Small regulatory RNAs (sRNAs) produced in one organism can sometimes function to silence mRNAs in another organism. These trans-species sRNAs seem especially prominent in plant/pathogen and plant/parasite interactions. Fungal plant pathogens produce sRNAs with complementarity to host mRNAs (Weiberg et al., 2013) and host plants produce trans-species sRNAs that silence mRNAs in both pathogenic fungi (Zhang et al., 2016; Cai et al., 2018) and oomycetes (Hou et al., 2019). The parasitic plant Cuscuta campestris produces trans-species microRNAs (miRNAs) which silence mRNAs in multiple host plants (Shahid et al., 2018). Silencing by plant trans-species sRNAs relies on extensive complementarity between the sRNA and target mRNA, similar to normal endogenous plant miRNAs (Liu et al., 2014).

Trans-species silencing is expected to benefit the source organism while being detrimental to the target organism in parasitic/pathogenic relationships. This implies that target sites are not under purifying selection to maintain complementarity to trans-species sRNAs. How could such a system be stable over evolutionary time and/or be useful against multiple species? One suggestion is a ‘shotgun’ strategy, in which a very diverse set of trans-species sRNAs is deployed to hit target mRNAs randomly. The plant response to Phytophthora may make use of this strategy (Hou et al., 2019). However, the fact that the trans-species sRNAs delivered to hosts from the parasitic plant C. campestris are miRNAs (Shahid et al., 2018) argues against the shotgun hypothesis in this case. MiRNAs are defined by the precise excision of a single mature, functional small RNA (Axtell and Meyers, 2018), which implies selection for the miRNA to target a particular sequence or closely related set of sequences. We examined Cuscuta trans-species sRNAs and their targets in detail to shed light on how this system may be evolutionarily stable and robust against diverse hosts.

We analyzed sRNA expression from four Cuscuta species (Figure 1A). Specimens from two or three distinct populations of C. pentagona and C. gronovii, respectively, were included, making a total of seven separate sRNA expression studies (identified with acronyms for brevity; Figure 1A–B, Supplementary files 1–2). All four Cuscuta species are generalists with documented hosts spanning multiple plant families (Figure 1—figure supplement 1). RNA samples (three biological replicates each) from host-parasite interfaces and parasite stems growing on the host Arabidopsis thaliana were obtained and used for sRNA sequencing (Figure 1B). Libraries were condensed to highly expressed sRNA variants and filtered to remove any sRNAs that came from the host (Figure 1—figure supplement 2). Differential expression analysis revealed several hundred Cuscuta sRNAs in each experiment that were significantly up-regulated in the interface tissue relative to parasite stems (FDR < 0.1) (Supplementary file 3); we dubbed these haustorially-induced (HI) sRNAs (Figure 1A; Supplementary file 4). HI-sRNAs are mostly 21 or 22 nucleotides long (Figure 1A), sizes consistent with either miRNAs or short interfering RNAs (siRNAs). Distinguishing miRNAs from siRNAs requires a genome assembly (Axtell and Meyers, 2018), a criterion met so far for only one of the four species (C. campestris) included in this study (Shahid et al., 2018; Vogel et al., 2018). Approximately half of the C. campestris HI-sRNAs (208/408) come from MIRNA hairpins (Supplementary file 5). C. campestris-derived HI-sRNAs were recovered from 40 of the 42 novel MIRNA loci described by Shahid et al. (2018), including representatives from all five miRNA families previously demonstrated to target host mRNAs.

Figure 1 with 2 supplements see all

Download asset Open asset

We next examined conservation of HI-sRNA accumulation between isolates and species. Some canonical plant miRNAs are highly conserved, with several ubiquitous families found in multiple plant orders, or even broadly in all land plants (Cuperus et al., 2011; Chávez Montes et al., 2014). Surprisingly, when using a strict cutoff (maximum edit distance of 1) we found that the majority of HI-sRNAs were not observed in more than one species (Figure 1C). In many cases HI-sRNAs were unique to single isolates of a single species (Figure 1C). This result implies that HI-sRNAs could be rapidly differentiating in expression, sequence, or both within these Cuscuta species.

Our previous work showed that C. campestris HI-sRNAs can target host mRNAs in several hosts (Shahid et al., 2018). We thus looked for evidence of interactions between our broader sets of HI-sRNAs with host (A. thaliana) mRNAs using two complementary methods: secondary siRNA accumulation (Shahid et al., 2018) and degradome analysis (Addo-Quaye et al., 2008). Secondary siRNAs can accumulate from mRNAs as a result of an initial miRNA- or siRNA-directed targeting event, especially when the initiating sRNA is 22 nucleotides long (Cuperus et al., 2010). A large portion of HI-sRNAs are 22-nt or are clustered from some sRNAs which are 22-nt in length (Figure 1—figure supplement 2), allowing us to detect their targeting with this approach. Degradome analysis made use of the NanoPARE method (Schon et al., 2018), which recovers 5’ ends of both capped and uncapped mRNAs. NanoPARE libraries were made from just one isolate from each of the four Cuscuta species, and comprised three biological replicates from the host portion of the interface (Supplementary file 2). A. thaliana xrn4 mutants were used as hosts for these experiments because they over-accumulate 5’ remnants of sRNA-mediated mRNA cleavage (Rymarquis et al., 2011; Schon et al., 2018). The CRCK2 mRNA is an example with both degradome and secondary siRNA evidence of targeting by a C. pentagona HI-sRNA (Figure 2A–E). Altogether these two analyses yielded a set of 61 target sites over 54 A. thaliana mRNAs targeted by Cuscuta HI-sRNAs confirmed by a single method and seven more confirmed by both (Figure 2F, Figure 2—figure supplement 1A, Supplementary file 6). Based on RNA-seq analysis, accumulation of confirmed target mRNAs is generally down-regulated in parasitized host stems (Figure 3). This greatly expands on the set of six mRNAs previously identified as host targets of C. campestris miRNAs (Shahid et al., 2018), and demonstrates that trans-species sRNAs are used by multiple Cuscuta species. Target predictions show that C. campestris homologs of targeted A. thaliana mRNAs invariably have lower complementarity to HI-sRNAs (Figure 4). Repeating the analysis pipeline to examine possible self-targeting of C. campestris mRNAs by HI-sRNAs found only four confirmed targeting interactions, an indication that HI-sRNAs may largely function in trans in the host (Figure 4—figure supplement 1).

Figure 2 with 2 supplements see all

Download asset Open asset

Figure 3

Download asset Open asset

Figure 4 with 1 supplement see all

Download asset Open asset

Some mRNAs were confirmed to be targeted by HI-sRNAs from multiple species, with the most frequent interaction being with SEOR1 (Figure 2F, Figure 2—figure supplement 1A). SEOR1 encodes a phloem protein that acts to reduce sap loss after wounding (Knoblauch et al., 2014). C. campestris growth is enhanced when A. thaliana seor1 mutants are used as hosts (Shahid et al., 2018). However, the majority of mRNAs confirmed as targets are unique to a single Cuscuta species or isolate (Figure 2F, Figure 2—figure supplement 1A). A possible explanation could be that Cuscuta trans-species sRNAs function to regulate similar host processes, while not necessarily the same target mRNAs. Additionally, our current analysis is likely to have missed many targets, both due to lack of sensitivity of our methods (secondary siRNA accumulation and/or degradome analysis both can miss true targets), and because A. thaliana is unlikely to be a major host of Cuscuta in nature.

Numerous target mRNAs are known to be involved in the same processes, both on a gene ontology level (Figure 2—figure supplement 2) and when manually examining known pathways. Genes involved in auxin signaling repeatedly appear, including the previously identified targets TIR1, AFB2, and AFB3 (Shahid et al., 2018) and new targets PXY (Etchells et al., 2012) and ARK2 (Sankaranarayanan and Samuel, 2015) with a proposed role in auxin response. Auxin signaling is involved in many processes in the plant, and is potentially connected to defense against Cuscuta through its role in glucosinolate production (Smith et al., 2016; Salehin et al., 2019). Phloem protein mRNAs are targeted, adding OPS (Truernit et al., 2012) to the previously identified SEOR1. A receptor-like kinase (CuRe1) from tomato is a resistance gene that prevents Cuscuta reflexa infestation (Hegenauer et al., 2016). Receptor-like kinases and kinases in general are well represented in the set of HI-sRNA targets, including several that are involved in defense responses. This includes the well-known defense regulator MPK3 (Asai et al., 2002) and previously discovered BIK1 (Veronese et al., 2006). Another targeted pathway is brassinosteroid (BR) signaling, with targets BRI1 (Planas-Riverola et al., 2019), MAPKKK5 (Yan et al., 2018), and PICKLE (Zhang et al., 2014). BR has a clear role in defense, with connections to both BIK1 and MPK3 (Zheng et al., 2018). An overall theme of targeting host immunity and vascular system function emerges from this set of confirmed targets.

Plant miRNAs that initially seem unrelated based on divergent sequences can sometimes be grouped into superfamilies (Xia et al., 2013). To discover potential superfamilies among Cuscuta HI-sRNAs, we clustered them with a cutoff of five substitutions and barring indels (Figure 5—figure supplement 1). This clustering strategy gives low rates of grouping by random chance (Figure 5—figure supplement 2). Many superfamilies of Cuscuta HI-sRNAs were found, with a substantial portion of them shared between species and isolates (Figure 5A). 19 superfamilies were shared between all isolates except C. indecora, and another 14 superfamilies were present in at least one isolate each of C. campestris, C. pentagona, and C. gronovii. Leveraging the prior C. campestris miRNA annotations, we can extrapolate that 158 out of 332 superfamilies which contain C. campestris HI-sRNAs are likely miRNAs. Furthermore, we extrapolate that of the superfamilies present in C. campestris with proven target relationships, 22 out of 23 are likely to be miRNAs (Figure 2—figure supplement 1B).

Figure 5 with 2 supplements see all

Download asset Open asset

HI-sRNAs within a superfamily vary at several positions both between and within species (Figure 5B). In many cases variation within superfamilies occurred in a visible three-nucleotide period (e.g. SupFam_24, SupFam_37; Figure 5B, Supplementary file 7). This pattern led us to investigate nucleotide variation in corresponding target sites among possible hosts. All four Cuscuta species in this study are generalists that parasitize eudicot hosts, so we aligned homologous target mRNAs from 36 eudicot species (Supplementary file 8). Analysis of translated target site conservation shows that HI-sRNAs target highly-conserved protein-coding positions (Figure 5C). Positional variations in HI-sRNA superfamilies precisely correspond to variable positions in homologous target sequences (Figure 5B). This variation is frequently apparent at synonymous sites, accounting for the three-nucleotide periodicity of superfamily variation. Modeling correlation of positional variation between HI-sRNA superfamilies and eudicot target sites found 18 significant (p-value<0.05, Pearson correlation) examples of this type of co-variation (Supplementary file 7, Figure 2—figure supplement 1C). Importantly, HI-sRNA superfamily variation occurs within single Cuscuta species (Figure 5B, Supplementary file 7), such that multiple HI-sRNA variants are commonly deployed by a given parasite during infestation. By targeting conserved sites, and making several HI-sRNA variants that collectively cover many/most possible synonymous target variants, Cuscuta may ensure successful targeting across a wide range of hosts.

Using sRNA-seq libraries made from C. campestris attachments on Nicotiana benthamiana (Shahid et al., 2018), we found evidence of targeting in transcripts homologous to known A. thaliana targets (Supplementary file 9). Additionally, N. benthamiana target mRNAs were generally down-regulated in interface tissues (Figure 6A, Supplementary file 10. Comparing targeting of TIR1 in A. thaliana and N. benthamiana homologs by SupFam_27 sRNAs illustrates differential complementarity of superfamily members to different mRNAs . The N. benthamiana TIR1 target sites encode identical amino acids, but vary at synonymous positions. Some SupFam_27 variants are more complementary than others from each of the different homologs (Figure 6B). This provides a direct example where variation in a Cuscuta sRNA superfamily accommodates synonymous-site variation in confirmed target mRNAs from different plant species.

Figure 6

Download asset Open asset

HI-sRNA superfamily diversity could also enable repression of multiple mRNAs with homologous target-sites within a single host. We examined target predictions within A. thaliana and found ten examples of gene family-specific motifs potentially targeted by Cuscuta HI-sRNA superfamilies (Supplementary file 11). These include a HI-sRNA superfamily predicted to target the mRNA region encoding the eponymous WRKY motifs within the well-known family of defense-related transcription factors (Pandey and Somssich, 2009). Several of the targets have been experimentally confirmed by secondary siRNA accumulation or degradome analysis but most remain predictions, including the WRKY family. However, false negatives are common with these methods of confirmation. The striking patterns of sequence covariation between the HI-sRNA superfamilies and their possible target mRNA families make a strong case for the reality of these interactions.

We conclude that multiple Cuscuta species use trans-species HI-sRNAs to target a substantial number of host mRNAs. Many if not most of these HI-siRNAs are likely to be miRNAs. Host genes involved in pathogen defense, hormone signaling, and vascular system function are common targets of Cuscuta trans-species HI-sRNAs. Cuscuta trans-species HI-sRNAs nearly always interact with highly conserved target sites within the coding sequences of host mRNAs. HI-sRNAs can often be grouped into superfamilies that have nucleotide diversity that corresponds with target site variation primarily at synonymous sites. It seems likely that host target sites are under purifying selection because they code for critical amino acids that have little variation even among distantly related eudicots. By targeting these already constrained protein-coding sites, and deploying an array of sRNA variants that cover most possible permutations of synonymous site variation, Cuscuta HI-sRNAs are likely to be robust against the evolution of host resistance by target site sequence changes. This is also a suitable strategy for a generalist parasite that interacts with diverse hosts. The strategy used by Cuscuta provides a novel paradigm for the molecular evolution of trans-species sRNA targeting during parasitism.

Sequencing data have been deposited in NCBI SRA under Bioproject number PRJNA543296.

1. 1. Johnson NR
   2. Axtell MJ

   (2019) NCBI SRA

   ID PRJNA543296. Small RNA-seq from multiple Cuscuta species parasitizing Arabidopsis.

   http://www.ncbi.nlm.nih.gov/bioproject/?term=PRJNA543296

1. 1. Addo-Quaye C
   2. Eshoo TW
   3. Bartel DP
   4. Axtell MJ

   (2008) Endogenous siRNA and miRNA targets identified by sequencing of the Arabidopsis degradome

   Current Biology 18:758–762.

   https://doi.org/10.1016/j.cub.2008.04.042

   • PubMed
   • Google Scholar
2. 1. Allen E
   2. Xie Z
   3. Gustafson AM
   4. Carrington JC

   (2005) microRNA-directed phasing during trans-acting siRNA biogenesis in plants

   Cell 121:207–221.

   https://doi.org/10.1016/j.cell.2005.04.004

   • PubMed
   • Google Scholar
3. 1. Arabidopsis Genome Initiative

   (2000) Analysis of the genome sequence of the flowering plant Arabidopsis thaliana

   Nature 408:796–815.

   https://doi.org/10.1038/35048692

   • PubMed
   • Google Scholar
4. 1. Asai T
   2. Tena G
   3. Plotnikova J
   4. Willmann MR
   5. Chiu WL
   6. Gomez-Gomez L
   7. Boller T
   8. Ausubel FM
   9. Sheen J

   (2002) MAP kinase signalling cascade in Arabidopsis innate immunity

   Nature 415:977–983.

   https://doi.org/10.1038/415977a

   • PubMed
   • Google Scholar
5. Software

   1. Axtell M

   (2014) GSTAr: Generic Small RNA-Transcriptome Aligner, version 2c96ca

   GitHub.

   https://github.com/MikeAxtell/GSTAr
6. Software

   1. Axtell M

   (2018) strucVis : Display small RNA depth of coverage on a predicted RNA secondary structure, version 0edc1ac

   GitHub.

   https://github.com/MikeAxtell/strucVis
7. 1. Axtell MJ
   2. Meyers BC

   (2018) Revisiting criteria for plant MicroRNA annotation in the era of big data

   The Plant Cell 30:272–284.

   https://doi.org/10.1105/tpc.17.00851

   • PubMed
   • Google Scholar
8. 1. Barnett DW
   2. Garrison EK
   3. Quinlan AR
   4. Strömberg MP
   5. Marth GT

   (2011) BamTools: a C++ API and toolkit for analyzing and managing BAM files

   Bioinformatics 27:1691–1692.

   https://doi.org/10.1093/bioinformatics/btr174

   • PubMed
   • Google Scholar
9. 1. Bombarely A
   2. Rosli HG
   3. Vrebalov J
   4. Moffett P
   5. Mueller LA
   6. Martin GB

   (2012) A draft genome sequence of Nicotiana benthamiana to enhance molecular plant-microbe biology research

   Molecular Plant-Microbe Interactions 25:1523–1530.

   https://doi.org/10.1094/MPMI-06-12-0148-TA

   • PubMed
   • Google Scholar
10. 1. Cai Q
    2. Qiao L
    3. Wang M
    4. He B
    5. Lin FM
    6. Palmquist J
    7. Huang SD
    8. Jin H

    (2018) Plants send small RNAs in extracellular vesicles to fungal pathogen to silence virulence genes

    Science 360:1126–1129.

    https://doi.org/10.1126/science.aar4142

    • PubMed
    • Google Scholar
11. 1. Camacho C
    2. Coulouris G
    3. Avagyan V
    4. Ma N
    5. Papadopoulos J
    6. Bealer K
    7. Madden TL

    (2009) BLAST+: architecture and applications

    BMC Bioinformatics 10:421.

    https://doi.org/10.1186/1471-2105-10-421

    • PubMed
    • Google Scholar
12. 1. Chávez Montes RA
    2. de Fátima Rosas-Cárdenas F
    3. De Paoli E
    4. Accerbi M
    5. Rymarquis LA
    6. Mahalingam G
    7. Marsch-Martínez N
    8. Meyers BC
    9. Green PJ
    10. de Folter S

    (2014) Sample sequencing of vascular plants demonstrates widespread conservation and divergence of microRNAs

    Nature Communications 5:3722.

    https://doi.org/10.1038/ncomms4722

    • PubMed
    • Google Scholar
13. 1. Cheng CY
    2. Krishnakumar V
    3. Chan AP
    4. Thibaud-Nissen F
    5. Schobel S
    6. Town CD

    (2017) Araport11: a complete reannotation of the Arabidopsis thaliana reference genome

    The Plant Journal : For Cell and Molecular Biology 89:789–804.

    https://doi.org/10.1111/tpj.13415

    • PubMed
    • Google Scholar
14. 1. Costea M
    2. García MA
    3. Baute K
    4. Stefanović S

    (2015) Entangled evolutionary history of Cuscuta pentagona clade: A story involving hybridization and Darwin in the Galapagos

    Taxon 64:1225–1242.

    https://doi.org/10.12705/646.7

    • Google Scholar
15. 1. Cuperus JT
    2. Carbonell A
    3. Fahlgren N
    4. Garcia-Ruiz H
    5. Burke RT
    6. Takeda A
    7. Sullivan CM
    8. Gilbert SD
    9. Montgomery TA
    10. Carrington JC

    (2010) Unique functionality of 22-nt miRNAs in triggering RDR6-dependent siRNA biogenesis from target transcripts in Arabidopsis

    Nature Structural & Molecular Biology 17:997–1003.

    https://doi.org/10.1038/nsmb.1866

    • PubMed
    • Google Scholar
16. 1. Cuperus JT
    2. Fahlgren N
    3. Carrington JC

    (2011) Evolution and functional diversification of MIRNA genes

    The Plant Cell 23:431–442.

    https://doi.org/10.1105/tpc.110.082784

    • PubMed
    • Google Scholar
17. 1. Edgar RC

    (2004) MUSCLE: multiple sequence alignment with high accuracy and high throughput

    Nucleic Acids Research 32:1792–1797.

    https://doi.org/10.1093/nar/gkh340

    • PubMed
    • Google Scholar
18. 1. Etchells JP
    2. Provost CM
    3. Turner SR

    (2012) Plant vascular cell division is maintained by an interaction between PXY and ethylene signalling

    PLOS Genetics 8:e1002997.

    https://doi.org/10.1371/journal.pgen.1002997

    • PubMed
    • Google Scholar
19. 1. Goodstein DM
    2. Shu S
    3. Howson R
    4. Neupane R
    5. Hayes RD
    6. Fazo J
    7. Mitros T
    8. Dirks W
    9. Hellsten U
    10. Putnam N
    11. Rokhsar DS

    (2012) Phytozome: a comparative platform for green plant genomics

    Nucleic Acids Research 40:D1178–D1186.

    https://doi.org/10.1093/nar/gkr944

    • PubMed
    • Google Scholar
20. 1. Götz S
    2. García-Gómez JM
    3. Terol J
    4. Williams TD
    5. Nagaraj SH
    6. Nueda MJ
    7. Robles M
    8. Talón M
    9. Dopazo J
    10. Conesa A

    (2008) High-throughput functional annotation and data mining with the Blast2GO suite

    Nucleic Acids Research 36:3420–3435.

    https://doi.org/10.1093/nar/gkn176

    • PubMed
    • Google Scholar
21. 1. Hegenauer V
    2. Fürst U
    3. Kaiser B
    4. Smoker M
    5. Zipfel C
    6. Felix G
    7. Stahl M
    8. Albert M

    (2016) Detection of the plant parasite cuscuta reflexa by a tomato cell surface receptor

    Science 353:478–481.

    https://doi.org/10.1126/science.aaf3919

    • PubMed
    • Google Scholar
22. 1. Hou Y
    2. Zhai Y
    3. Feng L
    4. Karimi HZ
    5. Rutter BD
    6. Zeng L
    7. Choi DS
    8. Zhang B
    9. Gu W
    10. Chen X
    11. Ye W
    12. Innes RW
    13. Zhai J
    14. Ma W

    (2019) A Phytophthora effector suppresses Trans-Kingdom RNAi to promote disease susceptibility

    Cell Host & Microbe 25:153–165.

    https://doi.org/10.1016/j.chom.2018.11.007

    • PubMed
    • Google Scholar
23. 1. Jiang M
    2. Anderson J
    3. Gillespie J
    4. Mayne M

    (2008) uShuffle: a useful tool for shuffling biological sequences while preserving the k-let counts

    BMC Bioinformatics 9:192.

    https://doi.org/10.1186/1471-2105-9-192

    • PubMed
    • Google Scholar
24. 1. Johnson NR
    2. Yeoh JM
    3. Coruh C
    4. Axtell MJ

    (2016) Improved placement of Multi-mapping small RNAs

    G3: Genes|Genomes|Genetics 6:2103–2111.

    https://doi.org/10.1534/g3.116.030452

    • Google Scholar
25. 1. Kasajima I
    2. Ide Y
    3. Ohkama-Ohtsu N
    4. Hayashi H
    5. Yoneyama T
    6. Fujiwara T

    (2004) A protocol for rapid DNA extraction from Arabidopsis thaliana for PCR analysis

    Plant Molecular Biology Reporter 22:49–52.

    https://doi.org/10.1007/BF02773348

    • Google Scholar
26. 1. Kim D
    2. Langmead B
    3. Salzberg SL

    (2015) HISAT: a fast spliced aligner with low memory requirements

    Nature Methods 12:357–360.

    https://doi.org/10.1038/nmeth.3317

    • PubMed
    • Google Scholar
27. 1. Knoblauch M
    2. Froelich DR
    3. Pickard WF
    4. Peters WS

    (2014) SEORious business: structural proteins in sieve tubes and their involvement in sieve element occlusion

    Journal of Experimental Botany 65:1879–1893.

    https://doi.org/10.1093/jxb/eru071

    • PubMed
    • Google Scholar
28. 1. Kumar S
    2. Stecher G
    3. Tamura K

    (2016) MEGA7: molecular evolutionary genetics analysis version 7.0 for bigger datasets

    Molecular Biology and Evolution 33:1870–1874.

    https://doi.org/10.1093/molbev/msw054

    • PubMed
    • Google Scholar
29. 1. Langmead B
    2. Trapnell C
    3. Pop M
    4. Salzberg SL

    (2009) Ultrafast and memory-efficient alignment of short DNA sequences to the human genome

    Genome Biology 10:R25.

    https://doi.org/10.1186/gb-2009-10-3-r25

    • PubMed
    • Google Scholar
30. 1. Liu Q
    2. Wang F
    3. Axtell MJ

    (2014) Analysis of complementarity requirements for plant microRNA targeting using a Nicotiana benthamiana quantitative transient assay

    The Plant Cell 26:741–753.

    https://doi.org/10.1105/tpc.113.120972

    • PubMed
    • Google Scholar
31. 1. Love MI
    2. Huber W
    3. Anders S

    (2014) Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

    Genome Biology 15:550.

    https://doi.org/10.1186/s13059-014-0550-8

    • PubMed
    • Google Scholar
32. 1. Martin M

    (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads

    EMBnet.journal 17:10–12.

    https://doi.org/10.14806/ej.17.1.200

    • Google Scholar
33. 1. Pandey SP
    2. Somssich IE

    (2009) The role of WRKY transcription factors in plant immunity

    Plant Physiology 150:1648–1655.

    https://doi.org/10.1104/pp.109.138990

    • PubMed
    • Google Scholar
34. 1. Planas-Riverola A
    2. Gupta A
    3. Betegón-Putze I
    4. Bosch N
    5. Ibañes M
    6. Caño-Delgado AI

    (2019) Brassinosteroid signaling in plant development and adaptation to stress

    Development 146:dev151894.

    https://doi.org/10.1242/dev.151894

    • Google Scholar
35. 1. Rymarquis LA
    2. Souret FF
    3. Green PJ

    (2011) Evidence that XRN4, an Arabidopsis homolog of exoribonuclease XRN1, preferentially impacts transcripts with certain sequences or in particular functional categories

    RNA 17:501–511.

    https://doi.org/10.1261/rna.2467911

    • PubMed
    • Google Scholar
36. Preprint

    1. Salehin M
    2. Li B
    3. Tang M
    4. Katz E
    5. Song L
    6. Ecker JR
    7. Kliebenstein D
    8. Estelle M

    (2019) Auxin-sensitive aux/IAA proteins mediate drought tolerance in Arabidopsis by regulating glucosinolate levels

    bioRxiv.

    https://doi.org/10.1038/s41467-019-12002-1

    • Google Scholar
37. 1. Sankaranarayanan S
    2. Samuel MA

    (2015) A proposed role for selective autophagy in regulating auxin-dependent lateral root development under phosphate starvation in Arabidopsis

    Plant Signaling & Behavior 10:e989749.

    https://doi.org/10.4161/15592324.2014.989749

    • PubMed
    • Google Scholar
38. 1. Schon MA
    2. Kellner MJ
    3. Plotnikova A
    4. Hofmann F
    5. Nodine MD

    (2018) NanoPARE: parallel analysis of RNA 5' ends from low-input RNA

    Genome Research 28:1931–1942.

    https://doi.org/10.1101/gr.239202.118

    • PubMed
    • Google Scholar
39. 1. Shahid S
    2. Kim G
    3. Johnson NR
    4. Wafula E
    5. Wang F
    6. Coruh C
    7. Bernal-Galeano V
    8. Phifer T
    9. dePamphilis CW
    10. Westwood JH
    11. Axtell MJ

    (2018) MicroRNAs from the parasitic plant cuscuta campestris target host messenger RNAs

    Nature 553:82–85.

    https://doi.org/10.1038/nature25027

    • PubMed
    • Google Scholar
40. 1. Smith JD
    2. Woldemariam MG
    3. Mescher MC
    4. Jander G
    5. De Moraes CM

    (2016) Glucosinolates from host plants influence growth of the parasitic plant cuscuta gronovii and its susceptibility to aphid feeding

    Plant Physiology 172:181–197.

    https://doi.org/10.1104/pp.16.00613

    • PubMed
    • Google Scholar
41. 1. Stefanovic S
    2. Kuzmina M
    3. Costea M

    (2007) Delimitation of major lineages within cuscuta subgenus grammica (Convolvulaceae) using plastid and nuclear DNA sequences

    American Journal of Botany 94:568–589.

    https://doi.org/10.3732/ajb.94.4.568

    • PubMed
    • Google Scholar
42. 1. Taberlet P
    2. Gielly L
    3. Pautou G
    4. Bouvet J

    (1991) Universal primers for amplification of three non-coding regions of chloroplast DNA

    Plant Molecular Biology 17:1105–1109.

    https://doi.org/10.1007/BF00037152

    • PubMed
    • Google Scholar
43. 1. Truernit E
    2. Bauby H
    3. Belcram K
    4. Barthélémy J
    5. Palauqui JC

    (2012) OCTOPUS, a polarly localised membrane-associated protein, regulates phloem differentiation entry in Arabidopsis thaliana

    Development 139:1306–1315.

    https://doi.org/10.1242/dev.072629

    • PubMed
    • Google Scholar
44. 1. Veronese P
    2. Nakagami H
    3. Bluhm B
    4. Abuqamar S
    5. Chen X
    6. Salmeron J
    7. Dietrich RA
    8. Hirt H
    9. Mengiste T

    (2006) The membrane-anchored BOTRYTIS-INDUCED KINASE1 plays distinct roles in Arabidopsis resistance to necrotrophic and biotrophic pathogens

    The Plant Cell 18:257–273.

    https://doi.org/10.1105/tpc.105.035576

    • PubMed
    • Google Scholar
45. 1. Vogel A
    2. Schwacke R
    3. Denton AK
    4. Usadel B
    5. Hollmann J
    6. Fischer K
    7. Bolger A
    8. Schmidt MH
    9. Bolger ME
    10. Gundlach H
    11. Mayer KFX
    12. Weiss-Schneeweiss H
    13. Temsch EM
    14. Krause K

    (2018) Footprints of parasitism in the genome of the parasitic flowering plant cuscuta campestris

    Nature Communications 9:2515.

    https://doi.org/10.1038/s41467-018-04344-z

    • PubMed
    • Google Scholar
46. 1. Weiberg A
    2. Wang M
    3. Lin FM
    4. Zhao H
    5. Zhang Z
    6. Kaloshian I
    7. Huang HD
    8. Jin H

    (2013) Fungal small RNAs suppress plant immunity by hijacking host RNA interference pathways

    Science 342:118–123.

    https://doi.org/10.1126/science.1239705

    • PubMed
    • Google Scholar
47. 1. Xia R
    2. Meyers BC
    3. Liu Z
    4. Beers EP
    5. Ye S
    6. Liu Z

    (2013) MicroRNA superfamilies descended from miR390 and their roles in secondary small interfering RNA biogenesis in eudicots

    The Plant Cell 25:1555–1572.

    https://doi.org/10.1105/tpc.113.110957

    • PubMed
    • Google Scholar
48. 1. Yan H
    2. Zhao Y
    3. Shi H
    4. Li J
    5. Wang Y
    6. Tang D

    (2018) BRASSINOSTEROID-SIGNALING KINASE1 phosphorylates MAPKKK5 to regulate immunity in Arabidopsis

    Plant Physiology 176:2991–3002.

    https://doi.org/10.1104/pp.17.01757

    • PubMed
    • Google Scholar
49. 1. Zhang D
    2. Jing Y
    3. Jiang Z
    4. Lin R

    (2014) The Chromatin-Remodeling factor PICKLE integrates brassinosteroid and gibberellin signaling during skotomorphogenic growth in Arabidopsis

    The Plant Cell 26:2472–2485.

    https://doi.org/10.1105/tpc.113.121848

    • PubMed
    • Google Scholar
50. 1. Zhang T
    2. Zhao YL
    3. Zhao JH
    4. Wang S
    5. Jin Y
    6. Chen ZQ
    7. Fang YY
    8. Hua CL
    9. Ding SW
    10. Guo HS

    (2016) Cotton plants export microRNAs to inhibit virulence gene expression in a fungal pathogen

    Nature Plants 2:16153.

    https://doi.org/10.1038/nplants.2016.153

    • PubMed
    • Google Scholar
51. 1. Zheng X
    2. Kang S
    3. Jing Y
    4. Ren Z
    5. Li L
    6. Zhou JM
    7. Berkowitz G
    8. Shi J
    9. Fu A
    10. Lan W
    11. Zhao F
    12. Luan S

    (2018) Danger-Associated peptides close stomata by OST1-Independent activation of anion channels in guard cells

    The Plant Cell 30:1132–1146.

    https://doi.org/10.1105/tpc.17.00701

    • PubMed
    • Google Scholar

Author details

1. Nathan R Johnson

   1. Intercollege PhD Program in Plant Biology, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, United States
   2. Department of Biology, The Pennsylvania State University, University Park, United States

   Contribution

   Conceptualization, Data curation, Formal analysis, Investigation, Methodology

   Competing interests

   No competing interests declared

2. Claude W dePamphilis

   1. Intercollege PhD Program in Plant Biology, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, United States
   2. Department of Biology, The Pennsylvania State University, University Park, United States

   Contribution

   Conceptualization, Funding acquisition, Methodology

   Competing interests

   No competing interests declared

3. Michael J Axtell

   1. Intercollege PhD Program in Plant Biology, Huck Institutes of the Life Sciences, The Pennsylvania State University, University Park, United States
   2. Department of Biology, The Pennsylvania State University, University Park, United States

   Contribution

   Conceptualization, Supervision, Funding acquisition, Methodology, Project administration

   For correspondence

   mja18@psu.edu

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-8951-7361

• 2,008

  views

• 219

  downloads

• 26

  citations

Views, downloads and citations are aggregated across all versions of this paper published by eLife.