RNA silencing safeguards plant fertility during viral infection and decreases turnip rosette virus vertical transmission

Aimer Gutiérrez-Díaz, Sanjana Holla, Inês Moura and Anders Hafrén*

affiliations: Department of Plant Biology, Uppsala BioCenter, Swedish University of Agricultural Sciences and Linnean Center for Plant Biology, Box 7080, 75007 Uppsala, Sweden.

*correspondence: anders.hafren@slu.se

Comparative viral transcriptomic analysis

RNA-seq expression data for uninfected and infected Arabidopsis thaliana were obtained from NCBI Bioprojects: TuMV PRJNA788379 [1], TuYV and CaMV PRJEB49403 [2], TYMV PRJNA1103879 [3], TCV PRJNA336058 [4], CMV PRJNA1124548 [5] and ArLV1 PRJNA863409 [6]. Read processing, alignment, and gene-level quantification was addressed by mapping with HISAT2 v2.2.1 [7] to TAIR10 reference genome and quantifying with featureCounts [8]. Differential expression was computed separately within each using DESeq2 DEGs were calculated using Deseq2 [9].

Libraries re-mapped

Virus	Project	Libraries	SeqType	Tissue	Ecotype	Genotypes	dpi	SRRs	Paper	Reference
CaMV	PRJEB49403	6	Paired-End	Rosette	Col-0	WT aphid Mp	14dold, 21dpi	ERR7738945, ERR7738946, ERR7738947, ERR7738939, ERR7738940, ERR7738941	10.1128/spectrum.00136-22	[2]
TuYV	PRJEB49403	3	Paired-End	Rosette	Col-0	WT aphid Mp	14dold, 21dpi	ERR7738939, ERR7738940, ERR7738941, ERR7738942, ERR7738943, ERR7738944	10.1128/spectrum.00136-22	[2]
TuMV	PRJNA788379	8	Paired-End	Rosette	Col-0 and Bar-0	Wt	28dold, 14dpi	SRR17227570, SRR17227569, SRR17227568, SRR17227567, SRR17227565, SRR17227564, SRR17227563, SRR17227562	10.1371/journal.pone.0275588	[1]
TCV	PRJNA336058	6 (12)	Paired-End	Leaves	Col-	WT and dcl1-/-	28dold, 8dpi	SRR3992506, SRR3992507, SRR3992508, SRR3992509, SRR3992510, SRR3992511	10.1038/srep36007.	[4]
CMV	PRJNA1124548	6	Paired-End	Rosette	Col-0	WT, CMV and CMV-Δ2b	14dpi	SRR29428980, SRR29428985, SRR29428982, SRR29428983	10.1038/s41467-025-65355-1	[5]
TYMV	PRJNA1103879	8 (16)	Single-End	Rosette	Col-0	WT and atg2	12dpi	SRR28785882, SRR28785883, SRR28785876, SRR28785877, SRR28785878	10.1101/2024.05.06.590709	[3]
ArLV1	PRJNA863409	7 (58)	Paired-End	Leaves	Col-0	WT	4% vs 80% infestation	SRR20705605, SRR20705604, SRR20705602, SRR20705607, SRR20705606, SRR20705603	10.1093/plphys/kiae581	[6]

From FeatureCouns

 Rscript Code/deseqDEG.R FeatureCounts/Liu_CMV_fcounts.txt "WTvsWT" "CMVvsMock"

 Rscript Code/deseqDEG.R FeatureCounts/Gyula_TVCV_TuMV_fcounts.txt "WTvsWT" "TuMVvsMock"

 Rscript Code/deseqDEG.R FeatureCounts/Gao_TCV_fcounts.txt  "WTvsWT" "TCVvsMock"
 
 Rscript Code/deseqDEG.R FeatureCounts/Chesnais_CaMV_TuYV_fcounts.txt  "WTvsWT" "CaMVvsMock"
 
 Rscript Code/deseqDEG.R FeatureCounts/Chesnais_CaMV_TuYV_fcounts.txt  "WTvsWT" "TuYVvsMock"
 
 Rscript Code/deseqDEG.R FeatureCounts/Clavel_TYMV_fcounts.txt  "WTvsWT" "TYMVvsMock"
 
 Rscript Code/deseqDEG.R FeatureCounts/Jiang_ArLV1_mArR1fcounts.txt "WTvsWT" "ArLV1vsMock"

Gene selection

(cat Data/deseq_head.txt && for gene in `cat Data/PRs_genes.csv | cut -f 1` ; do grep $gene FeatureCounts/*DESeq2*; done  )  > Data/PRs_Deseq2.txt

PRs genes expression

Heatmap PRs related genes transcriptionally induce/repress by 7 different viruses

DeseqOut <- read.delim("Data/PRs_Deseq2.txt", check.names = FALSE)
GeneNames <-  read.delim("Data/PRs_genes.csv", header = F)

colnames(GeneNames) <- c("gene","gene_name")

DeseqOut_clean <- DeseqOut %>%
  mutate(id = .[[1]]) %>%         
  select(-1) %>%
  separate(id, into = c("file", "gene"), sep = ":", extra = "merge", fill = "right") %>%
  mutate(
    file = basename(file),
    file = str_remove(file, "\\.tsv$"),
    Project = str_extract(file, "^[^_]+"),
    Condition =  gsub(".*_","",file),  
    Virus = str_match(file, "DESeq2_([^_]+)_vs_")[,2],
    sig = case_when(
      !is.na(padj) & padj < 0.001 ~ "***",
      !is.na(padj) & padj < 0.01  ~ "**",
      !is.na(padj) & padj < 0.05  ~ "*",
      TRUE ~ "ns"
    )
  )

DeseqOut_clean <- merge(GeneNames,DeseqOut_clean, by.x = "gene" )

Heatmap from Deseq2

DeseqOut_clean$ColKey <- DeseqOut_clean$Virus

log2fc_mat <- xtabs(log2FoldChange ~ gene_name + ColKey, data = DeseqOut_clean)
log2fc_mat <- as.matrix(log2fc_mat)

# helper for significance stars: take first non-empty star, else ""
first_star <- function(x) {
  x <- x[!is.na(x)]
  x <- x[x != ""]
  x <- x[!x %in% c("ns","NS","n.s.","N.S.","NA")]
  if (length(x) == 0) return("")
  x[1]
}


sig_mat <- tapply(DeseqOut_clean$sig,
                  list(DeseqOut_clean$gene_name, DeseqOut_clean$ColKey),
                  first_star)


# ---- colors for unscaled log2FC ----
mx <- max(abs(log2fc_mat), na.rm = TRUE)
col_fun <- colorRamp2(c(-mx, 0, mx), c("#2b8cbe", "white", "#de2d26"))
# ---- draw heatmap with stars overlaid on cells ----

colOrder <- c( "ArLV1","TuYV","TYMV","CaMV","TCV","TuMV","CMV")

sig_mat <- sig_mat[rowOrder,]

ht <- Heatmap(
  log2fc_mat,
  name = "log2FC",
  col = col_fun,
  na_col = "grey90",
  cluster_rows = F,
  cluster_columns = F,
  column_title = NULL, 
  row_title = NULL, 
  rect_gp = gpar(col = "grey80", lwd = 0.5),
  #  stars on top of each cell
  cell_fun = function(j, i, x, y, w, h, fill) {
    s <- sig_mat[i, j]
    if (!is.na(s) && nzchar(s)) {
      grid.text(s, x, y, gp = gpar(fontsize = 12, col = "black"))
    }
  }
)

svg(filename = "Figures/CompTranscriptome_PRs_sig.svg",   width = 4, height = 4)
ht
dev.off()

pdf(file = "Figures/CompTranscriptome_PRs_sig.pdf",   width = 4, height = 4)
ht
dev.off()

Single gene bar plot

PR1 expression log2FC ± lfcSErelative to Mock in 7 different viruses

DeseqOut_clean$Virus <- factor(DeseqOut_clean$Virus, levels = c("ArLV1","TuYV","TYMV","CaMV","TCV","TuMV","CMV"))

viral_gradient <-  c("Mock" ="#FA9EB4","TuYV" = "#fecf92","ArLV1" = "#feff92","TRoV" = "#641a80","CMV" = "#F768A1" ,"CaMV" ="#DD3497", "TYMV" = "#AE017E" ,"TCV" ="#b73779" ,"TuMV" = "#7A0177" )

PR1_plot <- ggplot(DeseqOut_clean[DeseqOut_clean$gene == "AT2G14610",], aes(x = Virus, y = log2FoldChange, fill = Virus)) +
  geom_col(width = 0.7) +
  geom_errorbar(aes(ymin = log2FoldChange - lfcSE, ymax = log2FoldChange + lfcSE),
                width = 0.2, linewidth = 0.6) +
  geom_text(aes(label = sig), position = position_dodge(width = 0.6),
            vjust = -0.8, size = 10, show.legend = FALSE) +
 scale_fill_manual(values = viral_gradient) +
    scale_y_continuous(expand = expansion(mult = c(0.05, 0.10))) +
#  facet_wrap(~ gene, scales = "free_y") +
  theme_bw( base_size = 30, base_family = "Helvetica")  +theme( legend.position = "none") +
  labs(x = NULL, y = "PR1 log2FC\n Relative to Mock")  

ggsave(PR1_plot, device = pdf, filename = "Figures/PR1_plot.pdf",  width=9, height=7.5)

PRs <- DeseqOut_clean[grepl( "AT2G14610|AT3G57260|AT1G75040", DeseqOut_clean$gene),-9]

out_list <- list(
  Deseq2Transcriptome = PRs
) 

ggsave(PR1_plot, device = svg, filename = "Figures/PR1_plot.svg",  width=9, height=7.5)
ggsave(PR1_plot, device = pdf, filename = "Figures/PR1_plot.pdf", width=9, height=7.5)


write.xlsx(out_list, file = "Results/SupTable5.PRs_comp_transcriptomics.xlsx", overwrite = TRUE)

PRs_plot <- ggplot(DeseqOut_clean[grepl( "AT3G57260|AT1G75040",DeseqOut_clean$gene ),], 
                   aes(x = Virus, y = log2FoldChange, fill = Virus)) +
  geom_col(width = 0.7) +
  geom_errorbar(aes(ymin = log2FoldChange - lfcSE, ymax = log2FoldChange + lfcSE),
                width = 0.2, linewidth = 0.6) +
  geom_text(aes(label = sig), position = position_dodge(width = 0.6),
            vjust = -0.8, size = 10, show.legend = FALSE) +
  scale_fill_manual(values = viral_gradient) +
    scale_y_continuous(expand = expansion(mult = c(0.05, 0.25))) +
 facet_grid(.~gene_name ) +
  theme_bw( base_size = 30, base_family = "Helvetica") + 
  labs(x = NULL, y = "log2FC ± lfcSE\n Relative to Mock")  +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1),legend.position = "none") 

ggsave(PRs_plot, device = pdf, filename = "Figures/PR2_5_plot.pdf",  width=12, height=8)
ggsave(PRs_plot, device = svg, filename = "Figures/PR2_5_plot.svg",  width=12, height=8)

PR2 and PR5 expression log2FC ± lfcSErelative to Mock in 7 different viruses

Viral RdRps structural prediction and phylogenetics

Protein sequences corresponding to CMV 2a, TRV 134K, TyMV 206K, TRoV P2ab, ALV1 P1, TuMV NIb, and PLrV, TuYV, RYMV, AhPV1, TMV, YoMV, TCV, P1AMV RdRps were curated prior to structure prediction. For viruses where the replication protein is polyprotein-derived (e.g., TRoV P2ab), sequences were processed to extract the annotated mature peptide corresponding to the RdRP-containing product, final sequences are available in this repository. Each curated protein was then folded using AlphaFold2 [10], while CaMV P5 PBD was the only RdRp experimentally elucidated (PDB: 8R0S) [11]. To build a structure-based phylogeny, an initial structural reconstruction was performed using the predicted RdRP models, and a non-LTR retrotransposon reverse transcriptase structure (PDB: 8GH6) was included as an outgroup, in a similar way to [12]. Finally, a consensus topology was obtained by performing an agreement analysis between trees generated from DALI [13] and Foldtree [14] outputs, retaining and scoring clades supported by both approaches.

PDB sequence post-processing

The sequence or chain selection applied to each structure in RdRps/ is summarized below. Residue ranges follow the numbering stored in each PDB file. “Residues in PDB” counts residues with ATOM records; consequently, experimentally determined structures can contain fewer coordinate-bearing residues than the retained sequence span because unresolved residues are absent. Mean pLDDT was recalculated for AlphaFold2 models as the arithmetic mean of the C$\alpha$-atom B-factor field, using one value per residue. For experimental structures, this field contains experimental B-factors or related quality values rather than pLDDT and is therefore reported as not applicable (N/A).

PDB file	Protein or construct	Sequence post-processing	Retained PDB range	Residues in PDB	Mean pLDDT	Published-structure citation
`ahpv1_rdrp_relax_m3_p0_plddt-90.pdb`	AhPV1 RdRp	Complete submitted RdRp sequence retained; no terminal trimming	A:1–585	585	90.61	–
`alv1_p1_1140_1610.pdb`	ALV1 P1	N-terminal region removed to retain the C-terminal RdRp region; the stored endpoint is residue 1610	A:1140–1610	471	87.78	–
`CaMV_P5_8R0S.pdb`	CaMV P5 reverse transcriptase	Protein chain A retained from the experimental structure; bound nucleic-acid chains were removed	A:1–475	470	N/A	[11]
`CMV_2a_273-750.pdb`	CMV 2a	Internal RdRp-containing region extracted from the replication protein	A:273–750	478	88.20	–
`nonLTR_RT_8gh6_1_924.pdb`	Bombyx mori R2 non-LTR reverse transcriptase	Protein chain A retained from the experimental structure; bound RNA and DNA chains were removed	A:111–924	715	N/A	[15]
`PlAMV_RdRp_q07518_895_1385.pdb`	PlAMV RdRp	C-terminal RdRp-containing region extracted from the replication protein	A:895–1385	491	85.61	–
`PLrV_Polerovirus_P11623_relax_m4_p0_plddt-78.pdb`	PLrV replication protein	Complete submitted replication-protein sequence retained; no terminal trimming	A:1–1062	1062	77.77	–
`rymv_rdrp_1_464.pdb`	RYMV RdRp	Complete submitted RdRp sequence retained; no terminal trimming	A:1–464	464	93.93	–
`tcv_rdrp_relax_m1_p0_plddt-92.pdb`	TCV RdRp	Complete submitted RdRp sequence retained; no terminal trimming	A:1–524	524	92.56	–
`tmv_rdrp_1117_relax_m3_p0_plddt-91.pdb`	TMV RdRp	C-terminal RdRp region beginning at source residue 1117 extracted and renumbered from 1 in the PDB	A:1–499	499	91.20	–
`trov_p2ab_428.pdb`	TRoV P2ab	N-terminal region removed to retain the mature RdRp-containing product	A:428–874	447	91.61	–
`trv_134k_1206_1707.pdb`	TRV 134K	C-terminal RdRp-containing region extracted	A:1206–1707	502	86.51	–
`TuMV_NIb_m2_plddt-93.pdb`	TuMV NIb	Mature NIb product extracted from the viral polyprotein and renumbered from 1	A:1–517	517	93.20	–
`TuYV_RdRP_p09507_relax_m1_p0_plddt-80.pdb`	TuYV replication protein	Complete submitted replication-protein sequence retained; no terminal trimming	A:1–1035	1035	80.63	–
`tymv_206k_1298.pdb`	TyMV 206K	N-terminal region removed to retain the C-terminal RdRp-containing region	A:1298–1844	547	82.12	–
`YoMV_RdRP_q66220_1120_1597.pdb`	YoMV RdRp	C-terminal RdRp-containing region extracted from the replication protein	A:1120–1597	478	88.25	–

Pairwise structural-alignment confidence and UPGMA clustering

To provide quantitative support for the structure-based groupings obtained with DALI and Foldtree, all 16 structures in RdRps/ were compared in an all-versus-all analysis with US-align v20260826 [16]. US-align performs sequence-independent monomeric structural alignment by maximizing TM-score. For every one of the 120 unique pairs, the analysis reports the RMSD over residues retained in the final structural alignment, aligned length, sequence identity, coverage, and TM-scores normalized independently by the length of each structure. Because length-specific TM-scores are directional, the symmetric TM-score matrix below uses the score normalized by the average length of the two structures (-a T); both directional scores remain available in the tidy results file.

TM-score ranges from 0 to 1, and values of at least approximately 0.5 generally indicate the same global protein fold. RMSD is sensitive to the number of aligned residues and was therefore interpreted jointly with TM-score and alignment coverage. These metrics provide an independent, quantitative check of the structural-similarity signal underlying the DALI/Foldtree topology; they are descriptive alignment measures rather than bootstrap or branch-support probabilities.

Summary statistic	Value
Structures	16
Unique pairwise alignments	120
RMSD, minimum / median / maximum	0.69 / 4.13 / 5.55 Å
Average-length-normalized TM-score, minimum / median / maximum	0.266 / 0.594 / 0.971
Pairs with TM-score >= 0.5	75/120 (62.5%)

The closest structural pairs illustrate that the high-similarity signal is not restricted to a single example. TMV–YoMV had the highest average-length-normalized TM-score (0.971; RMSD 0.69 Å across 478 aligned residues), followed by RYMV–TRoV (0.953; 1.32 Å across 445 residues), TRV–YoMV (0.937; 1.52 Å across 475 residues), and CMV–YoMV (0.921; 2.20 Å across 468 residues).

All pairwise aligned-residue RMSD and average-length-normalized TM-score values.

The complete numerical results are available as a tidy pairwise table, an RMSD matrix, and a TM-score matrix. The run metadata records the US-align version, executable checksum, command, and metric definitions.

UPGMA clustering of the pairwise metrics

UPGMA average-linkage clustering [17] was performed independently with (i) aligned-residue RMSD as the distance and (ii) 1 - average-length-normalized TM-score as the distance. The internal-node labels in the dendrograms are the arithmetic-mean linkage distances at which clusters were joined. Both analyses recover the particularly close TMV–YoMV and RYMV–TRoV pairs; additional differences between the dendrograms are expected because RMSD and TM-score weight alignment extent and length differently. UPGMA imposes an ultrametric hierarchy, so these dendrograms are a transparent descriptive clustering of the validation metrics and are not presented as replacements for the DALI/Foldtree evolutionary topology.

UPGMA clustering calculated independently from the RMSD and TM-distance matrices.

The exact linkage histories are provided in the RMSD merge table and TM-distance merge table. Machine-readable trees are provided in Newick format for RMSD and TM distance.

The complete analysis, including all alignments, matrices, UPGMA trees, and figures, can be regenerated with the repository script after downloading the official US-align executable:

python Code/pairwise_structure_validation.py --usalign /path/to/USalign

Representative RYMV–TRoV alignment

The existing structural visualization shows the close Sobemovirus pair TRoV (blue) and RYMV (orange). The values below were recalculated in the same all-pairwise US-align run.

Pair	Chains	RMSD (Å)	TM-score (average-length normalized)	Directional TM-scores (RYMV / TRoV length)	Identity	Aligned residues
RYMV–TRoV	A / A	1.32	0.953	0.936 / 0.971	51.5%	445

Structural alignment of RdRp between the closely related Sobemoviruses TRoV (blue) and RYMV (orange).

References

1. Gyula P, Tóth T, Gorcsa T, Nyikó T, Sós-Hegedűs A, Szittya G. Ecotype-specific blockage of tasiARF production by two different RNA viruses in arabidopsis. Plos one. 2022;17:e0275588.

2. Chesnais Q, Golyaev V, Velt A, Rustenholz C, Brault V, Pooggin MM, et al. Comparative plant transcriptome profiling of arabidopsis thaliana col-0 and camelina sativa var. Celine infested with myzus persicae aphids acquiring circulative and noncirculative viruses reveals virus-and plant-specific alterations relevant to aphid feeding behavior and transmission. Microbiology Spectrum. 2022;10:e00136–22.

3. Clavel M, Bianchi A, Kobylinska R, Groh R, Zhang X, Ma J, et al. Selective autophagy fine-tunes plant immunity to promote cell survival during viral infection. Science. 2026;392:eadu9554.

4. Wu C, Li X, Guo S, Wong S-M. Analyses of RNA-seq and sRNA-seq data reveal a complex network of anti-viral defense in TCV-infected arabidopsis thaliana. Scientific reports. 2016;6:36007.

5. Liu J-H, Lin Y, Li Y-X, Lang Z, Zhang Z, Duan C-G. A mutually antagonistic mechanism mediated by RNA m6A modification in plant-virus interactions. Nature Communications. 2025;16:10378.

6. Jiang Z, Verhoeven A, Li Y, Geertsma R, Sasidharan R, Zanten M van. Deciphering acclimation to sublethal combined and sequential abiotic stresses in arabidopsis thaliana. Plant Physiology. 2024;kiae581.

7. Kim D, Paggi JM, Park C, Bennett C, Salzberg SL. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nature biotechnology. 2019;37:907–15.

8. Liao Y, Smyth GK, Shi W. featureCounts: An efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014;30:923–30.

9. Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome biology. 2014;15:550.

10. Jumper J, Evans R, Pritzel A, Green T, Figurnov M, Ronneberger O, et al. Highly accurate protein structure prediction with AlphaFold. nature. 2021;596:583–9.

11. Prabaharan C, Figiel M, Szczepanowski RH, Skowronek K, Zajko W, Thangaraj V, et al. Structural and biochemical characterization of cauliflower mosaic virus reverse transcriptase. Journal of Biological Chemistry. 2024;300:107555. https://doi.org/10.1016/j.jbc.2024.107555.

12. Wolf YI, Kazlauskas D, Iranzo J, Lucı́a-Sanz A, Kuhn JH, Krupovic M, et al. Origins and evolution of the global RNA virome. MBio. 2018;9:10–1128.

13. Holm L. Dali server: Structural unification of protein families. Nucleic acids research. 2022;50:W210–5.

14. Moi D, Bernard C, Steinegger M, Nevers Y, Langleib M, Dessimoz C. Structural phylogenetics unravels the evolutionary diversification of communication systems in gram-positive bacteria and their viruses. Nature Structural & Molecular Biology. 2025;32:2492–502.

15. Wilkinson ME, Frangieh CJ, Macrae RK, Zhang F. Structure of the R2 non-LTR retrotransposon initiating target-primed reverse transcription. Science. 2023;380:301–8. https://doi.org/10.1126/science.adg7883.

16. Zhang C, Shine M, Pyle AM, Zhang Y. US-align: Universal structure alignments of proteins, nucleic acids, and macromolecular complexes. Nature Methods. 2022;19:1109–15. https://doi.org/10.1038/s41592-022-01585-1.

17. Sokal RR, Michener CD, et al. A statistical method for evaluating systematic relationships. 1958.

Name		Name	Last commit message	Last commit date
Code		Code
Data		Data
FeatureCounts		FeatureCounts
Figures		Figures
RdRps		RdRps
Results		Results
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
RYMV_TRoV_RdRps.gif		RYMV_TRoV_RdRps.gif
RYMV_TRoV_RdRps.png		RYMV_TRoV_RdRps.png
RdRpStructuralEvolution.Rproj		RdRpStructuralEvolution.Rproj
Readme.Rmd		Readme.Rmd
Readme.md		Readme.md
References.bib		References.bib

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RNA silencing safeguards plant fertility during viral infection and decreases turnip rosette virus vertical transmission

Contents

Comparative viral transcriptomic analysis

Libraries re-mapped

From FeatureCouns

Gene selection

PRs genes expression

Heatmap from Deseq2

Single gene bar plot

Viral RdRps structural prediction and phylogenetics

PDB sequence post-processing

Pairwise structural-alignment confidence and UPGMA clustering

UPGMA clustering of the pairwise metrics

Representative RYMV–TRoV alignment

References

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RNA silencing safeguards plant fertility during viral infection and decreases turnip rosette virus vertical transmission

Contents

Comparative viral transcriptomic analysis

Libraries re-mapped

From FeatureCouns

Gene selection

PRs genes expression

Heatmap from Deseq2

Single gene bar plot

Viral RdRps structural prediction and phylogenetics

PDB sequence post-processing

Pairwise structural-alignment confidence and UPGMA clustering

UPGMA clustering of the pairwise metrics

Representative RYMV–TRoV alignment

References

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