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 [ 1 – 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 [ 4 , 5 , 8 , 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 [ 2 , 9 ]. In an attempt to unveil the molecular basis of tolerance or recovery, Chen et al. (2013) and Gongora-Castillo et al. (2012) [ 3 , 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) [ 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 [ 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 [ 11 , 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 [ 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. 1 a), 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 &gt; 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 ( ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/assemblies/ )) with a mapping rate between 89 and &gt; 98% (Additional file 6 and Table 1 ). The PCA analysis of the three biological replicates for TYLCV and control samples is shown in Additional file 1 : Figure S1. 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% Fig. 1 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 7 : Table S2. e Validation of the expression of Rep and CP by qPCR. Expression values are relative to NbACT A total of 7561 and 4289 down- and up-regulated genes, respectively, were identified in these locally infected samples (Fig. 1 b; Additional file 6 : Table S1). The RNA-seq results were validated by qPCR analysis of selected genes (Fig. 1 c). The clean reads were also mapped to the TYLCV genome, with an average of 88,550 reads per million (RPM) (Fig. 1 d; Additional file 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 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. 1 e). 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 [ 12 ], and took samples at 14 days post-infection (dpi) (Fig. 2 a), when the virus is actively replicating in the apical leaves and the first symptoms have already appeared (Additional file 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 &gt; 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 ( ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/assemblies/ ) with a mapping rate between 97 and &gt; 98% (Additional file 6 : Table S1). The PCA analysis of the three biological replicates for TYLCV and control samples is shown in Additional file 1 : Figure S1. Fig. 2 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 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. 2 b; Additional file 8 : Table S3). The RNA-seq results were validated by qPCR analysis of selected genes (Fig. 2 c). The clean reads were also mapped to the TYLCV genome, with an average of 3344 reads per million (RPM) (Fig. 2 d; Additional file 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. 2 d; Additional file 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. 2 e). 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. 3 a, 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. 3 b) 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 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. 4 and 5 and Table 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 2 ). Fig. 4 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 &lt; 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 KEGG pathways of differentially expressed genes in local and systemic TYLCV infections. Heatmap shows the KEGG pathways that are significantly enriched (p &lt; 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 &lt; 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 Three KEGG pathways from Fig. 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 3 : Figure S3), Plant hormone signal transduction (Additional file 4 : Figure S4), and Plant-pathogen interaction (Additional file 5 : Figure S5). As observed in Additional file 3 : Fig. S3 and Additional file 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 3 : Figures S3; Additional file 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 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 [ 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. 6 a); 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. 6 b), 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 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 [ 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 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. 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 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 2 and 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 ([ 8 , 13 – 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 [ 17 ] and Rice tungro spherical virus [ 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 [ 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 [ 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 [ 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 [ 19 , 20 ]; it contains a partial dimer of the TYLCV genome (AJ489258; [ 21 ]) in the pGWB501 vector [ 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 [ 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 9 : Table S4. NbACT was used as the reference gene, using primers described in [ 23 ]. Preprocessing of RNA-Seq data We cleaned the paired-end reads by Trimimomatic [ 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: AJ489258.1) and its six ORFs by HISAT [ 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 [ 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 [ 27 ]. Reads mapping and quantification of N. benthamiana genes The N. benthamiana draft genome sequence [ 28 ] (v1.0.1) was downloaded from the Sol Genomics Network ( ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/assemblies/ ). 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 [ 28 ]. Differential gene expression analysis EdgeR [ 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 &lt; 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 ( ftp://ftp.solgenomics.net/genomes/Nicotiana_benthamiana/annotation/Niben101/ ). GO enrichment analysis of DEGs was implemented by topGO [ 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 [ 31 ]. The enriched KEGG pathways were visualized with DEGs by Pathview [ 32 ].
Original work titled: 'Transcriptional reprogramming caused by the geminivirus Tomato yellow leaf curl virus in local or systemic infections in Nicotiana benthamiana' (BMC Genomics/BioMed Central) by the following authors: Mengshi Wu; Xue Ding; Xing Fu; Rosa Lozano-Duran DOI: 10.1186/s12864-019-5842-7