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Research Article - (2018) Volume 9, Issue 11

Rep-PCR Genomic Fingerprinting Revealed Genetic Diversity and Population Structure among Ethiopian Isolates of Pseudocercospora griseola Pathogen of the Common Bean (Phaseolus vulgaris L.)

Yayis Rezene1,2*, Kassahun Tesfaye2,3, Mukankusi Clare4, Allan Male4 and Paul Gepts5
1Molecular Biotech Laboratory, Southern Agricultural Research Institute, Awassa, Ethiopia
2Department of Microbial, Cellular and Molecular Biology, Addis Ababa University, Ethiopia
3Ethiopian Biotechnology Institute, Addis Ababa, Ethiopia
4Centro Internacional de Agricultura Tropical (CIAT), Kampala, Uganda
5Department of Plant Sciences, University of California, Shields Avenue, Davis, California, USA
*Corresponding Author: Yayis Rezene, Molecular Biotech Laboratory, Southern Agricultural Research Institute, Awassa, Ethiopia, Tel: +251-462206575 Email:

Abstract

Angular leaf spot (Pseudocercospora griseola) is one of the most devastating diseases affecting common bean production in most parts of Ethiopia. Thus, use of common bean varieties with durable resistance is the most effective and economical control measure. Knowledge about the genetic variability and population structure of the pathogen populations is important for a successful common bean improvement program. The objective of this study was to determine the genomic diversity existing among and between P. griseola isolates which were obtained from the field survey collection of diverse common-bean growing areas of Ethiopia. The study used the repetitive extragenic palindromic elements-polymerase chain reaction protocol to fingerprinting DNA sequence diversity. To study the genetic diversity, we analysed molecular data from 79 single-spore isolates of the P. griseola pathogen. Hence, Molecular Analysis of Variance (AMOVA) and cluster analysis revealed the existence of high genetic diversity within and among P. griseola isolates. ERIC PCR produced 21 different patterns of clusters, whereas, REP-PCR and BOX PCR produced 11 and 5 different patterns of clusters respectively. This is because of some isolates that shared the same BOX patterns could be distinguished by the ERIC and REP finger printing patterns. The ERIC-, BOX- and REP-PCR combined fingerprinting patterns discriminated 25 different patterns among the 79 monosporic P. griseola isolates were produced at cut-off 77% genetic similarity matrix. These discriminated clusters revealed the existence of genetic diversity within and among the isolates of P. griseola collected from the diverse common bean growing regions of Ethiopia.

 

Keywords: Repetitive extragenic palindromic elements; Polymerase chain reaction; Genetic characterization; Pathogen differentiation; Pseudocercospora griseola

Introduction

Common bean (Phaseolus vulgaris L.) is the most cultivated pulse crop worldwide. It is one of the major food and cash crops with a significant contribution to national economy and also traditionally ensures food security in Ethiopia [1]. Several biotic and abiotic stress are limiting the productivity of common bean of which Angular leaf spot (ALS) caused by Pseudocercospora griseola is the most devastating disease its yield reduction is estimated to reach 80% [2]. The use of resistance common bean varieties with durable resistance is the most effective and economical control measure of ALS. However, getting common bean varieties with durable resistance is not easy. Knowledge about the genetic variability of the pathogen populations is important for a successful common bean improvement program that aims to develop disease resistant varieties [3]. Genetic structure is defined as the amount and distribution of genetic variation within and among populations [4]. Thus, knowledge of genetic structure gives insight into the evolutionary processes that shaped a population in the past. It is useful to differentiate between the two types of genetic diversity that contribute to genetic structure: gene diversity and genotype diversity, Gene diversity increases as the number of alleles increases and the relative frequencies of those alleles become more equal. Genotype diversity refers to the number and frequencies of multi-locus genotypes, or genetically distinct individuals, in a population. Genotype diversity is an important concept for plant pathogens that have a significant component of asexual reproduction in their life history [3]. The genomes of microbes contain a variety of repetitive DNA sequences, accounting for up to 5% of the genome [5]. Many of these repetitive DNA elements are of unknown function and have been localized to both intergenic and extragenic regions of the microbial genome. The Palindromic Units (PU) or Repetitive Extragenic Palindromes (REP) constitutes the characterized family of bacterial repetitive sequences. PUs are present in about 500-1000 copies in the chromosome of Escherichia coli and of Salmonella typhimurium. PU sequences consist of a 35-40 bp inverted repeat and are found in clusters. A second family of repetitive elements, called IRU (Intergenic Repeat Units) or ERIC (Enterobacterial Repetitive Intergenic Consensus), has been described [6]. IRU are 124-127 bp long in which successive copies (up to six) are arranged in alternate orientation [7,8]. Both PU and IRU families are similarly located in non-coding, probably transcribed, regions of the chromosome. Repetitive Element Polymorphism PCR (rep- PCR) fingerprinting has become a frequent method to discriminate bacteria species analysing the distribution of repetitive DNA sequences in prokaryotic genomes [6]. Rep-PCR is based on the observation that outwardly facing oligonucleotide primers, complementary to interspersed repeated sequences, enable the amplification of differently sized DNA fragments, consisting of sequences lying between these elements [9]. Multiple amplicons of different sizes can be resolved by electrophoresis, establishing DNA-fingerprint-specific patterns for bacterial strains [10]. Several of these interspersed repetitive elements are conserved in diverse genera of bacteria and, therefore, enable single primer sets to be used for DNA fingerprinting in many different microorganisms [9,10]. Although rep-PCR primers were developed for repetitive elements in prokaryotic genomes, these primers have been applied with success in the fingerprinting of eukaryotic genomes as well [11]. Thus, rep-PCR primers have been used to characterize the variability of several fungal genera [12-16]. Endogenous repetitive DNA elements have been identified in fungi and used to generate genomic fingerprints [11]. For example, repetitive sequences like microsatellites were shown to be useful targets for DNA-based typing because of their length variation and widespread occurrence [17]. Rep-PCR fingerprinting is a highly reproducible and simple method to distinguish closely related microbial strains, deduce phylogenic relationships, and study their diversity in different ecosystems [18]. However, few studies have been performed regarding the applicability of rep-PCR to the discrimination of fungal species. Currently, rep-PCR has been proved as a useful molecular method to identify and study the genetic variability in the fungal species. The aim of the work presented here was to study the genetic diversity and the population structure of P. griseola isolates obtained from the collected infected leaves of common bean from the various areas of Ethiopia using rep-PCR molecular finger printing methods. To the best of our knowledge, this is the first report of the use of rep-PCR genetic fingerprinting to study the genetic diversity of P. griseola from Ethiopia.

Materials and Methods

Sample collection and isolation of Pseudocercospora griseola

The experiment was conducted in the Molecular Biotech Lab at the Southern Agricultural Research Institute (SARI), Hawassa, Ethiopia. Leaves with lesions of ALS were sampled and collected from fields of common bean during the field survey in 2016 and 2017 in diverse agroecological zones of Ethiopia that are known major common bean production areas (Figure 1). A total of 78 pure and single spores were isolated from infected and diseased leaves collected from the various common-bean growing regions using methods developed by CIAT (Table 1). Moreover, one additional isolate already characterised isolate from Andean gene pools that was obtained from CIAT-Uganda was included in the study to differentiate the Ethiopian isolates into Middle American and Andean groups. Isolation and monosporic culture were done according to the method developed by Pastor-Corrales et al. [19]. Accordingly, freshly infected leaves of common bean were used and single spore were transferred from fungal structures formed on lesions to culture media, using a sterilized fine needle under a dissecting microscope (Motic compound microscope). Monosporic cultures of P. griseola were grown on V8 culture media in 12 h dark and light incubator for 20 days at 25°C until genomic DNA extraction.

plant-pathology-microbiology-griseola

Figure 1: Geographic location of Pseudocercospora griseola sampling sites in the study on the map of Ethiopia.

CODELocationAltitudeHostGenepoolOriginYear of Collection
pget001 WONDO 1742 masl PIC6 A Ethiopia 2017
pget002 WONDO 1742 masl ADP-0100 A Ethiopia 2017
pget003 GOFA 1400 masl SMALL RED M Ethiopia 2017
pget004 WONDO 1742 masl ADP-0095 A Ethiopia 2017
pget005 WONDO 1742 masl ADP-0468 A Ethiopia 2017
pget006 GOFA 1400 masl SMALL RED M Ethiopia 2017
pget007 HALABA 1872 masl TATU A Ethiopia 2017
pget008 GOFA 1400 masl HDUME M Ethiopia 2017
pget009 WONDO 1742 masl ADP-0668 A Ethiopia 2017
pget010 WONDO 1742 masl ADP-0518 A Ethiopia 2017
pget011 WONDO 1742 masl ADP-0037 A Ethiopia 2017
pget012 WONDO 1742 masl ADP-0037 A Ethiopia 2017
pget013 WONDO 1742 masl ADP-0675 A Ethiopia 2017
pget014 WONDO 1742 masl ADP-0675 A Ethiopia 2017
pget015 WONDO 1742 masl ADP-0675 A Ethiopia 2017
pget016 WONDO 1742 masl ADP-0675 A Ethiopia 2017
pget017 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget018 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget019 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget020 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget021 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget022 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget023 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget024 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget025 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget026 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget027 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget028 DOLLA 1865 masl RED WOLAITA M Ethiopia 2017
pget029 DOLLA 1219 masl NASIER M Ethiopia 2017
pget030 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget031 SOUTH OMO 1363 masl RED WOLAITA M Ethiopia 2017
pget032 SOUTH OMO 1363 masl RED WOLAITA M Ethiopia 2017
pget033 SOUTH OMO 1363 masl H DUME M Ethiopia 2017
pget034 SOUTH OMO 1363 masl H DUME M Ethiopia 2017
pget035 SOUTH OMO 1363 masl H DUME M Ethiopia 2017
pget036 SOUTH OMO 1363 masl H DUME M Ethiopia 2017
pget037 SOUTH OMO 1363 masl H DUME M Ethiopia 2017
pget038 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget039 BAKO GAZAR 1363 masl SMALL RED M Ethiopia 2017
pget040 BAKO GAZAR 1363 masl SMALL RED M Ethiopia 2017
pget041 SOUTH OMO 1363 masl HDUME M Ethiopia 2017
pget042 AREKA 1802 masl ADP A Ethiopia 2017
pget043 AREKA 1802 masl ADP A Ethiopia 2017
pget044 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget045 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget046 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget047 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget048 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget049 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget050 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget051 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget052 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget053 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget054 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget055 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget056 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget057 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget058 CHANO DORGA 1219 masl NASIER M Ethiopia 2017
pget059 CHANO MILE 1212 masl NASIER M Ethiopia 2017
pget060 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget061 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget062 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget063 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget064 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget065 CHANO MILE 1219 masl NASIER M Ethiopia 2017
pget066 CHANO MILE 1212 masl NASIER M Ethiopia 2017
pget067 CHANO MILE 1212 masl NASIER M Ethiopia 2017
pget068 CHANO MILE 1212 masl NASIER M Ethiopia 2017
pget069 CHANO MILE 1212 masl NASIER M Ethiopia 2017
pget070 HALABA 1872 masl HDUME M Ethiopia 2016
pget071 GURAGE 1604 masl NASIER M Ethiopia 2016
pget072 GURAGE 1770 masl NASIER M Ethiopia 2016
pget073 GURAGE 1772 masl NASIER M Ethiopia 2016
pget074 AREKA 1884 masl RED WOLAITA M Ethiopia 2016
pget075 GURAGE 1742 masl NASIER M Ethiopia 2016
pget076 KAO60 /CIAT -- -- A -- 2016
pget077 240 -- -- -- -- 2016
pget078 220 -- -- -- -- 2016
pget079 224 -- -- -- -- 2016

Table 1: Pseudocercospora griseola isolates collected from diverse common bean growing regions of Ethiopia.

Genomic deoxyribonucleic acid (DNA) extraction

Genomic DNA was extracted using a protocol described by Mahuku et al. [20-22] with minor modification. The harvested fresh fungal mycelium was transferred to sterilized 1.7 ml tube containing 500 microliter of TES extraction buffer (0.2 m Tris-HCl pH 8.0; 10 mm EDTA, pH 8.0, 0.5 m NaCl, 1% SDS); sterilized sand was added and grinded using mortar and pistil. The samples were mixed using vortex for 30s and then incubated in the water bath at 65°C for 30 minutes before it was centrifuged for 15 minutes at 20,800 g. The supernatant was transferred to a new tube and an equal volume of ice cold isopropanol was added. Tubes were then incubated at -20°C for 1 hour, followed by centrifugation for 10 minutes at 20,800 g to pellet the DNA. The supernatant was eliminated and the DNA pellet was washed with 800 microliters of cold 70% ethanol; the tubes were then turned upside down on clear sterile paper towel for 45 minutes to air dry. The dried DNA pellet was diluted with 1 x TAE buffer.

Rep-PCR fingerprinting

In rep-PCR, fingerprinting three families of repetitive sequences were used (Table 2). They included:

Genetic markers Sequences 5’ to 3’ Ta °C GC% Number of nucleotides References
REP 1R IIIICGICGICATCIGGC 49 52.9 18 [21]
REP 2 IIICGNCGNCATCNGGC 58 52.9 17 [21]
ERIC 1R ATGTAAGCTCCTGGGGATTCAC 58 50 22 [22]
ERIC 2 AAGTAAGTGACTGGGGTGAGCG 42 54.5 22 [22]
BOX A1R CTACGGCAAGGCGACGCTGACG 50 68.2 22 [6]

Table 2: Molecular markers used to amplify PCR fingerprinting products of Pseudocercospora griseola.

1) The Repetitive Extragenic Palindromic (REP) sequence REP1R-1/ REP2-1 (18 nucleotides in length), as described by Versalovic et al. [6].

2) The Enterobacterial Repetitive Intergenic Consensus (ERIC) in which two oligonucleotide primer pairs used for PCR amplification ERIC1R/ERIC2 (22 nucleotide in length).

3) BOX elements (22 nucleotide in length) [23].

Optimal PCR conditions for each of the primer sets used were as described by De-Bruijn [9] with minor modification of the annealing temperature. The reproducibility of rep-PCR was tested by amplifying DNA two times from ten randomly chosen strains. The PCR amplifications were performed with a thermal cycler 2710 using PCR premix (GEillustra) as described by the manufacturer [24]. The PCR products were electrophoresed in a 1.5% agarose gel for 2 h at a constant voltage of 90 V in 1 × TAE buffer (40 mMTris-Acetate, 1 mM EDTA, pH 8.0) at 4°C. Gels were stained in ethidium bromide and the rep-PCR profiles and fingerprinting patterns were visualized under UV light, and the image was captured using a Canon digital camera mounted on the visualization hood.

Data analysis and interpretation

Analysis of genetic similarity and dissimilarity: The results of PCR fingerprinting with ERIC, BOX, and REP primers were collected into matrices with scored presence (1) or absence (0), of banding pattern in each PCR analysis lane. In each case, a simple matrix was obtained by comparing pairs of isolates of P. griseola using a simple matching coefficient (SM). A dendrogram was constructed with all parameters together after cluster analysis with the Dice similarity matrix, the Jaccard dissimilarity matrix and the Euclidean distance [25]. As suggested by the Kosman diversity and distance measures [26] for populations with an asexual and mixed mode of reproduction were considered in this specific study to measure the genetic diversity with populations and distance between populations. The Kosman distance and diversity measures for populations were calculated using different measures of dissimilarity between individuals (the simple mismatch, Jaccard, and Dice coefficients of dissimilarity). Similarity among the profiles was calculated using the Dice similarity matrix. The clustering was based on an average linkage or unweighted pair group method with arithmetic averages (UPGMA).

Analysis of molecular variance (AMOVA) and genetic diversity: An analysis of molecular variance (AMOVA) was performed using GenAlEx6.1 [27] to assess genotypic variations across all the populations studied. The analysis included partitioning of total genetic variation into within-groups and among groups variance components, hence, it provided a measure of intergroup genetic distance as proportion of the total variation residing among populations. The significance of analysis was tested using 999 random permutations.

Results

Analysis of molecular markers

Rep-PCR amplification in Pseudocercospora griseola: Rep-PCR analysis using primer sets REP, ERIC and BOX of highly conserved repetitive sequences resulted in differential banding patterns among and within P. griseola populations collected from the diverse common bean growing regions of Ethiopia. In Rep-PCR, three families of repetitive sequences were used, including the repetitive extragenic palindromic (REP) sequences, enterobacterial repetitive extragenic palindromic sequence, enterobacterial repetitive intergenic consensus (ERIC), and BOX elements [23]. Amplification of genomic DNA from the P. griseola isolates collected from the diverse common bean growing regions of Ethiopia with rep-PCR resulted in complex fingerprint patterns (Figures 2-4). Rep-PCR fingerprint patterns for isolates of P. griseola were examined. The size of amplification products ranged from 100bp to 1500bp. Analysis of the ERIC PCR fingerprinting patterns by UPGMA using Dice similarity coefficient resulted 17 distinct groups among the 79 P. griseola at 77% similarity cut of level (Figure 2). While BOX and REP PCR fingerprinting pattern discriminated 5 and 11 distinct groups among the 79 P. griseola at cut-off 60 and 66% similarities level respectively (Figures 3 and 4). Hence, ERIC-PCR was the most informative to differentiate isolates of P. griseola collected from the diverse common bean growing regions of Ethiopia. The dendrogram obtained from the cluster analysis using combined ERIC-, REP- and BOX-PCR genomic fingerprints revealed the overall grouping of the P. griseola isolates collected from the diverse areas of Ethiopia. Thus, combined REP-PCR fingerprinting discriminated 26 distinct groups among the 79 isolates of P. griseola at a cut off 77% similarity molecular level (Figure 5). Previously the distribution of ERIC, REP and BOX elements has been examined and reported in diverse prokaryotic genomes [6]. Previous report of repetitive PCR primers matching with these repetitive sequences has been described for the molecular characterization of bacterial strains [9,10]. Our results were consistent with many reports that indicates these repetitive elements, which are highly conserved in the bacterial kingdom, are also presents in our specific study in the fungus P. griseola populations collected from diverse bean growing regions of Ethiopia, which allowed us to differentiate the 79 isolates of P. griseola.

plant-pathology-microbiology-ecologies

Figure 2: Agarose gel showing polymerase chain reaction genomic fingerprinting pattern and cluster analysis based on UPGMA and Dice similarity coefficients with ERIC-PCR gDNA extracted by the Mahuku (2004) method from 79 (1-79) single spore isolates of Pseudocercospora griseola. The isolates were collected from the diverse common bean growing agro-ecologies of Ethiopia. M=100 bp genetic marker.

plant-pathology-microbiology-genomic

Figure 3: Agarose gel showing polymerase chain reaction (PCR) and genomic fingerprinting pattern and cluster analysis based on UPGMA and Dice similarity coefficients with BOX -PCR from the gDNA extracted by the Mahuku (2004) method from 79 (lane 1 to 79) single spore isolates of Pseudocercospora griseola collected from diverse common bean growing regions of Ethiopia. M: genetic marker 100bp, NC: Negative Control.

plant-pathology-microbiology-polymerase

Figure 4: Agarose gel showing polymerase chain reaction produced genomic fingerprinting pattern and Cluster analysis based on UPGMA and Dice similarity coefficients with REP-PCR from 79 (lane 1-79) single spore isolates of Pseudocercospora griseola collected from diverse common bean growing regions of Ethiopia. M: molecular marker 100bp, NC; Negative Control.

plant-pathology-microbiology-molecular

Figure 5: Pie chart indicating the percentage of molecular variance partition.

Analysis of molecular variance (AMOVA): The analysis of molecular variance (AMOVA), which revealed 83% and 17% genetic variations (p<0.05) within and among the monosporic isolates of P. griseola obtained from the collections of the diverse common bean growing areas of Ethiopia (Figures 5-8).

plant-pathology-microbiology-cluster

Figure 6: Cluster analysis based on UPGMA and Dice similarity coefficients obtained from the combined REP, BOX and ERIC genomic fingerprinting patterns of 79 single-spore isolates of Pseudocercospora griseola collected from diverse common bean growing regions of Ethiopia. Same colors within the cluster indicates genetic similarity of P. griseola isolates and the dendrogram in the right side indicates the genetically discriminated 25 clusters among the 79 monosporic isolates.

plant-pathology-microbiology-evolutionary

Figure 7: The evolutionary history was inferred using the Minimum Evolution method. The optimal tree with the sum of branch length=0.75085937 is shown. The tree is drawn to scale, with branch lengths (next to the branches) in the same units as those of the evolutionary distances used to infer the phylogenetic tree. The ME tree was searched using the Close-Neighbour-Interchange (CNI) algorithm at a search level of 1. The Neighbour-joining algorithm was used to generate the initial tree. Evolutionary analyses were conducted in MEGA6.

plant-pathology-microbiology-dendrogram

Figure 8: Cluster analysis dendrogram of 79 Pseudocercospora griseola isolates collected from the diverse common bean growing areas of Ethiopia using rep-PCR molecular fingerprinting data.

Cluster analysis of BOX, REP and ERIC-PCR fingerprinting pattern: Cluster analysis was performed on the combined DNA fingerprints produced from BOX, REP and ERIC PCR products (Figure 5). The dendrogram obtained from the cluster analysis of combined (REP/BOX/ERIC) Rep-PCR fingerprinting patterns discriminated the entire monosporic P. griseola isolates, that were collected from various common bean regions of Ethiopia into 25 distinct types among the 79 P. griseola isolates. The results of the present study determined primarily the usefulness of Rep-PCR genomic fingerprinting as complimentary or as an alternative strategy to other methods of genomic diversity study of P. griseola isolates of the angular leaf spot of the common beans.

Discussion

The genomic DNA fingerprinting patterns found among the P.griseola isolates obtained from infected common bean leaves collected from various common bean growing areas of Ethiopia were found to be varied in size and number depending on each P. griseola isolates indicating the existence of diverse genetic variability within each isolate. However, some of the isolates showed similar DNA fingerprinting patterns with only minor differences; hence, these isolates with similar genomic DNA fingerprinting pattern were clustered in the same group. The Rep-PCR primers set families of ERIC, REP and BOX generated multiple distinct DNA genomic fingerprints ranging from 100 bp to 1500 bp (Figures 1-3). The results of genomic DNA fingerprint profiles obtained from monosporic isolates complement with the many of the previous reports and can be reproducible from one experiment to another [13,14,16,17]. The observed significant variation within the monosporic isolates of P. griseola, among the isolates of the same geographic locations were due to the co-exitance of diverse host genotypes and based on many of the reports the pathogen might undergone parasexual that facilitates exchange of genetic material within and between isolates. It might also because of chromosomal inversion, deletion and presence of transposons because all are reported to have capability to increase the variability in P. griseola [28,29]. The genetic structure of P. griseola revealed no geographical differentiation. The small reds & white coloured beans from the Mesoamerican gene pool have been predominantly cultivated in Ethiopia with the exception of a few areas known for the cultivation large and speckled red beans from the Andean gene pool [1].

Therefore, geographical specialization was not evident. This has important implications for the deployment of angular leaf spot resistance genes and the development of common bean cultivars for the ALS disease resistance. High genetic variability of P. griseola was observed in areas typically cultivating Mesoamerican common bean. Since Mesoamerican common beans are predominantly cultivated in Ethiopia, the greatest challenge to manage angular leaf spot of the common bean is in areas that are known for the cultivation of beans from the Mesoamerican gene pools. The lack of isolation by distance among the isolates of P. griseola from the diverse common bean growing areas of Ethiopia indicates the P. griseola fungi have efficient dispersal at the common bean growing areas of the region. From our study we confirmed that the genetic divergence between the populations was very low which was 13% whereas, 87% of the molecular variance was attributed to the variation within populations this was indicated with sharing of rep-PCR genomic finger printing pattern between geographic populations from distinct locations of common bean growing areas of Ethiopia, (for example genomic finger printing patterns between Dolla and south Omo the two locations are far away about 450 km from each other). The observed gene flow and sharing same genomic fingerprints between isolates of the two distinct locations could be due to different possibilities; one of the possibilities for the long-distance gene flow might be due to the longdistance gene flow nature of the pathogen and due to spore dispersal without human interference because of the wind and other natural influence. Moreover, the long distance geneflow over hundreds to thousands of kilometers has been reported in many of fungi [30] or the other possibility for the long-distance gene flow could be also due to the human involvement and the seed born nature of P. griseola. The informal seed system, which is common practice and is associated with movement of infected planting materials between different locations or common bean growing areas, including wind dispersal could be the main causes for the observed genomic fingerprinting pattern between distinct locations. This was explained with the presence of P. griseola isolates from different geographical regions in the same branch of the dendrogram. Human activities were reported and found to be responsible for the long-distance dispersal of may fungi and pathogens [31-33]. This study is the first report using rep-PCR genomic finger printing on genomic variation and population structure of P. griseola isolates that were collected from diverse common bean growing regions of Ethiopian. The results revealed that P. griseola in Ethiopia demonstrates with high level of genomic diversity. As previously reported, Rep-PCR fingerprinting was a highly reproducible and a simple method to distinguish closely related fungal isolates. To infer the phylogenic relationships and to study their diversity in different ecosystems [9,19]. The majority of our P. griseola samples were from the southern parts Ethiopia which is known for its wider and potential common bean production areas of Ethiopia. The area is known for its hotspot area for the angular leaf spot and majority of the isolates of P.griseola from this area were confirmed to be genetically very diverse and this area might not represent other parts of Ethiopia. The analysis of additional samples from other areas as well as more genes might allow defining the population structure of P. griseola existing in Ethiopia. We believe this study represents an important step towards understanding the presence of high genetic diversity within the P. griseola existing in common bean production areas of Ethiopia and hence the common bean breeding program aiming to develop durable resistance varsities should consider this information during the deployment of resistance genes to develop resistance common bean varieties.

Conclusion

This study was the first report on the genomic variation and population structure of P. griseola that were collected from the diverse common bean growing regions and the result revealed that P. griseola in Ethiopia displays with high level of genomic diversity. The genetic structure of P. griseola reveals no geographic differentiation. Moreover, the result from this specific study compliments with many of the reports that confirms the sources of genomic variability existed within and among the monosporic isolates of P. griseola obtained from the diverse common bean growing areas of Ethiopia might be the informal seed system that was dominantly practiced with common bean seed system within the small-scale farming community. In addition of that the movement of infected planting materials between different location in the common bean growing areas including wind dispersal of spores also the main contributors to the presence of P. griseola isolates from the different geographic regions in the same group and for the absence of geographic differentiation between common bean growing locations. As disease management strategy common bean seed multipliers and should give attention to produce pathogen free clean seed common bean for wider dissemination. Rep-PCR fingerprinting was a highly reproducible and a simple method to distinguish closely related fungal isolates. The regional and national common bean improvement programs in Ethiopia should also give priorities for gene deployment and marker aided gene pyramiding techniques in developing broad and multiple disease resistance common bean varieties along with identification of new sources of resistance common bean cultivar.

Acknowledgements

The study was part of the PhD research work for the first author (Yayis Rezene). The authors acknowledge the support, funding of the research and provision of molecular lab facilities from the KIRKHOUSE TRUST. We also extend our thanks to the lab technicians Mihiret Tadesse and Bethel Mulugeta for their support during pathogen collection and isolations activities, and also our thanks to Alan Male (CIAT-Uganda) for his technical advice during the molecular lab work. We also thank the Southern Agricultural Research Institute (SARI), the Centro Internacional de Agricultura Tropical (CIAT), and the African Bean Consortium (ABC) partner countries.

References

Citation: Rezene Y, Tesfaye K, Clare M, Male A, Gepts P (2018) Rep-PCR Genomic Fingerprinting Revealed Genetic Diversity and Population Structure among Ethiopian Isolates of Pseudocercospora griseola Pathogen of the Common Bean (Phaseolus vulgaris L.). J Plant Pathol Microbiol 9: 463.

Copyright: © 2018 Rezene Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.