Abstract

Background

   Viruses have evolved to create a cellular environment permissive for
   viral replication in susceptible hosts. Possibly both enabling and
   resulting from these virus-triggered changes, infected hosts undergo a
   dramatic transcriptional reprogramming, the analysis of which can shed
   light on the molecular processes underlying the outcome of virus-host
   interactions. The study of the transcriptional changes triggered by the
   plant DNA viruses geminiviruses is potentially hampered by the low
   representation of infected cells in the total population, a situation
   that becomes extreme in those cases, like that of Tomato yellow leaf
   curl virus (TYLCV), in which the virus is restricted to phloem
   companion cells.

Results

   In order to gain insight into how different the transcriptional
   landscapes of TYLCV-infected cells or whole tissues of TYLCV-infected
   plants might be, here we compare the transcriptional changes in leaf
   patches infected with TYLCV by agroinfiltration or in systemic leaves
   of TYLCV-infected plants in Nicotiana benthamiana. Our results show
   that, in agreement with previous works, infection by TYLCV induces a
   dramatic transcriptional reprogramming; the detected changes, however,
   are not equivalent in local and systemic infections, with a much larger
   number of genes differentially expressed locally, and some genes
   responding in an opposite manner. Interestingly, a transcriptional
   repression of the auxin signalling pathway and a transcriptional
   activation of the ethylene signalling pathway were detected in both
   local and systemically infected samples. A transcriptional activation
   of defence was also detectable in both cases. Comparison with the
   transcriptional changes induced by systemic infection by the
   geminivirus Tobacco curly shoot virus (TbSV) shows common subsets of
   up- and down-regulated genes similarly affected by both viral species,
   unveiling a common transcriptional repression of terpenoid
   biosynthesis, a process also suppressed by the geminivirus Tomato
   yellow leaf curl China virus.

Conclusions

   Taken together, the results presented here not only offer insight into
   the transcriptional changes derived from the infection by TYLCV in N.
   benthamiana, but also demonstrate that the resolution provided by local
   and systemic infection approaches largely differs, highlighting the
   urge to come up with a better system to gain an accurate view of the
   molecular and physiological changes caused by the viral invasion.

Electronic supplementary material

   The online version of this article (10.1186/s12864-019-5842-7) contains
   supplementary material, which is available to authorized users.

   Keywords: Geminivirus, Transcriptome, Nicotiana benthamiana, TYLCV,
   Agroinoculation

Background

   As intracellular parasites, viruses have evolved to create a cellular
   environment permissive for viral replication in susceptible hosts; for
   this purpose, viruses induce a re-wiring of the host’s physiology and
   development concomitant to the establishment of a successful infection.
   In plants, these virus-induced changes can be easily visualized and
   quantified, and infection by different viruses frequently produces some
   of a common array of symptoms, including stunting, chlorosis, and leaf
   curling. Possibly both enabling and resulting from these
   virus-triggered changes, infected hosts undergo a dramatic
   transcriptional reprogramming; the analysis of the modifications in the
   transcriptional landscape of the host upon the viral infection can shed
   light on the molecular processes underlying the outcome of virus-host
   interactions. Such transcriptional studies have proliferated in the
   past decade, possibly owing to technical advances allowing for in-depth
   sequencing, availability of genomic information, and the increased
   affordability of these approaches.

   Geminiviruses are insect-transmitted DNA viruses causing severe
   diseases in crops worldwide, and currently pose a serious threat to
   food security; however, our understanding of the molecular basis of the
   infection is still partial, which limits the development of effective
   anti-geminiviral strategies for crop protection. The transcriptional
   changes triggered by the infection by geminiviruses have been studied
   in a number of plant-virus interactions [[31]1–[32]8]. When comparing
   the results obtained in these studies, few commonalities arise: plant
   hormone signaling pathways, especially those for jasmonates (JA) and
   brassinosteroids (BR), frequently appear as altered, although the
   direction of the change is not consistent [[33]4, [34]5, [35]8, [36]9];
   and cell cycle-related genes, which need to be reactivated in the
   virus-infected terminally differentiated cells to allow for viral DNA
   replication, are detected as differentially expressed in a couple of
   cases only [[37]2, [38]9]. In an attempt to unveil the molecular basis
   of tolerance or recovery, Chen et al. (2013) and Gongora-Castillo et
   al. (2012) [[39]3, [40]4] compared the transcriptome of susceptible or
   tolerant tomato cultivars infected with Tomato yellow leaf curl virus
   (TYLCV) and that of recovered and symptomatic leaves of pepper infected
   with Pepper golden mosaic virus (PepGMV), respectively, by RNA-seq;
   however, and somewhat surprisingly, only limited differences were
   detected in both cases.

   One factor frequently neglected in these transcriptional studies of
   geminivirus-infected plants is the low representation of infected cells
   in the total population: in an average infection, only some cells will
   be supporting viral replication at a given time. This situation becomes
   extreme in those cases, like that of TYLCV, in which the virus is
   restricted to phloem companion cells. The global transcriptome of
   infected plants will be the average of that in all cells, infected and
   non-infected, and all tissues: this not only creates a serious dilution
   issue, but also averages the potential transcriptional responses in
   infected and systemic, uninfected cells, which could be opposite, hence
   generating potentially misleading results difficult to interpret.
   However, and since to date no transcriptional profile of isolated
   infected cells is available, the extent to which this could be
   different to that obtained from complete aerial organs of an infected
   plant is unclear.

   In order to gain insight into how different the transcriptional
   landscapes of TYLCV-infected cells or whole tissues of TYLCV-infected
   plants might be, here we compare the transcriptional changes in leaf
   patches infected with TYLCV by agroinfiltration or in systemic leaves
   of TYLCV-infected plants in Nicotiana benthamiana by RNA-seq.
   Strikingly, our results show that, as expected and in agreement with
   previous works, infection by TYLCV induces a dramatic transcriptional
   reprogramming; the detected changes, however, are not equivalent in
   local and systemic infections, with a much larger number of genes
   differentially expressed locally, and some genes responding in an
   opposite manner in local and systemic samples. Interestingly, a
   transcriptional repression of the auxin signaling pathway and a
   transcriptional activation of ethylene signaling and defence responses
   were detected both in local and systemic infections. Despite more
   limited changes detected in the systemically infected samples,
   comparison with the transcriptional changes induced by systemic
   infection by the geminivirus Tobacco curly shoot virus (TbSV) [[41]5]
   unveiled common subsets of up- and down-regulated genes similarly
   affected by both viral species. Among the common biological processes
   potentially affected by the transcriptional changes we find terpenoid
   biosynthesis as transcriptionally repressed; notably, another
   geminivirus species, Tomato yellow leaf curl China virus (TYLCCN), has
   been shown to suppress terpenoid biosynthesis [[42]10], making it
   tempting to speculate that depletion of terpenoids might be a
   requirement for geminiviruses to establish a successful infection in
   nature. Taken together, the results presented here not only shed light
   on the transcriptional changes derived from the infection by TYLCV in
   N. benthamiana, but also demonstrate that the resolution provided by
   local and systemic infection approaches largely differs, highlighting
   the urge to come up with a better system to gain an accurate view of
   the molecular and physiological changes caused by the viral invasion.

Results

Transcriptional changes upon local infection by TYLCV in N. benthamiana

   From the observation that only a fraction of cells support active
   replication by a geminivirus at a given time [[43]11, [44]12] logically
   follows the idea that an accurate study of the cellular changes
   triggered by the viral invasion will require the isolation of the
   infected cells specifically, and their comparison with similar cells
   from an uninfected sample. Analysis of whole organs of systemically
   infected plants, which is the common practice due to the lack of a more
   precise approach, would presumably result in potential dilution and
   masking issues. In order to test this idea, we decided to compare the
   transcriptional changes detectable by RNA sequencing (RNA-seq) upon
   local or systemic infection in the model plant N. benthamiana by the
   geminivirus TYLCV.

   The leaf patch infection results in the viral replication in most cells
   [[45]11], which the virus must effectively manipulate to generate a
   permissive environment, hence serving as a good surrogate system to
   study the viral infection. For the local infection, we performed
   agroinfection in leaf patches of four-week-old N. benthamiana leaves
   and took samples at 6 days post-infiltration (dpi) (Fig. [46]1a), when
   the virus is still actively replicating. In order to exclude the
   potential effect of the bacteria on plant transcription, N. benthamiana
   leaves inoculated with an Agrobacterium clone containing the empty
   vector were used as control; three independent biological replicates
   were used in each case. RNA-seq was performed by Illumina sequencing as
   indicated in the methods section. The raw HiSeq reads were filtered and
   trimmed, and between 34 and > 51 million clean pair-end reads were
   obtained per sample; these clean reads were mapped to the N.
   benthamiana draft genome (v1.0.1) from the Sol Genomics Network
   ([47]ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/assemblies
   /)) with a mapping rate between 89 and > 98% (Additional file [48]6 and
   Table [49]1). The PCA analysis of the three biological replicates for
   TYLCV and control samples is shown in Additional file [50]1: Figure S1.

Fig. 1.

   Fig. 1
   [51]Open in a new tab

   Transcriptional changes upon local infection by TYLCV in N.
   benthamiana. a Schematic representation of the experimental design. b
   Differentially expressed genes in the local infection are shown by
   volcano plot. The x-axis shows the log2 transformed gene expression
   fold change between infected and control samples. The y-axis indicates
   the negative log10 transformed adjusted p-values (FDR) of the
   differential expression test calculated by R package edgeR. The
   up-regulated and down-regulated genes are represented by red and blue
   dots, respectively. Pie chart shows the number of up−/down-regulated
   genes. c Validation of selected DEGs by qPCR. Values are the average of
   three biological replicates, relative to mock. NbACT was used as the
   normalizer. d Mapping of viral reads to the TYLCV genome. Three
   biological replicates (inner concentric circles) are represented; the
   upper side of each circle represents the virion (+) strand; the lower
   side of each circle represents the complementary (−) strand. ORFs are
   depicted in blue. Please note that the accumulated reads in the area
   containing the CP and V2 ORFs have been trimmed; the numbers of total
   reads are shown in Additional file [52]7: Table S2. e Validation of the
   expression of Rep and CP by qPCR. Expression values are relative to
   NbACT

Table 1.

   Summary of the RNA-seq results
   Infection Sample Replicate Raw reads Clean reads Mapped reads Mapping
   rate
   local EV 1 56,095,920 49,607,102 48,784,974 98.34%
   local EV 2 40,783,494 35,461,778 34,878,028 98.35%
   local EV 3 59,215,650 50,797,194 49,957,654 98.35%
   local TLCV 1 46,414,226 42,273,896 38,070,811 90.06%
   local TYLCV 2 40,583,678 34,799,420 31,263,478 89.84%
   local TYLCV 3 42,441,872 36,642,412 32,897,690 89.78%
   systemic EV 1 45,831,882 42,069,696 41,242,974 98.03%
   systemic EV 2 40,432,870 36,748,216 35,955,596 97.84%
   systemic EV 3 48,885,308 45,262,860 44,405,978 98.11%
   systemic TYLCV 1 47,349,296 43,508,220 42,484,166 97.65%
   systemic TYLCV 2 55,797,434 51,761,828 50,618,532 97.79%
   systemic TYLCV 3 49,035,824 45,797,334 44,821,767 97.87%
   [53]Open in a new tab

   A total of 7561 and 4289 down- and up-regulated genes, respectively,
   were identified in these locally infected samples (Fig. [54]1b;
   Additional file [55]6: Table S1). The RNA-seq results were validated by
   qPCR analysis of selected genes (Fig. [56]1c).

   The clean reads were also mapped to the TYLCV genome, with an average
   of 88,550 reads per million (RPM) (Fig. [57]1d; Additional file [58]7:
   Table S2). Unexpectedly, reads for both strands of the virus were
   detected throughout the viral genome with uneven and non-perfectly
   symmetrical distribution, and not restricted to the described open
   reading frames (ORFs); the number of reads was much higher in the
   region of the genome containing the V2 and CP (late) genes (Additional
   file [59]7: Table S2). The accumulation of viral reads was confirmed by
   qPCR to detect expression of the viral genes encoding the Rep
   (Replication-associated protein) and the CP (capsid protein), contained
   in the complementary and the virion strand of the viral genome,
   respectively (Fig. [60]1e).

Transcriptional changes upon systemic infection by TYLCV in N. benthamiana

   In order to analyze the transcriptional changes detectable during the
   systemic infection by TYLCV, we performed agroinfection of two-week-old
   N. benthamiana plants as described in [[61]12], and took samples at
   14 days post-infection (dpi) (Fig. [62]2a), when the virus is actively
   replicating in the apical leaves and the first symptoms have already
   appeared (Additional file [63]2: Figure S2). Apical leaves of N.
   benthamiana plants inoculated with an Agrobacterium clone containing
   the empty vector were used as control; three independent biological
   replicates were used in each case. RNA-seq was performed by Illumina
   sequencing as indicated in the methods section. The raw HiSeq reads
   were filtered and trimmed, and between 36 and > 52 million clean
   pair-end reads were obtained per sample; these clean reads were mapped
   to the N. benthamiana draft genome (v1.0.1) from the Sol Genomics
   Network
   ([64]ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/assemblies
   /) with a mapping rate between 97 and > 98% (Additional file [65]6:
   Table S1). The PCA analysis of the three biological replicates for
   TYLCV and control samples is shown in Additional file [66]1: Figure S1.

Fig. 2.

   Fig. 2
   [67]Open in a new tab

   Transcriptional changes upon local infection by TYLCV in N.
   benthamiana. a Schematic representation of the experimental design. b
   Differentially expressed genes in the systemic infection are shown by
   volcano plot. The x-axis shows the log2 transformed gene expression
   fold change between infected and control samples. The y-axis indicates
   the negative log10 transformed adjusted p-values (FDR) of the
   differential expression test calculated by R package edgeR. The
   up-regulated and down-regulated genes are represented by red and blue
   dots, respectively. Pie chart shows the number of up−/down-regulated
   genes. c Validation of selected DEGs by qPCR. Values are the average of
   three biological replicates, relative to mock. NbACT was used as the
   normalizer. d Mapping of viral reads to the TYLCV genome. Three
   biological replicates (inner concentric circles) are represented; the
   upper side of each circle represents the virion (+) strand; the lower
   side of each circle represents the complementary (−) strand. ORFs are
   depicted in blue. The number of total reads are shown in Additional
   file [68]7: Table S2. e Validation of the expression of Rep and CP by
   qPCR. Expression values are relative to NbACT

   A total of 247 and 1290 down- and up-regulated genes, respectively,
   were identified in these systemically infected samples (Fig. [69]2b;
   Additional file [70]8: Table S3). The RNA-seq results were validated by
   qPCR analysis of selected genes (Fig. [71]2c).

   The clean reads were also mapped to the TYLCV genome, with an average
   of 3344 reads per million (RPM) (Fig. [72]2d; Additional file [73]7:
   Table S2). Also in this case, reads for both strands of the virus were
   detected throughout the viral genome with uneven and non-perfectly
   symmetrical distribution, and not restricted to the described ORFs; as
   observed in the locally infected samples, the number of reads was
   notably higher in the region of the genome containing the V2 and CP
   (late) genes (Fig. [74]2d; Additional file [75]7: Table S2). The
   accumulation of viral reads was confirmed by qPCR to detect expression
   of the viral genes encoding the Rep and the CP (Fig. [76]2e).

Distinct landscapes of transcriptional changes detectable in local and
systemic infections by TYLCV in N. benthamiana

   In both the locally and the systemically infected samples used, the
   virus is actively replicating, and therefore those putative
   transcriptional changes underlying successful viral multiplication must
   have been established. The vast difference in the number of
   differentially expressed genes (DEGs) between these infected samples
   raises the possibility that the larger proportion of infected cells in
   the agroinfected leaf patches might provide an increase in resolution,
   which may result from a lower dilution of infection-induced
   transcriptional changes due to a higher infected-to-uninfected cell
   ratio and/or negligible masking from potential non-cell-autonomous
   plant responses to the viral infection.

   In order to gain further insight into the differences between locally
   and systemically infected samples, we set out to compare the subsets of
   induced or repressed genes in each case. Strikingly, as shown in
   Fig. [77]3a, both the up- and the down-regulated genes in systemic
   infections show only a partial overlap with those in local infections:
   only 56.7% of the repressed genes are also repressed in local
   infections, while, surprisingly, 6% are induced; among the induced
   genes in systemic infections, only 24% are also up-regulated in local
   infections, with a 23.8% down-regulated in these samples. Hierarchical
   clustering (Fig. [78]3b) shows that the systemically infected samples
   cluster closer to their control than to the locally infected samples.
   Taken together, these results clearly indicate that the differences
   detected between datasets go beyond a higher sensitivity in locally
   infected samples, which would explain the quantitative differences in
   DEGs, but not the apparent opposite behavior of some of them.

Fig. 3.

   Fig. 3
   [79]Open in a new tab

   Different transcriptional changes are detected upon local and systemic
   infections by TYLCV in N. benthamiana. a Venn diagram of DEGs in local
   and systemic infections. b The differential gene expressions of local
   and systemic infection samples are shown in heatmap with hierarchical
   clustering, which is drawn by R package pheatmap. The values in the
   gene expression matrix are centered and scaled in the row direction.
   The color bar indicates the Z-score. Loc: local infections; sys:
   systemic infections. EV: empty vector

   With the aim of determining whether the distinct transcriptional
   landscapes of local and systemic infections may nevertheless result in
   similar functional outputs, we performed functional enrichment analysis
   using Gene Ontology and KEGG pathways annotations. As shown in
   Figs. [80]4 and [81]5 and Table [82]2, the overlap in over-represented
   GO categories (Biological Process Ontology) or KEGG pathways for
   systemic and local infections is only marginal. Common over-represented
   functional categories in both subsets of DEGs include trehalose
   biosynthetic process and defence response among the induced genes, and
   mitotic nuclear division and cellulose biosynthetic process among the
   repressed genes (Table [83]2).

Fig. 4.

   Fig. 4
   [84]Open in a new tab

   Functional enrichment analysis of differentially expressed genes in
   local and systemic TYLCV infections. Heatmap shows the Gene Ontology
   terms of biological processes that are significantly enriched
   (p < 0.01) in the up−/down-regulated genes in the local infection,
   the systemic infection, and their overlaps. Each square is colored
   according to the value of –log[10](p), where p is the p-value for the
   significance of GO term enrichment. The color bar indicates the
   –log[10](p). loc: local infection; sys: systemic infection; olp:
   overlapping between local and systemic infections. up: up-regulated;
   dn: down-regulated

Fig. 5.

   Fig. 5
   [85]Open in a new tab

   KEGG pathways of differentially expressed genes in local and systemic
   TYLCV infections. Heatmap shows the KEGG pathways that are
   significantly enriched (p < 0.05) in the up−/down-regulated genes in
   the local infection, the systemic infection, and their overlaps. Each
   square is colored according to the value of –log[10](p), where p is the
   p-value for the significance of KEGG pathway enrichment. The color bar
   indicates the –log[10](p). up: up-regulated; dn: down-regulated

Table 2.

   Significantly enriched GO terms (Biological Process Ontology) in the
   subsets of differentially expressed genes (p-value < 0.05)
   GO.ID Term Annotated Significant Expected p-value
   Overrepresented in the subset of up-regulated in local infection
    GO:0006351 transcription, DNA-templated 2046 271 151.48 1.8E-22
    GO:0006310 DNA recombination 47 15 3.48 2.5E-06
    GO:0005992 trehalose biosynthetic process 34 12 2.52 3.1E-06
    GO:0048544 recognition of pollen 69 16 5.11 0.000034
    GO:0006302 double-strand break repair 16 7 1.18 0.000076
    GO:0006464 cellular protein modification process 2490 237 184.35
   0.00008
    GO:0006012 galactose metabolic process 23 8 1.7 0.00016
    GO:0006950 response to stress 873 108 64.63 0.00022
    GO:0016310 phosphorylation 2148 196 159.03 0.00023
    GO:0006376 mRNA splice site selection 6 4 0.44 0.0004
    GO:0032968 positive regulation of transcription elongation from RNA
   polymerase II promoter 3 3 0.22 0.00041
    GO:0006887 exocytosis 72 14 5.33 0.00071
    GO:0006635 fatty acid beta-oxidation 7 4 0.52 0.00087
    GO:0006281 DNA repair 271 38 20.06 0.00415
    GO:0043562 cellular response to nitrogen levels 2 2 0.15 0.00548
    GO:0009744 response to sucrose 2 2 0.15 0.00548
    GO:0045454 cell redox homeostasis 229 28 16.95 0.00608
    GO:0008272 sulfate transport 31 7 2.3 0.00653
    GO:0000160 phosphorelay signal transduction system 102 15 7.55 0.00799
    GO:0006334 nucleosome assembly 59 10 4.37 0.01074
    GO:0009298 GDP-mannose biosynthetic process 7 3 0.52 0.0113
    GO:0070966 nuclear-transcribed mRNA catabolic process, no-go decay 3 2
   0.22 0.01562
    GO:0070481 nuclear-transcribed mRNA catabolic process, non-stop decay
   3 2 0.22 0.01562
    GO:0071025 RNA surveillance 3 2 0.22 0.01562
    GO:0015706 nitrate transport 3 2 0.22 0.01562
    GO:0010167 response to nitrate 3 2 0.22 0.01562
    GO:0007064 mitotic sister chromatid cohesion 8 3 0.59 0.01709
    GO:0006032 chitin catabolic process 31 6 2.3 0.02419
    GO:0035434 copper ion transmembrane transport 9 3 0.67 0.02425
    GO:0050832 defense response to fungus 4 2 0.3 0.02972
    GO:0042742 defense response to bacterium 4 2 0.3 0.02972
    GO:0007050 cell cycle arrest 10 3 0.74 0.03277
    GO:0006529 asparagine biosynthetic process 11 3 0.81 0.04263
    GO:0006020 inositol metabolic process 11 3 0.81 0.04263
   Overrepresented in the subset of down-regulated in local infection
    GO:0009765 photosynthesis, light harvesting 74 61 12.92 1E-30
    GO:0007017 microtubule-based process 254 105 44.35 4.6E-19
    GO:0033014 tetrapyrrole biosynthetic process 53 32 9.25 3.3E-12
    GO:0015979 photosynthesis 387 169 67.57 4.9E-12
    GO:0000079 regulation of cyclin-dependent protein serine/threonine
   kinase activity 48 29 8.38 3.4E-11
    GO:0006270 DNA replication initiation 18 16 3.14 7.7E-11
    GO:0072330 monocarboxylic acid biosynthetic process 171 61 29.85
   8.1E-09
    GO:0051258 protein polymerization 86 34 15.01 8.8E-09
    GO:0006546 glycine catabolic process 11 9 1.92 5.8E-06
    GO:0005975 carbohydrate metabolic process 1345 320 234.82 0.000013
    GO:0006073 cellular glucan metabolic process 142 56 24.79 0.000026
    GO:0006260 DNA replication 119 55 20.78 0.000027
    GO:0000910 cytokinesis 24 12 4.19 0.000033
    GO:0006629 lipid metabolic process 928 234 162.02 0.00028
    GO:0030244 cellulose biosynthetic process 62 22 10.82 0.00051
    GO:0000096 sulfur amino acid metabolic process 50 18 8.73 0.00084
    GO:0006656 phosphatidylcholine biosynthetic process 4 4 0.7 0.00093
    GO:0018160 peptidyl-pyrromethane cofactor linkage 4 4 0.7 0.00093
    GO:1901070 guanosine-containing compound biosynthetic process 17 9
   2.97 0.00094
    GO:0006418 tRNA aminoacylation for protein translation 95 29 16.59
   0.00125
    GO:0009132 nucleoside diphosphate metabolic process 135 40 23.57
   0.00155
    GO:0006228 UTP biosynthetic process 15 8 2.62 0.00171
    GO:0006241 CTP biosynthetic process 15 8 2.62 0.00171
    GO:0006269 DNA replication, synthesis of RNA primer 5 4 0.87 0.00399
    GO:0045132 meiotic chromosome segregation 5 4 0.87 0.00399
    GO:0009084 glutamine family amino acid biosynthetic process 31 12 5.41
   0.00416
    GO:1901617 organic hydroxy compound biosynthetic process 23 14 4.02
   0.00531
    GO:0009234 menaquinone biosynthetic process 3 3 0.52 0.00532
    GO:0016117 carotenoid biosynthetic process 14 7 2.44 0.00536
    GO:0009065 glutamine family amino acid catabolic process 12 6 2.1
   0.00997
    GO:0046168 glycerol-3-phosphate catabolic process 6 4 1.05 0.01031
    GO:0015671 oxygen transport 6 4 1.05 0.01031
    GO:0006801 superoxide metabolic process 23 9 4.02 0.01161
    GO:0007076 mitotic chromosome condensation 13 6 2.27 0.01582
    GO:0006166 purine ribonucleoside salvage 4 3 0.7 0.01849
    GO:0006096 glycolytic process 114 29 19.9 0.02014
    GO:0046274 lignin catabolic process 33 11 5.76 0.02045
    GO:0042549 photosystem II stabilization 7 4 1.22 0.02075
    GO:0042026 protein refolding 14 6 2.44 0.02366
    GO:0019307 mannose biosynthetic process 2 2 0.35 0.03047
    GO:0007155 cell adhesion 2 2 0.35 0.03047
    GO:0045038 protein import into chloroplast thylakoid membrane 2 2 0.35
   0.03047
    GO:0048478 replication fork protection 2 2 0.35 0.03047
    GO:0007623 circadian rhythm 2 2 0.35 0.03047
    GO:0010027 thylakoid membrane organization 2 2 0.35 0.03047
    GO:0006450 regulation of translational fidelity 2 2 0.35 0.03047
    GO:0030259 lipid glycosylation 15 6 2.62 0.03373
    GO:0009082 branched-chain amino acid biosynthetic process 23 8 4.02
   0.03537
    GO:0006168 adenine salvage 5 3 0.87 0.04023
    GO:0010207 photosystem II assembly 5 3 0.87 0.04023
    GO:0006353 DNA-templated transcription, termination 5 3 0.87 0.04023
    GO:0006662 glycerol ether metabolic process 68 18 11.87 0.04094
   Overrepresented in the subset of up-regulated in systemic infection
    GO:0006032 chitin catabolic process 31 7 0.78 9.1E-06
    GO:0006355 regulation of transcription, DNA-templated 1764 69 44.11
   0.00012
    GO:0005975 carbohydrate metabolic process 1345 63 33.64 0.00022
    GO:0043562 cellular response to nitrogen levels 2 2 0.05 0.00062
    GO:0009744 response to sucrose 2 2 0.05 0.00062
    GO:0016998 cell wall macromolecule catabolic process 29 5 0.73 0.00069
    GO:0019310 inositol catabolic process 8 3 0.2 0.00079
    GO:0005992 trehalose biosynthetic process 34 5 0.85 0.00146
    GO:0015770 sucrose transport 3 2 0.08 0.00184
    GO:0019953 sexual reproduction 11 3 0.28 0.00221
    GO:0009607 response to biotic stimulus 43 6 1.08 0.00271
    GO:0006570 tyrosine metabolic process 5 2 0.13 0.00594
    GO:0006094 gluconeogenesis 8 2 0.2 0.01581
    GO:0006952 defense response 112 8 2.8 0.01862
    GO:0006541 glutamine metabolic process 23 3 0.58 0.01898
    GO:0000272 polysaccharide catabolic process 25 3 0.63 0.02376
    GO:0006662 glycerol ether metabolic process 68 5 1.7 0.02756
    GO:0003333 amino acid transmembrane transport 46 4 1.15 0.02758
    GO:0006529 asparagine biosynthetic process 11 2 0.28 0.02956
    GO:0008283 cell proliferation 11 2 0.28 0.02956
    GO:0044264 cellular polysaccharide metabolic process 148 7 3.7 0.03961
    GO:0008272 sulfate transport 31 3 0.78 0.04163
    GO:0006857 oligopeptide transport 33 3 0.83 0.04874
   Overrepresented in the subset of down-regulated in systemic infection
    GO:0007018 microtubule-based movement 129 11 0.7 1.2E-10
    GO:0000079 regulation of cyclin-dependent protein serine/threonine
   kinase activity 48 7 0.26 7.2E-09
    GO:0007067 mitotic nuclear division 51 5 0.28 0.0001
    GO:0016114 terpenoid biosynthetic process 30 2 0.16 0.0116
    GO:0006265 DNA topological change 30 2 0.16 0.0116
    GO:0007094 mitotic spindle assembly checkpoint 3 1 0.02 0.0163
    GO:0006952 defense response 112 3 0.61 0.0235
    GO:0009813 flavonoid biosynthetic process 6 1 0.03 0.0323
    GO:0030244 cellulose biosynthetic process 62 2 0.34 0.0452
   Overrepresented in the subset of up-regulated genes common to local and
   systemic infections
    GO:0043562 cellular response to nitrogen levels 2 2 0.01 0.000041
    GO:0009744 response to sucrose 2 2 0.01 0.000041
    GO:0005992 trehalose biosynthetic process 34 4 0.22 0.000066
    GO:0006094 gluconeogenesis 8 2 0.05 0.0011
    GO:0019310 inositol catabolic process 8 2 0.05 0.0011
    GO:0006529 asparagine biosynthetic process 11 2 0.07 0.0022
    GO:0006355 regulation of transcription, DNA-templated 1764 21 11.37
   0.0043
    GO:0007205 protein kinase C-activating G-protein coupled receptor
   signaling pathway 24 2 0.15 0.0104
    GO:0006659 phosphatidylserine biosynthetic process 3 1 0.02 0.0192
    GO:0042742 defense response to bacterium 4 1 0.03 0.0255
    GO:0006562 proline catabolic process 4 1 0.03 0.0255
    GO:0050832 defense response to fungus 4 1 0.03 0.0255
    GO:0006570 tyrosine metabolic process 5 1 0.03 0.0318
    GO:0003333 amino acid transmembrane transport 46 2 0.3 0.0355
    GO:0009064 glutamine family amino acid metabolic process 57 3 0.37
   0.0453
   Overrepresented in the subset of down-regulated genes common to local
   and systemic infections
    GO:0007018 microtubule-based movement 129 10 0.43 1.4E-11
    GO:0000079 regulation of cyclin-dependent protein serine/threonine
   kinase activity 48 7 0.16 2.1E-10
    GO:0007067 mitotic nuclear division 51 5 0.17 0.000014
    GO:0006265 DNA topological change 30 2 0.1 0.0045
    GO:0007094 mitotic spindle assembly checkpoint 3 1 0.01 0.0099
    GO:0030244 cellulose biosynthetic process 62 2 0.21 0.0181
    GO:0006606 protein import into nucleus 12 1 0.04 0.0392
   [86]Open in a new tab

   Three KEGG pathways from Fig. [87]5 were selected to be graphically
   displayed, allowing for visualization and easy comparison of the
   transcriptional regulation of their components: MAPK signaling pathway
   (Additional file [88]3: Figure S3), Plant hormone signal transduction
   (Additional file [89]4: Figure S4), and Plant-pathogen interaction
   (Additional file [90]5: Figure S5). As observed in Additional file
   [91]3: Fig. [92]S3 and Additional file [93]4: Figure S4, although not
   statistically significant in all cases, both local and systemic
   infections have an impact on these pathways, with the local infections
   having the strongest effect. As previously mentioned, the presence of
   the virus seems to activate plant defence responses (Additional file
   [94]3: Figures S3; Additional file [95]5: Figure S5). Notably, both
   types of infection trigger a detectable transcriptional repression of
   auxin signaling and a transcriptional activation of ethylene signaling,
   while only local infections resulted in a repression of the
   brassinosteroid signaling pathway (Additional file [96]4: Figure S4).

TYLCV and TbSCV modify the expression of a set of common genes upon systemic
infection in N. benthamiana

   In an attempt to identify potential central targets of the
   transcriptional geminiviral manipulation and/or effectors of the plant
   anti-geminiviral response, we decided to compare the transcriptional
   changes triggered by systemic infections by TYLCV and the geminivirus
   TbSCV, in combination with or without its associated satellite, in N.
   benthamiana [[97]5]. Remarkably, a proportion of DEG were commonly
   affected by both TYLCV and TbSCV (12.6% of up-regulated genes by TYLCV
   infection, and 9.7% of down-regulated genes by TYLCV infection)
   (Fig. [98]6a); the proportion of induced, but not repressed, genes
   largely increased (to 28.7% of up-regulated genes by TYLCV infection)
   when TbSCV was inoculated in combination with its satellite (Fig.
   [99]6b), suggesting that some of the virulence functions provided by
   this ancillary molecule result in the activation of host genes and are
   already encoded in the TYLCV genome. Functional enrichment analysis of
   the genes commonly activated or repressed by TYLCV and TbSCV revealed
   the existence of a number of GO categories over-represented in these
   subsets (Table [100]3), including trehalose biosynthetic process and
   defence responses (in the common up-regulated gene set), and terpenoid
   biosynthetic process (in the common down-regulated gene set).
   Interestingly, a third geminiviral species, TYLCCN, has been proven to
   suppress terpenoid biosynthesis and release, and this effect in turn
   improves performance of its insect vector, the whitefly Bemisia tabaci
   [[101]10]. Considering this negative regulation by three different
   geminivirus species, it is tempting to speculate that depletion of
   terpenoids is a requirement for geminiviruses to establish a successful
   infection in nature, perhaps at least partly through an indirect effect
   on favouring the insect vector-virus mutualism.

Fig. 6.

   Fig. 6
   [102]Open in a new tab

   Systemic infection by TYLCV and TbSCV in N. benthamiana trigger
   partially overlapping transcriptional changes. The transcriptional
   changes triggered by the systemic infection by TYLCV were compared to
   those triggered by the geminivirus TbSCV, without (a) or with (b) its
   associated satellite, in N. benthamiana; the TbSCV data are from Li et
   al., 2018

Table 3.

   GO enrichment of DEGs common to systemic infections by TYLCV and TbSCV
   in N. benthamiana. Over-represented GO categories (Biological Process
   Ontology) in the different subsets of DEGs represented in Fig. [103]6
   GO.ID Term Annotated Significant Expected p value
   Y35A_up+up
    GO:0006108 malate metabolic process 36 3 0.13 0.00027
    GO:0046470 phosphatidylcholine metabolic process 19 2 0.07 0.00197
    GO:0000272 polysaccharide catabolic process 25 2 0.09 0.00341
    GO:0016998 cell wall macromolecule catabolic process 29 2 0.1 0.00457
    GO:0006032 chitin catabolic process 31 2 0.11 0.00521
    GO:0045454 cell redox homeostasis 229 4 0.8 0.00835
    GO:0050832 defense response to fungus 4 1 0.01 0.01386
    GO:0042742 defense response to bacterium 4 1 0.01 0.01386
    GO:0046373 L-arabinose metabolic process 5 1 0.02 0.0173
    GO:1902358 sulfate transmembrane transport 7 1 0.02 0.02413
    GO:0019953 sexual reproduction 11 1 0.04 0.03767
   Y35A_down+down
    GO:0016114 terpenoid biosynthetic process 30 2 0.02 0.00028
    GO:0009813 flavonoid biosynthetic process 6 1 0 0.00498
    GO:0006012 galactose metabolic process 23 1 0.02 0.01897
   Y35AB_up+up
    GO:0006355 regulation of transcription, DNA-templated 1764 27 11.74
   0.000032
    GO:0006529 asparagine biosynthetic process 11 2 0.07 0.0023
    GO:0006952 defense response 112 4 0.75 0.0067
    GO:0046470 phosphatidylcholine metabolic process 19 2 0.13 0.007
    GO:0006571 tyrosine biosynthetic process 3 1 0.02 0.0198
    GO:0005992 trehalose biosynthetic process 34 2 0.23 0.0215
    GO:0005975 carbohydrate metabolic process 1345 19 8.95 0.0215
    GO:0006979 response to oxidative stress 257 5 1.71 0.0288
    GO:0006421 asparaginyl-tRNA aminoacylation 5 1 0.03 0.0328
    GO:0046373 L-arabinose metabolic process 5 1 0.03 0.0328
    GO:0009607 response to biotic stimulus 43 2 0.29 0.0332
   Y35AB_down+down
    GO:0016114 terpenoid biosynthetic process 30 2 0.03 0.00032
    GO:0009813 flavonoid biosynthetic process 6 1 0.01 0.00529
   [104]Open in a new tab

Discussion

   In this work, we describe and compare the genome-wide transcriptional
   changes detectable by RNA-seq occurring in N. benthamiana upon local or
   systemic infection by the geminivirus TYLCV. Our results show that, as
   expected and in agreement with previous works, infection by TYLCV
   causes a strong transcriptional reprogramming in the host; however, the
   detectable changes are more dramatic in our local infection system. It
   is possible that the local infection offers higher resolution owing to
   lower dilution of the infected cells; however, the opposite behavior of
   some DEGs in local and systemic samples suggests that the absence of
   transcriptional changes coming from non-infected cells and masking
   local changes also plays a role in the differences detected.
   Additionally, we cannot rule out that some differences between the two
   infected samples could be due to differences in the stage of the
   infection: although in both cases the virus is actively replicating in
   infected cells, in the leaf patch assay the infection is supposed to be
   more or less synchronized, while in the systemic infection a mixture of
   cells at different phases of the infection is most likely present in
   the samples. Another potentially relevant difference between local and
   systemic samples is the presence of Agrobacterium in the former;
   nevertheless, Agrobacterium is also present in the respective control.
   It is relevant to note that, if local and systemic samples show
   different results due to dilution and masking issues, these problems
   would potentially not only affect the study of transcriptional changes,
   but also the analysis of other molecular or physiological alterations
   resulting from the viral infection.

   Functional enrichment analyses can help us gain insight into the
   biological processes underlying infection by TYLCV (Tables [105]2 and
   [106]3). In locally infected samples, over-represented GO categories
   included DNA recombination among the up-regulated genes, and
   cytokinesis among the down-regulated genes. These processes are
   expected to be connected to the viral manipulation of DNA replication
   and cell cycle, and might not be detectable in the systemically
   infected samples as a result of dilution. Photosynthesis also appears
   as transcriptionally down-regulated; photosynthetic shut-down seems to
   be a common outcome in viral infections ([[107]8, [108]13–[109]16],
   among others). A transcriptional negative regulation of BR signaling
   can also be detected in locally infected samples; these results are in
   agreement with a recent work by Seo et al. (2018), in which suppression
   of BR signaling was shown to underpin symptom development triggered by
   TYLCV in tomato.

   Among those categories over-represented in both local and systemic
   infections, we can find cellulose biosynthetic process as
   transcriptionally repressed, suggesting that the viral infection might
   be affecting cell wall composition; changes in cell wall dynamics have
   been recently shown as triggered by Potato virus Y [[110]17] and Rice
   tungro spherical virus [[111]18], and in the latter case they have been
   proposed to correlate with virus-induced stunting. Intriguingly, both
   viruses negatively impact the cellulose biosynthetic machinery; whether
   impaired cellulose biosynthesis is a general plant response to the
   viral invasion is an idea that will require further investigation.
   Auxin signaling is also transcriptionally repressed upon the viral
   infection; these changes may also mediate or modulate the impact of the
   viral infection on plant development.

   The identification of common subsets of up- and down-regulated genes in
   the systemic infections by TYLCV and TbSV [[112]5] indicates that
   geminiviral manipulation of the cell and/or plant defence responses to
   geminiviral infection follow common transcriptional routes in different
   geminivirus/N. benthamiana interactions. Perhaps of particular interest
   is the finding that defence responses are activated in response to the
   viral infection; this observation reveals that these geminiviruses are
   being efficiently perceived as non-self by the plant, which in turn
   triggers a defence response. Although the activated plant defence
   reponses are not sufficient to fend off the virus, since the infection
   is established successfully, the fact that the plant is capable of
   detecting these pathogens in the first place paves the way for future
   engineering of the host, potentially boosting defences downstream of
   perception of the virus and hence tilting the balance in favour of the
   plant. Another pathway that emerges as a potential valuable target for
   engineering anti-geminiviral resistance is terpenoid biosynthesis,
   which is transcriptionally repressed by TYLCV and TbSV and has been
   proven to be suppressed by TYLCCN [[113]10], raising the idea that its
   down-regulation might underpin a successful geminiviral infection.

   Our results support the idea that the leaf patch assay entails great
   potential for the study of geminiviruses. Not only does this system
   result in a relatively synchronic infection and provides high
   resolution to detect virus-induced changes, but it also allows for the
   study of mutant viruses. All TYLCV null mutants for single genes, with
   the exception of those mutated in Rep, are capable of replicating their
   genome, but unable to infect the plant systemically; the use of local
   infections makes it possible to analyze the differences between the
   cellular changes triggered by wild-type and mutant viruses, therefore
   providing insight into the function of the viral genes in the context
   of the infection. An inherent limitation of this surrogate system,
   however, is the inevitable loss of cell type specificity: while TYLCV
   naturally infects phloem companion cells exclusively, in a leaf patch
   assay the virus is forced to replicate in mesophyll cells. Another
   obvious shortcoming is the impossibility of studying those mechanisms
   involved in cell-to-cell or long-distance transport, or the
   interactions between virus and vector.

   All things considered, and while the analysis of systemic and local
   infections has provided and will continue to provide useful insight
   into the molecular events underlying the infection by geminiviruses,
   both approaches are imperfect for a number of reasons, as mentioned
   above. In order to considerably deepen our view, offering a substantial
   leap in our understanding of the molecular and physiological changes
   occurring during the plant-geminivirus interaction, isolating those
   infected cells, ideally based on the stage of the infection, will be
   crucial. Several approaches would enable the isolation of geminivirus
   infected cells: the use of transgenic plants harbouring a
   replicon-based system to label those cells sustaining active viral
   replication (like those described in [[114]11]) could be combined with
   Fluorescence Activated Cell Sorting (FACS) or Laser Capture
   Microdissection (LCM), leading to the separation of infected from
   non-infected cells; high-throughput single-cell sequencing would also
   allow the unbiased identification and analysis of those cells
   containing the virus in the context of the infected plant. The increase
   in precision and resolution provided by the isolation of infected cells
   and their comparison to uninfected cells in the same plant will
   foreseeably result in an unprecedented view of the molecular
   landscaping triggered by the viral invasion.

Conclusions

   Our results show that TYLCV induces a dramatic transcriptional
   reprogramming in N. benthamiana, the detection of which largely differs
   in local and systemic infections. Nevertheless, some responses,
   including a transcriptional repression of the auxin signaling pathway
   and a transcriptional activation of defence, can be commonly detected.

   Comparison with the transcriptional changes induced by systemic
   infection by the geminivirus TbSV shows common subsets of up- and
   down-regulated genes similarly affected by both viral species, among
   which the suppression of terpenoid biosynthesis might be a general
   change triggered by geminiviruses. Taken together, our results not only
   provide insight into the transcriptional changes resulting from the
   infection by TYLCV in N. benthamiana, but also highlight the need to
   come up with an optimized system to gain a precise overview of the
   molecular and physiological changes caused in the host by the viral
   invasion.

Methods

Plant material and growth conditions

   Wild-type N. benthamiana plants were grown in a controlled growth
   chamber in long day conditions (16 h light/8 h dark) at 25 °C.

Viral infections

   The TYLCV infectious clone is described in [[115]19, [116]20]; it
   contains a partial dimer of the TYLCV genome ([117]AJ489258; [[118]21])
   in the pGWB501 vector [[119]22]. Agrobacterium tumefaciens GV3101
   strain was used for the delivery of TYLCV infectious clone and empty
   pGWB501 vector. Agrobacterium cells carrying these constructs were
   liquid-cultured in LB with appropriate antibiotics at 28 °C overnight.
   Bacterial cultures were centrifuged at 4000 g for 10 min and
   resuspended in the infiltration buffer (10 mM MgCl[2], 10 mM MES
   pH 5.6, 150 μM acetosyringone) to an OD[600] = 0.5. Bacterial
   suspensions were incubated in the buffer at room temperature and in the
   dark for 4 h before using them to infiltrate 4-week-old N. benthamiana
   for leave patch assays (local infections) and three-week-old N.
   benthamiana for systemic infection as described in [[120]12].

RNA extraction

   Total RNA was extracted from 8 mm leaf discs using the RNeasy plant
   mini kit (Qiagen) following the manufacturer’s instructions.

RNA sequencing

   Transcriptome analyses were performed at the Genomic Core Facility,
   Shanghai Center for Plant Stress Biology, CAS. Three biological
   replicates were used. Total RNA (1 μg) from each sample was used for
   library preparation with NEBNext Ultra Directional RNA Library Prep Kit
   for Illumina (New England BioLabs, E7420L) following the manufacturer’s
   instructions. Prepared libraries were assessed for quality using NGS
   High-Sensitivity kit on a Fragment Analyzer (AATI) and for quantity
   using Qubit 2.0 fluorometer (Thermo Fisher Scientific). All libraries
   were sequenced in paired-end 125 bases protocol (PE125) on an Illumina
   HiSeq sequencer.

Quantitative RT-PCR

   First-strand cDNA synthesis was performed with the iScriptTM cDNA
   Synthesis Kit (Bio-Rad #1708890) according to the manufacturer’s
   instructions. For qPCR reactions, the reaction mixture consisted of
   cDNA first-strand template, primers (500 nM each) and iTaqTM Universal
   SYBRR Green Supermix (Bio-Rad, #1725120). qPCR was performed in a
   BioRad CFX96 real-time system. Expression result was determined using
   the comparative Ct method (2^-ΔΔCt). Primers used are described in
   Additional file [121]9: Table S4. NbACT was used as the reference gene,
   using primers described in [[122]23].

Preprocessing of RNA-Seq data

   We cleaned the paired-end reads by Trimimomatic [[123]24] (version
   0.36). After trimming the adapter sequence, removing low quality bases
   and filtering short reads, clear read pairs were retained for further
   analysis.

Mapping and quantification of TYLCV reads

   Cleaned reads were mapped to TYLCV DNA (GenBank: [124]AJ489258.1) and
   its six ORFs by HISAT [[125]25] (version 2.1.0) with default
   parameters. The RPM (Reads per Million) was used to quantify the
   expression level of each ORF and the whole viral genome. The read
   coverage of each base on the reference DNA was calculated by samtools
   [[126]26] (version 1.5) with maximum coverage depth 8000 (−d 8000) and
   normalized to RPM. The expression level and read coverage were
   calculated for forward and reverse strand, respectively. The circular
   viral genome and read coverage of RNA-Seq data were visualized by
   CGView [[127]27].

Reads mapping and quantification of N. benthamiana genes

   The N. benthamiana draft genome sequence [[128]28] (v1.0.1) was
   downloaded from the Sol Genomics Network
   ([129]ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/assemblie
   s/). Cleaned reads were mapped to the genome sequence by HISAT with
   default parameters. Number of reads that were mapped to each N.
   benthamiana gene was calculated with the htseq-count script in HTSeq
   [[130]28].

Differential gene expression analysis

   EdgeR [[131]29] was used to identify genes that were differentially
   expressed between control and experiment samples. Genes with at least
   two-fold change in expression and had a FDR < 0.05 were considered
   differentially expressed genes (DEGs).

GO enrichment analysis

   The Gene Ontology (GO) terms assigned to N. benthamiana genes were
   extracted from annotation file downloaded from the SGN
   ([132]ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/annotatio
   n/Niben101/). GO enrichment analysis of DEGs was implemented by topGO
   [[133]30] with custom gene-to-GOs mapping annotations.

KEGG pathway enrichment analysis

   Since N. benthamiana is not supported by KEGG, we searched its ortholog
   genes in Arabidopsis by BLAST+ (version 2.5.0, evalue = 1e-5). The
   ortholog genes of the DEGs were used to perform KEGG enrichment
   analysis by clusterProfiler [[134]31]. The enriched KEGG pathways were
   visualized with DEGs by Pathview [[135]32].

Additional files

   [136]Additional file 1:^ (529KB, jpg)

   Figure S1. PCA analysis of the RNA-seq datasets (local and systemic
   TYLCV infections) (JPG 529 kb)
   [137]Additional file 2:^ (299.5KB, jpg)

   Figure S2. Pictures of plants systemically infected by TYLCV at the
   time of sample collection (JPG 299 kb)
   [138]Additional file 3:^ (919.4KB, jpg)

   Figure S3. Differentially expressed genes of local (A) and systemic (B)
   TYLCV infections in the MAPK signaling pathway. The log[2] transformed
   expression fold changes of DEGs are converted to pseudo colors using
   default limit (e.g. from − 1 to 1), and mapped to KEGG pathway by R
   package pathview. The up- and down- regulated genes are labeled in red
   and green, respectively. (JPG 919 kb)
   [139]Additional file 4:^ (759.8KB, jpg)

   Figure S4. Differentially expressed genes of local (A) and systemic (B)
   TYLCV infections in the plant hormone signal transduction pathway. The
   log[2] transformed expression fold changes of DEGs are converted to
   pseudo colors using default limit (e.g. from − 1 to 1), and mapped to
   KEGG pathway by R package pathview. The up- and down- regulated genes
   are labeled in red and green, respectively. (JPG 759 kb)
   [140]Additional file 5:^ (640.9KB, jpg)

   Figure S5. Differentially expressed genes of local (A) and systemic (B)
   TYLCV infections in the plant-pathogen interaction pathway. The log[2]
   transformed expression fold changes of DEGs are converted to pseudo
   colors using default limit (e.g. from − 1 to 1), and mapped to KEGG
   pathway by R package pathview. The up- and down- regulated genes are
   labeled in red and green, respectively. (JPG 640 kb)
   [141]Additional file 6:^ (1MB, xlsx)

   Table S1. DEGs in local TYLCV infections (XLSX 1067 kb)
   [142]Additional file 7:^ (10.5KB, xlsx)

   Table S2. Reads mapping to the viral genome in local and systemic
   infections (XLSX 10 kb)
   [143]Additional file 8:^ (190.4KB, xlsx)

   Table S3. DEGs in systemic TYLCV infections (XLSX 146 kb)
   [144]Additional file 9:^ (27.9KB, docx)

   Table S4. Primers used in this work (DOCX 14 kb)

Acknowledgements