporter 10 IRT1_Iron-regulated transporter 1 IRT1_Iron-regulated transporter 1 ATIRT3_Iron regulated transporter 3 ATIRT3_Iron regulated transporter 3 bZIP23 - transcription factor family bZIP19 - transcription factor family bZIP44 | basic leucine-zipper 44 Homology to M. truncatula 3E-63 0 4E-37 9.5E-42 3E-156 2.1E-46 2E-43 6.5E-23 1E-132 4E-130 1E-129 5.7E-52 1.5E-56 1.2E-14 2E-158 3.2E-33 0 1.3E-20 1E-102 4E-126 5E-112 2.2E-68 9.5E-42 7E-49 1E-109 2E-52 ZIP-like zinc transporter -Medtr1g016120.1 Zinc transporter 5 -Medtr3g082050.1 Zinc transporter 5 -Medtr3g082050.4 Zinc transporter 5 Zinc transporter - Medtr3g082050.3 Iron regulated transporter -Medtr2g097580.1 Iron regulated transporter -Medtr2g097580.1 Zinc transporter 5 -Medtr4g065640.1 Zinc transporter -Medtr3g082050.1 Zinc transporter -Medtr3g082050.1 Zinc transporter zupT -Medtr3g082050.1 Zinc transporter 5 -Medtr2g097580.1 Zinc transporter 5 -Medtr5g071990.1 Zinc transporter 5 -Medtr7g074060.1 Zinc transporter -Medtr5g071990.1 Zinc transporter 6-Medtr3g082050.3 ZIP transporter -Medtr7g074060.1 Zinc transporter 6, -Medtr4g065640.1 Zinc transporter Zinc transporter Zinc/iron permease-Medtr8g105030.1 ZIP transporter - Medtr3g104400.1 Zinc transporter 4 - Medtr4g083570.1 Basic leucine zipper - Medtr4g073100.1 Basic leucine zipper - Medtr4g073100.1 bZIP transcription factor - Medtr4g070860.1 4E-145 4E-21 2E-22 2E-109 5E-99 2E-161 2E-130 1E-150 4E-44 7E-147 3E-113 7E-141 0 2E-139 7E-91 0 3E-124 3E-174 3E-169 6E-129 2E-131 3E-174 5E-57 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. 53 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. 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 54 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). 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. 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). 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 9 (cont’d) 57 Figure 9 (cont’d) 58 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. 59 Table 11. Quantitative trait loci (QTL) for iron and zinc concentration identified with composite interval mapping in the DOR364 x G19833 population. Trait QTL Iron Zinc LOD R2 Additive effect Source 361.1 5.7 10.6 3.1 G19833 BMb1188 - BMb1259 194.8 5.7 11.1 1.8 G19833 3 g1388 - Leg213 226.2 PvIRT1 – PvIRT2 9.4 19.7 2.4 DOR364 4.5 10.5 1.3 DOR364 3.3 5.9 2.3 DOR364 Tissue Environment Chromosome Marker interval Position (cM) Iron2.1 Cotyledon Darien 2003 2 g680 - BSNP6 Iron3.1DG Whole seed Darien 2003 3 Iron3.2 DG Whole seed Darien 2003 DG* ZIP genes nearby1 Genomic position (bp) Iron3.3 DG Whole seed Darien 2006 3 G1388 – BSNP59 239.5 PvIRT1 – PvIRT2 Iron 3.4 DG* Cotyledon Darien 2003 3 BSNP59 245.5 PvIRT1 – PvIRT2 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 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 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 3.6 9.8 1.0 DOR364 3.2 6.7 1.0 G19833 6.6 17.7 1.5 G19833 11.5 28.0 1.1 G19833 7.8 17.5 1.8 G19833 5.5 16.0 0.8 G19833 Zinc9.1 DG Whole seed Popayan 1998 9 G1286 0.0 Zinc11.1 DG Whole seed Popayan 1998 11 BMd27 - BSNPc27 53.4 Zinc11.2 DG Whole seed Darien 2003 11 Bng187 - BMa145 92.7 Zinc11.3 DG* Cotyledon Darien 2003 11 BSNP82 - BN 22.0 Zinc11.4 DG* Whole seed Darien 2006 11 Bng001 - BMa6 92.7 Pv bZIP2-Pv bZIP3 49,001,506 - 49,013,793 3,134,797 - 3,709,270 PvZIP12 - PvZIP13 17,174,396 - 18,954,219 PvZIP18 5,071,268 Pv bZIP2 3,134,797 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, LopezMillan 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. 60 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). 61 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 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. 63 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). 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 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). 65 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). 66 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. 67 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 68 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. 69 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; 70 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. 71 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 size No. QT Chr Environment Map distance (cM) Total Markers Dor364 x G198331 MxA 87 13 2, 3, 6, 7, 9, 11 2 1,703 236 G21242 x G210782 AxA 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 AxA 77 11 1, 6, 5, 11 2 1,105 167 AxM 72 3 1, 11 1 Total 41 1 Blair et al., 2009, 2Blair et al., 2010, 3Blair et al., 2010b, 4Cichy et al., 2009, 5 non-published 1,364 9,443 217 Bat93 x Jalo EEP5 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. Colocation 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 72 Table 13. Summary of QTLs used in the meta-QTL analysis. QTL Chromosome LOD R2 Position From To Palmira Pv02 4 18.63 133 125 134 Zn 2.1 Darien Pv02 3.1 8.47 310.11 300 311 ZnDG2.2 ZnDG2.3 ZnAG6.1 Shelled seed_Darien Shelled seed_Darien Low P_Darien_2000 Pv02 Pv02 Pv06 5.31 2.71 4.71 17.58 7.64 24.13 267.51 306.11 76.26 300 316 76.26 311 320 79.26 ZnAG6.2 High P_Darien_2000 Pv06 2.56 7.39 18.44 18.44 21.44 AG High P_Darien_2006 Pv06 3.01 10.08 18.01 18.01 21.01 DG Zn 6.1 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 AA Zn 2.1 DG Environment Zn 6.3 Darien Pv11 3.65 15.06 26.91 26.91 29.91 DG Popayan_1998 Pv11 5.35 17.77 53.41 43.3 60 DG Darien_2003 Pv11 5.08 16.47 96.31 83 106.5 DG Zn 11.3 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 Zn 11.1 Zn 11.2 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. 73 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 74 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. 75 Table 14. Characteristics of meta-QTL identified for Zn concentration in common bean MQTL Chr MQTL2.1 MQTL2.2 MQTL6.1 MQTL6.2 MQTL11.1 2 2 6 6 11 Flanking markers AIC value No. QTL model No of initial QTL Mean R2 QTL CI (cM) MetaQTL CI (cM) BM152 - BMb495 BMb97 – g2540 BMb182 – PvZIP BM170 – AGAC01 26.3 4 371.1 4 36.9 3 2 2 3 2 4 18.1 7.8 9.2 15.9 14.9 34.0 18 96.8 58 46.2 8 12.0 36.4 37.5 23.2 BMd22 – g1932 Physical length MQTL (Mb) 12.0 18.0 2.1 3.3 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 metaQTL 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 76 domains, a leucine zipper dimerization and histidine-rich motifs, essential in the response to low Zn supply in Arabidopsis (Assuncao et al., 2010). 77 2 145 146 147 148 149 150 152 153 154 160 162 164 165 166 169 172 143 144 145 146 147 148 149 150 152 153 154 160 162 164 165 166 169 172 Bng080 41 g471 51 BMd12 60 BMb419 71 BMb519 77 BMb182 88 93 95 97 101 102 104 108 110 114 116 126 131 133 136 142 146 148 155 157 162 166 170 171 172 174 178 182 185 189 196 200 BMb341 NAS2 BMb539 Leg736 CAC1 BMb1108 BES41H07.r BMb1279 BM137 OD12 BMb1061 BMb1158 BMb342 g2553 YS1 GCTC03 g739 PVBR163 PVBR198 ZIP10 Bng046 Leg81 BMb1105 BMc238 Bng009 BMd76 BMb1157 BM170 FRO1 Bng027 Leg58 PVBR14 PVBR20 BMd37 AGAT05171 CTTA05 PVBR5 Bng094 AGAC01 g1998 g1757 BSNP67 Bng104 g1174 g2480 ZnDG_6.1 8 ZnAG_6.2 ZnDG_6.1 Meta-QTL_1 Bng084 ZnAG_6.1 136 137 138 140 141 142 Consensus Chr 6 0 ZnAG_6.3 144 135 ZnAG_6.2 143 134 ZnAG_6.2 136 137 138 140 141 142 ZnDG_2.3 ZnAA_2.1 Meta-QTL_1 ZnDG_2.1 Meta-QTL_2 ZnDG_2.2 ZnDG_2.3 Meta-QTL_2 Meta-QTL_1 135 133 b)6 Meta_QTL_2 134 131 b) ZnAG_6.1 133 130 Meta-QTL_1 131 119 121 124 125 127 129 ZnAA_2.1 130 118 Meta_QTL_2 119 121 124 125 127 129 ZnDG_2.1 118 A0102B P0903B H1901A H1902B E0403A L0205B BM167 BM139 BM164 BM172 BMb1166 BMb1266 BMb180 BMb125 BMb122 NFP_2 PVBR18 g2020 Bng011 BMb365 PVBR94 BMb1163 BM156 BM152 BMd18 GATS91 PVBR243 PVBR78 PVBR15 BMb137 BMb80 NORK_2 BMb259 BMb1289 BMe30 PVBR11 BMb497 BMb527 BSNP41 BMb252 BMb712 BMa133 BMb1131 PVBR25 BMb420 BMb97 Bng117 BMd17 BMd47 g1801 CCS52_3 BM143 SSR-1AC29 BM142 CA5 BMb1194 SSR-1AC13 BMa269 g2581 SSR-1AC57 BMb1126 BMd02 PG02 BMa7 BMa150B BMa180 g321 BMb1192 BSNP85 BMa150 BMa07 g1148 BMa16 BMb1286 SSR-1AC46 BMb495 Leg188 Bng108 BMc280 g2540 g680 Leg301 g774 g2427 BSNP6 BSNP4 ZnDG_2.2 0 7 11 12 20 24 30 38 40 51 67 69 73 79 86 93 98 101 103 104 107 108 111 116 A0102B P0903B H1901A H1902B E0403A L0205B BM167 BM139 BM164 BM172 BMb1166 BMb1266 BMb180 BMb125 BMb122 NFP_2 PVBR18 g2020 Bng011 BMb365 PVBR94 BMb1163 BM156 BM152 BMd18 GATS91 PVBR243 PVBR78 PVBR15 BMb137 BMb80 NORK_2 BMb259 BMb1289 BMe30 PVBR11 BMb497 BMb527 BSNP41 BMb252 BMb712 BMa133 BMb1131 PVBR25 BMb420 BMb97 Bng117 BMd17 BMd47 g1801 CCS52_3 BM143 SSR-1AC29 BM142 CA5 BMb1194 SSR-1AC13 BMa269 g2581 SSR-1AC57 BMb1126 BMd02 PG02 BMa7 BMa150B BMa180 g321 BMb1192 BSNP85 BMa150 BMa07 g1148 BMa16 BMb1286 SSR-1AC46 BMb495 Leg188 Bng108 BMc280 g2540 g680 Leg301 g774 g2427 BSNP6 BSNP4 ZnAG_6.3 Consensus 2 Chr 2 ZnAG_6.2 a) 0 7 11 12 20 24 30 38 40 51 67 69 73 79 86 93 98 101 103 104 107 108 111 116 6 0 Bng084 8 Bng080 41 g471 51 BMd12 60 BMb419 71 BMb519 77 BMb182 88 93 95 97 101 102 104 108 110 114 116 126 131 133 136 142 146 148 155 157 162 166 170 171 172 174 178 182 185 189 196 200 BMb341 NAS2 BMb539 Leg736 CAC1 BMb1108 BES41H07.r BMb1279 BM137 OD12 BMb1061 BMb1158 BMb342 g2553 YS1 GCTC03 g739 PVBR163 PVBR198 ZIP10 Bng046 Leg81 BMb1105 BMc238 Bng009 BMd76 BMb1157 BM170 FRO1 Bng027 Leg58 PVBR14 PVBR20 BMd37 AGAT05171 CTTA05 PVBR5 Bng094 AGAC01 g1998 g1757 BSNP67 Bng104 g1174 g2480 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 12 (cont’d) c) 0 10 12 17 21 23 26 27 29 ZnDG_11.3 ZnBJ_11.1 ZnDG_11.2 ZnDG_11.1 30 Meta-QTL_1 11 Consensus Chr 11 32 33 36 38 43 44 49 50 52 55 56 57 59 60 61 62 64 66 67 68 69 70 71 72 73 74 80 81 82 83 84 86 90 91 97 99 106 113 124 126 132 139 Leg183 Leg241 Leg100 Leg470 BMa116 BMd22 BSNP39 BSNP82 g811 BM441 g735 BN BMd33 Leg133 g2273 BMd27 g1731 g1415 g1932 g2307 BSNPc27 Bng167 Bng001 g1438 Bng025 BMa6 BMb185 BMa145 Bmb2150w g2285 Bmb2149w Bmb1344w Bmb32 g835 Bmb310 Bmb1228 Bmb1093 Bmb654 g2527 g156 Bmb484 g1510 g1489 g1168 Bmb653 Bng145 BMa241a BM10 BMb588 g1598 BMb619 DMI3-1 BMa324 BMa32 BM1074 BMb1072 Baja Leg43 BMy2 Leg236 Leg449 Leg220 Leg218 Leg208 g1215 g188 g1983 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 79 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. 80 Table 15. Candidate genes reported in the identified meta-QTL regions ID Locus Phvul.002G066000 Phvul.002G076800 Phvul.002G080500 Phvul.002G144100 Phvul.002G206100 Phvul.006G055600 Phvul.006G085700 Phvul.006G105100 Chr Pv02 Pv02 Pv02 Pv02 Pv02 Pv06 Pv06 Pv06 Position 7,686,359 11,395,433 12,067,481 28,039,140 36,601,189 17,167,752 20,410,218 22,145,643 Gene Function Zinc ion binding Interacting selectively and non-covalently with zinc (Zn) ions Phvul.002G099700 Phvul.002G184200 Phvul.006G055800 Phvul.006G070200 Phvul.011G058500 Pv02 Pv02 Pv06 Pv06 Pv11 19,642,778 33,721,809 17,175,727 18,954,796 5,068,287 ZIP metal ion transporter family Zinc transport proteins responsible for zinc uptake in the plant Phvul.002G108300 Pv02 21,890,013 Zinc induced facilitator-like 2 Transporter involved in Zn homeostasis and its transport to the vacuole Phvul.002G110600 Phvul.002G203300 Phvul.006G029200 Phvul.006G071300 Phvul.006G101700 Phvul.011G038200 Phvul.011G042600 Pv02 Pv02 Pv06 Pv06 Pv06 Pv11 Pv11 22,492,108 36,344,588 12,406,073 19,074,132 21,870,121 3,309,633 3,708,498 Basic-leucine zipper (bZIP) Transcription factors regulating function for the adaptation of plants to zinc deficiency Phvul.002G156800 Phvul.002G156900 Pv02 Pv02 29,860,709 29,878,666 Heavy metal atpase 5 (HMA) Some members have been involved in Zn and Cd ions loading to the shoots (Hanikenne et al., 2008) Phvul.002G113500 Phvul.002G205000 Phvul.002G205100 Phvul.002G205200 Phvul.002G205300 Pv02 Pv02 Pv02 Pv02 Pv02 23,134,245 36,507,752 36,521,460 36,533,751 36,541,077 Vacuolar iron transporter (VIT) Transport and load of Fe into the vacuoles during embryo development (Kim et al., 2006) Phvul.006G090100 Pv06 20,862,354 Phvul.006G093700 Phvul.006G139700 Phvul.006G139900 Phvul.006G153400 Phvul.006G173800 Phvul.011G068500 Pv06 Pv06 Pv06 Pv06 Pv06 Pv11 21,160,159 25,474,240 25,481,346 26,649,216 28,443,517 5,918,539 Phvul.006G117300 Pv06 23,218,312 Phvul.006G133600 Pv06 24,818,368 Heavy metal transport/detoxification (HMA) Metallochaperones for safe transport of metallic ions. They contain a metal binding domain involved in heavy metal homeostasis and detoxify Nicotianamine synthase 4 Phloem chelator capacity to bind Cu, Co, Fe(II) and Fe(III), Mn, Ni, and Zn and transport to seed Ferric reduction oxidase 7 (FRO) 81 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. 82 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. 83 APPENDIX 84 Table 16. Forward and reverse sequence for all primer pairs used to validate putative SNPs in genotypes Albion and Voyager. Name Forward primer Reverse primer Product 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 85 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. 86 Table 17. Genotypes scored taking QTL donor allele as a base. CAP entry number Genotype Type Zn (ug/g) black 68.5 66.8 66.2 65.9 65.8 65.4 64.8 64.4 62.9 62.8 62.5 36.0 36.0 35.7 35.2 35.0 34.7 32.7 32.4 31.9 31.0 332 CDC Jet 329 CDC Crocus GN BelMiNeb-RMR-3 GN 2 259 Hyden 274 Silver Cloud 296 GN9-4 331 CDC Expresso 6 BelMiNeb-RMR-4 small white WK GN black GN 258 NW-395 160 UI-537 pink 159 UI-37 small red 118 pinto 16 Lassen Ensign Poncho Beluga Bill Z 47 F07-004-9-1 navy 20 Montrose Sonora Baja Agassiz pinto 128 109 83 123 121 114 small white WK navy WK pinto pinto pinto pinto 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). 87 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 HMA_1-1-2 GCGGGCCACCTTAGTTGGTCTTGGGA HMA_2-1-1 GCGGGCAGGGCGGCCACGGAGTCCTCGAAGCT HMA_2-1-2 GCGGGCTTCACGGAGTCCTCGAAGCC HMA_2-2-1 GCGGGCCTTACAAACCGGACGTCACT HMA_2-2-2 GCGGGCAGGGCGGCCTTACAAACCGGACGTCACA HMA_2-4-1 GCGGGCAGGGCGGCGGCTTGCCTGGTTTTTGGCG HMA_2-4-2 GCGGGCGGCTTGCCTGGTTTTTGGCT HMA_2-5-1 GCGGGCAGGGCGGCCTGTCTTCTAGGTAAACTGC HMA_2-5-2 GCGGGCCTGTCTTCTAGGTAAACTGT *Numbers indicate member gene – SNP - and allele. 88 TGTGGCCAGCAACTAATTGA TGGCTTGCAGAATGTCGTTTG TTCTTTGTCCTTGTTCCGTA CCAAACTGCAAAGCAAGCTC AATAATTCTGTTCTCACTAT 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-Dthiogalactopyranoside (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 89 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. 90 BIBLIOGRAPHY 91 BIBLIOGRAPHY Afoufa-Bastien, D., Medici, A., Jeauffre, J., Coutos-Thevenot, RA., Lemoine, R., Atanassova, R., Laloi, M. (2010). The Vitis vinifera sugar transporter gene family: phylogenetic overview and macroarray expression profiling. BMC Plant Biol 10, 245. Aggett P.J. (2012). Iron. In: Erdman JW, Macdonald IA, Zeisel SH, eds. Present Knowledge in Nutrition. 10th ed. Washington, DC: Wiley-Blackwell. 506-20. Anand, R., Dorrestein, P. C., Kinsland, C., Begley, T. P., Ealick, S. E. (2002). Structure of oxalate decarboxylase from Bacillus subtilis at 1.75 angstrom resolution. Biochemistry, 41(24), 7659-7669. doi: Doi 10.1021/Bi0200965 Arumuganathan, K., Earle, E. D. (1991). Nuclear DNA content of some important plant species. Plant molecular biology reporter, 9(3), 208-218. Assuncao, A. G. L., Herrero, E., Lin, Y. F., Huettel, B., Talukdar, S., Smaczniak, C., Aarts, M. G. M. (2010). Arabidopsis thaliana transcription factors bZIP19 and bZIP23 regulate the adaptation to zinc deficiency. Proceedings of the National Academy of Sciences of the United States of America, 107(22), 10296-10301. doi: 10.1073/pnas.1004788107 Assuncao, Agl., Ten, B., Nelissen HJM., Vooijs, R., Schat, H., Ernst, W. (2003). A cosegregation analysis of zinc (Zn) accumulation and Zn tolerance in the Zn hyperaccumulatorThlaspicaerulescens. New Phytologist 159, 383-90. Astudillo, C., Fernandez, A. C., Blair, M. W., Cichy, K. A. (2013). The Phaseolus vulgaris ZIP gene family: identification, characterization, mapping, and gene expression. Frontiers in plant science, 4. doi: Artn 286. Doi 10.3389/Fpls.2013.00286 Bailey, R. L., Gahche, J. J., Lentino, C. V., Dwyer, J. T., Engel, J. S., Thomas, P. R., ... Picciano, M. F. (2010). Dietary supplement use in the United States, 2003–2006. The Journal of nutrition, jn-110. Bauer, P., Bereczky, Z., Brumbarova, T., Klatte, M., Wang, H. Y. (2004). Molecular regulation of iron uptake in the dicot species Lycopersiconesculentum and Arabidopsis thaliana. Soil Science and Plant Nutrition, 50(7), 997-1001. Beebe, S., Gonzalez, A. V., Rengifo, J. (2000). Research on trace minerals in the common bean. Food Nutrition Bulletin, 21(4), 387-391. Beebe, Se., Rojas-Pierce, M., Yan, Xl., Blair, MW., Pedraza, F., Muñoz, F., Tohme, J., Lynch, JP. (2006). Quantitative trait loci for root architecture traits correlated with phosphorus acquisition in common bean. Crop Science 46, 413-23. 92 Bitocchi, E., Nanni, L., Bellucci, E., Rossi, M., Giardini, A., Zeuli, P. S., Papa, R. (2012). Mesoamerican origin of the common bean (Phaseolus vulgaris L.) is revealed by sequence data. Proceedings of the National Academy of Sciences, 109(14), E788-E796. Blair, M. W. (2013). Mineral biofortification strategies for food staples: the example of common bean. Journal of agricultural and food chemistry, 61(35), 8287-8294. Blair, M. W., Astudillo, C., Grusak, M., Graham, R., Beebe, S. (2009). Inheritance of seed iron and zinc concentrations in common bean (Phaseolus vulgaris L.). Molecular Breeding 23, 197207. Blair, M. W., Astudillo, C., Rengifo, J., Beebe, S. E., Graham, R. (2011). QTL analyses for seed iron and zinc concentrations in an intra-genepool population of Andean common beans (Phaseolus vulgaris L.). Theoretical and Applied Genetics, 122(3), 511-521. doi: DOI 10.1007/s00122-010-1465-8 Blair, M. W., Diaz, J. M., Hidalgo, R., Diaz, L. M., Duque, M. C. (2007). Microsatellite characterization of Andean races of common bean (Phaseolus vulgaris L.). Theoretical and Applied Genetics, 116(1), 29-43. Blair, M. W., Gonzalez, L. F., Kimani, P. M., Butare, L. (2010). Genetic diversity, inter-gene pool introgression and nutritional quality of common beans (Phaseolus vulgaris L.) from Central Africa. Theoretical and Applied Genetics, 121(2), 237-248. doi: DOI 10.1007/s00122-010-1305Blair, M. W., Izquierdo, P., Astudillo, C., Grusak, M. A. (2013). A legume biofortification quandary: variability and genetic control of seed coat micronutrient accumulation in common beans. Frontiers in plant science, 4. Blair, M. W., Medina, J. I., Astudillo, C., Rengifo, J., Beebe, S. E., Machado, G., Graham, R. (2010). QTL for seed iron and zinc concentration and content in a Mesoamerican common bean (Phaseolus vulgaris L.) population. Theoretical and Applied Genetics, 121(6), 1059-1070. Blair, M. W., Pedraza, F., Buendia, H. F., Gaitán-Solís, E., Beebe, S. E., Gepts, P., Tohme, J. (2003). Development of a genome-wide anchored microsatellite map for common bean (Phaseolus vulgaris L.). Theoretical and Applied Genetics 107, 1362-74. Blair, M. W.; Astudillo, C.; Rengifo, J.; Beebe, S. E.; Graham, R. (2011). QTL for seed iron and zinc concentrations in a recombinant inbred line population of Andean common beans (Phaseolus vulgaris L.) Theor. Appl. Genet. 122, 511– 523 Blair, M. W., Knewtson, S. J. B., Astudillo, C., Li, C. M., Fernandez, A. C., Grusak, M.A. (2010). Variation and inheritance of iron reductase activity in the roots of common bean (Phaseolus vulgaris L.) and association with seed iron accumulation QTL BMC Plant Biol.10, 215. 93 Blair, M. W., Monserrate, F., Beebe, S. E., Restrepo, J., Flores, J. O (2010). Registration of high mineral common bean germplasm lines NUA35 and NUA56 from the red-mottled seed class. Journal of Plant Registrations, 4(1), 55-59. Bobb, A. J., Eiben, H. G., Bustos, M. M. (1995). Pvalf, an Embryo-Specific Acidic Transcriptional Activator Enhances Gene-Expression from Phaseolin and Phytohemaglutinin Promoters. Plant Journal, 8(3), 331-343. doi: DOI 10.1046/j.1365-313X.1995.08030331.x Bouis, H. E., Hotz, C., McClafferty, B., Meenakshi J. V, Pfeiffer W. H. (2011). Biofortification: A new tool to reduce micronutrient malnutrition. Food Nutrition Bulletin 32 (1), 31S-40. Brotanek J. M., Gosz J. Weitzman M. Flores G. (2007). Iron deficiency in early childhood in the United States: Risk factors and racial/ethnic disparities. Pediatrics. 120:568–575. Broughton, W. J., Hernandez, G., Blair, M., Beebe, S., Gepts, P., Vanderleyden, J. (2003). Beans (Phaseolus spp.)–model food legumes. Plant and soil, 252(1), 55-128. Brown, K. H., Wuehler, S. E., Peerson, J. M. (2001). The importance of zinc in human nutrition and estimation of the global prevalence of zinc deficiency. Food Nutrition Bulletin, 22(2), 113125. Cakmak, I., Pfeiffer, W. H., McClafferty, B. (2010). Biofortification of Durum Wheat with Zinc and Iron. Cereal Chemistry, 87(1), 10-20. doi: Doi 10.1094/Cchem-87-1-0010 Chappell, J., Chrispeels, M. J. (1986). Transcriptional and Posttranscriptional Control of Phaseolin and Phytohemagglutinin Gene-Expression in Developing Cotyledons of Phaseolus vulgaris. Plant Physiology, 81(1), 50-54. doi: Doi 10.1104/Pp.81.1.50 Chasapis, C. T., Loutsidou, A. C., Spiliopoulou, C. A., Stefanidou, M. E. (2012). Zinc and human health: an update. Archives of Toxicology, 86(4), 521-534. doi: DOI 10.1007/s00204011-0775-1 Chen W.R., Feng, Y., Chao Y. E. (2008). Genomic analysis and expression pattern of OsZIP1, OsZIP3, and OsZIP4 in two rice (Oryza sativa L.) genotypes with different zinc efficiency. Russian Journal of Plant Physiology 55, 400-9. Chinoy, C., Welham, T., Turner, L., Moreau, C., Domoney, C. (2011). The genetic control of seed quality traits: effects of allelic variation at the Tri and Vc-2 genetic loci in Pisum sativum L. Euphytica, 180(1), 107-122. doi: DOI 10.1007/s10681-011-0363-8 Cichy, K. A., Caldas, G. V., Snapp, S. S., Blair, M. W. (2009). QTL Analysis of Seed Iron, Zinc, and Phosphorus Levels in an Andean Bean Population. Crop Science, 49(5), 1742-1750. doi:10.2135/cropsci2008.10.0605 Cichy, K. A., Forster, S., Grafton, K. F., Hosfield, G. L. (2005). Inheritance of seed zinc accumulation in navy bean. Crop Science, 45(3), 864-870. doi: DOI 10.2135/cropsci2004.0104 94 Cichy, K. A.; Caldas, G. V.; Snapp, S. S.; Blair, M. W. 2009. QTL analysis of seed iron, zinc, and phosphorus levels in an Andean bean population Crop Sci., 49, 1742– 1750 Cichy, K.A., Blair, M. W., Galeano, C. H., Snapp, S. S., and Kelly, J. D. (2009). QTL analysis of root architecture traits and low phosphorus tolerance in an Andean bean population. Crop Sci. 49:59–68. Connolly, E., Fett J. P., Guerinot M. (2002). Expression of the IRT1 metal transporter is controlled by metals at the levels of transcript and protein accumulation. Plant Cell 14, 1347-57. Courtois, B., Ahmadi, N., Khowaja, F., Price, A. H., Rami, J. F., Frouin, J., ... Ruiz, M. (2009). Rice root genetic architecture: meta-analysis from a drought QTL database. Rice, 2(2-3), 115128. Curie, C., Cassin, G., Couch, D., Divol, F., Higuchi, K., Jean, M., Mari, S. (2009). Metal movement within the plant: contribution of nicotianamine and yellow stripe 1-like transporters. Annals of Botany, 103(1), 1-11. doi: 10.1093/aob/mcn207 D’Ovidio, R., Raiola, A., Capodicasa C., Devoto, A., Pontiggia, D., Roberti, S., Galletti, R., Conti, E., O'Sullivan, D., and De Lorenzo G. (2004). Characterization of the complex locus of bean encoding polygalacturonase-inhibiting proteins reveals subfunctionalization for defense against fungi and insects. Plant Physiol 135, 2424-2435. Debouck, D. G., Toro, O., Paredes, O. M., Johnson, W. C., Gepts, P. (1993). Genetic Diversity and Ecological Distribution of Phaseolus vulgaris (Fabaceae) in Northwestern South-America. Economic botany, 47(4), 408-423. doi: Doi 10.1007/Bf02907356 Duarte, Y., Gutierrez, M., Astudillo, L., Brito, I., Cardenas, A., Bolte, M., Lopez-Rodriguez, M. (2013). Crystal structure of 1,3-bis(6-methoxyquinolin-2-yl) benzene, C26H20N2O2. Zeitschrift Fur Kristallographie-New Crystal Structures, 228(3), 371-372. doi: DOI 10.1524/ncrs.2013.0184 Dubbs, W. E., Grimes, H. D. (2000). Specific lipoxygenase isoforms accumulate in distinct regions of soybean pod walls and mark a unique cell layer. Plant Physiology, 123(4), 1269-1279. doi: DOI 10.1104/pp.123.4.1269 Eide, D., M., Broderius, J. F., and Guerinot, M. L. (1996). A novel iron‐regulated metal transporter from plants identified by functional expression in yeast. Proceedings of the National Academy of Sciences 93(11), 5624–5628. Eng, B.H., Guerinot, M.L., Eide, D., Saier, M. H. (1998). Sequence analyses and phylogenetic characterization of the ZIP family of metal ion transport proteins. Journal of Membrane Biology 166, 1-7. Fageria, N. K., Dos Santos, A. B., Cobucci, T. (2011). Zinc nutrition of lowland rice. Comm. Soil Sci. Plant Anal. 42, 1719-1727. 95 Fernandez, A. C., Galeano, C. H., Cichy, K. A., Blair, M. W. (2011). MapSynteny: Software to create images of synteny. Plant Breeding Genetics and Biotechnology Symposium East Lansing, USA. Dec 16. Gainza-Cortes, F., Perez-Diaz R., Perez-Castro, R., Tapia, J., Casaretto, J. A., González, S., Peña-Cortés, H., Ruiz-Lara, S., and González, E., (2012). Characterization of a putative grapevine Zn transporter, VvZIP3, suggests its involvement in early reproductive development in Vitisvinifera L. BMC Plant Biology 12:111, doi:10.1186/1471-2229-12-111. Gaither, L. A., Eide, D. J. (2001). Eukaryotic zinc transporters and their regulation. Biometals 14, 251-70. Galeano, C. H., Fernandez, A. C., Cichy, K. A., Blair, M. W. (2009). Single strand conformation polymorphism based SNP and Indel markers for genetic mapping and synteny analysis of common bean (Phaseolus vulgaris L.). BMC Genomics 10:629 Galeano, C., Fernandez, AC.,, Franco-Herrera, Natalia., Cichy, K. A., McClean, P. E., Vanderleyden, J., and Blair, M. W. (2011). Saturation of an Intra-Gene Pool Linkage Map: Towards a Unified Consensus Linkage Map for Fine Mapping and Synteny Analysis in Common Bean. Plos one 6(12), e28135 doi:10.1371/journal.pone.0028135. Gelin, J. P., Forster, S., Grafton, K. F., McClean, P. E., Kooas-Cifuentes, G. A. (2007). Analysis of seed zinc and other minerals in a recombinant inbred population of navy bean (Phaseolus vulgaris L.). Crop Science, 47(4), 1361-1366. doi: DOI 10.2135/cropsci2006.08.0510 Genc, Y., McDonald, G.K., and Graham, R.D. (2006). Contribution of different mechanisms to zinc efficiency in bread wheat during early vegetative stage. Plant and Soil 281:353–367. Gepts, P., Aragão, F. J., de Barros, E., Blair, M. W., Brondani, R., Broughton, W., ... Yu, K. (2008). Genomics of Phaseolus beans, a major source of dietary protein and micronutrients in the tropics. In Genomics of Tropical Crop Plants (pp. 113-143). Springer New York. Gepts, P., Osborn, T. C., Rashka, K., Bliss, F. A. (1986). Phaseolin-protein Variability in Wild Forms and Landraces of the Common Bean (Phaseolus vulgaris): Evidence for Multiple Centers of Domestication. Economic botany, 40(4), 451-468. Goffinet B., Gerber S. (2000). Quantitative trait loci: a meta-analysis. Genetics 155:463-473. Graham, R. D., Knez, M., Welch, R. M. (2012). 1 How Much Nutritional Iron Deficiency in Humans Globally Is due to an Underlying Zinc Deficiency?. Advances in Agronomy, 115(1). Grotz, N., Fox, T., Connolly, E., Park, W., Guerinot M., Eide D. (1998). Identification of a family of zinc transporter genes from Arabidopsis that respond to zinc deficiency. Proceedings of the National Academy of Sciences of the United States of America 95, 7220-4. Guerinot M. L, (2000). The ZIP family of metal transporters. BiochimicaEtBiophysicaActaBiomembranes 1465, 190-8. 96 Guo, W. J., Meetam, M., Goldsbrough, P. B. (2008). Examining the specific contributions of individual Arabidopsis metallothioneins to copper distribution and metal tolerance. Plant Physiology, 146(4), 1697-1706. doi: DOI 10.1104/pp.108.115782 Hacisalihoglu, G., Ozturk, L., Cakmak, I., Welch, R. M., Kochian, L. (2004). Genotypic variation in common bean in response to zinc deficiency in calcareous soil. Plant and Soil 259, 71-83. Hambidge, M. (2000). Human Zinc Deficiency. J. Nutr 130 (5), 1344S-1349S. Hambidge, M., Cousins, R. J., Costello, R. B. (2000). Zinc and health: Current status and future directions - Introduction. Journal of Nutrition, 130(5), 1341s-1343s. Hao, Z., Li, X., Liu, X., Xie, C., Li, M., Zhang, D., Zhang, S. (2010). Meta-analysis of constitutive and adaptive QTL for drought tolerance in maize. Euphytica, 174(2), 165-177. Hara, M., Fujinaga, M., Kuboi, T. (2005). Metal binding by citrus dehydrin with histidine-rich domains. Journal of Experimental Botany, 56(420), 2695-2703. doi: Doi 10.1093/Jxb/Eri262 Haydon, M. J., Kawachi, M., Wirtz, M., Hillmer, S., Hell, R., Kramer, U. (2012). Vacuolar Nicotianamine Has Critical and Distinct Roles under Iron Deficiency and for Zinc Sequestration in Arabidopsis. [Article]. Plant Cell, 24(2), 724-737. doi: 10.1105/tpc.111.095042 Hell, R., Stephan, U. W. (2003). Iron uptake, trafficking and homeostasis in plants. Planta, 216(4), 541-551. doi: DOI 10.1007/s00425-002-0920-4 Henriques, R., Jasik, J., Klein, M., Feller, U., Schell, J., Pais, M. S., Koncz, C. (2002). Knockout of Arabidopsis metal transporter gene IRT1 results in iron deficiency accompanied by cell differentiation defects. Plant Molecular Biology 50, 587-97. Hernández, G., Valdés-López, O., Ramírez, M., Goffard, N., Weiller, G., Aparicio-Fabre, R., ... Vance, C. P. (2009). Global changes in the transcript and metabolic profiles during symbiotic nitrogen fixation in phosphorus-stressed common bean plants. Plant physiology, 151(3), 12211238. Huang, X. Q., Madan, A. (1999). CAP3: A DNA sequence assembly program. Genome Research, 9(9), 868-877. doi: Doi 10.1101/Gr.9.9.868 Islam, F. M. A., Basford, K. E., Jara, C., Redden, R. J., Beebe, S. (2002). Seed compositional and disease resistance differences among gene pools in cultivated common bean. Genetic Resources and Crop Evolution, 49(3), 285-293. Islam, F. M. A., Basford, K. E., Jara, C., Redden, R. J., Beebe, S. (2002). Seed compositional and disease resistance differences among gene pools in cultivated common bean. Genetic Resources and Crop Evolution, 49(3), 285-293. 97 Islam, F. M. A., Basford, K. E., Redden, R. J., Gonzalez, A. V., Kroonenberg, P. M., and Beebe, S. (2002). Genetic variability in cultivated common bean beyond the two major gene pools. Genetic Resources and Crop Evolution 49 (3) 271-283. Jakoby, M., Weisshaar, B., Dröge-Laser, W., Vicente-Carbajosa, J., Tiedemann, J., Kroj, T., Parcy, F. (2002). bZIP transcription factors in Arabidopsis. Trends in Plant Science 7 (3), 106111. Jiang, W., Struik, P.C., Van Keulen, H., Zhao, M., Jin L.N., Stomph, T.J. (2008). Does increased zinc uptake enhance grain zinc mass concentration in rice?. Annals of Applied Biology 153 (1), 135–147, August 2008. Jin, T., Zhou, J., Chen, J., Zhu, L., Zhao, Y., Huang, Y. (2013). The genetic architecture of zinc and iron content in maize grains as revealed by QTL mapping and meta-analysis. Breeding science, 63(3), 317. Juliano, B.O. (1999). Comparative nutritive value of various staple foods. Food Rev 15, 399-434. Kalavacharla, V., Liu, Z., Meyers, B. C., Thimmapuram, J., Melmaiee, K. (2011). Identification and analysis of common bean (Phaseolus vulgaris L.) transcriptomes by massively parallel pyrosequencing. BMC plant biology, 11(1), 135. Kerkeb, L., Mukherjee, I., Chatterjee, I., Lahner, B., Salt, D. E, Connolly E. (2008). Iron-induced turnover of the Arabidopsis IRON- REGULATED TRANSPORTER metal transporter requires lysine residues. Plant Physiology 146, 1964-73. Khowaja, F. S., Norton, G. J., Courtois, B., Price, A. H. (2009). Improved resolution in the position of drought-related QTLs in a single mapping population of rice by meta-analysis. BMC genomics, 10(1), 276. Kim, S. A., Punshon, T., Lanzirotti, A., Li, L. T., Alonso, J. M., Ecker, J. R., Guerinot, M. L. (2006). Localization of iron in Arabidopsis seed requires the vacuolar membrane transporter VIT1. Science, 314(5803), 1295-1298. doi: DOI 10.1126/science.1132563 Kmet, J., Mahboubi, E. (1972). Esophageal cancer in the Caspian littoral of Iran: initial studies. Science, 175(4024), 846-853. Korshunova, Yo.,Eide, D., Clark, W. G, Guerinot, M., Pakrasi, H.B. (1999). The IRT1 protein from Arabidopsis thaliana is a metal transporter with a broad substrate range. Plant Molecular Biology 40, 37-44. Kuboyama, T., Shintaku, Y., Takeda, G. (1991). Hybrid plant of Phaseolus vulgaris L. and P. lunatus L. obtained by means of embryo rescue and confirmed by restriction endonuclease analysis of rDNA. Euphytica, 54(2), 177-182. 98 Langmead, B., Trapnell, C., Pop, M., Salzberg, S. L. (2009). Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biology, 10(3). doi: Artn R25 Doi 10.1186/Gb-2009-10-3-R25 Lanquar, V., Ramos, M. S., Lelievre, F., Barbier-Brygoo, H., Krieger-Liszkay, A., Kramer, U., Thomine, S. (2010). Export of Vacuolar Manganese by AtNRAMP3 and AtNRAMP4 Is Required for Optimal Photosynthesis and Growth under Manganese Deficiency. Plant Physiology, 152(4), 1986-1999. doi:DOI 10.1104/pp.109.150946 Larkin, M. A., Blackshields, G., Brown, N. P., Chenna, R., McGettigan, P. A., McWilliam, H., Valentin, F., Wallace, I. M., Wilm, A., Lopez, R., Thompson, J. D., Gibson, T. J., Higgins, D. G. (2007). Clustal W and Clustal X version 2.0. Bioinformatics 23(21), 2947-2948. Leonard, M. F., Stephens, L. C., Summers, W. L. (1987). Effect of maternal genotype on development ofPhaseolus vulgaris L.× P. lunatus L. interspecific hybrid embryos. Euphytica, 36(1), 327-332. Li, Z., and Trick, H. N. (2005). Rapid method for high-quality RNA isolation from seed endosperm containing high levels of starch. BioTechniques 38, 872-876 Li, H., Handsaker, B., Wysoker, A., Fennell, T., Ruan, J., Homer, N., Proc, G. P. D. (2009). The Sequence Alignment/Map format and SAMtools. Bioinformatics, 25(16), 2078-2079. doi: DOI 10.1093/bioinformatics/btp352 Li, J. Z., Zhang, Z. W., Li, Y. L., Wang, Q. L., Zhou, Y. G. (2011). QTL consistency and metaanalysis for grain yield components in three generations in maize. Theoretical and applied genetics, 122(4), 771-782. Li, R. Q., Zhu, H. M., Ruan, J., Qian, W. B., Fang, X. D., Shi, Z. B., Wang, J. (2010). De novo assembly of human genomes with massively parallel short read sequencing. Genome Research, 20(2), 265-272. doi: DOI 10.1101/gr.097261.109 Li, S. Z., Zhou, X. J., Huang, Y. Q., Zhu, L. Y., Zhang, S. J., Zhao, Y. F., Chen, R. M. (2013). Identification and characterization of the zinc-regulated transporters, iron-regulated transporterlike protein (ZIP) gene family in maize. BMC Plant Biology, 13. doi: Artn 114 Doi 10.1186/1471-2229-13-114 Lin, Y. F., Liang, H. M., Yang, S.Y., Boch, A., Clemens, S., Chen, C. C., Wu, J. F., Huang, J. L., Yeh, K. C. (2009). Arabidopsis IRT3 is a zinc-regulated and plasma membrane localized zinc/iron transporter. New Phytologist 182 (2), 392–404. Liu, Zh., Wang, H. Y, Wang, X. E, Zhang G. P., Chen, P. D., Liu, D. J. (2006). Genotypic and spike positional difference in grain phytase activity, phytate, inorganic phosphorus, iron, and zinc contents in wheat (Triticumaestivum L.). Journal of Cereal Science 44, 212-9. 99 Livak, K. J, Schmittgen, T. D. (2001). Analysis of relative gene expression data using real-time quantitative PCR and the 2(T)(-Delta Delta C) method. Methods 25, 402-8. Lopez-Marin, H. D., Rao, I. M., Blair, M. W. (2009) Quantitative trait loci for root morphology traits under aluminum stress in common bean (Phaseolus vulgaris L.). TheorAppl Genet 119, 449–458. Lopez-Millan A. F., Ellis, D. R., Grusak, M. A. (2004). Identification and characterization of several new members of the ZIP family of metal ion transporters in Medicagotruncatula. Plant Molecular Biology 54, 583-96. Lorieux, M. (2012). MapDisto: fast and efficient computation of genetic linkage maps. Molecular Breeding 30, 1231-1235. Melotto, M., Monteiro-Vitorello, C. B., Bruschi, A. G., Camargo, L. E. (2005). Comparative bioinformatic analysis of genes expressed in common bean (Phaseolus vulgaris L.) seedlings. Genome, 48(3), 562-570. Miller, B. D. D., Welch, R. M. (2013). Food system strategies for preventing micronutrient malnutrition. Food Policy, 42, 115-128. Milner, M. J, Craft, E., Yamaji, N., Koyama, E., Ma, J., Kochian, L. V. (2012). Characterization of the high affinity Zn transporter from Noccaeacaerulescens, NcZNT1, and dissection of its promoter for its role in Zn uptake and hyperaccumulation. New Phytologist 195, 113-23. Mochida, K., Shinozaki, K. (2010). Genomics and bioinformatics resources for crop improvement. Plant and Cell Physiology, 51(4), 497-523. Moraghan, J. T., Grafton, K. (1999). Seed-zinc concentration and the zinc-efficiency trait in navy bean. Soil Science Society of America Journal, 63(4), 918-922. Moreau, S., Rowena, M., Thomson, B. N., Kaiser, B. T., Guerinot, M. L., Udvardi, M. K., Puppo, A., and David A. Day. (2001). GmZIP1 Encodes a Symbiosis-specific Zinc Transporter in Soybean. J. Biol. Chem. 277, 4738-4746. Morel, M., Crouzet, J., Gravot, A., Auroy, P., Leonhardt, N., Vavasseur, A., Richaud, P. (2009). AtHMA3, a P-1B-ATPase Allowing Cd/Zn/Co/Pb Vacuolar Storage in Arabidopsis. Plant Physiology, 149(2), 894-904. doi: DOI 10.1104/pp.108.130294 Mott, R., Flint, J. (2013). Dissecting quantitative traits in mice. Annual review of genomics and human genetics, 14, 421-439. Muhammad, S., and ,Uebersax M. A. (2012). Dry Beans and Pulses Production and Consumption—An Overview, in Dry Beans and Pulses Production, Processing and Nutrition (eds M. Siddiq and M. A. Uebersax), Blackwell Publishing Ltd., Oxford, UK. doi: 10.1002/9781118448298.ch1. 100 Myers, J. R., Baggett, J. R. (1999). Improvement of snap bean. In Common bean improvement in the twenty-first century (pp. 289-329). Springer Netherlands. Nriagu, J. (2010). Zinc deficiency in human health. Encyclopedia of Environmental Health, 789800. Oliker, M., Poljakoffmayber, A., Mayer, A. M. (1978). Changes in Weight, Nitrogen Accumulation, Respiration and Photosynthesis during Growth and Development of Seeds and Pods of Phaseolus vulgaris. American Journal of Botany, 65(3), 366-371. doi: Doi 10.2307/2442279 Olsen, L. I., Palmgren, M. G. (2014). Many rivers to cross: the journey of zinc from soil to seed. Frontiers in plant science, 5. doi: Artn 30 Doi 10.3389/Fpls.2014.00030 Penny, M. E., Marin, R. M., Duran, A., Peerson, J. M., Lanata, C. F., Lönnerdal, B., ... Brown, K. H. (2004). Randomized controlled trial of the effect of daily supplementation with zinc or multiple micronutrients on the morbidity, growth, and micronutrient status of young Peruvian children. The American journal of clinical nutrition, 79(3), 457-465. Pinheiro, C., Baeta, J. P., Pereira, A. M., Domingues, H., Ricardo, C. P. (2010). Diversity of seed mineral composition of Phaseolus vulgaris L. germplasm. Journal of Food Composition and Analysis, 23(4), 319-325. doi: DOI 10.1016/j.jfca.2010.01.005 Ramírez, M., Graham, M. A., Blanco-López, L., Silvente, S., Medrano-Soto, A., Blair, M. W., ... Lara, M. (2005). Sequencing and analysis of common bean ESTs. Building a foundation for functional genomics. Plant physiology, 137(4), 1211-1227. Rastogi, T., and Mathers, C. D. (2002b). "Global Burden of Iron Deficiency Anaemia in the Year 2000." GBD 2000 Working Paper, World Health Organization, Geneva. http://www.who.int/evidence/bod. Ren, Y. J., Liu, Y., Chen, H. Y., Li, G., Zhang, X. L., Zhao, J. (2012). Type 4 metallothionein genes are involved in regulating Zn ion accumulation in late embryo and in controlling early seedling growth in Arabidopsis. Plant Cell and Environment, 35(4), 770-789. doi: DOI 10.1111/j.1365-3040.2011.02450.x Ricachenevsky, F. K., Sperotto, R. A., Menguer, P. K., Sperb, E. R., Lopes, K. L., Fett, J. P. (2011). ZINC-INDUCED FACILITATOR-LIKE family in plants: lineage-specific expansion in monocotyledons and conserved genomic and expression features among rice (Oryza sativa) paralogs. BMC Plant Doi 10.1186/1471-2229-11-20 Biology, 11. doi: Artn 20 Ryu, M. S. (2011). Identification of biomarker responses in humans under experimentally induced zinc depletion. University of Florida. 101 Salinas, A. D. (1988). Variation, taxonomy, domestication, and germplasm potentialities in Phaseolus coccineus. In Genetic resources of Phaseolus beans (pp. 441-463). Springer Netherlands. Saltzman, A., Birol, E., Bouis, H. E., Boy, E., De Moura, F. F., Islam, Y. Pfeiffer, W. H. (2013). Biofortification: Progress toward a more nourishing future, Global Food Security. ISSN 2211-9124, 10.1016/j.gfs.2012.12.003. Sandstead, H. H. (1991). Zinc deficiency: a public health problem. Am J. Dis Child 145, 835 859. Sandstead, H. H. (2000). Zinc: growth, development, and function. The Journal of Trace Elements in Experimental Medicine, 13(1), 41-49. Sankaran, R. P., Grusak, M. A. (2014). Whole shoot mineral partitioning and accumulation in pea (Pisumsativum). Plant Nutrition, 5, 149. Sankaran, R. P., Huguet, T., Grusak, M. A. (2009). Identification of QTL affecting seed mineral concentrations and content in the model legume Medicagotruncatula. Theoretical and Applied Genetics, 119(2), 241-253. doi: 10.1007/s00122-009-1033-2 Schmutz, J., McClean, P. E., Mamidi, S., Wu, G. A., Cannon, S. B., Grimwood, J., ... Jackson, S. A. (2014). A reference genome for common bean and genome-wide analysis of dual domestications. Nature genetics, 46(7), 707-713. Schuler, M., Rellan-Alvarez, R., Fink-Straube, C., Abadia, J., Bauer, P. (2012). Nicotianamine Functions in the Phloem-Based Transport of Iron to Sink Organs, in Pollen Development and Pollen Tube Growth in Arabidopsis. Plant Cell, 24(6), 2380-2400. doi: DOI 10.1105/tpc.112.099077 Sekara, A., Poniedzialek, M., Giura, J., Jedrszczyk, E. (2005). Zinc and copper accumulation and distribution in the tissues of nine crops: Implications for phytoremediation. Polish Journal of Environmental Studies 14, 829-35. Seo, Y., Wessling-Resnick, M. (2014). Ferroportin deficiency impairs manganese metabolism in flatiron mice (995.1). The FASEB Journal, 28(1 Supplement), 995-1. Shanmugam, V., Lo, J. C., Wu, C. L., Wang, S. L., Lai, C. C., Connolly, E. L., Huang, J. L., Yeh, K. C. (2011). Differential expression and regulation of iron-regulated metal transporters in Arabidopsis halleri and Arabidopsis thaliana - the role in zinc tolerance. New Phytologist 190, 125-37. Shellie, K. C., Hosfield, G. L. (1991). Genotype× environmental effects on food quality of common bean: resource-efficient testing procedures. Journal of the American Society for Horticultural Science, 116(4), 732-736. 102 Sheokand, S., Dahiya, P., Vincent, J. L., Brewin, N. J. (2005). Modified expression of cysteine protease affects seed germination, vegetative growth and nodule development in transgenic lines of Medicagotruncatula. Plant Science, 169(5), 966-975. doi: DOI 10.1016/j.plantsci.2005.07.003 Singh, S. P., Gepts, P., Debouck, D. G. (1991). Races of common bean (Phaseolus vulgaris, Fabaceae). Economic Botany, 45(3), 379-396. Stephan, U. W., Scholz, G. (1993). Nicotianamine - Mediator of Transport of Iron and HeavyMetals in the Phloem. PhysiologiaPlantarum, 88(3), 522-529. doi: DOI 10.1034/j.13993054.1993.880318.x Swamy, B. P., Kumar, A. (2013). Genomics-based precision breeding approaches to improve drought tolerance in rice. Biotechnology advances, 31(8), 1308-1318. Talke, I. N., Hanikenne, M., Kramer, U. (2006). Zinc-dependent global transcriptional control, transcriptional deregulation, and higher gene copy number for genes in metal homeostasis of the hyperaccumulator Arabidopsis halleri. Plant Physiol 142, 148-67. Thomine, S., Lelievre, F., Debarbieux, E., Schroeder, J. I., Barbier-Brygoo, H. (2003). AtNRAMP3, a multispecific vacuolar metal transporter involved in plant responses to iron deficiency. Plant Journal, 34(5), 685-695. doi: 10.1046/j.1365-313X.2003.01760.x Tian, J., Venkatachalam, P., Liao, H., Yan, X., Raghothama, K. (2007). Molecular cloning and characterization of phosphorus starvation responsive genes in common bean (Phaseolus vulgaris L.). Planta, 227(1), 151-165. Trapnell, C., Hendrickson, D. G., Sauvageau, M., Goff, L., Rinn, J. L., Pachter, L. (2013). Differential analysis of gene regulation at transcript resolution with RNA-seq. Nature Biotechnology, 31(1), 46-53. doi: Doi 10.1038/Nbt.2450 Trapnell, C., Pachter, L., Salzberg, S. L. (2009). TopHat: discovering splice junctions with RNASeq. Bioinformatics, 25(9), 1105-1111. doi: DOI 10.1093/bioinformatics/btp120 Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Pachter, L. (2010). Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nature Biotechnology, 28(5), 511-U174. doi:Doi 10.1038/Nbt.1621 Truntzler, M., Barrière, Y., Sawkins, M. C., Lespinasse, D., Betran, J., Charcosset, A., Moreau, L. (2010). Meta-analysis of QTL involved in silage quality of maize and comparison with the position of candidate genes. Theoretical and applied genetics, 121(8), 1465-1482. Van De Mortel, J. E., Villanueva, A. L., Schat, H. Kwekkeboom, J., Coughlan, S., Moerland, P., van Themaat, E. V. L., and Koornneef, M., Aarts, M. G. M. (2006a). Large expression differences in genes for iron and zinc homeostasis, stress response, and lignin biosynthesis 103 distinguish roots of Arabidopsis thaliana and the related metal hyperaccumulator Thlaspicaerulescens. Plant Physiol 142, 1127-47. Velu, G., Rai, K. N., Muralidharan, V., Kulkarni, V. N., Longvah, T., Raveendran, T. S. (2007). Prospects of breeding biofortified pearl millet with high grain iron and zinc content. Plant Breeding, 126(2), 182-185. Vert, G., Barberon, M., Zelazny, E., Seguela, M., Briat, J. F., Curie, C. (2009). Arabidopsis IRT2 cooperates with the high-affinity iron uptake system to maintain iron homeostasis in root epidermal cells. Planta 229, 1171-9. Vert, G., Briat, J.F., Curie, C. (2001). Arabidopsis IRT2 gene encodes a root-periphery iron transporter. Plant Journal 26, 181-9. Wang, N., Daun, J. K. (2004). Effect of variety and crude protein content on nutrients and certain antinutrients in field peas (Pisumsativum). Journal of the Science of Food and Agriculture 84, 1021-9. Wang, S., Basten, C. J., and Zeng, Z. B. (2012). Windows QTL Cartographer 2.5. Department of Statistics, North Carolina State University, Raleigh, NC Wang, Z., Gerstein, M., Snyder, M. (2009). RNA-Seq: a revolutionary tool for transcriptomics. Nature Reviews Genetics, 10(1), 57-63. Waters, B. M., Chu, H. H., DiDonato, R. J., Roberts, L. A., Eisley, R. B., Lahner, B., Walker, E. L. (2006). Mutations in Arabidopsis Yellow Stripe-Like1 and Yellow Stripe-Like3 reveal their roles in metal ion homeostasis and loading of metal ions in seeds. Plant Physiology, 141(4), 1446-1458. doi: DOI 10.1104/pp.106.082586 Waters, B. M., Sankaran, R. P. (2011). Moving micronutrients from the soil to the seeds: Genes and physiological processes from a biofortification perspective. Plant Science 180, 562-74. Weber, M., Harada, E., Vess, C., Roepenack-Lahaye, E. V., Clemens, S., (2004). Comparative microarray analysis of Arabidopsis thaliana and Arabidopsis halleri roots identifies nicotianamine synthase, a ZIP transporter and other genes as potential metal hyperaccumulation factors. The Plant Journal 37, 269-81. Welch, R. M. (2002). Breeding strategies for biofortified staple plant foods to reduce micronutrient malnutrition globally. The Journal of nutrition, 132(3), 495S-499S. Welch, R. M., Graham, R. D. (2004). Breeding for micronutrients in staple food crops from a human nutrition perspective. Journal of Experimental Botany, 55(396), 353-364. Wen, K., Seguin, P., St.-Arnaud M., and Jabaji-Hare S. (2005). Real time quantitative RT PCR of defense gene transcripts of Rhizoctoniasolani infected bean seedlings in response to inoculation with a non pathogenicbinucleateRhizoctonia isolate. Phytopathology 95, 345-353. 104 Wu, J., Zhao, F-J., Ghandilyan, A., Logoteta, B., Guzman, M., Schat, H.,Wang, X., Aarts, M. (2009). Identification and functional analysis of two ZIP metal transporters of the hyperaccumulatorThlaspicaerulescens. Plant Soil 325, 79–95. Xu, Y., Crouch, J. H. (2008). Marker-assisted selection in plant breeding: from publications to practice. Crop Science, 48(2), 391-407. Yang, X., Huang, J., Jiang, Y., Zhang, H. S., (2009). Cloning and functional identification of two members of the ZIP (Zrt, Irt-like protein) gene family in rice (Oryza sativa L.). Molecular Biology Reports 36, 281-7. Zhao, H., and Eide, D. (1996). The yeast ZRT1 gene encodes the zinc transporter of a high affinity uptake system induced by zinc limitation. Proc. Natl. Acad. Sci. 93, 2454–2458. Zuo, Y., Zhang, F. (2008). Effect of peanut mixed cropping with gramineous species on micronutrient concentrations and iron chlorosis of peanut plants grown in a calcareous soil. Plant and soil, 306(1-2), 23-36. 105