{"paper_id":"05d24379-b941-4fb2-b4d7-eb28a5860d6b","body_text":"Unveiling Cassava Mosaic Virus Diversity in Zimbabwe: A Nanopore Sequencing Approach | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Unveiling Cassava Mosaic Virus Diversity in Zimbabwe: A Nanopore Sequencing Approach Tapiwa Nyakauru, Fiona Robertson This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7759354/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Mar, 2026 Read the published version in Journal of Agriculture and Horticulture Research → Version 1 posted You are reading this latest preprint version Abstract Cassava ( Manihot esculenta Crantz: Euphorbiaceae ) is the only species in its genus that is grown as a food crop. Africa is the largest centre for cassava production, producing over 100 million tonnes per year. However, cassava is affected by several diseases including Cassava Mosaic Disease (CMD) which is caused by Cassava Mosaic Viruses (CMVs). Symptoms of CMD include distortion of leaf lamina, mottling, unordered growth and malformation of the leaves, formation of chlorotic mosaics, and narrowing of the leaves. Several strains of CMVs that have been recorded include Africa cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), East African cassava mosaic Malawi virus (EACMMV), East African cassava mosaic Ugandan virus (EACMV-UG), East African cassava mosaic Cameroon virus (EACMCV), East African cassava mosaic Zanzibar virus (EACMZV), East African cassava mosaic Kenya virus (EACMKV) and South African cassava mosaic virus (SACMV). However, limited information is available with regards to CMV strains in Zimbabwe. This work focused on identifying specific strains of CMV that are affecting cassava plants in Zimbabwe. Using nanopore sequencing, several strains of CMV including ACMV, EACMV, EACMV-UG, EACMV-K, and SACMV were detected in cassava plants from Zimbabwe. These findings provide the first comprehensive evidence of CMV strain diversity in Zimbabwe and highlight the potential of nanopore sequencing as a rapid and cost-effective tool for virus surveillance, early detection, and management of CMD in cassava production systems. Cassava Mosaic Virus Cassava mosaic disease Nanopore sequencing cassava bioinformatics Figures Figure 1 Figure 2 Introduction Cassava ( Manihot esculenta Crantz ) is the sole species within the genus Manihot cultivated extensively for human consumption. Globally, it serves as a major source of dietary carbohydrates, particularly in tropical and subtropical regions. Africa is the leading centre of cassava production, with annual outputs exceeding 100 million metric tons, and more than 70 million people on the continent rely on cassava as a staple food source (Adebayo, 2023 ; Mohidin et al., 2023 ). While the production of cassava is set to increase as its tubers can be used for ethanol production and livestock feed (Borku, 2025 ; Fathima et al., 2023 ), cassava is affected by several diseases, including Cassava Mosaic Disease (CMD) caused by Cassava Mosaic Viruses (CMVs). CMVs belong to the family Geminiviridae of the genus Begomovirus (Dye et al., 2023 ). While symptoms of CMD vary by season and cassava variety, general symptoms of CMDs include distortion of the leaf lamina, mottling, unordered growth and malformation of the leaves, formation of chlorotic mosaics, and narrowing of the leaves, which all results in reduction in photosynthesis (Chikoti et al., 2019 ; Hillocks & Thresh, 2000 ). CMVs strains recorded in Africa include African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), East African cassava mosaic Malawi virus (EACMMV), East African cassava mosaic Cameroon virus (EACMCV), East African cassava mosaic Zanzibar virus (EACMZV), East African cassava mosaic Kenya virus (EACMKV), and South African cassava mosaic virus (SACMV) (Ndunguru et al. 2005 ; Patil and Fauquet, 2009 ; Zinga et al. 2013 ; Dye et al. 2023 ). Rapid, accurate, and sensitive detection of CMV is essential for effective control and management of CMD. Early identification of CMV infections, prior to the appearance of visible symptoms, allows for timely implementation of protective measures, thereby reducing the risk of virus transmission to healthy cassava plants (Boykin et al., 2019). The detection of CMVs is performed using different methods, including enzyme-linked immunosorbent assay (ELISA) (Ogbe et al., 2003 ) and polymerase chain reaction (PCR) (Legg et al., 2004 ). In Zimbabwe, Berry and Rey, ( 2001 ) identified the presence of CMVs in cassava plants grown in the northern part of Zimbabwe using novel degenerate primers that amplify ACMV, EACMV, and SACMV. With the emergence of new strains of the CMV, ELISA is incompatible with the detection of these different strains. Although PCR is still widely used in viral detection, in some circumstances, it is incompatible in detecting new strains of the virus due to viral mutation or absence of sequences for primer design (Sanjuán & Domingo-Calap, 2016 ). Oxford Nanopore MinION sequencing has gained much attention in identifying plant viruses because of its capability to generate whole genome sequences which enables the identification of known and novel viral strains (Sun et al., 2022 ; Boykin et al., 2019). With the increase in cassava production in Zimbabwe, there is a need to investigate the prevalence and presence of CMV strains, hence, the objective of the project was to detect strain/s of CMV affecting cassava plants in Chiredzi, southeast of Zimbabwe, using the latest MinION nanopore sequencing technology. In this study, Oxford Nanopore sequencing was employed to screen cassava plants for the presence of CMVs. Multiple strains were identified in samples from Zimbabwe, including ACMV, EACMV, EACMV-UG, EACMV-K, and SACMV, demonstrating the capability of oxford nanopore to be used as a rapid diagnostic tool for early virus detection in plants. These findings provide important insights into the diversity and distribution of CMVs in Zimbabwe and contribute to the development of effective disease management strategies. Methodology Sample preparation and DNA extraction. Leaf samples exhibiting cassava mosaic disease symptoms were collected from a field in Chiredzi, Zimbabwe. Samples were flash-frozen in liquid nitrogen and stored at − 80°C until DNA extraction. Total genomic DNA (gDNA) was extracted following the cetyl trimethylammonium bromide (CTAB) protocol described by Doyle and Doyle (1990). Briefly, 300 mg of leaf tissue was rinsed with distilled water, blotted dry with a sterile paper towel, and surface-sterilized with 70% ethanol. The tissue was then ground to a fine powder in liquid nitrogen using a sterile mortar and pestle and transferred to 1.5 mL microcentrifuge tubes containing 400 µL of preheated (65°C) CTAB extraction buffer [2% (w/v) CTAB, 1% (w/v) polyvinylpyrrolidone (PVP), 100 mM Tris-HCl (pH 8.0), 1.4 M NaCl, 20 mM EDTA, and 5% (v/v) β-mercaptoethanol]. Samples were mixed by gentle inversion and incubated at 65°C for 20 minutes, with mixing every 5 minutes. Following incubation, 400 µL of chloroform:isoamyl alcohol (24:1, v/v) was added, and tubes were shaken horizontally on a rotary shaker (200 rpm) at room temperature for 15 min. The mixtures were centrifuged at 10,000 rpm for 5 minutes, and the aqueous phase was transferred to fresh tubes. DNA was precipitated by adding 400 µL of isopropanol, mixing thoroughly, and incubating at − 20°C for 1 h, followed by centrifugation at 10,000 rpm for 5 minutes. The supernatant was discarded, and the DNA pellet was washed with 400 µL of cold 100% ethanol, centrifuged for 5 minutes at 10,000 rpm, and air-dried. DNA was resuspended in 100 µL of nuclease-free deionized water and further purified using a Genomic DNA Clean & Concentrator kit (D4010, Zymo Research). DNA concentration and purity were determined using a NanoDrop spectrophotometer, and DNA integrity was confirmed by electrophoresis on a 1% agarose gel stained with ethidium bromide and visualized using an E-Gel Imager (Life Technologies, Israel). Whole genome sequencing and bioinformatics analysis DNA sequencing libraries were prepared using the Rapid Barcoding Kit (SQK-RBK004) with R9.4.1 flow cells (Oxford Nanopore Technologies), following the manufacturer’s instructions. Libraries were loaded onto a MinION device connected to a MinIT for real-time basecalling. Raw fastq sequences, generated via the ONT Albacore pipeline, were demultiplexed using Porechop ( https://github.com/rrwick/Porechop ) and was deposited in SRA NCBI database under accession PRJNA1308336 under biosample accessions SAMN50694092-3. BLASTn searches against a custom CMV database were performed to identify CMV reads, which were assembled de novo in Geneious Prime v11.0.5 to produce viral contigs which were deposited in SRA NCBI database under accession SAMN50736739. The novelty of CMV contigs was assessed using BLASTn searches against the NCBI database. Phylogenetic relationships of CMV sequences were inferred using the Maximum Likelihood method in MEGA-X, incorporating published CMV reference genomes. Results Genomic DNA was successfully extracted, and DNA yield and purity were evaluated with a NanoDrop spectrophotometer. Whole-genome sequencing was performed on the Oxford Nanopore MinION platform. Basecalled reads were demultiplexed using Porechop , yielding a total of 109574 raw reads across all samples (Fig. 1 ). For sample 96/0037, sequencing generated a mean read length of 5604 bp, with the longest read spanning 113,791 bp. For sample XM6, a comparable mean read length of 3718 bp was observed, with the maximum read length also reaching 39,327 bp. Sequences were deposited in SRA NCBI database under accession PRJNA1308336. Nanopore sequencing and bioinformatics analysis BLAST analysis was performed against a custom CMV database under default parameters and the number of CMV-assigned reads is presented in Table 1 . Table 1 Nanopore sequencing results and custom BLAST analysis obtained after whole genome sequencing of cassava plants affected with CMV Sample name CMD severity score Total reads Max. seq length CMV-A BLAST hits Number of contigs formed 96/0037 2 81720 113791 911 47 XM6 5 27845 39327 4571 215 De novo assembly, defined as the reconstruction of genomes in the absence of a reference sequence (Faria et al., 2016), was subsequently performed on reads with high similarity to CMV, as identified by the custom BLAST search. BLAST analysis of the resulting contigs showed strong sequence homology with previously published CMVs documented in other studies (Table 2 ). Cassava variety 96_0037 was found to be infected with three strains: SACMV, EACMV-Ke, and EACMV-Malawi. Variety XM6 was infected with SACMV, EACMV, and EACMV variants from Kenya, Malawi, and Uganda. Furthermore, phylogenetic analysis confirmed the close relationship between known CMV sequences and novel contigs (Fig. 2 ). Table 2 BLAST analysis of novel contigs obtained from De novo assembly of CMV reads obtained through nanopore sequencing. Query Description Query Coverage Pairwise Identity Accession XM6 South African cassava mosaic virus 75.00-100.00% 78.00-85.60% OP971522.1, KJ887649, AJ575560 East African cassava mosaic Kenya virus 76–93.00% 79.20-85.36% JF909222.1, JF909092.1, JF909174, JF909096, JF909188, JF909069, JF909069, JF909157, JF909163 East African cassava mosaic virus-Malawi 100.00% 76.60–84.00% KP890350.1, KT869119, KY885005, KY885004, KT869121, NC_022645, MT821892.1 East African cassava mosaic virus 68.89–72.75% 77.70–81.5% AJ717558, AJ717554, MZ494486 East African cassava mosaic virus-Uganda 93.32–100.00% 80.30–82.5% FN668380, AM502332 96_0037 East African cassava mosaic Malawi virus 99.81–100.00% 81.70–86.10% MT821892 South African cassava mosaic virus 96.04–100% 83.00-85.90% OP971522 East African cassava mosaic virus-Kenya 100.00% 78.10–82.60% JF909069, JF909177 Discussion Cassava is a critical food security crop in sub-Saharan Africa, providing a staple source of carbohydrates for millions of people. However, cassava production is severely threatened by cassava mosaic viruses which are responsible for cassava mosaic disease (CMD) (Chikoti et al., 2019 ). CMD is one of the most devastating viral diseases of cassava, causing significant yield losses and threatening both food and income security (Ristaino et al., 2021 ). The accurate and timely identification of CMV strains is therefore essential for implementing effective disease management strategies (Alejandro et al., 2025 ). Historically, the detection of CMV has relied on methods such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), which have been widely applied in cassava research (Houngue et al., 2019 ; Jimenez et al., 2021 ). However, these techniques are often limited by their dependency on prior knowledge of viral sequences, relatively long turnaround times, and the inability to detect novel or recombinant strains (Hareesh et al., 2023 ). In many farming communities, diagnosis is still largely based on visual assessment of symptoms on cassava leaves, which can be misleading due to symptom similarity among different viral strains and environmental stressors (Chikoti et al., 2019 ). In Zimbabwe, the information available on CMV strains is particularly outdated, with the most recent study dating back to 2004, which reported the occurrence of South African cassava mosaic virus (SACMV) in the country (Chikoti et al., 2019 ; Legg & Fauquet, 2004 ; Sseruwagi et al., 2004 ). Since then, farmers have continued to report CMD outbreaks, with some CMV strains exhibiting higher virulence and adaptability, showing the urgent need for updated molecular surveillance of CMVs circulating in the country. The identification of CMV strains currently affecting cassava in Zimbabwe is crucial for designing targeted management strategies to reduce yield losses and curb viral spread. This study applied nanopore sequencing, one of the latest next-generation sequencing platforms, to characterize cassava viruses in Zimbabwe. Nanopore sequencing offers distinct advantages over traditional detection and sequencing methods, including rapid, real-time data generation, portability, relatively low cost, and the ability to generate long reads that improve genome assembly (Boykin et al., 2019; Zheng et al., 2023 ). Importantly, nanopore sequencing can be deployed directly in the field, enabling virus detection and preliminary analysis within hours, compared to days or weeks required for conventional methods (Boykin et al., 2019; Sun et al., 2022 ). For a country like Zimbabwe, where cassava cultivation is expanding and resources for plant health monitoring are often limited, the ability to identify viral pathogens within hours represents a transformative step toward proactive disease management. Our bioinformatic analyses, including BLAST searches and phylogenetic tree construction, revealed that cassava variety 96_0037 was infected with three strains: SACMV, EACMV-Ke, and EACMV-Malawi, and variety XM6 was infected with SACMV, EACMV, and EACMV variants from Kenya, Malawi, and Uganda. Such mixed infections have been associated with more severe disease symptoms and increased potential for recombination, thereby accelerating the emergence of novel, more virulent variants (Fondong, 2017 ). Interestingly, several viral contigs from different plants were found to be highly similar to known CMV strains, suggesting that novel variants may be emerging due to point mutations or recombination events, a phenomenon previously documented in plant viruses (Duffy & Seah, 2010 ; Ng et al., 2011 ; Sánchez-Campos et al., 2018 ). The detection of such potential new strains highlights the evolutionary dynamics of CMV populations in Zimbabwe and reinforces the need for continuous genomic surveillance. This study is particularly important for Zimbabwe as it provides the first updated molecular data on CMV strains in nearly two decades. The findings will inform cassava breeding programs by identifying the most prevalent and virulent strains to target in the development of resistant varieties. Furthermore, real-time diagnostic capacity using nanopore sequencing can enable agricultural extension services to make rapid, evidence-based decisions, reducing the spread of infection and safeguarding farmers’ livelihoods. Importantly, the viral contigs published in this study on NCBI database can serve as a valuable reference resource for future research aimed at monitoring, comparing, and identifying emerging CMV strains in Zimbabwe and other regions of Southern Africa. This data can be used to track viral evolution, improve regional surveillance systems, and guide integrated disease management strategies. In the long term, the application of such sequencing-based surveillance systems can strengthen Zimbabwe’s preparedness against viral threats not only in cassava but also in other staple crops. Conclusion Nanopore sequencing offers a rapid, reliable, and field-deployable tool for cassava virus detection in Zimbabwe. Several strains of cassava mosaic virus were detected, including ACMV, EACMV, EACMV-UG, EACMV-K, and SACMV. By generating updated genomic data, this study provides essential insights that will benefit cassava farmers, breeders, and policymakers in Zimbabwe and the broader Southern African region. The potential of new strains of CMV in Zimbabwe highlights the need to further investigate the presence of new strains in Zimbabwe. Declarations Authors’ contribution : Tapiwa Nyakauru: Conceptualization, formal analysis, investigation, methodology, visualization, writing—original draft, review and editing. Fiona Robertson: Conceptualization, investigation, methodology, supervision, visualization, reviewing draft and editing. Funding: This work was funded by the Alliance for Global Health of the Centre for Emerging and Neglected Diseases, USA project managed by the University of Zimbabwe, College of Health Sciences, Research Support Centre. Conflict of interest : The authors declare that they have no known conflict of interest Ethics declaration : Not applicable. Consent to Publish declaration : Not applicable Consent to Participate declaration : Not applicable References Adebayo, W. G. (2023). Cassava production in africa: A panel analysis of the drivers and trends. Heliyon , 9 (9), e19939. https://doi.org/10.1016/j.heliyon.2023.e19939 Alejandro, J. A. G., Mausisa, J. H. M., & Paglinawan, C. C. (2025). 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Cite Share Download PDF Status: Published Journal Publication published 08 Mar, 2026 Read the published version in Journal of Agriculture and Horticulture Research → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-7759354\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":524509042,\"identity\":\"56bee9b7-b29e-4c0e-b7cb-4f68fb43e8b1\",\"order_by\":0,\"name\":\"Tapiwa 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08:48:41\",\"extension\":\"html\",\"order_by\":8,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"acdc-reference\",\"size\":81015,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"earlyproof.html\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7759354/v1/cc0e562b0742a0bb35b885a3.html\"},{\"id\":92929530,\"identity\":\"84d55214-abeb-481a-adfc-d4bc9b9d87e0\",\"added_by\":\"auto\",\"created_at\":\"2025-10-07 08:48:40\",\"extension\":\"png\",\"order_by\":1,\"title\":\"Figure 1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":32361,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eCumulative density curves showing sequenced reads and their lengths.\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7759354/v1/77749885882a9189223a2661.png\"},{\"id\":92929531,\"identity\":\"7e79fdd4-8376-4ecb-892a-894ecebe5866\",\"added_by\":\"auto\",\"created_at\":\"2025-10-07 08:48:40\",\"extension\":\"png\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":174730,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eA Maximum Likelihood phylogenetic tree showing the evolutionary relationships between novel contigs and known CMV sequences\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"2.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7759354/v1/6191660f46513cee18f688ea.png\"},{\"id\":104731106,\"identity\":\"acd49f60-8ff0-4aed-bb7b-70af1fe8d0da\",\"added_by\":\"auto\",\"created_at\":\"2026-03-16 14:30:46\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":704071,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-7759354/v1/d30b8939-8e84-4260-b93a-cf29658fb5c4.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Unveiling Cassava Mosaic Virus Diversity in Zimbabwe: A Nanopore Sequencing Approach\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eCassava (\\u003cem\\u003eManihot esculenta Crantz\\u003c/em\\u003e) is the sole species within the genus \\u003cem\\u003eManihot\\u003c/em\\u003e cultivated extensively for human consumption. Globally, it serves as a major source of dietary carbohydrates, particularly in tropical and subtropical regions. Africa is the leading centre of cassava production, with annual outputs exceeding 100\\u0026nbsp;million metric tons, and more than 70\\u0026nbsp;million people on the continent rely on cassava as a staple food source (Adebayo, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Mohidin et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eWhile the production of cassava is set to increase as its tubers can be used for ethanol production and livestock feed (Borku, \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e; Fathima et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e), cassava is affected by several diseases, including Cassava Mosaic Disease (CMD) caused by Cassava Mosaic Viruses (CMVs). CMVs belong to the family \\u003cem\\u003eGeminiviridae\\u003c/em\\u003e of the genus Begomovirus (Dye et al., \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). While symptoms of CMD vary by season and cassava variety, general symptoms of CMDs include distortion of the leaf lamina, mottling, unordered growth and malformation of the leaves, formation of chlorotic mosaics, and narrowing of the leaves, which all results in reduction in photosynthesis (Chikoti et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Hillocks \\u0026amp; Thresh, \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2000\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eCMVs strains recorded in Africa include African cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), East African cassava mosaic Malawi virus (EACMMV), East African cassava mosaic Cameroon virus (EACMCV), East African cassava mosaic Zanzibar virus (EACMZV), East African cassava mosaic Kenya virus (EACMKV), and South African cassava mosaic virus (SACMV) (Ndunguru et al. \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Patil and Fauquet, \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Zinga et al. \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Dye et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Rapid, accurate, and sensitive detection of CMV is essential for effective control and management of CMD. Early identification of CMV infections, prior to the appearance of visible symptoms, allows for timely implementation of protective measures, thereby reducing the risk of virus transmission to healthy cassava plants (Boykin et al., 2019). The detection of CMVs is performed using different methods, including enzyme-linked immunosorbent assay (ELISA) (Ogbe et al., \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e) and polymerase chain reaction (PCR) (Legg et al., \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). In Zimbabwe, Berry and Rey, (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2001\\u003c/span\\u003e) identified the presence of CMVs in cassava plants grown in the northern part of Zimbabwe using novel degenerate primers that amplify ACMV, EACMV, and SACMV. With the emergence of new strains of the CMV, ELISA is incompatible with the detection of these different strains. Although PCR is still widely used in viral detection, in some circumstances, it is incompatible in detecting new strains of the virus due to viral mutation or absence of sequences for primer design (Sanju\\u0026aacute;n \\u0026amp; Domingo-Calap, \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eOxford Nanopore MinION sequencing has gained much attention in identifying plant viruses because of its capability to generate whole genome sequences which enables the identification of known and novel viral strains (Sun et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; Boykin et al., 2019). With the increase in cassava production in Zimbabwe, there is a need to investigate the prevalence and presence of CMV strains, hence, the objective of the project was to detect strain/s of CMV affecting cassava plants in Chiredzi, southeast of Zimbabwe, using the latest MinION nanopore sequencing technology.\\u003c/p\\u003e\\u003cp\\u003eIn this study, Oxford Nanopore sequencing was employed to screen cassava plants for the presence of CMVs. Multiple strains were identified in samples from Zimbabwe, including ACMV, EACMV, EACMV-UG, EACMV-K, and SACMV, demonstrating the capability of oxford nanopore to be used as a rapid diagnostic tool for early virus detection in plants. These findings provide important insights into the diversity and distribution of CMVs in Zimbabwe and contribute to the development of effective disease management strategies.\\u003c/p\\u003e\"},{\"header\":\"Methodology\",\"content\":\"\\u003cp\\u003e\\u003cb\\u003eSample preparation and DNA extraction.\\u003c/b\\u003e\\u003c/p\\u003e\\u003cp\\u003eLeaf samples exhibiting cassava mosaic disease symptoms were collected from a field in Chiredzi, Zimbabwe. Samples were flash-frozen in liquid nitrogen and stored at \\u0026minus;\\u0026thinsp;80\\u0026deg;C until DNA extraction. Total genomic DNA (gDNA) was extracted following the cetyl trimethylammonium bromide (CTAB) protocol described by Doyle and Doyle (1990). Briefly, 300 mg of leaf tissue was rinsed with distilled water, blotted dry with a sterile paper towel, and surface-sterilized with 70% ethanol. The tissue was then ground to a fine powder in liquid nitrogen using a sterile mortar and pestle and transferred to 1.5 mL microcentrifuge tubes containing 400 \\u0026micro;L of preheated (65\\u0026deg;C) CTAB extraction buffer [2% (w/v) CTAB, 1% (w/v) polyvinylpyrrolidone (PVP), 100 mM Tris-HCl (pH 8.0), 1.4 M NaCl, 20 mM EDTA, and 5% (v/v) β-mercaptoethanol]. Samples were mixed by gentle inversion and incubated at 65\\u0026deg;C for 20 minutes, with mixing every 5 minutes.\\u003c/p\\u003e\\u003cp\\u003eFollowing incubation, 400 \\u0026micro;L of chloroform:isoamyl alcohol (24:1, v/v) was added, and tubes were shaken horizontally on a rotary shaker (200 rpm) at room temperature for 15 min. The mixtures were centrifuged at 10,000 rpm for 5 minutes, and the aqueous phase was transferred to fresh tubes. DNA was precipitated by adding 400 \\u0026micro;L of isopropanol, mixing thoroughly, and incubating at \\u0026minus;\\u0026thinsp;20\\u0026deg;C for 1 h, followed by centrifugation at 10,000 rpm for 5 minutes. The supernatant was discarded, and the DNA pellet was washed with 400 \\u0026micro;L of cold 100% ethanol, centrifuged for 5 minutes at 10,000 rpm, and air-dried. DNA was resuspended in 100 \\u0026micro;L of nuclease-free deionized water and further purified using a Genomic DNA Clean \\u0026amp; Concentrator kit (D4010, Zymo Research). DNA concentration and purity were determined using a NanoDrop spectrophotometer, and DNA integrity was confirmed by electrophoresis on a 1% agarose gel stained with ethidium bromide and visualized using an E-Gel Imager (Life Technologies, Israel).\\u003c/p\\u003e\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e\\u003ch2\\u003eWhole genome sequencing and bioinformatics analysis\\u003c/h2\\u003e\\u003cp\\u003eDNA sequencing libraries were prepared using the Rapid Barcoding Kit (SQK-RBK004) with R9.4.1 flow cells (Oxford Nanopore Technologies), following the manufacturer\\u0026rsquo;s instructions. Libraries were loaded onto a MinION device connected to a MinIT for real-time basecalling. Raw fastq sequences, generated via the ONT Albacore pipeline, were demultiplexed using Porechop (\\u003cspan class=\\\"ExternalRef\\\"\\u003e\\u003cspan class=\\\"RefSource\\\"\\u003ehttps://github.com/rrwick/Porechop\\u003c/span\\u003e\\u003cspan address=\\\"https://github.com/rrwick/Porechop\\\" targettype=\\\"URL\\\" class=\\\"RefTarget\\\"\\u003e\\u003c/span\\u003e\\u003c/span\\u003e) and was deposited in SRA NCBI database under accession PRJNA1308336 under biosample accessions SAMN50694092-3. BLASTn searches against a custom CMV database were performed to identify CMV reads, which were assembled \\u003cem\\u003ede novo\\u003c/em\\u003e in Geneious Prime v11.0.5 to produce viral contigs which were deposited in SRA NCBI database under accession SAMN50736739. The novelty of CMV contigs was assessed using BLASTn searches against the NCBI database. Phylogenetic relationships of CMV sequences were inferred using the Maximum Likelihood method in MEGA-X, incorporating published CMV reference genomes.\\u003c/p\\u003e\\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cp\\u003eGenomic DNA was successfully extracted, and DNA yield and purity were evaluated with a NanoDrop spectrophotometer. Whole-genome sequencing was performed on the Oxford Nanopore MinION platform. Basecalled reads were demultiplexed using \\u003cem\\u003ePorechop\\u003c/em\\u003e, yielding a total of 109574 raw reads across all samples (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\\u003cp\\u003eFor sample 96/0037, sequencing generated a mean read length of 5604 bp, with the longest read spanning 113,791 bp. For sample XM6, a comparable mean read length of 3718 bp was observed, with the maximum read length also reaching 39,327 bp. Sequences were deposited in SRA NCBI database under accession PRJNA1308336.\\u003c/p\\u003e\\n\\u003ch3\\u003eNanopore sequencing and bioinformatics analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eBLAST analysis was performed against a custom CMV database under default parameters and the number of CMV-assigned reads is presented in Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e.\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eNanopore sequencing results and custom BLAST analysis obtained after whole genome sequencing of cassava plants affected with CMV\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"6\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"char\\\" char=\\\".\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eSample name\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eCMD severity score\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eTotal reads\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003eMax. seq length\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eCMV-A BLAST hits\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003eNumber of contigs formed\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003e96/0037\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e2\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e81720\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e113791\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e911\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e47\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eXM6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003e5\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e27845\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e39327\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003e4571\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"char\\\" char=\\\".\\\" colname=\\\"c6\\\"\\u003e\\u003cp\\u003e215\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003eDe novo assembly, defined as the reconstruction of genomes in the absence of a reference sequence (Faria et al., 2016), was subsequently performed on reads with high similarity to CMV, as identified by the custom BLAST search. BLAST analysis of the resulting contigs showed strong sequence homology with previously published CMVs documented in other studies (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Cassava variety 96_0037 was found to be infected with three strains: SACMV, EACMV-Ke, and EACMV-Malawi. Variety XM6 was infected with SACMV, EACMV, and EACMV variants from Kenya, Malawi, and Uganda. Furthermore, phylogenetic analysis confirmed the close relationship between known CMV sequences and novel contigs (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003e\\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e\\u003ccaption language=\\\"En\\\"\\u003e\\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e\\u003cdiv class=\\\"CaptionContent\\\"\\u003e\\u003cp\\u003eBLAST analysis of novel contigs obtained from \\u003cem\\u003eDe novo\\u003c/em\\u003e assembly of CMV reads obtained through nanopore sequencing.\\u003c/p\\u003e\\u003c/div\\u003e\\u003c/caption\\u003e\\u003ccolgroup cols=\\\"5\\\"\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e\\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e\\u003cthead\\u003e\\u003ctr\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u003cp\\u003eQuery\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eDescription\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003eQuery Coverage\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003ePairwise Identity\\u003c/p\\u003e\\u003c/th\\u003e\\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAccession\\u003c/p\\u003e\\u003c/th\\u003e\\u003c/tr\\u003e\\u003c/thead\\u003e\\u003ctbody\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"4\\\" rowspan=\\\"5\\\"\\u003e\\u003cp\\u003eXM6\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSouth African cassava mosaic virus\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e75.00-100.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e78.00-85.60%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eOP971522.1, KJ887649, AJ575560\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEast African cassava mosaic Kenya virus\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e76\\u0026ndash;93.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e79.20-85.36%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eJF909222.1, JF909092.1, JF909174, JF909096, JF909188, JF909069, JF909069, JF909157, JF909163\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEast African cassava mosaic virus-Malawi\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e76.60\\u0026ndash;84.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eKP890350.1, KT869119, KY885005, KY885004, KT869121, NC_022645, MT821892.1\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEast African cassava mosaic virus\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e68.89\\u0026ndash;72.75%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e77.70\\u0026ndash;81.5%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eAJ717558, AJ717554, MZ494486\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEast African cassava mosaic virus-Uganda\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e93.32\\u0026ndash;100.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e80.30\\u0026ndash;82.5%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eFN668380, AM502332\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e\\u003cp\\u003e96_0037\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEast African cassava mosaic Malawi virus\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e99.81\\u0026ndash;100.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e81.70\\u0026ndash;86.10%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eMT821892\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eSouth African cassava mosaic virus\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e96.04\\u0026ndash;100%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e83.00-85.90%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eOP971522\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003ctr\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u003cp\\u003eEast African cassava mosaic virus-Kenya\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e\\u003cp\\u003e100.00%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u003cp\\u003e78.10\\u0026ndash;82.60%\\u003c/p\\u003e\\u003c/td\\u003e\\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e\\u003cp\\u003eJF909069, JF909177\\u003c/p\\u003e\\u003c/td\\u003e\\u003c/tr\\u003e\\u003c/tbody\\u003e\\u003c/colgroup\\u003e\\u003c/table\\u003e\\u003c/div\\u003e\\u003c/p\\u003e\\u003cp\\u003e\\u003c/p\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cp\\u003eCassava is a critical food security crop in sub-Saharan Africa, providing a staple source of carbohydrates for millions of people. However, cassava production is severely threatened by cassava mosaic viruses which are responsible for cassava mosaic disease (CMD) (Chikoti et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). CMD is one of the most devastating viral diseases of cassava, causing significant yield losses and threatening both food and income security (Ristaino et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). The accurate and timely identification of CMV strains is therefore essential for implementing effective disease management strategies (Alejandro et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eHistorically, the detection of CMV has relied on methods such as polymerase chain reaction (PCR) and enzyme-linked immunosorbent assay (ELISA), which have been widely applied in cassava research (Houngue et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Jimenez et al., \\u003cspan citationid=\\\"CR14\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). However, these techniques are often limited by their dependency on prior knowledge of viral sequences, relatively long turnaround times, and the inability to detect novel or recombinant strains (Hareesh et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). In many farming communities, diagnosis is still largely based on visual assessment of symptoms on cassava leaves, which can be misleading due to symptom similarity among different viral strains and environmental stressors (Chikoti et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eIn Zimbabwe, the information available on CMV strains is particularly outdated, with the most recent study dating back to 2004, which reported the occurrence of South African cassava mosaic virus (SACMV) in the country (Chikoti et al., \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Legg \\u0026amp; Fauquet, \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Sseruwagi et al., \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e). Since then, farmers have continued to report CMD outbreaks, with some CMV strains exhibiting higher virulence and adaptability, showing the urgent need for updated molecular surveillance of CMVs circulating in the country. The identification of CMV strains currently affecting cassava in Zimbabwe is crucial for designing targeted management strategies to reduce yield losses and curb viral spread.\\u003c/p\\u003e\\u003cp\\u003eThis study applied nanopore sequencing, one of the latest next-generation sequencing platforms, to characterize cassava viruses in Zimbabwe. Nanopore sequencing offers distinct advantages over traditional detection and sequencing methods, including rapid, real-time data generation, portability, relatively low cost, and the ability to generate long reads that improve genome assembly (Boykin et al., 2019; Zheng et al., \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Importantly, nanopore sequencing can be deployed directly in the field, enabling virus detection and preliminary analysis within hours, compared to days or weeks required for conventional methods (Boykin et al., 2019; Sun et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). For a country like Zimbabwe, where cassava cultivation is expanding and resources for plant health monitoring are often limited, the ability to identify viral pathogens within hours represents a transformative step toward proactive disease management.\\u003c/p\\u003e\\u003cp\\u003eOur bioinformatic analyses, including BLAST searches and phylogenetic tree construction, revealed that cassava variety 96_0037 was infected with three strains: SACMV, EACMV-Ke, and EACMV-Malawi, and variety XM6 was infected with SACMV, EACMV, and EACMV variants from Kenya, Malawi, and Uganda. Such mixed infections have been associated with more severe disease symptoms and increased potential for recombination, thereby accelerating the emergence of novel, more virulent variants (Fondong, \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e\\u003cp\\u003eInterestingly, several viral contigs from different plants were found to be highly similar to known CMV strains, suggesting that novel variants may be emerging due to point mutations or recombination events, a phenomenon previously documented in plant viruses (Duffy \\u0026amp; Seah, \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e; Ng et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; S\\u0026aacute;nchez-Campos et al., \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). The detection of such potential new strains highlights the evolutionary dynamics of CMV populations in Zimbabwe and reinforces the need for continuous genomic surveillance.\\u003c/p\\u003e\\u003cp\\u003eThis study is particularly important for Zimbabwe as it provides the first updated molecular data on CMV strains in nearly two decades. The findings will inform cassava breeding programs by identifying the most prevalent and virulent strains to target in the development of resistant varieties. Furthermore, real-time diagnostic capacity using nanopore sequencing can enable agricultural extension services to make rapid, evidence-based decisions, reducing the spread of infection and safeguarding farmers\\u0026rsquo; livelihoods. Importantly, the viral contigs published in this study on NCBI database can serve as a valuable reference resource for future research aimed at monitoring, comparing, and identifying emerging CMV strains in Zimbabwe and other regions of Southern Africa. This data can be used to track viral evolution, improve regional surveillance systems, and guide integrated disease management strategies. In the long term, the application of such sequencing-based surveillance systems can strengthen Zimbabwe\\u0026rsquo;s preparedness against viral threats not only in cassava but also in other staple crops.\\u003c/p\\u003e\"},{\"header\":\"Conclusion\",\"content\":\"\\u003cp\\u003eNanopore sequencing offers a rapid, reliable, and field-deployable tool for cassava virus detection in Zimbabwe. Several strains of cassava mosaic virus were detected, including ACMV, EACMV, EACMV-UG, EACMV-K, and SACMV. By generating updated genomic data, this study provides essential insights that will benefit cassava farmers, breeders, and policymakers in Zimbabwe and the broader Southern African region. The potential of new strains of CMV in Zimbabwe highlights the need to further investigate the presence of new strains in Zimbabwe.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eAuthors\\u0026rsquo; contribution\\u003c/strong\\u003e: Tapiwa Nyakauru: Conceptualization, formal analysis, investigation, methodology, visualization, writing\\u0026mdash;original draft, review and editing. Fiona Robertson: Conceptualization, investigation, methodology, supervision, visualization, reviewing draft and editing.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding:\\u003c/strong\\u003e This work was funded by the Alliance for Global Health of the Centre for Emerging and Neglected Diseases, USA project managed by the University of Zimbabwe, College of Health Sciences, Research Support Centre.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e: The authors declare that they have no known conflict of interest\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthics declaration\\u003c/strong\\u003e: Not applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Publish declaration\\u003c/strong\\u003e: Not applicable\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent to Participate declaration\\u003c/strong\\u003e: Not applicable\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAdebayo, W. G. (2023). Cassava production in africa: A panel analysis of the drivers and trends. \\u003cem\\u003eHeliyon\\u003c/em\\u003e, \\u003cem\\u003e9\\u003c/em\\u003e(9), e19939. https://doi.org/10.1016/j.heliyon.2023.e19939\\u003c/li\\u003e\\n\\u003cli\\u003eAlejandro, J. A. G., Mausisa, J. H. M., \\u0026amp; Paglinawan, C. C. (2025). Deep Learning Approach to Cassava Disease Detection Using EfficientNetB0 and Image Augmentation. \\u003cem\\u003e2024 IEEE 6th Eurasia Conference on IoT, Communication and Engineering\\u003c/em\\u003e, 28. https://doi.org/10.3390/engproc2025092028\\u003c/li\\u003e\\n\\u003cli\\u003eBerry, S., \\u0026amp; Rey, M. E. C. (2001). Molecular evidence for diverse populations of cassava-infecting begomoviruses in southern Africa. \\u003cem\\u003eArchives of Virology\\u003c/em\\u003e, \\u003cem\\u003e146\\u003c/em\\u003e(9), 1795\\u0026ndash;1802. https://doi.org/10.1007/s007050170065\\u003c/li\\u003e\\n\\u003cli\\u003eBorku, A. W. (2025). 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Cassava begomovirus species diversity changes during plant vegetative cycles. \\u003cem\\u003eFrontiers in Microbiology\\u003c/em\\u003e, \\u003cem\\u003e14\\u003c/em\\u003e. https://doi.org/10.3389/fmicb.2023.1163566\\u003c/li\\u003e\\n\\u003cli\\u003eFathima, A. A., Sanitha, M., Tripathi, L., \\u0026amp; Muiruri, S. (2023). Cassava ( Manihot esculenta ) dual use for food and bioenergy: A review. \\u003cem\\u003eFood and Energy Security\\u003c/em\\u003e, \\u003cem\\u003e12\\u003c/em\\u003e(1). https://doi.org/10.1002/fes3.380\\u003c/li\\u003e\\n\\u003cli\\u003eFondong, V. N. (2017). The Search for Resistance to Cassava Mosaic Geminiviruses: How Much We Have Accomplished, and What Lies Ahead. \\u003cem\\u003eFrontiers in Plant Science\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e. https://doi.org/10.3389/fpls.2017.00408\\u003c/li\\u003e\\n\\u003cli\\u003eHareesh, P. S., Resmi, T. R., Sheela, M. N., \\u0026amp; Makeshkumar, T. (2023). Cassava mosaic disease in South and Southeast Asia: current status and prospects. \\u003cem\\u003eFrontiers in Sustainable Food Systems\\u003c/em\\u003e, \\u003cem\\u003e7\\u003c/em\\u003e. https://doi.org/10.3389/fsufs.2023.1086660\\u003c/li\\u003e\\n\\u003cli\\u003eHillocks, R. J., \\u0026amp; Thresh, J. M. (2000). \\u003cem\\u003eCassava Mosaic and Cassava Brown Streak Virus Diseases in Africa: A Comparative Guide to Symptoms and Etiologies\\u003c/em\\u003e. \\u003cem\\u003e7\\u003c/em\\u003e(December), 1\\u0026ndash;8.\\u003c/li\\u003e\\n\\u003cli\\u003eHoungue, J. A., Zandjanakou-Tachin, M., Ngalle, H. B., Pita, J. S., Caca\\u0026iuml;, G. H. T., Ngatat, S. E., Bell, J. M., \\u0026amp; Ahanhanzo, C. (2019). Evaluation of resistance to cassava mosaic disease in selected African cassava cultivars using combined molecular and greenhouse grafting tools. \\u003cem\\u003ePhysiological and Molecular Plant Pathology\\u003c/em\\u003e, \\u003cem\\u003e105\\u003c/em\\u003e, 47\\u0026ndash;53. https://doi.org/10.1016/j.pmpp.2018.07.003\\u003c/li\\u003e\\n\\u003cli\\u003eJimenez, J., Leiva, A. M., Olaya, C., Acosta-Trujillo, D., \\u0026amp; Cuellar, W. J. (2021). An optimized nucleic acid isolation protocol for virus diagnostics in cassava (Manihot esculenta Crantz.). \\u003cem\\u003eMethodsX\\u003c/em\\u003e, \\u003cem\\u003e8\\u003c/em\\u003e, 101496. https://doi.org/10.1016/j.mex.2021.101496\\u003c/li\\u003e\\n\\u003cli\\u003eLegg, J. P., \\u0026amp; Fauquet, C. M. (2004). Cassava mosaic geminiviruses in Africa. \\u003cem\\u003ePlant Molecular Biology\\u003c/em\\u003e, \\u003cem\\u003e56\\u003c/em\\u003e(4), 585\\u0026ndash;599. https://doi.org/10.1007/s11103-004-1651-7\\u003c/li\\u003e\\n\\u003cli\\u003eLegg, J. P., Ndjelassili, F., \\u0026amp; Okao-Okuja, G. (2004). First report of cassava mosaic disease and cassava mosaic geminiviruses in Gabon. \\u003cem\\u003ePlant Pathology\\u003c/em\\u003e, \\u003cem\\u003e53\\u003c/em\\u003e(2), 232\\u0026ndash;232. https://doi.org/10.1111/j.0032-0862.2004.00972.x\\u003c/li\\u003e\\n\\u003cli\\u003eMohidin, S. R. N. S. P., Moshawih, S., Hermansyah, A., Asmuni, M. I., Shafqat, N., \\u0026amp; Ming, L. C. (2023). Cassava ( Manihot esculenta Crantz): A Systematic Review for the Pharmacological Activities, Traditional Uses, Nutritional Values, and Phytochemistry. \\u003cem\\u003eJournal of Evidence-Based Integrative Medicine\\u003c/em\\u003e, \\u003cem\\u003e28\\u003c/em\\u003e. https://doi.org/10.1177/2515690X231206227\\u003c/li\\u003e\\n\\u003cli\\u003eNdunguru, J., Legg, J., Aveling, T., Thompson, G., \\u0026amp; Fauquet, C. (2005). Molecular biodiversity of cassava begomoviruses in Tanzania: evolution of cassava geminiviruses in Africa and evidence for East Africa being a center of diversity of cassava geminiviruses. \\u003cem\\u003eVirology Journal\\u003c/em\\u003e, \\u003cem\\u003e2\\u003c/em\\u003e(1), 21. https://doi.org/10.1186/1743-422X-2-21\\u003c/li\\u003e\\n\\u003cli\\u003eNg, T. F. F., Duffy, S., Polston, J. E., Bixby, E., Vallad, G. E., \\u0026amp; Breitbart, M. (2011). Exploring the Diversity of Plant DNA Viruses and Their Satellites Using Vector-Enabled Metagenomics on Whiteflies. \\u003cem\\u003ePLoS ONE\\u003c/em\\u003e, \\u003cem\\u003e6\\u003c/em\\u003e(4), e19050. https://doi.org/10.1371/journal.pone.0019050\\u003c/li\\u003e\\n\\u003cli\\u003eOgbe, F. O., Atiri, G. I., Dixon, A. G. O., \\u0026amp; Thottappilly, G. (2003). Symptom severity of cassava mosaic disease in relation to concentration of African cassava mosaic virus in different cassava genotypes. \\u003cem\\u003ePlant Pathology\\u003c/em\\u003e, \\u003cem\\u003e52\\u003c/em\\u003e(1), 84\\u0026ndash;91. https://doi.org/10.1046/j.1365-3059.2003.00805.x\\u003c/li\\u003e\\n\\u003cli\\u003ePATIL, B. L., \\u0026amp; FAUQUET, C. M. (2009). Cassava mosaic geminiviruses: actual knowledge and perspectives. \\u003cem\\u003eMolecular Plant Pathology\\u003c/em\\u003e, \\u003cem\\u003e10\\u003c/em\\u003e(5), 685\\u0026ndash;701. https://doi.org/10.1111/j.1364-3703.2009.00559.x\\u003c/li\\u003e\\n\\u003cli\\u003eRistaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V., Finegold, C., Garrett, K. A., Gilligan, C. A., Jones, C. M., Martin, M. D., MacDonald, G. K., Neenan, P., Records, A., Schmale, D. G., Tateosian, L., \\u0026amp; Wei, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. \\u003cem\\u003eProceedings of the National Academy of Sciences\\u003c/em\\u003e, \\u003cem\\u003e118\\u003c/em\\u003e(23). https://doi.org/10.1073/pnas.2022239118\\u003c/li\\u003e\\n\\u003cli\\u003eS\\u0026aacute;nchez-Campos, S., Dom\\u0026iacute;nguez-Huerta, G., D\\u0026iacute;az-Mart\\u0026iacute;nez, L., Tom\\u0026aacute;s, D. M., Navas-Castillo, J., Moriones, E., \\u0026amp; Grande-P\\u0026eacute;rez, A. (2018). Differential Shape of Geminivirus Mutant Spectra Across Cultivated and Wild Hosts With Invariant Viral Consensus Sequences. \\u003cem\\u003eFrontiers in Plant Science\\u003c/em\\u003e, \\u003cem\\u003e9\\u003c/em\\u003e. https://doi.org/10.3389/fpls.2018.00932\\u003c/li\\u003e\\n\\u003cli\\u003eSanju\\u0026aacute;n, R., \\u0026amp; Domingo-Calap, P. (2016). Mechanisms of viral mutation. \\u003cem\\u003eCellular and Molecular Life Sciences\\u003c/em\\u003e, \\u003cem\\u003e73\\u003c/em\\u003e(23), 4433\\u0026ndash;4448. https://doi.org/10.1007/s00018-016-2299-6\\u003c/li\\u003e\\n\\u003cli\\u003eSseruwagi, P., Sserubombwe, W. S., Legg, J. P., Ndunguru, J., \\u0026amp; Thresh, J. M. (2004). Methods of surveying the incidence and severity of cassava mosaic disease and whitefly vector populations on cassava in Africa: a review. \\u003cem\\u003eVirus Research\\u003c/em\\u003e, \\u003cem\\u003e100\\u003c/em\\u003e(1), 129\\u0026ndash;142. https://doi.org/10.1016/j.virusres.2003.12.021\\u003c/li\\u003e\\n\\u003cli\\u003eSun, K., Liu, Y., Zhou, X., Yin, C., Zhang, P., Yang, Q., Mao, L., Shentu, X., \\u0026amp; Yu, X. (2022). Nanopore sequencing technology and its application in plant virus diagnostics. \\u003cem\\u003eFrontiers in Microbiology\\u003c/em\\u003e, \\u003cem\\u003e13\\u003c/em\\u003e. https://doi.org/10.3389/fmicb.2022.939666\\u003c/li\\u003e\\n\\u003cli\\u003eZheng, P., Zhou, C., Ding, Y., Liu, B., Lu, L., Zhu, F., \\u0026amp; Duan, S. (2023). Nanopore sequencing technology and its applications. \\u003cem\\u003eMedComm\\u003c/em\\u003e, \\u003cem\\u003e4\\u003c/em\\u003e(4). https://doi.org/10.1002/mco2.316\\u003c/li\\u003e\\n\\u003cli\\u003eZinga, I., Chiroleu, F., Legg, J., Lefeuvre, P., Komba, E. K., Semballa, S., Yandia, S. P., Mandakombo, N. B., Reynaud, B., \\u0026amp; Lett, J.-M. (2013). Epidemiological assessment of cassava mosaic disease in Central African Republic reveals the importance of mixed viral infection and poor health of plant cuttings. \\u003cem\\u003eCrop Protection\\u003c/em\\u003e, \\u003cem\\u003e44\\u003c/em\\u003e, 6\\u0026ndash;12. https://doi.org/10.1016/j.cropro.2012.10.010\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":true,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":true,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":true,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Cassava Mosaic Virus, Cassava mosaic disease, Nanopore sequencing, cassava, bioinformatics\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-7759354/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-7759354/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eCassava (\\u003cem\\u003eManihot esculenta Crantz: Euphorbiaceae\\u003c/em\\u003e) is the only species in its genus that is grown as a food crop. Africa is the largest centre for cassava production, producing over 100\\u0026nbsp;million tonnes per year. However, cassava is affected by several diseases including Cassava Mosaic Disease (CMD) which is caused by Cassava Mosaic Viruses (CMVs). Symptoms of CMD include distortion of leaf lamina, mottling, unordered growth and malformation of the leaves, formation of chlorotic mosaics, and narrowing of the leaves. Several strains of CMVs that have been recorded include Africa cassava mosaic virus (ACMV), East African cassava mosaic virus (EACMV), East African cassava mosaic Malawi virus (EACMMV), East African cassava mosaic Ugandan virus (EACMV-UG), East African cassava mosaic Cameroon virus (EACMCV), East African cassava mosaic Zanzibar virus (EACMZV), East African cassava mosaic Kenya virus (EACMKV) and South African cassava mosaic virus (SACMV). However, limited information is available with regards to CMV strains in Zimbabwe. This work focused on identifying specific strains of CMV that are affecting cassava plants in Zimbabwe. Using nanopore sequencing, several strains of CMV including ACMV, EACMV, EACMV-UG, EACMV-K, and SACMV were detected in cassava plants from Zimbabwe. These findings provide the first comprehensive evidence of CMV strain diversity in Zimbabwe and highlight the potential of nanopore sequencing as a rapid and cost-effective tool for virus surveillance, early detection, and management of CMD in cassava production systems.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Unveiling Cassava Mosaic Virus Diversity in Zimbabwe: A Nanopore Sequencing Approach\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-10-07 08:48:36\",\"doi\":\"10.21203/rs.3.rs-7759354/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"b6ce17ea-28ce-4bbd-b598-cfed0239ca7c\",\"owner\":[],\"postedDate\":\"October 7th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"published-in-journal\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-16T14:30:40+00:00\",\"versionOfRecord\":{\"articleIdentity\":\"rs-7759354\",\"link\":\"https://doi.org/10.33140/JAHR.09.01.16\",\"journal\":{\"identity\":\"journal-of-agriculture-and-horticulture-research\",\"isVorOnly\":true,\"title\":\"Journal of Agriculture and Horticulture Research\"},\"publishedOn\":\"2026-03-09 00:00:00\",\"publishedOnDateReadable\":\"March 9th, 2026\"},\"versionCreatedAt\":\"2025-10-07 08:48:36\",\"video\":\"\",\"vorDoi\":\"10.33140/JAHR.09.01.16\",\"vorDoiUrl\":\"https://doi.org/10.33140/JAHR.09.01.16\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-7759354\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-7759354\",\"identity\":\"rs-7759354\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}