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Extensive recombination challenges the utility of Sugarcane mosaic virus phylogeny and strain typing

Sugarcane mosaic virus (SCMV) is distributed worldwide and infects three major crops: sugarcane, maize, and sorghum. The impact of SCMV is increased by its interaction with Maize chlorotic mottle virus which causes the synergistic maize disease maize lethal necrosis. Here, we characterised maize lethal necrosis-infected maize from multiple sites in East Africa, and found that SCMV was present in all thirty samples. This distribution pattern indicates that SCMV is a major partner virus in the East African maize lethal necrosis outbreak. Consistent with previous studies, our SCMV isolates were highly variable with several statistically supported recombination hot- and cold-spots across the SCMV genome. The recombination events generate conflicting phylogenetic signals from different fragments of the SCMV genome, so it is not appropriate to group SCMV genomes by simple similarity.

NIb, NIa-VPg, and NIa-Pro, and the 6K1-VPg-NIa-Pro-NIb region has been called a recombination hot-spot, although without statistical support [28][29][30][31][32][33][34] . Recombination complicates phylogenetic analyses because various genome regions in a single individual may have different evolutionary histories. Accordingly, constructing phylogenies using different sections of SCMV and other potyviral genomes produces conflicting trees 35,36 . Recombination may also impede virus detection because increased genomic variation may lead to false negative results with common techniques such as PCR and antibody ELISA 11,37 . There are multiple potyviruses present in East Africa which could act as partner viruses to MCMV. Therefore, we decided to survey MLN-infected maize in Kenya and Ethiopia using next-generation sequencing (NGS) to allow identification and analysis of the partner viruses in this region. The only partner virus we detected was SCMV, and these data were then used to look for signals of historical recombination in the SCMV genome. We also assessed the suitability of traditional phylogenetic methods for SCMV genomic data.

Results and Discussion
Sequencing of MLn-infected maize reveals ScMV. In August 2014 we collected 23 MLN-symptomatic (mosaic and chlorosis on leaves) maize samples from 13 Kenyan and 4 Ethiopian sites (Table S1) and performed NGS RNA-seq (ArrayExpress accession: E-MTAB-7002). All samples contained both MCMV, characterised previously 38 , and SCMV (Fig. S1). The 23 assembled SCMV sequences ranged from 2,191 bp to 9,632 bp, which is 23% to 100% of the longest previously reported SCMV sequence (available in GenBank: MH093717-MH093739). We aligned and manually trimmed the long (>8000 bp) SCMV contigs to the full SCMV genomes available in GenBank for further analysis.
We assessed nucleotide polymorphism diversity of multiple sequence alignments using DNAsp5 39 . Diversity was high with 4,289 mutations spread over 2,831 sites and an average of 1121.1 nucleotide differences between sequences. Nucleotide diversity across the genome was 0.17, higher than for most RNA viruses but within the range previously reported for SCMV 30,33 . There were high polymorphism regions in the N-termini of P1 and CP and the most conserved regions were in the central domain of P3 and the 3′ UTR (Fig. 2b,c). P1 is a serine protease with a known hyper-variable region 40 . The variable region in the CP N-terminus (Figs. 2b,c and S2, S3) corresponds to a domain with variable amino acid length and low conservation 41,42 , with episodic positive selection detected by MEME analysis (Fig. S4) 43,44 . This N-terminal domain is surface located, raising the possibility that variation in this region may alter interactions with host or vector proteins 45,46 .
The conserved P3 protein is essential for potyviral replication but it is also the locus of the cryptic fusion protein P3N-PIPO and this overlapping open reading frame is an extra constraint to evolution of the nucleotide sequence 47 . Interestingly, in the P3N-PIPO region, there were also sites with episodic positive selection detected by MEME (Fig. S4). The 3′ UTR of potyviruses contains a poly-A tail to promote genome stability and translation which is completely conserved. evidence for ScMV recombination. The alignment patterns of several samples suggested recombination, with different regions of the same sample genome showing closest alignment to divergent reference genomes (Fig. 3a). To simplify the analysis whilst retaining maximum diversity, we subsampled the alignment of 116 sequences. We generated a nucleotide identity matrix (Table S2) and grouped sequences with >99% similarity, then kept the longest sequence in each group. This produced a final dataset of 55 SCMV genomes/contigs, including 13 from our NGS libraries.
Splits network analysis can detect and visualize conflicting signals from a phylogenetic dataset 48 . Conflicting signals imply that the relationship between sequences is different depending on the part of the sequence being analysed, and they can be caused by recombination or horizontal gene transfer. Splits networks with reticulate shapes rather than bifurcating tree shapes indicate conflicting phylogenetic signals. Here, we found the splits network derived from our SCMV sequences to be very different from a bifurcating tree indicating conflicting phylogenetic signals and implying recombination (Fig. 3b). Additional independent evidence for SCMV recombination comes from the distribution of multiple indels that do not correlate with geographic or phylogenetic proximity (Figs. 2a and S2, S3).
To estimate the number and location of recombination breakpoints we used Recombination Detection Programme 4 (RDP4) to predict the locations of recombination events in SCMV 49 . RDP uses multiple algorithms to locate sites in an alignment at which phylogenetic signals change rapidly, which is indicative of a recombination event. The recombination scheme suggested multiple recombination events, with many between geographic regions (Fig. 3c,d). There was notable reciprocal exchange of recombinant fragments between European and Chinese isolates (Fig. 3c,d), and between Chinese and African isolates. There was also evidence for intra-region recombination in the regions with more than five isolates (China and Africa). Recombination was more frequent between strains within a region than between strains in different region, as expected. Additionally, there were 28 recombination events with unknown parents (i.e. genome fragments not closely related to any known isolates), demonstrating that more sequencing data will be required to fully describe worldwide recombination patterns.
To search for regions of the genome with an over-or under-representation of recombination, we counted the recombination breakpoints in sliding windows across the SCMV genome (Fig. 4a), calculated likelihood ratios, and used permutation testing to identify statistically significant regions (Fig. 4b). The permutation test www.nature.com/scientificreports www.nature.com/scientificreports/ randomly places recombinant fragments spanning the same number of variable nucleotide positions as each detected recombinant fragment, which controls for sequence variability and generates a density map of where recombination is more likely to be detected in the SCMV alignment.
'Global hot/cold-spots' were defined as those with more breakpoints than 95% of the sliding windows across the genome, and 'local hot/cold-spots' were those with more breakpoints than 99% of sliding windows at that position. Global recombination hot-spots were present at the 5′ and 3′ genomic termini. We detected nine local recombination hot-spots, and twelve local recombination cold-spots (Fig. 4). The hot-spots were concentrated in the 3′ region encoding NIb and CP, with single hot-spots in CI, P3N-PIPO, and the P1/HC-Pro junction (Fig. 4c). The cold-spots were distributed more uniformly across the first 7500 bp of the genome. These are the first statistically supported recombination hot-and cold-spots reported in SCMV. www.nature.com/scientificreports www.nature.com/scientificreports/ Recombination can promote nucleotide diversity by mixing lineages, and we note that the 3′ genomic region encoding CP in SCMV has high nucleotide diversity (Fig. 2), and a concentration of recombination hot-spots (Fig. 4). Potyvirus recombination hot-spots have previously been observed in the C-terminal region of CI, which we also observed, and in P1, which we did not [23][24][25][26][27] . Recombination is clearly a major force in SCMV evolution (Fig. 3), as in the Potyviridae generally 20,50 . is making an ScMV phylogeny useful?. The purpose of a phylogeny is to describe the evolutionary history of biological entities. This exercise has academic value, in tracing the history of life, and practical value, in organising similar biological entities into clades. If a phylogeny does not describe evolutionary history, and does not group biological entities into self-similar clades, it is an inappropriate analysis (due to the methodology or the underlying data) and does not contain useful information. Given the high levels of recombination between our SCMV sequences, we decided to investigate whether further phylogenetic analysis is appropriate.
There are many published SCMV phylogenies, based on CP sequences and whole genomes, which place isolates into two to six strains with variable names, see Gao et al. (2011) for a helpful summary 51 . Whole genome phylogenies of SCMV group isolates into four strains (IA, IB, II, III, IV), with around 80% nucleotide similarity between strains 33,51 . African SCMV genomes sequenced in this study form two novel clusters (AI and AII) of sequence identity, decreasing the separation between previously reported strains (Fig. 3b).
Simulations show that phylogenetic analyses are most severely impacted by recombination when breakpoints occur near the centre of alignments, and by recent recombination between diverged taxa 52 . Our recombination analysis shows evidence of recombination between divergent (<80% nucleotide identity) SCMV isolates, in the centre of both genomes and CP sequences (Figs. 3 and S5, Table S3). Therefore, there is no single evolutionary history for phylogenetic analyses to infer. To statistically test for conflicting phylogenetic signals, we constructed phylogenies by maximum likelihood using the whole SCMV genome, and three sections of the alignment (section 1: positions 963-2,764 in the original alignment, section 2: 2,875-5,103, and section 3: 5,181-8,036) without recombination hot-spots (Fig. 4, Supplemental Data d1). We chose alignment sections with a minimum number of recombination events (i.e. containing cold-spots) which were separated with recombination hot-spots (Fig. 4c). Tree incongruence was tested statistically using a Shimodaira-Hasegawa test (SH-test) for each pair of trees. The log likelihood differences were 12,222 between sections 1 and 2, 12,739 between 1 and 3, and 15,692 between 2 and 3 (p < 1e-7 and n = 2 trees for all comparisons), confirming significant differences between the trees, which can be visualised using tanglegrams or identity matrices (Fig. S6).
conclusion Viral studies often present a phylogeny followed by evidence of extensive recombination, showing that the central assumption of the phylogenetic analysis was violated 23,30,31,33 . Multiple evolutionary histories within a genome are valid and averaging these different histories does not produce the true evolutionary history of the genome 52 . Imposing a bifurcating tree structure on a dataset which does not have a single, bifurcating evolutionary history will introduce systematic error. We argue that in organisms with unknown or high recombination rates, such as RNA viruses, recombination analyses should be performed initially, then used to inform the phylogenetic approach taken, as in Ohshima et al. 25 . Splits network analysis is appropriate for all alignments, but standard phylogenetic methods may not be, depending on the splits network results. Phylogenetic analyses of whole genomes may be desirable to identify viral strains containing isolates with a broadly similar evolutionary history. However, the presence of sequences from different strains which have entered due to recombination may confound phenotyping and molecular attempts at strain identification in the field.
We have shown that SCMV is in complex with MCMV in MLN-infected maize in East Africa, and that producing SCMV phylogenies does not produce useful classification systems or describe biological truth (Fig. S6) 52 . We conclude that constructing phylogenetic trees is inappropriate for SCMV due to extensive historical recombination between divergent isolates. This may also have implications for studies of other RNA viruses, and phylogenies of other organisms with high recombination rates. There are multiple avenues for progress in this field -for example the statistical framework for assessing splits networks is not well developed, there are no automated approaches for locating viral recombination hot-spots, and visualisation of reticulate recombination networks.
Methods nGS of MLn-infected maize. We collected maize leaf samples from Kenya and Ethiopia in August 2014 (Table S1) nGS quality control. We used a custom python script to demultiplex the libraries allowing one error in index sequences, then trimmed adaptors using Trim galore! (parameters:-phred64-fastQC-illumina-length 30-paired_retain_unpaired input_1.fq input_2.fq) 53,54 . String matching deduplication (deletion of identical reads) was performed using Quality Assessment of Short Read (QUASR) pipeline scripts 55 .
ScMV alignment and diversity analysis. SCMV genomes generated in this study were combined with those in GenBank and aligned using MUSCLE (gap extension cost: 800, other settings default) in MEGA6, with separate alignments for genomes called with and without ambiguous bases 59 . We checked the alignments manually in JALview and refined where necessary 60 . To construct a nucleotide identity matrix we used the dist.alignment function from the R package seqinr. We obtained diversity metrics using the alignment without ambiguous base calls in DnaSP v5 39 . ScMV recombination analysis. Recombination analysis was performed with the alignment containing no ambiguous base calls. To generate splits networks, we used SplitsTree4 using default settings -distances calculated by uncorrected P, and network generated by neighbour-net 48 . To generate more specific predictions of recombination, we used RDP4, using the algorithms RDP, GENECONV, MaxChi, BootScan, and SiScan (all default settings), and reviewed all breakpoints manually. Recombination network diagrams were generated by constructing interaction matrices for regions and SCMV isolates. The interaction matrix was converted into a regional recombination network (Fig. 3c) using the ggraph function from the ggraph R package, while the individual interaction networks (Fig. 3d) were constructed using the ggnet2 function of the R package GGally.
Dendrogram and phylogeny construction. The nucleotide identity dendrogram was constructed from the identity matrix using the heatmap.2 function of the gplots R package. Phylogenies were constructed from the alignment without ambiguous bases using RAxML-HPC2 (8.2.10) on XSEDE, hosted by the CIPRES science gateway (parameters: -T 4 -N autoMRE -n result -s infile.txt -m GTRCAT -q part.txt -c 25 -p 12345 -f a -x 12345asc-corr lewis) 61 . Phylogenies were compared using the tanglegram function of Dendroscope (3.5.9) 62 .

Statistical analysis.
We used an SH-test to test for tree incongruence 63 between phylogenies constructed using the three alignment sections. Trees for each section were generated using the methods above and compared using the SH-test as implemented in the R-package phangorn 64 in a pairwise fashion. To determine whether the SH-test is appropriate for these data we created a negative control dataset from our alignment in which we did not expect tree disagreement. In the negative control dataset, the null hypothesis (of identical tree architectures) should not be rejected. To generate the negative control dataset, we divided a region containing a recombination cold spot (positions 2,224 to 2,586 of the original alignment), which should have had little recombination and therefore have a consistent evolutionary history into two sections (positions 2,224 to 2,400 and 2,401 to 2,586). The log likelihoods of the tree topologies constructed from these sections were −5,895 and −5,735 respectively with a difference of 59.8. Subsequent testing with the SH-test did not reject the null hypothesis of the topologies agreeing (p = 0.21).