# Movement of the A-strain maize streak virus in and out of Madagascar

Kehinde A. Oyeniran, Darren Patrick Martin, Jean-Michel Lett, Mbolarinosy

Rasoafalimanana Rakotomalala, Abdou Azali Hamza, Arvind Varsani

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Kehinde A. Oyeniran, Darren Patrick Martin, Jean-Michel Lett, Mbolarinosy Rasoafalimanana Rakotomalala, Abdou Azali Hamza, et al. Movement of the A-strain maize streak virus in and out of Madagascar. Virology, 2024, 600, pp.110222. 10.1016/j.virol.2024.110222. hal-05183142

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# Movement of the A-strain maize streak virus in and out of Madagascar

Kehinde A. Oyeniran<sup>a,b,*</sup>, Darren P. Martin<sup>a</sup>, Jean-Michel Lett<sup>c</sup>, Mbolarinosy Rasoafalimanana Rakotomalala<sup>d</sup>, Hamza A. Azali<sup>e</sup>, Arvind Varsani<sup>f,g</sup>

## ARTICLE INFO

Handling Editor: Prof. Xue-Ping Zhou

**Keywords:**

Maize streak virus

Geminiviruses

Maize streak disease

Mastrevirus

Madagascar

## ABSTRACT

The maize streak virus belongs in the genus *Mastrevirus*, in the family *Geminiviridae*. The A-strain of the virus (MSV-A) is recognised as the principal causative agent of the most severe manifestation of maize streak disease (MSD). This disease continues to be a persistent limitation on maize output across sub-Saharan Africa and the nearby Indian Ocean islands. Irrespective of the causes behind the spread of MSV-A, we can determine the paths and speeds with which MSV-A spreads by analysing MSV genome sequence data along with information on when and where samples were taken. This information is valuable for identifying the geographical origins of viral strains that cause sporadic MSD epidemics in specific places and the geographical regions where viruses remain in reservoirs and contribute to prolonged epidemics during outbreaks. Our aim is to utilise these analyses to estimate the timing and origin of the MSV-A that arrived on the island of Madagascar in the Indian Ocean. Specifically, we employ model-based phylogeographic analyses on 524 complete MSV-A genome sequences, which consist of 56 newly obtained genomes from infected maize plants collected in Madagascar. These studies allow us to reconstruct the most likely paths of MSV-A to Madagascar. We found strong evidence for the existence of at least four separate movements of MSV-A variants from East and southern Africa to Madagascar. These movements took place between roughly 1979 (with a 95% highest probability density interval [HPD] ranging from 1976 to 1982) and 2003 (with a 95% HPD ranging from 2002 to 2003). While we inferred that MSV-A variants are spreading at an average rate of 38.9 km/year (with a 95% highest posterior density interval of 34.0–44.4) across their geographical range. Since their arrival in Madagascar, MSV-A variants have been migrating at an average rate of 47.6 km/year (with a 95% highest posterior density interval of 36.05–61.70). Human influences are likely significant contributors to both sporadic long-range movements of MSV-A between mainland Africa and Madagascar, as well as shorter to medium range movements within the island.

## 1. Introduction

The maize streak disease (MSD) poses a significant risk to maize production, which is a crucial staple grain in most regions of sub-Saharan Africa (Charles, 2014; Shepherd et al., 2010). MSD outbreaks, which happen every five to 20 years in a specific area, continue to intermittently cause severe damage to maize output and remain a persistent potential cause for future regional famines (Charles, 2014;

Martin and Shepherd, 2009). The intermittent outbreaks of MSD are caused by complicated and not well understood interactions between the viruses that primarily cause the disease, particularly the A-strain of maize streak virus (MSV-A) (Martin and Shepherd, 2009). Maize streak virus, a member of the *Mastrevirus* genus within the *Geminiviridae* family is commonly spread by species of leafhopper in the genus *Cicadulina* (Magenya et al., 2009; Page et al., 1999). MSV has been found to infect at least 120 different types of grass (nearly 100 of which are not

<footnote><sup>a</sup> Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Observatory, Cape Town, 7925, South Africa</footnote>

<footnote><sup>b</sup> Department of Biological Sciences, Bamidele Olumilua University of Education, Science and Technology, Ikere-Ekiti, Nigeria</footnote>

<footnote><sup>c</sup> CIRAD, UMR PVBMT, F-97410, Saint Pierre, La Réunion, France</footnote>

<footnote><sup>d</sup> FOFIFA Ampandrianomby Antananarivo 101, Madagascar, Madagascar</footnote>

<footnote><sup>e</sup> INRAPE, Mde Ngazidja, Moroni, Comoros</footnote>

<footnote><sup>f</sup> The Biodesign Centre for Fundamental and Applied Microbiomics, Centre for Evolution and Medicine, School of Life Sciences, Arizona State University, Tempe, AZ, USA</footnote>

<footnote><sup>g</sup> Structural Biology Research Unit, Department of Integrative Biomedical Sciences, University of Cape Town, Rondebosch, Cape Town 7700, South Africa</footnote>

<footnote>* Corresponding author. Computational Biology Division, Department of Integrative Biomedical Sciences, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, Observatory, Cape Town, 7925, South Africa.

E-mail address: oyenirankehinde@gmail.com (K.A. Oyeniran).</footnote>

<footnote>https://doi.org/10.1016/j.virol.2024.110222</footnote>

<footnote>Received 28 May 2024; Received in revised form 26 August 2024; Accepted 3 September 2024</footnote>

<footnote>Available online 6 September 2024</footnote>

<footnote>0042-6822/© 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).</footnote>
cultivated) (Damsteegt, 1983).

The incidence of MSV epidemics is common in sub-Saharan Africa and the neighbouring islands of the Indian Ocean (Martin and Shepherd, 2009; Shepherd et al., 2010). Similar to other mastreviruses, this virus has a circular genome of approximately 2.7 kilobases. The genome is made up of a single-stranded DNA (ssDNA) and contains three genes. The virion sense genome strand encodes a movement protein (mp) and a coat protein (cp) gene. On the complementary sense genome strand, there is a gene that expresses a replication-associated protein (rep) and a repA protein from an alternatively spliced transcript (Martin et al., 1999; Martin and Rybicki, 2002).

Comparative studies examining the genetic diversity and geographic ranges of MSV-A, as well as other MSV strains and African mastreviruses, have suggested that MSV-A has a fairly high rate of dispersal (Varsani et al., 2008, 2009). The increased dispersal rate of MSV-A can be attributed to its broader host range compared to other related viruses. Additionally, it is likely that human-mediated long-distance transfer also contributes to this dispersal (Martin and Shepherd, 2009; Varsani et al., 2009). Furthermore, when compared to other similar strains of MSV species, the extensive cultivation of maize in Africa is likely to result in a larger and more evenly spread population of vulnerable hosts for MSV-A. This factor is expected to have an influence on the speed at which MSV spreads through natural transmission by insect vectors (CABI; EPPO, 1997; Fajinmi et al., 2012).

Gaining insight into the movement patterns of MSV-A throughout Africa is essential for devising measures to halt the transmission of periodic localised MSD outbreaks on the continent and its islands. An optimal method for monitoring previous movements of MSV-A involves analysing genomic sequence data in relation to the locations and time-stamps of the sampled genomic sequences. By utilising this data, it is practicable to determine the geographical sources of MSV-A variants that cause outbreaks and calculate the rates or frequencies of MSV-A spread between different locations (Firth et al., 2009; Lemey et al., 2009).

The previous MSV-A phylogeographic analysis by Monjane et al. (2011) described the emergence of MSV-A as a maize pathogen in southern Africa around the 1870s and its subsequent spread across continents. However, this analysis did not include samples from northern African countries like Sudan, Ethiopia, and Egypt. In addition, there were no samples from numerous islands along the eastern shores of Africa, notably Madagascar, the largest among them. Considering the established presence of MSD on Madagascar, it is reasonable to believe that Madagascar could have had, and may currently have, an unrecognised role in the development and spread of new MSV-A variants.

In this study, we analysed a total of 524 complete MSV-A genome sequences collected from southern Africa, East Africa, and different Indian Ocean islands from 1979 to 2010. Additionally, we included 56 newly obtained MSV-A genome sequences from Madagascar (n = 53) and the Comoros (n = 3) that were collected between 2009 and 2010. The aim was to determine the frequency, timing, and sources of MSV-A movements between Madagascar and mainland Africa. In addition to providing more evidence that human activities are causing the rapid spread of MSV-A variants over vast distances, we also show that the rate of virus transmission inside Madagascar is slightly higher than those estimated for continental Africa.

# 2. Materials and methods

## 2.1. Sampling

A total of 56 MSV-A genomes were isolated from symptomatic maize samples collected between 2009 and 2010 at 12 distinct locations in Madagascar (n = 53) and the Comoros (n = 3). The DNA extraction process involved the use of the Extract-N-Amp™ Plant Tissue PCR Kit to extract the total DNA from the leaves. Subsequently, the circular molecules present in the recovered DNA were selectively amplified using

phi29 DNA polymerase (TempliPhi; GE Healthcare), following the method reported by Shepherd et al. (2008). The circular, amplified MSV genome concatemers were restricted using either KpnI or BamHI enzymes, resulting in linearized viral genomes of approximately 2.7 kilobases. These linearized genomes were then ligated to similarly linearized pGEMZf + plasmids (Promega Biotech) and introduced into Escherichia coli DH5alpha competent cells through transformation. The plasmids obtained were subjected to Sanger sequencing by primer walking using primers previously described in Owor et al. (2007) and Shepherd et al. (2008) at Macrogen Inc. South Korea. The viral genome sequences were assembled and edited using DNAm an software (version 5.2.9; Lynnon Bio-soft, Canada).

## 2.2. Recombination analyses

The 56 MSV-A genome sequences isolated from samples collected in Madagascar and Comoros were aligned with 630 other MSV-A sequences and 182 non-MSV-A sequences (representing strains of MSV-B through K) that were publicly accessible from GenBank. The alignment was performed using the MUSCLE algorithm (Edgar, 2004), and refined through visual examination using the computer program IMPALE (Khoosal, 2015). The alignment was examined for recombination using the recombination detection programme (RDP) version 4.46 (Martin et al., 2015) in order to create a dataset with no recombinant sequences. Recombinant screening was conducted in two steps as described in Monjane et al. (2011) using the default RDP settings that include inbuilt RDP, GENECONV, Boostscan, Maxchi, Chimaera, SiScan, Phylpro, LARD and 3Seq methods were used followed by Bonferroni multiple tests p-value correction to minimise errors. Recombinant sequence fragments were identified and removed by replacing the corresponding sequence and their tracts with the standard "gap" characters ("-"), without altering the alignment of the remaining nucleotides. This recombinant-free dataset was then utilised for subsequent phylogenetic analyses.

The reason for not including West and Central African sequences in the phylogeographic analyses was that they formed sub-clades within larger clades of East African isolates in the phylogenetic trees. Additionally, none of these sequences were closely related to the branches where Madagascan sequences were located. This suggests that there is no evidence of MSV-A movements between Madagascar and West Africa. Thus, in order to ensure prompt convergence of Bayesian phylogeographic results, all 144 irrelevant West African sequences were excluded from subsequent phylogeographic analyses.

## 2.3. Phylogenetic analysis

### 2.3.1. Nucleotide substitution model test

We utilised jModeltest version 2.1.10 (Darriba et al., 2012) on the CIPRES server located at http://phylo.org (Miller et al., 2010) to estimate the most suitable nucleotide substitution model for the MSV-A dataset.

## 2.4. Maximum likelihood phylogeny

A maximum likelihood (ML) tree was generated for the MSV-A sequence dataset using IQ-TREE version 1.6.12 (Nguyen et al., 2015). The determination of branch supports in the tree involved the use of 2000 repetitions of non-parametric Shimodaira-Hasegawa-like approximation likelihood ratio tests (SH-aLRT) (Guindon et al., 2010; Shimodaira and Hasegawa, 1999), together with 5000 ultrafast bootstrap replicates (Hoang et al., 2018). A 95% majority-rule consensus tree was created from branch support analyses, using 5000 bootstrap trees. The tree was then visualised using FigTree version 1.4.4 (http://tree.bio.ed.ac.uk/software/figtree/) and the ggtree R package (Yu et al., 2017) with custom scripts.
## 2.5. Test for temporal signal

We utilised TempEst version 1.5.3 (Rambaut et al., 2016) to evaluate the temporal signal in the MSV-A dataset. This assessment was conducted on the maximum likelihood phylogeny generated using IQ-TREE. The correlation coefficient (r) and determination coefficient (R2) of the linear regression were then estimated. The p-values were computed using the approach outlined by Murray et al. (2016) and were derived from 1000 random, clustered permutations of the sequence sampling dates.

## 2.6. Phylogeographic analyses

Unlike continuous models of spatio-temporal diffusion, discrete phylogeographic movement models do not assume that homogeneous Brownian motion or a relaxed random walk process accurately describes virus movement (Lemey et al., 2009). Thus, when examining the spread of viruses across long distances (such as between continental Africa and Madagascar), it is preferable to use discrete phylogeographic models. This is because viruses are anticipated to make big jumps from one location to another, rather than gradually spreading between the locations. Discrete phylogeography models can offer strong statistical evidence, in the form of a robust Bayes factor (BF), to support well-established epidemiological connections between different sampling locations (Lemey et al., 2009). In this study, we employed the computer program Bayesian evolutionary analysis sampling of trees (BEAST) version 1.10.4 (Suchard et al., 2018) to estimate the nucleotide substitution rate of MSV-A (per site per year). We used a discrete phylogeography model, the best fitting nucleotide substitution model, a strict molecular clock model implemented following marginal likelihoods via path sampling, and a coalescent constant population size model for the tree prior to prevent mixing and convergence issues associated with complex model parameterizations. The estimation was done through replicate BEAST runs involving Markov chain Monte Carlo (MCMC) sampling and a chain length of $10^8$. Previous studies by Harkins et al. (2009) and Monjane et al. (2011) have described similar methods for MSV.

In order to ensure adequate mixing of the Markov Chain Monte Carlo (MCMC), all analyses were conducted until the effective sample size (ESS) estimations for all model parameters pertaining to the dispersal process exceeded 200 (Drummond and Rambaut, 2007). The log-combiner version 1.10.4 was used to combine log and tree files that had similar values from separate independent replicate runs. The first 10% of each sample of trees was discarded as burn-in before combining them. Resulting log files were examined in Tracer version 1.7.1 (Rambaut et al., 2018) and a Maximum Clade Credibility (MCC) tree was generated based on 592 post burn-in, posterior trees. The MCC tree was annotated using Tree Annotator version 1.10.4 and visualised using Figtree version 1.4.4.

We utilised the MCC tree, which includes the most likely location states of ancestral viral lineages, to analyse the movements of MSV-A. Our focus was on Madagascar, and we employed the program SPREAD3 (version 0.9.7.1; https://rega.kuleuven.be/cev/ecv/software/SpreaD3; Bielejec et al., 2016) for spatial phylogeographic reconstruction of evolutionary dynamics. The occurrence of these movements within the inferred timeframe was further validated by estimating the timing of transitions between mainland Africa and Madagascar using 100 post burn-in, posterior trees. The Markov jumps, indicating the least positive statistical support for MSV-A movements between pairs of locations, were estimated using the Bayesian stochastic search variable selection (BSSVS) analysis procedure. These estimates were based on the asymmetric, non-reversible substitution model (Lemey et al., 2009). The results were reported following the interpretation scale defined by Kass and Raftery (1995).

## 2.7. Continuous phylogeographic and post hoc analyses

In order to analyse the spread of MSV-A in Madagascar, we employed a continuous diffusion model (Lemey et al., 2010) implemented in BEAST 1.10.4 (Rambaut et al., 2018). This model allowed us to create a detailed phylogeographic reconstruction of the MSV-A dissemination history in Madagascar and the Comoros. More precisely, a rigorous Brownian diffusion model was used to generate posterior tree distributions within which internal nodes of the trees were associated with geographic locations or coordinates following a method of Lemey et al. (2010) that utilised a Brownian random walk to model the variation in diffusion velocity among branches. We accomplished continuous phylogeographic reconstructions for a total of 524 sequences. The Markov chains were set to run for $10^8$ generations, with a sampling interval of 0.4 million states. The Tracer software was used to evaluate the MCMC stationary convergence properties after removing 10% of the sampled trees as burn-in, similar to the discrete phylogeographic inference. A maximum clade credibility (MCC) tree, which included continuous geographical coordinate trait data, was visualised using the R package, Seraphim (Dellicour et al., 2016). In order to calculate dispersal statistics, we utilised the available functions in Seraphim to extract spatio-temporal data from 1000 posterior, post-burn-in trees. We then estimated the weighted lineage dispersal velocity, which is determined by the geographic distance travelled (measured in kilometres using the great-circle distance) and the time elapsed (measured in years).

$$ v^{\text{weighted}} = \sum_{i=1}^{n} di \sum_{i=1}^{n} ti $$

# 3. Results

## 3.1. Field sampling regions

From 2009 to 2010, we collected samples of maize plants showing signs that are typical of maize streak disease. These samples were taken from 12 different locations in Madagascar and the Comoros (Fig. 1). 60% of the sampling locations in Madagascar were located in regions where maize is actively cultivated. Maize is a primary food source in the

<figure><img src="image_4.png" /><figcaption>Fig. 1. Sampling sites for maize streak virus in Madagascar and the Comoros.</figcaption></figure>
southern regions of Madagascar and is predominantly grown in the elevated plateaus (Masoni et al., 2019; Palchetti et al., 2019). In general, these places where maize is grown do not experience similar tropical climatic conditions. Specifically, the middle zone of Madagascar's high plateaux experiences an altitude climate characterised by alternating hot and cold periods. Maize plants in the eastern region of the island typically encounter greater MSD loads, potentially because the climatic conditions on this side of the island are more favourable for the spread of MSD (Mckinnon et al., 2015; see Supplementary Table S1).

The 56 genomes obtained from Madagascar are classified as MSV-A variants based on their similarity to previously identified MSV-A genomes. The MSV-A variants from Madagascar exhibit the highest genetic similarity to those found in the MSV-A1 subtype.

## 3.2. Phylogenetic evidence of multiple MSV introductions to Madagascar from Africa

We created a ML tree to visually represent the evolutionary connections among the new Madagascan and Comorian MSV-A isolates, along with other MSV-A collected in Africa, La Réunion, and Mauritius. The tree was utilised for both determining the most suitable nucleotide substitution model and evaluating the temporal signal of the MSV-A dataset. The most suitable DNA substitution model for the MSV-A sequence alignment was general time reversible GTR + I + G4, which agrees with earlier MSV-A phylogeographic investigations conducted by Harkins et al. (2009) and Monjane et al. (2011). The GTR model represents the empirical DNA model developed by Tavare (1986). It allows for unequal rates of reversible nucleotide substitutions and unequal base frequencies. The parameter I represents the proportion of invariant sites. The G4 model, developed by Yang (1994), is a discrete Gamma model with four rate categories. The proportions of sites and relative rates for each category are: category 1 (0.1568: 0.05669), category 2 (0.1568: 0.4136), category 3 (0.1568: 1.321), and category 4 (0.1568: 4.586). The maximum likelihood tree derived from the MSV-A sequences is displayed in Fig. 2. Additionally, a ML tree that displays the year of sampling for the represented MSV-A isolates can be found in Supplementary Fig. S1.

## 3.3. Sufficient temporal signal

Before constructing time-calibrated phylogenetic trees using the Bayesian genealogical inference framework for a fast-evolving virus like MSV, it is crucial to assess the presence of an adequate temporal signal in the dataset. This signal refers to the amount of divergence that accumulates between samples over successive sampling years. The evolutionary rate is a measurable metric that represents the rate of divergence over time, allowing for the conversion of genetic distance to time on a tree. The results showed the sequences had a fairly strong temporal signal (R2 = 0.111, p-value <0.001), with a correlation coefficient of 0.333 when comparing sample dates from root to tip divergence estimated using the TempEst program (Rambaut et al., 2016) and p-value assessment from 1000 random, clustered permutations of the sequence sampling dates (Murray et al., 2016). The level of temporal signal in the data, together with the fact that samples were collected over a span of around 35 years, suggests that the data is appropriate for phylogenetic reconstructions using molecular clock models (see Supplementary Fig. S2).

## 3.4. Estimating the MSV-A substitution rate

The rate of substitution measures the frequency or rate at which a pathogen, gene, genome, or population gains new nucleotide changes. The calculated average nucleotide substitution rate of MSV-A since the most recent common ancestor of all investigated MSV-A isolates was $4.8687 \times 10^{-4}$ substitutions per genome site per year (with a 95% maximum probability density interval [HPD] ranging from $4.0924 \times 10^{-4}$ to $4.8687 \times 10^{-4}$).

<figure><img src="image_5.png" /><figcaption>**Fig. 2.** MSV-A maximum likelihood phylogeny. The phylogeny was rooted using the outgroup Reunion and Mauritius isolates and coloured based on the regions where the samples were collected. The model used for the phylogeny was GTR + I + G4. The clade labels represent the MSV-A subtypes. The bar represents the number of nucleotide substitution per site.</figcaption></figure>

4 to $5.5181 \times 10^{-4}$. This estimate falls within the confidence intervals of previous estimates of MSV-A substitution rates. These estimates were determined through model-based analyses of MSV-A genome sequences obtained from nature (Harkins et al., 2009; Monjane et al., 2011; Pande et al., 2017), as well as from evolution experiments lasting between one and six years (Harkins et al., 2009a).

## 3.5. Where, when and how many times has MSV-A been introduced to Madagascar?

In order to identify the origin of MSV-A in Madagascar, we examined the complete genome dataset (n = 524), which consisted of isolates from East Africa, southern Africa, Madagascar, Reunion, and Mauritius. Our analyses utilised discrete sampling year and sampling country metadata to inform strict molecular clock and discrete phylogeography models. These models allowed us to infer the geographic origins of ancestral sequences and the timing of their movements to Madagascar from the regions where viruses were sampled in the past decades. The temporal and spatial history of MSV-A, including its travels between different locations, was summarized into a maximum clade credibility (MCC) tree by averaging 592 posterior, post burn-in trees (Fig. 3 A). Among the nine tree nodes representing ancestral sequences of monophyletic Madagascan MSV-A clades at the branch tips (indicated by arrows in Fig. 3A), four movements from Uganda and Zambia had posterior probability supports greater than 0.9 (Table 1). This suggests that the most probable locations from which the ancestral viruses entered Madagascar were most likely East and southern African.

The earliest movement from East Africa to Madagascar, as determined by our analyses, was estimated to have taken place in 1979 (with a 95% highest posterior density interval of 1976–1982). The most recent movement from East Africa to Madagascar, as inferred, occurred around
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** (A) The MSV-A maximum clade credibility (MCC) phylogeny represents the average of 592 posterior trees inferred using the discrete phylogeographic diffusion model. The branches are coloured based on either the most likely country where the ancestral sequences represented by the branches existed (for the internal branches) or the country where the isolates were sampled (for the terminal branch tips). The clade labels represent the MSV-A subtypes. Changes in colour along branches are a sign of ancestral viral transitions between countries. Black arrows denote branches, symbolising migrations between East or southern Africa and Madagascar. (B) Geospatial representations of MSV-A dissemination and sampling. (C) Directions of MSV-A movements from East or southern Africa to Madagascar. The two-letter country codes for the countries are: Ethiopia [ET], Kenya [KE], Comoros Islands [KM], Lesotho [LS], Madagascar [MG], Mauritius [MU], Mozambique [MZ], Reunion Island [RE], Tanzania [TZ], Uganda [UG], South Africa [ZA], Zambia [ZM], and Zimbabwe [ZW].</figcaption></figure>

<table><caption>**Table 1**
MSV-A movements into Madagascar.</caption><thead><tr><td>Movements</td><td>Posterior probability</td><td>Year (95% HPD)</td></tr></thead><tbody><tr><td>1</td><td>1.00</td><td>1979 (1976–1982)</td></tr><tr><td>2</td><td>0.01</td><td>1991 (1989–1993)</td></tr><tr><td>3</td><td>0.01</td><td>1998 (1992–2001)</td></tr><tr><td>4</td><td>0.03</td><td>1999 (1994–2000)</td></tr><tr><td>5</td><td>0.95</td><td>2002 (1999–2005)</td></tr><tr><td>6</td><td>0.95</td><td>2003 (1999–2006)</td></tr><tr><td>7</td><td>0.18</td><td>2002 (1998–2005)</td></tr><tr><td>8</td><td>0.93</td><td>2004 (2001–2006)</td></tr><tr><td>9</td><td>0.03</td><td>2003 (2002–2003)</td></tr></tbody></table>

2003 (with a 95% highest posterior density interval of 2002–2003). When examining the well-established MSV-A movement routes, which encompassed five pathways in eastern Africa, southern Africa, and the Indian Ocean islands (Fig. 3B), the most significant one for our study was the East Africa to Madagascar pathway, which had a strong level of support based on Bayes factor analysis (BF > 10; Fig. 4 B). However, the estimated Bayes factor support for movements from Mauritius had no phylogenetic support.

### 3.6. The spread of MSV-A within Madagascar

In addition to examining the MSV-A imports to and exports from Madagascar, it is also important to consider the internal spread of MSV-A within the country. In order to accomplish this, we conducted a continuous analysis of MSV-A sequences based on the GPS coordinates of their sampling locations. The continuous analysis estimated a mean substitution rate of $4.269 \times 10^{-4}$ (95% HPD $3.6186 \times 10^{-4}$ - $4.9627 \times 10^{-4}$), which is slightly lower than the rate of $4.8687 \times 10^{-4}$ (95% HPD $4.0924 \times 10^{-4}$ - $5.5181 \times 10^{-4}$) estimated using the discrete analysis.

The estimated root date for the most recent common ancestor of all the MSV-A sequences used in this analysis was 1882, with a 95% highest posterior density (HPD) interval of 1861–1901. This date is earlier than the estimated date of the most recent common ancestor (MRCA) of MSV-A, which is 1900 with a 95% HPD interval of 1882–1916, as inferred using the discrete analysis.

The continuous phylogeographic reconstructions of MSV-A dispersal within Madagascar over the last decade, shown in Fig. 5, support the findings from the discrete phylogeographic analyses. These reconstructions reveal that there were at least five introductions of MSV-A from mainland Africa to Madagascar. These introductions occurred in 1990 (with a 95% highest posterior density interval of 1988–1993), 1991 (with a 95% highest posterior density interval of 1983–1998), 1995 (with a 95% highest posterior density interval of 1992–1999), 1995 (with a 95% highest posterior density interval of 1991–1999), and 1999 (with a 95% highest posterior density interval of 1994–2002). The introduction of MSV-A to the Comoros from Madagascar was estimated to have taken place around 1999, with a 95% highest posterior density (HPD) interval of 1994–2002. This estimate is roughly six years earlier than the estimate obtained using discrete phylogeographic analysis. For more details, refer to Supplementary Fig. S3, which shows the continuous maximum clade credibility (MCC) tree.

### 4. Discussion

The MSV-A virus has been categorised into different subtypes based on the similarity of their genetic sequences. Typically, isolates that have a sequence similarity of over 98% are assigned to a subtype. The
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** (A) The provided maximum clade credibility (MCC) tree displays the estimated rewards for Markov jump transition states associated with MSV-A spread from eastern or southern Africa to Madagascar (highlighted in green). These rewards were inferred using the discrete phylogeographic diffusion model. (B) A heat map displaying the Bayes factor support for the MSV-A movement pathways to Madagascar, determined using a Bayesian Stochastic Search Variable Selection (BSSVS) procedure with an asymmetric substitution model. (C) The timing of the transmission rates of MSV-A into Madagascar over time, calculated by averaging across a posterior sample of 100 trees.</figcaption></figure>

subtypes are primarily used to describe the relationships between sequences. However, they also indicate the severity of symptoms caused by different groups of MSV-A isolates (Martin et al., 2001) and the geographical distribution of these isolates across Africa (Harkins et al., 2009; Monjane et al., 2011). MSV-A isolates of subtypes A1 and A4 have been categorised into 19 MSV-A recombinant lineages based on patterns of previous recombination within and across strains. Out of these lineages, 14 are mainly composed of sequences derived from MSV-A1 (Martin et al., 2001; Monjane et al., 2011; Owor et al., 2007). According to this categorization, it was found that all Madagascan isolates belong to the MSV-A1 subtype. Additionally, it was discovered that these isolates may be grouped into three different recombinant lineages: XI, IV, and V. It is worth noting that these lineages have been previously identified in East Africa (refer to Supplementary Table S1).

All of these recombinant lineages originated from intra-MSV-A strain recombinants, which are viruses in the MSV-A strain that resulted from recombination between two MSV-A viruses. Recombinant forms are frequently observed, as seen with over 50% of the isolates collected from Uganda in 2005 (Owor et al., 2007), which were part of recombinant lineages comparable to the ones found in Madagascan viruses. Furthermore, the finding that the Madagascan MSV-A viruses are likely to be instances of recombinant lineages initially identified in Uganda aligns well with other evidence indicating that East Africa, especially the East African Great Lakes region, is significant in the development of new MSV-A variants and their spread to other areas of the continent (Monjane et al., 2011). The MSV-A1 lineages, which have been detected across continental Africa, most likely originated from East Africa. Our finding of this subtype in Madagascar provides more evidence of its significance as the primary cause of severe MSD over the whole geographical distribution of MSV.

While the MSV-A variants analysed in this study arrived in Madagascar around 1979, it is highly improbable that these lineages were the first to reach the island. Although the first record of MSD in Madagascar dates back to 1974 (CABI; EPPO, 1997; Charles, 2014), it is possible that the disease had been present on the island for many

decades prior to these reports. It is also challenging to determine the exact time when MSD initially spread to other islands such as the Comoros, and very plausible that MSD has been present on the Comoros for the same duration as it has been on Madagascar. The probability of this being the initial introduction of MSV-A to the island chain is highly improbable.

The spread of MSV-A from mainland Africa to Madagascar is likely linked to trade between these regions, particularly in the importation of fresh vegetables. Madagascar implemented trade liberalisations in 1982 and subsequently joined regional trade agreements with the member states of the East African Community (EAC) and the Indian Ocean Commission (IOC). These agreements, as reported by Adar (2011) and Wangia et al. (2002), may have influenced the frequency of unintentional transportation of viruses like MSV to and from Madagascar. Madagascar has undergone a significant increase in imports, which continues to rise to this day. The MSV-A movements to Madagascar coincided with a period in Madagascar's agricultural history known as the "boom corn years," during which maize cultivation was rapidly increasing. This situation likely created favourable conditions for the growth and spread of any MSV-A variants that were introduced to the island in the past 40 years (Maret, 2007).

Our continuous phylogeographic analysis suggested fewer independent introductions of MSV-A to Madagascar compared to our discrete phylogeographic analysis. However, the estimated timings of all five introductions inferred by the continuous analysis fell within the estimated time window inferred for the nine introductions inferred by the discrete analysis. The present study of the geographic distribution of MSV-A in Madagascar has shown that the virus has spread at an average rate of roughly 47.6 km per year (with a 95% highest posterior density interval ranging from 36.05 to 61.70 km per year) after many introductions to the island. The rate of dissemination is somewhat higher than the estimated rate of around 38.9 km/year (95% HPD 34.0 to 44.4) for the dispersal of MSV-A on mainland Africa, as estimated by us and other researchers (Monjane et al., 2011). It is important to be cautious when interpreting the meaning of this inference as the estimates of
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Phylogeographic analysis of the spatial distribution of MSV-A throughout Madagascar. The maximum clade credibility (MCC) tree and the 80% highest posterior density areas depict the level of uncertainty associated with the phylogeographic inference. The nodes of the MCC tree were assigned colours based on their chronological order. Arrows indicate the direction of movements. The red and blue arrows symbolise inbound and outbound movements to and from Madagascar, respectively.</figcaption></figure>

dispersal rate can be greatly influenced by factors such as sample sizes, the type of introductions (whether they are from a single or multiple sources), and the reliability of the temporal information in the analysed nucleotide sequence datasets (Lemey et al., 2010).

## 5. Conclusion

All of these estimated movements of MSV-A into Madagascar during the last 30 years seem to have originated from East or southern Africa. The movement of MSV-A from East Africa to Madagascar is primarily influenced by human and socio-political factors rather than natural ecological events. This is because the chances of viruliferous insect vectors being carried across the ocean between mainland Africa and Madagascar are very low. The coronavirus pandemic has demonstrated that island states are in an advantageous position to effectively avoid the introduction of viruses. Potential strategies to restrict the introduction of pathogens in the future include implementing bio-sanitation regulations, such as imposing limitations on the importation of cuttings from plant species that are vulnerable to MSV, such as sugarcane, until they have been confirmed to be free of MSV. Measures can be implemented to eradicate leafhoppers in cargo containers and airline cabins. In addition, restricting the influx of illicit imports could also contribute to reducing the spread of MSV-A and other plant diseases to the island.

## Funding

KAO was supported by the National Research Foundation (NRF) South Africa and The World Academy of Science (TWAS) Grant number 105461. J-ML was supported by the European Union: European regional development fund (ERDF, programme INTERREG VI), the *Conseil Régional de La Réunion*, and CIRAD. The funders had no role in study design, data collection, or interpretation or in the decision to submit the work for publication.

## CRediT authorship contribution statement

**Kehinde A. Oyeniran:** Writing – review & editing, Writing – original draft, Visualization, Software, Methodology, Investigation, Formal analysis, Data curation. **Darren P. Martin:** Writing – review & editing, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Jean-Michel Lett:** Writing – review & editing, Validation, Supervision, Project administration, Methodology, Investigation, Formal analysis, Data curation. **Mbolarinosy Rasoafalimanana Rakotomalala:** Writing – review & editing, Resources, Project administration, Methodology, Investigation, Formal analysis. **Hamza A. Azali:** Writing – review & editing, Resources, Methodology, Investigation. **Arvind Varsani:** Writing – review & editing, Validation, Supervision, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Data availability

GenBank sequence accession numbers are provided in the supplemental Table S1. Additional data will be made available on request.

## Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.virol.2024.110222.

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