Malaysian Journal of Sustainable Agriculture (MJSA)

This study examines the phenotypic traits and genetic diversity of soybean genotypes resistant to soybeanrust (Phakopsora pachyrhizi) using simple sequence repeat (SSR) markers. Five soybean varieties, includingboth resistant and susceptible types, were evaluated under controlled conditions. Molecular analysis wasconducted using SSR markers (CP171/172, SSR1, RB32, and RB34) to assess genetic variation. Phenotypicassessments were performed to correlate molecular data with resistance traits, focusing on disease responseand yield potential. The results showed that plant height ranged from 87.3 cm (TGx1951-3F) to 60.3 cm(TGx1835-10). Pathological analysis revealed that some varieties, such as TGx1448-2E and TGx1951-4F,exhibited resistance to rust, while TGx1904-6F had the highest disease incidence. Overall, the findingshighlighted significant genetic diversity among the evaluated genotypes, with several accessionsdemonstrating strong resistance and high yield potential. This research enhances the understanding of thegenetic basis of rust resistance in soybean and offers valuable insights for future breeding strategies toimprove crop resilience against this major pathogen.

Soybean (Glycine max (L.) Merrill), a member of the pea family, is a highlyversatile legume cultivated for its nutrient-rich seeds. It plays a crucial rolein global agriculture, providing an essential source of protein and keynutrients for both human consumption and animal feed. However,soybean’s vulnerability to various diseases, such as rust, presents a majorchallenge to its productivity and overall yield (Mishra et al., 2024).

Soybean rust, caused by Phakopsora pachyrhizi, is a serious threat tosoybean cultivation worldwide (Ono et al., 1992; Schneider et al., 2005).This fungal disease primarily affects the leaves, stems, and pods of theplant. Early symptoms typically manifest as small brown or yellow spotson the leaves, followed by lesions and cracks on the stems, as well assunken patches on the pods. These symptoms often result in prematureleaf drop, weakened plant structures, and significantly reduced yield andpod formation (Wikipedia). The pathogen spreads through airborneurediniospores, allowing for rapid disease progression. In regions wherethe disease is prevalent, yield losses can reach as high as 80% (Li et al.,2012).

The primary method for managing soybean rust involves the applicationof fungicides, which, although effective, significantly increase productioncosts and pose environmental concerns. Additionally, some P. pachyrhizistrains have shown increased resistance to specific fungicides (Godoy,2009). As a result, developing and cultivating rust-resistant soybeanvarieties is considered the most efficient and sustainable strategy fordisease control. Resistant varieties offer a cost-effective and environmentally friendly alternative that simplifies disease management.

The genetic variation responsible for resistance arises from multiplefactors, including insertions, deletions, substitutions, and nucleotiderearrangements within the DNA (Redelings et al., 2024). Current researchfocuses on evaluating genetic diversity and phenotypic traits among rustresistantsoybean varieties using molecular markers, which play a criticalrole in breeding programs. Molecular markers aid in the identification andincorporation of resistance genes, enhancing breeding efficiency and thedevelopment of improved cultivars (Tabien et al., 2000).

Additionally, analyzing DNA polymorphism in soybean varieties providesvaluable insights that can contribute to the development of molecularmarkers for rust resistance. These markers streamline the screeningprocess, allowing breeders to efficiently identify and cultivate rustresistantsoybean varieties (Miah et al., 2013). By minimizing yield lossescaused by rust, molecular marker-assisted breeding enhances soybeanquality, increases farmers’ income, and reduces dependence on fungicides,thereby mitigating environmental risks.

Future research in this area may focus on identifying additional DNApolymorphisms linked to rust resistance, refining molecular markers toimprove accuracy and efficiency, and employing molecular breedingtechniques to develop soybean varieties with enhanced traits beyond rustresistance—such as higher yield potential and improved droughttolerance (Li et al., 2023). Advancements in this research hold significantpotential for strengthening soybean production, ensuring greater foodsecurity, and promoting sustainable agricultural practices.

Despite progress in developing rust-resistant soybean genotypes, geneticdiversity among these genotypes remains relatively narrow. This limited variation poses a challenge to the long-term effectiveness of resistance, asthe emergence of new rust pathogen races could overcome existingdefenses. Therefore, this study utilizes molecular markers to evaluategenetic diversity and guide breeding strategies aimed at enhancing rustresistance and broadening genetic variation.

The objective of this research was to examine the phenotypic and geneticdiversity of rust-resistant and susceptible soybean genotypes usingSimple Sequence Repeat (SSR) markers.

2. MATERIALS AND METHODS

2.1 Experimental Site and Location.

The study was carried out in the Molecular Biology Laboratory at JosephSarwuan Tarka University, Makurdi, Benue State. The university issituated at a latitude of 7.45°N and a longitude of 8.32°E, with an elevationranging from 97 m to 111.2 m above sea level. The planting phase tookplace in the laboratory’s screen house, which is positioned behind theVeterinary Medicine auditorium, South Core area.

2.2 Planting Materials

Seeds from five distinct soybean varieties were sourced from the seedstore of the Molecular Biology Laboratory at Joseph Sarwuan TarkaUniversity. The selected varieties included:

i. TG X 1835-10E
ii. TG X 1951-3F
iii. TG X 1951-4F
iv. TG X 1904-6F
v. TG X 1448-2E

To prevent any mixing, the seeds were stored in separate, labeled packets.

2.3 Planting of Soybean Seeds in the Screen House

The five soybean varieties acquired from the Molecular BiologyLaboratory were sown in pots filled with topsoil. Initially, three seeds fromeach variety were planted per pot. After ten days, thinning was carried out,leaving two plants per pot to promote optimal growth conditions.

2.4 Collection of Leaf samples

Leaf samples were collected from young soybean plants of each varietyfourteen (14) days after planting. The samples were placed in polythenezip-lock bags containing silica gel and left to dry for three days.

The equipment used for sample collection included:
i. Blade
ii. Polythene zip-lock bags
iii. 70% ethanol
iv. Paper towel

2.5 DNA Extraction using the CTAB Method

The Polymerase Chain Reaction (PCR) technique was employed to amplifya small number of copies of a specific DNA segment, thereby producingmultiple replicas of that particular DNA sequence. The PCR procedure wasconducted with a total reaction volume of 15μl. The components of thereaction comprised:

PuReTaqTM Ready-go-goTM PCR beads (containing PCR Buffer, MgCl2,DNTP’s, and Taq Polymerase)

Distilled water

1μl of each primer and

1μl of DNA (50ng)

SSR based PCR protocol was used in carrying out PCR amplifications(Omoigui et al., 2015). 25 μl of Molecular Biology Grade water was addedinto 0.2 ml eppendorf tubes containing the PCR beads. The mixture wasthen divided into two for two PCR reaction, 1 μl primer (marker) and 1 μlDNA sample to serve as template was added into each 0.2 ml eppendorftube. Tubes were covered and centrifuged for 15 seconds in other toassemble all components at the base of the tubes. The 0.2 ml eppendorfPCR tubes were arranged properly into the thermal cycler (PCR machine)to begin amplification.

2.6 Polymerase Chain Reaction (PCR) Mixture

The Polymerase Chain Reaction (PCR) technique was utilized to amplifyspecific DNA segments, generating multiple copies of the target sequence. The reaction was carried out in a total volume of 15 μL. The reactionmixture included the following components:

• PuReTaq™ Ready-To-Go™ PCR beads (containing PCR buffer, MgCl₂,dNTPs, and Taq polymerase)
• Distilled water
• 1 μL of each primer
• 1 μL of DNA (50 ng)

The SSR-based PCR protocol described by a group researcher wasfollowed for amplification (Omoigui et al., 2015). Twenty-five microlitersof Molecular Biology Grade water was first added to 0.2 mL Eppendorftubes containing the PCR beads. The solution was then split into twoseparate tubes for two PCR reactions. One microliter of primer (marker)and 1 μL of the DNA sample were added to each tube, serving as thetemplate for amplification.

After sealing the tubes, they were centrifuged for 15 seconds to ensure allcomponents settled at the bottom. Finally, the 0.2 mL Eppendorf tubeswere carefully placed in the thermal cycler (PCR machine), where theamplification process was initiated.

2.6.1 Polymerase chain reaction cycle

The PCR cycling protocol involved an initial denaturation step at 94°C for4 minutes, followed by denaturation at 94°C for 30 seconds, annealing at55°C for 1 minute, and extension at 72°C for 1 minute. The reaction wasthen held at 60°C indefinitely to maintain the amplified DNA.

2.7 Agarose Gel Electrophoresis

The methodology described was adopted for gel electrophoresis. A 3.5%agarose gel was prepared by weighing 3.5 g of agarose powder anddissolving it in 350 mL of 1x TAE buffer (Omoigui et al., 2015). The mixturewas gently swirled and heated in a microwave until it became clear. Aftercooling, 30 μL of ethidium bromide (EtBr) was added and mixedthoroughly. The gel solution was then poured into a pre-prepared gelcasting tray with a comb to form wells.

Once the gel solidified, it was carefully placed in the electrophoresis tank,and the comb was gently removed to prevent well damage. To prepare theDNA samples, 1 μL of DNA was mixed with 1 μL of 6x loading dye in a PCRtube and briefly spun. The prepared samples were then carefully loadedinto the wells using a micropipette. Additionally, 5 μL of a DNA ladder wasloaded into a separate well as a reference marker. The electrophoresissystem was sealed, and the gel was run at 120V for 45 minutes.

DNA purity and quality were assessed using UV spectrophotometry. Thebanding patterns of the DNA samples, resolved on the agarose gel, werevisualized under a UV transilluminator, and the gel image was captured forband scoring. Only distinct bands were recorded, with presence scored as(1) and absence as (0) (Omoigui et al., 2015).

2.8 Data Analysis

The MINITAB 17 software was used for statistical analysis. Phenotypicdata were analyzed using descriptive statistics, and cluster analysis wasperformed. A dendrogram was generated using the complete linkagemethod to assess genetic relationships among the samples.

3. RESULTS AND DISCUSSION

Table 1 provides information on the growth, yield and pathologicalcharacters assessed during the field work. Plant height varieties from 87.3to 60.3(TGx-1951-3F and TGx-1835-10E) is shown in figure 1. VarietyTGx-1835-10E had the highest maturity which was recorded as 109 isshown in figure 2. The data showed that variety TGx-1951-3F (723.7) hadthe highest seed yield/plot with variety TGx-1835-10E (555.6) as thelowest is shown in figure 3. Days to 50% flowering vary from 41 days to45 days (TGx-1835-10E) and (TGx-1904-6F) is shown in figure 4.Pathological data shows that some of the varieties are resistant to rustdisease, the variety that recorded the highest incident of rust disease wasTGx-1904-6F is shown in figure 5, Frogeye leaf spot disease was 1.8 (TGx-1448-2E). Mosaic disease ranged from 1 (TGx-1904-6F) and 1.7 (TGx-1951-4F), RTNOD 1-5 ranged from 2.5 to 3.5 (TGx-1835-10E and TGx-1951-4F). Lodg 1-5 ranged from 1 to 2.2 (TGx-1951-4F and TGx-1904-6F).Pod shat late ranged from 1 to 2 (TGx-1835-10E and TGx1904-6F), 100seed weight ranged from 11.7 to 13.7 (TGx-1448-2E and TGx-1951-4F).The variety with the Lowest Pod height was TGx-1835-10E 3.7.

Plates 1 and 2 display the agarose gel images of four screened SimpleSequence Repeat (SSR) markers used in DNA amplification to assesspolymorphism between rust-resistant (TGx1835-10E) and rustsusceptible(TGx1951-3F and TGx1951-4F) soybean varieties. Theprimers exhibited varying levels of genetic polymorphism, depending onthe soybean DNA amplified and the SSR primers used. While all primersgenerated visible bands, SSR 1 showed no clear resolution in either rustresistantor susceptible varieties. However, distinct bands were wellresolved using primers CP 171/172, RB 32, and RB 34.

= TG X 1835-10E

= TG X 1951-3F

= TG X 1951-4F

= TG X 1904-6F

= TG X 1448-2E

L = 50bp Ladder.

Primers = CP 171/172 and SSR 1

L = 50bp Ladder

B = Blank

Primers = RB32 and RB34

= TG X 1835-10E

= TG X 1951-3F

= TG X 1951-4F

= TG X 1904- 6F

= TG X 1448-2E

4. DISCUSSION

Genetic diversity among rust-resistant soybean genotypes is essential forimproving breeding programs aimed at managing soybean rust, a diseasecaused by Phakopsora pachyrhizi. Molecular markers are valuable toolsfor evaluating genetic variation and identifying resistance traits bypinpointing genomic regions associated with disease resistance. Plate 1presents the agarose gel image comparing rust-resistant and susceptiblesoybean varieties. The two markers used in this study were only amplifiedin resistant varieties, suggesting a potential link between these markersand rust resistance. This observation aligns with the findings of whoidentified SSR markers associated with rust resistance (Zhong et al., 2024).The application of these markers in breeding programs enables moreprecise selection of resistant plants, thereby accelerating the developmentof improved soybean varieties for farmers facing rust disease challenges.

Plate 2 shows the screening results of SSR markers for polymorphismbetween rust-resistant and susceptible soybean varieties. The testedmarkers revealed genetic variation in both groups, indicating theireffectiveness in distinguishing between resistant and susceptiblegenotypes. Notably, RB32 was amplified only in the resistant parent, whileRB34 was amplified exclusively in susceptible varieties. This suggests astrong genetic linkage between RB32 and rust resistance, and betweenRB34 and rust susceptibility. These findings are consistent with previousresearch by further supporting the potential of these markers in markerassistedselection for rust resistance (Li et al., 2023).

A phenotypic assessment was conducted to correlate molecular data withresistance traits, particularly focusing on disease incidence and yieldpotential. Some varieties exhibited early maturity and demonstratedresistance to rust and other diseases. Among the genotypes analyzed, TGX-1904-6F recorded the highest rust disease incidence rate (2.2). In terms ofyield performance, TGX-1951-3F had the highest seed yield per plot(723.7), while TGX-1835-10E recorded the lowest (555.6).

5. CONCLUSION

The results from Plate 1 and Plate 2 demonstrate that SSR markers areeffective tools for identifying rust resistance in soybean varieties. Theobserved polymorphism across the two markers indicates their ability todistinguish between resistant and susceptible genotypes. Specifically,RB32 and RB34 showed strong associations with rust resistance andsusceptibility, respectively, suggesting a close genetic link to theunderlying resistance and susceptibility genes. These findings align withprevious research and highlight the potential of SSR markers in breedingprograms for rust-resistant soybean varieties.

RECOMMENDATIONS

• Explore additional markers – Further research should focus on identifying additional molecular markers associated with rustresistance. Expanding the pool of markers will provide breeders with more precise tools for selecting resistant soybean varieties.

• Incorporate markers into breeding programs – Validated markers should be integrated into soybean breeding programs to expedite the development of rust-resistant varieties. Collaboration among researchers, breeders, and farmers will be essential for the successful implementation and adoption of marker-assisted selection.

• Increase awareness and adoption – Outreach programs and educational initiatives should be conducted to inform farmers and other stakeholders about the benefits of rust-resistant soybean varieties. Promoting awareness will encourage wider adoption and enhance the impact of these technologies on soybean production.

• Enhance breeding strategies – Molecular markers should be systematically integrated into breeding programs to develop improved soybean varieties with resistance to rust and other diseases. A multi-disciplinary approach involving geneticists, breeders, and farmers will ensure the successful deployment of these advancements in real-world agricultural settings.

REFERENCES

Godoy, C., 2009. Changes in performance of soybean rust fungicides over years and new management strategies adopted in Brazil. National Soybean Rust Symposium, New Orleans, LA, United States.

Li, H., Hua, L., Zhao, S., Hao, M., Song, R., Pang, S. and Chen, S., 2023. Cloning of the wheat leaf rust resistance gene Lr47 introgressed from Aegilops speltoides. Nature Communications, 14 (1), Pp. 6072.

Li, L., Smith, J.R., Ray, J.D., and Frederick, R.D., 2012. Identification of a new soybean rust resistance gene in PI 567102B. Theoretical and Applied Genetics, 125 (1), Pp. 133–142. https://doi.org/10.1007/s00122-012-1821-y

Li, M.W., Wang, X., Ching-Ching, S., Yung, W.S., Wong, F.L., Zhang, G., Chung, G., Chan, T.F., and Lam, H.M., 2023. Development of a set of polymorphic DNA markers for soybean (Glycine max L.) applications. Agronomy, 13 (11), Pp. 2708. https://doi.org/10.3390/agronomy13112708

Miah, G., Rafii, M.Y., Ismail, M.R., Puteh, A.B., Rahim, H.A., Asfaliza, R., and Latif, M.A., 2013. Blast resistance in rice: A review of conventional breeding to molecular approaches. Molecular Biology Reports, 40 (3), Pp. 2369–2388

Mishra, R., Tripathi, M.K., Sikarwar, R.S., Singh, Y., and Tripathi, N., 2024. Soybean (Glycine max L. Merrill): A multipurpose legume shaping our world. Plant Cell Biotechnology and Molecular Biology, 25 (3–4), Pp. 17–37. https://doi.org/10.56557/pcbmb/2024/v25i3-48643

Omoigui, L.O., Ishyaku, M.F., Gwoda, B.S., Kamara, A.Y., and Timko, M.P., 2015. Suitability and use of two molecular markers to track race-specific resistance Striga gesnerioides in cowpea (Vigna unguiculata (L.) Walp.). African Journal of Biotechnology, 14 (27), Pp. 2179–2190.

Ono, Y., Buritica, P., and Hennen, J.F., 1992. Delimitation of Phakopsora, Physopella, Cerotelium and their species on Leguminosae. Mycological Research, 96 (10), Pp. 825–850. https://doi.org/10.1016/S0953-7562(09)81029-0

Redelings, B.D., Holmes, I., Lunter, G., Pupko, T., and Anisimova, M., 2024. Insertions and Deletions: Computational Methods, Evolutionary Dynamics, and Biological Applications. Molecular Biology and Evolution, 41 (9), PP. 177.

Schneider, R.W., Hollier, C.A., Whitam, H.K., Palm, M.E., McKemy, J.M., Hernández, J.R., Levy, L., and DeVries-Paterson, R., 2005. First report of soybean rust caused by Phakopsora pachyrhizi in the continental United States. Plant Disease, 89 (7), PP. 774. https://doi.org/10.1094/PD-89-0774A.

Tabien, R.E., Li, Z., Paterson, A.H., Marchetti, M.A., Stansel, J.W., Pinson, S.R.M., and Park, W.D., 2000. Mapping of four major rice blast resistance genes from ‘Lemont’ and ‘Teqing’ and evaluation of their combinatorial effect for field resistance. Theoretical and Applied Genetics, 101, Pp. 1215–1225. https://doi.org/10.1007/s001220051599

Tersoo, K., Iheukwumere, C.C., Omoigui, L.O., Olasan, J.O., and Ugbaa, M.S., 2021. Characterization of some selected tomato (Solanum lycopersicum L.) accessions using simple sequence repeat markers. Agricultural and Biological Sciences Journal, 7 (2), Pp. 46–50.

Zhong, H., Li, C., Yu, W., Zhou, H.P., Lieber, T., Su, X., Wang, W., Bumann, E., Lunny Castro, R. M., Jiang, Y., Gu, W., Liu, Q., Barco, B., Zhang, C., Shi, L., and Que, Q., 2024. A fast and genotype-independent in planta Agrobacterium-mediated transformation method for soybean. Plant Communications 5 (12), Pp. 101063. https://doi.org/10.1016/j.xplc.2024.101063