By Yanda, FE; Tryphone, GM (2023). Greener Journal of Plant Breeding and Crop Science 11(1): 1-12.
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ISSN: 2354-2292
Vol. 11(1), pp. 1-12, 2023
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DOI: https://doi.org/10.5281/zenodo.7655383
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1Ministry of Agriculture, Manyoni District Council, P.O. Box 60, Manyoni, Tanzania.
2Department of Crop Science and Horticulture, College of Agriculture, Sokoine University of Agriculture, P. O. Box 3005, Chuo Kikuu, Morogoro, Tanzania.
Type: Research
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DOI: 10.5281/zenodo.7655383
Common bean (Phaseolus vulgaris L.) grain has big dietary role in supplying protein, energy, vitamins, dietary fibre and micronutrients for millions of people worldwide. In Tanzania, reports have shown existence of Fe and Zn micronutrient variabilities among the common bean genotypes. Eighty-four (84) genotypes were collected to study such variability by seed biochemical and leaf molecular marker analysis. The analyses revealed significant (p < 0.001) genetic variability for seed Zn and Fe nutrient contents. Highest seed Fe content was 118 ppm for genotype Kashiransoni and for seed Zn was 51.81 ppm for Imponzo 5 genotype (both were collected from NPGRC). With seven (7) SSR primers associated with Fe and Zn traits, genetic diversity was evaluated. A marker BM154 scored PIC value 0.967 mean while marker BM160 had a lowest PIC score of 0.899. Using an estimated genetic similarity value, two main clusters with sub clusters in the dendrogram were developed. To corroborate the UPGMA analysis results, a Principal Coordinate Analysis (PCoA) was performed and displayed outputs into scattered plot presentation. For Fe micronutrient improvement purposes, Inula was proposed to be crossed with Kashiransoni while Roba with Imponzo 5 for Zn micronutrient improvement. Selection was based on both concentrations of nutrients for each genotype and their genetic similarity distances.
Published: 15/02/2023
George Muhamba Tryphone
E-mail: muhatry@ gmail.com
1.0 INTRODUCTION
Common bean (Phaseolus vulgaris L.) is one of the principal foods and cash crop legumes grown in the tropics and most of the production takes place in developing countries (Hillocks et al. 2006). Common bean grains have big dietary role in supplying protein, energy, dietary fiber and micronutrients for millions of people worldwide although its intake, in terms of nutrient content level, does not satisfy their nutrient requirements (Hillocks et al. 2006). Although common beans are grown largely by smallholder farmers for subsistence and mainly by women, about 40% of the total production from Africa is marketed (Wortmann et al. 1998). It is estimated that over 75% of rural households in Tanzania depend on common beans for daily sustenance (CIAT, 2008).
Micronutrients like zinc and iron are among the important nutrients required in small amounts in the diet to fulfill the nutritional needs of people. Both of these micronutrients are critical to human well-being and an adequate supply of iron and zinc helps to prevent iron deficiency which may lead to reduced oxygen transportation in blood, immune function and consequently to anemia (Blair et al. 2009). Zinc deficiency may lead to reduced immune function, fetal brain cell development and child’s growth and cognitive development as well as other health problems of the developing world (Black, 2003). Zinc is also an anti-cancer element because a potential anti-oxidant. Identifying common bean with high level of these nutrients and integrating them in daily diet will help to improve the situation.
In Tanzania, Tryphone and Nchimbi-Msolla (2010) reported a large variability among common bean genotypes for Fe and Zn nutrient concentrations. Such variability is influenced by genotype environmental interaction and suggested improving climate adapted genotypes has greater potential than improving common bean based on the difference in content of Fe and Zn, for exploiting existing genetic diversity in Tanzania. Unfortunately, there is limited information on how the common bean genotypes being adopted in Tanzania vary genetically for iron and zinc. The knowledge of genetic diversity patterns increases the efficiency for conservation, utilization and genetic improvement of common beans (Beebe et al. 2000). Tremendous efforts have been made to delve deeper into the genetic diversity of these traits in several crop species, including common bean through the use of molecular markers (Talukder et al. 2010). Molecular markers used in estimating the genetic diversity and level of heterozygosity among plants and animals, thus have a great potential to help breeders develop new and improved varieties (Kumar et al., 2008). Therefore, this study will investigate the genetic diversity among germplasm collection of common bean to obtain suitable parental genotype (s) based on both genetic similarity distances and the nutrient levels of Fe and Zn.
2.0 MATERIALS AND METHOD
2.1 Location of the study
The study was conducted at Sokoine University of Agriculture (SUA) in Morogoro, Tanzania in the screen housed behind African seed building located at latitude 6.84795 S and 37.65904 E at 543 m above the sea level. The study was conducted for the period of December 2017 to October 2018.
2.2 Genotypes
Eighty-four (84) common bean genotypes seed were collected from National Plant Genetic Resource Center (NPGRC) at Arusha, Uyole National Research Institute (UNRI) at Mbeya and SUA at Morogoro. The seed was stored in a cold room (12˚C) until planting at Sokoine University of Agriculture, Department of Crop Science and Horticulture. Genotypes collected were diverse, representing a range of seed types, ranging from different seed coat colors, size and shape. During accessioning, there were no specific and strict criteria in choosing a genotype to collect. The collected accessions are as indicated in Table 1.
Table 1: Common bean (Phaseolus vulgarism L.) genotype accessions from different regions of Tanzania
Key: UYL – Uyole, SUA – Sokoine University of Agriculture, NPGRC- National Plant Genetic Resource Center, SEM- Standard error of means and %CV- Percentage coefficient of variation
2.3 Soil sampling and chemical analysis
Composite soil samples obtained were analysed as described by Carter (1993). Bulk soil samples were taken at a depth of 0 – 20 cm on an area of 2 × 2 m2. Composite soil constituted nine sub-samples randomly collected from forestry area covering 1.0 ha. For pot experimentation, sub-samples were thoroughly mixed, sterilized, air dried and ground to pass through an 8.0 mm mesh. Further preparations for soil analysis were carried whereby, the 2.0 mm sieved composite soil samples were used for laboratory physical and chemical analyses. Composite soil samples obtained were analysed for pH, cation exchange capacity, exchangeable bases (Ca, K, Mg and Na), micronutrients (Fe, Zn, Mn and Cu), Nitrogen (N), available phosphorus, particle size distribution and organic carbon (OC) as described by Carter (1993). The soil pH was determined in water at a soil: water ratio of 1:2.5 suspension using pH meter. Electrical conductivity was measured in 1:2.5 soil: water using the electric conductivity meter (Thomas, 1996). Cation exchange capacity (CEC) was determined by the ammonium-acetate saturation method and quantification of exchangeable bases: K, Ca, Na and Mg were determined from the ammonium-acetate filtrates following the Lindsay and Norvell (1978) methods. Exchangeable calcium (Ca) and magnesium (Mg) were determined by atomic absorption spectrophotometry whereas K and Na were extracted using ammonium acetate and analysed by flame spectrophotometry. The DTPA extractable Cu, Fe, Mn and Zn were determined by atomic absorption spectrophotometry (Lindsay and Norvell, 1978). Total nitrogen was determined by the micro-Kjeldahl digestion distillation method (Bremner and Mulvaney, 1982). Soil extractable P was determined by using the Bray-1-P method by Kuo, (1996) and colour was developed by the ascorbic acid-molybdate blue method (Murphy and Rilley, 1962). Particle size analysis was determined by the hydrometer method after dispersing the soil samples with sodium hexametaphosphate solution (Gee and Bauder, 1986). Soil textural classes were determined using the USDA textural class triangle (USDA, 1975).
2.4 Screen house experimentation and agronomical practices
Eighty-four (84) genotypes were arranged in a completely randomized design (CRD) and replicated three times. Before sowing, the 4 kg potted soil was watered and allowed to stay for one day. Four seeds were sown; thinning was done at age of 10 days after emergence. Irrigation by re-introducing trapped water (infiltrates) on bottomed trays was carried out regularly to maintain the moisture content.
2.5 Chemical analysis for iron and zinc nutrients in common bean seeds
After harvesting, bean seeds from each pod of individual plant were mixed thoroughly and taken for chemical analysis. Dry ash method of extraction was undertaken to determine iron and zinc content (Jorhem, 1993). Digested plant samples were taken for reading absorbance values using the Atomic Absorption Spectrophotometer (AAS) method. A standard curve was computed and used to determine the concentration of each sample using Microsoft excel program (version 2016).
2.6 Genomic DNA extraction
Total genomic DNA was extracted from a trifoliate leaf tissue sample of 14 days old plants by using the Quick-DNA Plant/Seed Mini-Prep Kit protocols. DNA quality and quantity were assessed on gel electrophoresis stained with safe view DNA loading dye (EZ-vision) EZ vision. Seven simple sequence repeat (SSR) markers were used to detect the polymorphisms and assess genetic diversity of 84 common bean genotypes collected from two organizations, with several sources of their origin and marker diversity as shown in Table 1and 3 respectively.
2.7 SSR markers and PCR amplification
SSR primer pairs were selected on the basis of the published genetic diversity analysis on common bean by (Talukder et al. 2010). Seven microsatellite primers with high polymorphism levels (≥0.5) were selected and used for final polymerase chain reaction (PCR) amplification (Table 10) for investigating the genetic diversity on Fe and Zn micronutrients in the dry bean accession. The DNA amplification was carried out according Talukder et al. (2010) protocol with minor modifications. A final volume of 25 μl for the PCR reaction mix included 1.5 μl genomic DNA, 0.1 μl of forward and reverse primers, 12.5 μl of 2X× TaqMaster mix and 10 μl of nucleotide free sterile double distilled water. The temperature profile in the thermocycler was of one cycle of 95 0C for 3 min; 40 cycles of 95 0C for 1 min, from 55 to 57 0C for 1 min, and 72 0C for 2 min; and one cycle of 72 0C for 10 min.
2.8 Electrophoretic separation and visualization of amplicons
Superfine agarose (2 %) gel preparation involved the addition of 150 ml of chilled 1x TBE buffer (pH 8.0) to a flask with 3g of superfine agarose powder. Twenty microlitres (20μl) of safe view DNA loading dye (EZ-vision) was added to the gel and cast on a tray to set at room temperature. A gel was then carefully placed in a tank with plenty of 1x TBE buffer and into each well, 3 μl of the samples were carefully loaded with the first well having 1 μl of DNA ladder (100 bp). Gel electrophoresis was run for 200 min at 120 volts. The bands were visualized under an ultra violet trans-illuminator and photo-shopped using the Picasa software.
2.9 Data analysis
2.9.1 Chemical analysis
Obtained absorbance values were processed using MS excel (2018) to determine the concentrations through R square value by establishment of standard curve. The treatment effects were analyzed by analysis of variance (ANOVA) using GenStat V. 25 software. In additional, Fisher’s least significant difference (LSD) was used for mean separation at 5% level of probability.
2.9.2 Genetic diversity
Gel images obtained after electrophoresis were processed using IMAGE J software to determine the molecular weight of the SSR marker bands in reference to the loaded DNA ladder. The determined molecular weights of the SSR marker bands were analyzed by POWERMARKER V.3.25 for estimating the mean number of observable alleles (Na), major allele frequency, allele frequency, observed heterozygosity, expected heterozygosity, gene diversity and the polymorphic information content (PIC).
2.9.3 Cluster analysis
Genetic diversity analyses were conducted using numerical taxonomy and the multivariate analysis system, NTSYSpc V. 2.2. Genetic similarity values were computed between all possible pairs with the SIMQUAL option and ordered in a similarity matrix. The similarity matrix was run by sequential, agglomerative, hierarchical, nested (SAHN) clustering with the unweighted pair group with arithmetic averaging (UPGMA) method as an option. The dendrogram and cluster groupings were constructed by the UPGMA clustering algorithm from the SAHN option of NTSYSpc v.2.2.
2.9.4 Principal coordinate analysis
To corroborate the interrelationships between the common bean genotypes, a scatter plot representation was built using principal coordinate analysis (PCoA) with the Nei’s similarity coefficient. The analyses were performed using GenAlEx statistical software V 6.5 (Peakall and Smouse, 2012).
3.0 RESULTS
3.1 Soil chemical analysis
The experimental forestry soils had medium to high chemical and sandy clay loam textural class as physical characteristics (Table 2). The analysed composed forestry soil based on the selected soil parameters, showed optimal condition that favour growth of common bean as described by Landon, (1991). Therefore, the forestry soils were suitable for production of common beans and other field crops like cereals.
Table 2: Physical-chemical properties of the experimental forestry soils
3.2 Chemical analysis for iron and zinc contents on common bean seeds
The iron content in seed for collected genotypes of Common bean differed significantly (P<0.001) (Table 1). It varied from 32.36 to 118.24 ppm, with a mean of 62.533ppm. Genotypes of Kashiransoni, Kalambi and Maliwalinda were observed with the highest seed iron (Fe) contents of 118.24 ppm, 107.29 ppm and 93.63 ppm respectively. On the other hand, 32.36 ppm, 34.37 ppm and 36.92 ppm were the lowest contents of iron observed in genotypes of Inula, Uyole84 and Maharage-karanga respectively. The genotypes differed significantly (P<0.001) in seed zinc contents (Table 1). Values varied from 18.23 to 51.81 ppm with a mean of 34.44 ppm. Genotypes Imponzo5, Imponzo7 and Tichakuronza were observed with the highest seed zinc (Zn) contents of 51.81 ppm, 47.89 ppm and 47.23 ppm respectively meanwhile 18.23 ppm, 20.5 ppm and 20.68 ppm were the lowest contents of zinc observed in genotypes Roba, Inula and Uyole 84 respectively.
3.3 Allelic diversity of common bean accessions
The key parameters used to define genetic diversity among the common bean accessions from Tanzania are presented in Table 3. There was a high polymorphism with a mean of 24.857 alleles per locus and a range of 17 to 40 alleles in the germplasm. The frequency for the major allele ranged from 0.064 for primer BM154 to 0.208 for the primer BMd16 with mean of 0.139. In total, the 7 markers detected 174 alleles and PIC ranging from 0.892 for the primer BMd16 to 0.967 for the primer BM154 with the mean of 0.923. The overall mean for the expected heterozygosity ranged from 0.899 for BMd16 primer to 0.967 for BM154 primer. The observed heterozygosity was observed on the two primers of BM160 and BM181 with value 0.025 and 0.027 respectively.
Table 3: Genetic information for the markers used to detect the genetic diversity of 84 Common bean genotypes collected from different regions of Tanzania for the Fe and Zn traits.
3.4 Cluster analysis
For a better understanding of the genetic relationship among common bean genotypes, the genetic similarity (GS) values were submitted to hierarchical clustering by UPGMA. The dendrogram based on Nei’s coefficient of genetic distance suggested the existence of two clusters with sub clusters (Figure 1). The Cluster I was further subdivided into two sub clusters of A and B in total comprised of 31 and 2 genotypes of common bean respectively. Cluster II was also divided into two sub cluster designated as A and B with a total of 28 and 23 genotypes respectively (Table 4).
Table 4: Distribution of genotypes to different clusters based on the UPGMA methods
cluster
3.5 Principal co-ordinate analysis
The common bean genotypes were represented by a scatter plot in 2-dimensions using the results of the principal coordinate analysis (PCoA) obtained by estimating the Nei’s genetic similarity distance. It revealed the global structure similar to the dendrogram analysis, but the distribution of these accessions was shown more clearly in scatter plot (Figure 2). Overall, the clustering pattern of the genotypes in the principal coordinates analysis corresponds with the dendrogram derived from UPGMA (Figure 1).
Figure 1: Dendrogram representing the genetic diversity among the 84 accessions of Common bean of Tanzania based on Dice coefficient
of similarity matrix data using UPGMA cluster analysis
Figure 2: Principal coordinate analysis (PCoA) of common bean accessions from the microsatellite diversity based on molecular weight (band sizes) of alleles.
4.0 DISCUSSION
4.1 Iron and zinc contents in common bean
The high seed Fe and Zn content were expressed with genotypes of Kashiransoni and Imponzo5. Both genotypes were sourced at the gene bank of Tanzania (NPGRC) being collected from Kigoma (Kashiransoni) and Mbeya (Imponzo 5) regions. There was no environmental contribution to the significantly (p<0.001) variability of both seed Fe and Zn content on the experimented genotypes of common bean because the study was conducted in screen-house conditions hence differences in their genetic makeup could have resulted into such variability.
4.2 Genetic diversity of common bean genotypes
The employed UPGMA clustering method divided the common bean genotypes into two main clusters. The main two branches of the dendrogram have showed a considerable range of genetic diversity of genotypes in respect to the genetic similarity distance being less than 9e-04, suggesting existence of low genetic diversity based on Fe and Zn traits. This existence could be due to either the genotypes in equation comes from the similar origin or the SSR markers used could not clearly detect large range of diversity. Similar results for the diversity of common bean genotypes for iron and zinc nutrients of Tanzania corresponds with numerous and deliberate efforts done by other researchers worldwide by using various molecular techniques including SSR marker. Talukder et al. (2010) observed low level of genetic diversity because since the GS for the common bean genotypes was more than 50%, hence suggesting low genetic variability. Low genetic variability between the two main clusters does hinder selection of parental genotypes for cross breeding programs due to significant (p<0.001) difference in variabilities of seed Fe and Zn content. Thus, it can be expected that the introgression of a desired trait would be easier for this cross without sacrificing valuable trait(s) and/or adding unwanted trait(s) as a consequence of linkage drag (Talukder et al. 2010).
4.3 Selection of genotypes for breeding programs
Selection of genotypes for breeding purpose based on micronutrient variability and genetic diversity has been suggested in many crop species including common bean by Talukder et al. (2010) and rice (Gregorio, 2000). From the current results of genetic diversity analysis, several breeding strategies can be derived; however, the interest at present is to develop populations to map Zn and Fe content traits and to breed common bean cultivars with enhanced Zn and Fe contents.
Based on seed Iron (Fe) and Zinc (Zn) contents of the genotypes and genotypic variability at molecular level, the cross-breeding program for seed Fe content was designed to cross the genotypes with high Fe content namely Kashiransoni, Kalambi and Maliwalinda while low Fe content genotypes were Inula, Uyole84 and Maharage-karanga. The high Fe contents genotypes Kashiransoni, Kalambi and Maliwalinda belonged to clusters II-A, II-B and II-A respectively and those of low Fe contents viz. Inula, Uyole 84 and Maharage-karanga belonged to clusters; II-A, II-B and II-A respectively. The genetic similarity distance values of genotypes Inula with Kashiransoni, Kalambi and Maliwalinda are 1.704e-4, 0 and 1.0116e-3 respectively and those of Uyole 84 with Kashiransoni, Kalambi and Maliwalinda are 3.91e-3, 3.31e-3 and 3.20e-3 respectively and those of Maharage-karanga with Kashiransoni, Kalambi and Maliwalinda are 8.89e-4, 2.79e-3 and 1.03e-3 respectively. These values indicate that the selected genotypes are divergent at the molecular level with significant differences in seed Fe content and could result in better segregation and recombination of the desired alleles in successive generations during population development.
For the crossing program to enhance seed Zn content, genotype Imponzo 5 with high Zn content (ppm) was selected against the genotypes of Roba and Inula with low zinc contents. The high Zn content genotype of Imponzo 5 belonged to clusters II-A and the low Zn content genotypes of Roba and Inula belonged to clusters II-B and II-A respectively. The genetic similarity distances value of genotype Roba with Imponzo 5 was 8.72e-4 and that of Inula with Imponzo 5 was 5.28e-4. These values indicate that the selected genotypes are divergent at the molecular level with significant differences in seed Zn content and could result in better segregation and recombination of the desired alleles in successive generations during population development. Crossing combinations with genotypes between and within cluster having high genetic diversity and mineral content would be expected to accumulate positive alleles derived from unique sources and generate breeding lines with even higher seed mineral contents.
Also, crossing of the selected genotypes would be of significant important for genetic studies because they’re contrasting for the observed traits. Genetic studies offer an opportunity of utilizing the genetic information like inheritance patterns of Fe and Zn, additive effect and dominance effects of gene can be determined as knowledge of heritability is crucial for decisions making concerning screening and breeding methodologies and the scale of breeding.
5.0 CONCLUSION
The variability in iron and zinc content was significant (p<0.001) across all experimented genotypes. The range of seed iron content was 32.36 for Inula to 118.24 ppm for Kashiransoni genotype with mean of 62.533 ppm. For seed zinc content it ranged from 18.23 ppm for Roba to 51.81 ppm for Imponzo 5 with a mean of 34.44 ppm. The SSR primers used in this study showed to be effective on detecting the alleles bonded to the trait of iron and zinc. The high PIC (0.967) value was shown by BM154 primer which detected 40 alleles for 78 samples of genotypes that were amplified. The overall results obtained by SSR analysis of the common bean genotypes in the present study has shown that there is genetic divergence among the collected genotypes based on the traits. The UPGMA analysis together with the scattered plot presentation (PCoA) has successfully grouped the genotypes into sense of similarity by using the estimated genetic similarity distance. It is recommended that the genotypes Kashiransoni and Inula for iron and Roba and Imponzo 5 for zinc as breeding materials as sources Fe and Zn. Introgression of gene for Fe and Zn enrichment should rely on consumer preferred common bean genotypes as a key group during participatory plant breeding (PPB) programs. Further, the inheritance patterns of Fe and Zn traits has to be studied since the knowledge of heritability is crucial for decisions making concerning screening and breeding methodologies and the scale of breeding.
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Cite this Article: Yanda, FE; Tryphone, GM (2023). Microsatellite Analysis of Common Bean (Phaseolus vulgaris L.) Genotypes in Tanzania for Diversity in Seed Iron and Zinc Micronutrients. Greener Journal of Plant breeding and Crop Science, 11(1): 1-12. https://doi.org/10.5281/zenodo.7655383
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