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Rhizobial tRNA-derived small RNAs are signal molecules regulating plant nodulation


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Microbial tRNA pieces regulate nodulation To fix nitrogen, leguminous plants enter into a symbiotic relationship with nodulating bacteria. Ren et al. now reveal the bacteria as active regulators in this process (see the Perspective by Baldrich and Meyers). Small fragments cleaved from rhizobial tRNA molecules tap into the hosts' RNA interference machinery to silence key host genes. Thus, both host and microbe shape the symbiotic environment. Science , this issue p. 919 ; see also p. 868
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Cite as: B. Ren et al., Science
10.1126/science.aav8907 (2019).
First release: 25 July 2019 (Page numbers not final at time of first release) 1
Symbiotic nitrogen fixation by the bacteria Rhizobia, which
occurs in specialized root organs known as nodules of leg-
umes, provides usable nitrogen to plants in agricultural and
natural eco-systems. The establishment of rhizobia-legume
symbiosis is dependent on recognition of signal molecules be-
tween the partners. Upon perception of plant flavonoids, rhi-
zobia secret lipo-chitooligosaccharidic nodulation factors,
which induce root hairs to curl around the bacteria and de-
velop infection treads that allow bacteria to penetrate into
the cortical cells of the roots to form nodules (1). As symbiotic
nitrogen fixation is resource intensive, legumes have evolved
a number of mechanisms to control nodule numbers (2).
Here we describe a mechanism by which the bacteria regulate
nodule numbers.
Transfer RNA (tRNA)-derived small RNA fragments
(tRFs) are found in both prokaryotes and eukaryotes. Origi-
nally thought to be random degradation products, tRFs are
specifically cleaved from mature tRNAs and often accumulate
in stressed or virally infected cells (3). Some tRFs, akin to mi-
croRNAs (miRNAs), are bound by Argonaute (AGO) proteins,
suggesting that they may employ an miRNA-like mechanism
to regulate gene expression (4). tRFs can target and repress
retrotransposons through an RNAi-mediated silencing path-
way (5). Here we ask whether tRFs are involved in cross-king-
dom communications.
We studied the soybean (Glycine max) and the rhizobium
(Bradyrhizobium japonicum) as symbiotic partners to ad-
dress this question. All 50 rhizobial tRNAs produced tRFs in
both the Rhizobium (strain USDA110) culture and the 10-d
and 20-d old soybean (cultivar Williams 82) nodules, most
were 18 to 24 nt in size, and abundance varied (Fig. 1A and
figs. S1 and S2). Overall, the tRFs in the nodules are more
abundant than those in the culture, with 21-nt tRFs, primarily
derived from the 3ends of the tRNAs, most abundant (figs.
S2 and S3 and table S1).
A total of 52 soybean genes in the soybean genome (6)
were predicted to be targets of 25 unique 21- or 22-nt rhizo-
bial tRFs with a relative abundance of >100 copies per million
rhizobial small RNA reads (table S1). These tRFs were neither
found in small RNA libraries from non-nodule soybean tis-
sues (table S2) (7) nor predicted to target rhizobial genes. Of
the 52 soybean genes, GmRHD3a/GmRHD3b,
GmHAM4a/GmHAM4b, and GmLRX5, which are orthologs of
the Arabidopsis Root Hair Defective 3 (RHD3), Hairy Meri-
stem 4 (HAM4), and Leucine-Rich Repeat Extensin-Like 5
(LRX5), respectively, attracted our attention, as these Ara-
bidopsis genes are important for root hair/plant development
(810). These soybean genes were predicted to be the targets
of three rhizobial tRFs, dubbed Bj-tRF001, Bj-tRF002, and Bj-
tRF003, the predominant products derived from three
tRNAs, Val-1-tRNA(CAC), Gly-1-tRNA(UCC), and Gln-1-
tRNA(CUG), respectively (Fig. 1A). Enrichment of the three
tRFs in the nodules compared with the rhizobium culture
was further validated by stem-loop qRT-PCR (Fig. 1B and fig.
S4), and reduced expression of the five soybean genes in the
nodules compared with the uninoculated roots was revealed
by qRT-PCR (Fig. 1C).
Rhizobial tRNA-derived small RNAs are signal molecules
regulating plant nodulation
Bo Ren1*, Xutong Wang1*, Jingbo Duan1, Jianxin Ma1,2
1Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA. 2Center for Plant Biology, Purdue University, West Lafayette, IN 47907, USA.
*These authors contributed equally to this work.
†Corresponding author. Email:
Rhizobial infection and root nodule formation in legumes requires recognition of signal molecules
produced by the bacteria and their hosts. Here we show that rhizobial tRNA-derived small RNA fragments
(tRFs) are signal molecules that modulate host nodulation. Three families of rhizobial tRFs were
confirmed to regulate host genes associated with nodule initiation and development via hijacking the host
RNAi machinery that involves ARGONAUTE 1. Silencing individual tRFs with the use of short tandem target
mimics or by overexpressing their targets represses root hair curling and nodule formation, whereas
repressing these targets with artificial miRNAs identical to the respective tRFs or mutating these targets
with CRISPR-Cas9 promotes nodulation. Our findings thus uncover a bacterial small RNA-mediated
mechanism for prokaryote-eukaryote interaction and may pave the way for enhancing nodulation
efficiency in legumes.
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Assuming that the reduced expression of these soybean genes
was caused by the rhizobial tRFs through miRNA-like post-
transcriptional regulation, cleavage of the mRNAs from these
genes at the predicted tRF target sites would have occurred.
Indeed, the mRNAs of these genes were predominantly
cleaved at the predicted tRF target sites in the 20-d nodules,
whereas none of these sites were cleaved in the uninoculated
roots (Fig. 1D and fig. S5). None of these sites are complemen-
tary to or were predicted to be targeted by previously identi-
fied soybean small RNAs (7) or newly produced ones in this
study. Soybean miR171k is the only small RNA predicted to
target GmHAM4a/GmHAM4b, but it was primarily expressed
in the uninoculated roots (9.38 counts per million reads, in-
stead of the nodules (0.27 counts per million reads), and thus
unlikely to be responsible for the observed repression of
GmHAM4a/GmHAM4b in the nodules.
To determine whether the repression of the
GmRHD3a/GmRHD3b, GmHAM4a/GmHAM4b, and
GmLRX5 expression in the nodules is associated with nodu-
lation, we created root mutants (fig. S6) for each of the five
genes and for both copies of each of the two duplicated gene
pairs by CRISPR-Cas9, which were inoculated with USDA110.
In all cases examined, expression of the edited genes was re-
duced (fig. S7), the roots with edited genes produced more
nodules than the empty-vector transgenic controls, and the
double mutants produced the greatest number of nodules
(Fig. 2, A and D). We also developed transgenic roots that
overexpress GmRHD3b, GmHAM4a, or GmLRX5 by the cau-
liflower mosaic virus (CaMV) 35S promoter, which exhibited
increased expression of these genes and reduced nodule num-
bers compared with the controls (Fig. 2, B and D, and fig.
S8A). Thus, these genes are negative regulators of nodulation.
To examine the effects of individual rhizobial tRFs on
nodulation, we generated transgenic short tandem target
mimic (STTM) soybean roots to silence each of the three rhi-
zobial tRFs (fig. S9). Nodule numbers in the STTM roots were
decreased compared with the empty-vector transgenic con-
trols (Fig. 2, C and D). As expected, relative abundance of the
three tRFs was decreased, and expression of their putative
targets was increased (figs. S8B and S10), suggesting that
these tRFs are positive regulators of nodulation and may
function through repressing their putative target genes.
To understand by which mechanism rhizobial tRFs regu-
late nodulation, two artificial miRNA precursors, aMIR-
tRF001 and aMIR-tRF003, were constructed by replacing the
miR172a and miR172a* sequences from the soybean miR172a
precursor MIR172a with rhizobial tRF001 and its comple-
mentary tRF001* or with tRF003 and its complementary
tRF003* (fig. S11). aMIR-tRF001 and aMIR-tRF003 were ex-
pressed separately in Williams 82 hairy roots, under the con-
trol of the 35S promoter, to produce artificial miRNAs amiR-
tFR001 and amiR-tFR-003 in the transgenic roots (Fig. 3A).
Expression of the putative amiR-tFR001 and amiR-tFR003
targets GmRHD3a/3b and GmLRX5 was reduced compared
with empty-vector transgenic controls (Fig. 3B), and more
nodules were produced in the aMIR-tRF001 and aMIR-
tRF003 transgenic roots than in respective controls (Fig. 3C).
These observations suggest that the artificial miRNA/tRF se-
quences directly repressed their putative targets to promote
To determine whether such sequence complementarity
was necessary for the amiRNA/tRF-mediated gene regula-
tion, two sets of fusion genes were made by adding each of
the 21-base pair (bp) of DNA fragments corresponding to the
three putative tRF target sites (wild type) and each of the 21-
bp of DNA fragments with 4-bp modification at the detected
cleavage site (mutation type) to the coding sequence of the
green fluorescence protein (GFP) gene. The fusion genes were
expressed under the control of the 35S promoter in Williams
82 hairy roots separately (fig. S12A). Reduction of the GFP
activity in the “wild-type” roots ~24-hours after inoculation
with USDA110 was detected, whereas no change of the GFP
activity in the “mutation type” roots was observed (Fig. 3D
and fig. S12B). The relative abundance of GFP transcripts was
consistent with the GFP activity (fig. S12C). These observa-
tions indicate that the “wild-type” fusion genes were nega-
tively regulated through base-pairing of their mRNAs at the
integrated “target sites” with the rhizobial tRFs.
In Arabidopsis, AGO1 is a component of the RNA-induced
silencing complexes that mediate miRNA-guided cleavage of
target mRNAs (11). To determine whether the rhizobial tRFs
act through the functional counterpart of AGO1 in soybean,
one (GmAGO1b) of the two soybean orthologs of the Ara-
bidopsis AGO1 (12), whose transcripts are relatively more
abundant than those of the other (GmAGO1a) in soybean root
nodules (13), was fused with the Myc epitope tag and ex-
pressed in the hairy roots of Williams 82. The fusion protein
was immunoprecipitated by the Myc antibody from the 20-d
nodules induced by USDA110. All three rhizobial tRFs were
detected in the GmAGO1b-Myc-associated fraction pulled
down by the Myc antibody but not detected in the nodule ly-
sate incubated without the antibody, suggesting that these
rhizobial tRFs hijacked the soybean AGO1 to catalyze tRF-
guided cleavage of target mRNAs in the host cells (Fig. 3E).
Actually, the tRF-mediated regulation of host gene expres-
sion was detected at early stages of rhizobial infection. At all
five time points from 6 to 72 hours post-inoculation with
USDA110, the abundance of the three tRFs was increased in
the inoculated root hairs compared with the uninoculated
root hairs (fig. S13A), whereas the expression of their targets
was decreased (fig. S13B). No differences in root hair number
and length were observed between the GmRHD3b,
GmHAM4a, and GmLRX5 overexpression roots and the con-
trols or between the tRF-silencing STTM roots and the
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controls (fig. S14), but the proportions of deformed/curled
root hairs were decreased in the overexpression and STTM
roots compared with respective controls (Fig. 4, A to C), sug-
gesting that rhizobial tRFs promote rhizobial infection.
To shed light on the evolutionary conservation and diver-
gence of rhizobial tRF-mediated host gene regulation, we an-
alyzed sequence data from four legumes, soybean, common
bean (Phaseolus vulgaris), Medicago trunctrula, and Lotus
japonica (6), and 12 rhizobium species (14), as well as the
GmRHD3a/GmRHD3b, GmHAM4a/GmHAM4b, and
GmLRX5 sequences from soybean populations (15, 16).
Among 699 soybean accessions, no sequence variation at the
three tRF target sites within the five genes was found (fig.
S15). Among eight B. japonicum strains, no sequence varia-
tion at the three tRF sites within respective rhizobial tRNAs
was detected (fig. S16). By contrast, sequences at the target
sites diverged among the four legumes (fig. S17). In particu-
lar, no orthologs of GmLRX5 were found in the other three
legumes (fig. S17). The counterparts of the three rhizobial tRF
sequences in respective tRNAs also showed interspecific di-
vergence (fig. S16). PvRHD3 in common bean, the ortholog of
GmRHD3a/3b, does have a tRF001 target site identical to that
of GmRHD3a/3b (fig. S17), but Rhizobium etli, a compatible
symbiotic partner of common bean (17), does not have the B.
japonicum Val-1-tRNA(CAC) from which tRF001 was derived.
Using the small RNA data from the common bean nodules
induced by a R. etli strain (17), 38 R. etli tRNAs were identi-
fied to have produced 21-nt tRFs. These tRFs were primarily
derived from the 3ends of the tRNAs (fig. S18). Ten unique
21-nt tRFs, each with a relative abundance of >100 counts per
million rhizobial small RNA reads in the common bean nod-
ules, were predicted to target 14 common bean genes, includ-
ing genes encoding a protein kinase, a GRAS transcription
factor, and an APETALA2-like transcription factor that may
be involved in nodulation regulation (table S3) (18). Never-
theless, none of these 14 putative R. etli tRF targets in com-
mon bean are orthologs of the 25 putative B. japonicum tRF
targets in soybean (table S1).
We demonstrate that rhizobial tRFs are positive regula-
tors of rhizobial infection and nodule formation in soybean,
thereby playing an important role in balancing plant growth
and symbiosis (fig. S19). In addition to the three rhizobial
tRFs we investigated, other rhizobial tRFs were predicted to
target soybean genes annotated to encode auxin receptors
and efflux carriers, RING/U-box proteins, and protein kinases
(table S1), which may also affect nodulation (19). Such cross-
kingdom communications may be common among symbiotic
partners, but the nodes of rhizobial tRFs-host gene interac-
tions appear to be diverse.
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42. P. Danecek, A. Auton, G. Abecasis, C. A. Albers, E. Banks, M. A. DePristo, R. E.
Handsaker, G. Lunter, G. T. Marth, S. T. Sherry, G. McVean, R. Durbin; 1000
Genomes Project Analysis Group, The variant call format and VCFtools.
Bioinformatics 27, 21562158 (2011). doi:10.1093/bioinformatics/btr330
We thank D. Yu for the pSC1 vector, and B. C. Meyers for suggestions. Funding: This
work was partially supported by the Agriculture and Food Research Initiative of
the USDA National Institute of Food and Agriculture (grant nos. 2015-67013-
22811 and 2018-67013-27425), Purdue AgSEED program, North Central
Soybean Research Program, and Indiana Soybean Alliance. Author
contributions: B.R., X.W., and J.D. performed the research, B.R., X.W., and J.M.
analyzed the data, and J.M. designed the research and wrote the manuscript
with input from B.R., X.W., and J.D. Competing interests: This work has been
filed for a U.S. Provisional Patent Application. Data and materials availability:
All data are available in NCBI (accession no. SRR7986781 to SRR7986788, and
SRR7985373), main text, or supplementary materials.
Materials and Methods
Figs. S1 to S19
Tables S1 to S4
References (2042)
30 October 2018; resubmitted 22 April 2019
Accepted 10 July 2019
Published online 25 July 2019
on July 25, 2019 from
First release: 25 July 2019 (Page numbers not final at time of first release) 5
Fig. 1. Rhizobial tRFs and their
putative target genes in soybean.
) Origins of three rhizobial tRFs.
Anticodons in corresponding tRNAs
are colored in blue. (
) Abundance of
the three tRFs, measured by stem-
loop-qRT-PCR, in free-living
Bradyrhizobium japonicum (B. j.)
USDA110 (1), and 10-d and 20-d post
inoculation (dpi) nodules (2 and 3,
respectively). (
) Expression of the
putative tRF target genes, measured
by qRT-PCR, in the 10-d and 20-d
nodules (2 and 4) and uninoculated
soybean roots (1 and 3). Values in (B)
and (C), with one set as “1” and the
others adjusted accordingly, are
shown as means ± standard errors
(s.e.) from three biological replicates.
Asterisks indicate the significance
level at P<0.01 (t-test). (
) The three
tRFs, their putative target transcripts,
and the cleavage sites and
frequencies (indicated by arrows and
ratios) detected in the 20-dpi
on July 25, 2019 from
First release: 25 July 2019 (Page numbers not final at time of first release) 6
targets by CRISPR-
numbers. (
) Overexpression (OX)
of the putative tRF targets resulted
in decreased nodule numbers. (
Silencing of individual tRFs by STTM
numbers. (
) Nodule numbers, with
all data points represented by dots,
are shown as box and whisker plots
displaying 95%-
three biological replicates (12 plants
Controls are transgenic roots of
empty vectors used for the CRISPR-
Cas9 knockouts, the gene-
overexpression roots, and the STTM
Asterisks indicate the significance
level of P < 0.01 (t-test).
on July 25, 2019 from
First release: 25 July 2019 (Page numbers not final at time of first release) 7
Fig. 3. Rhizobial tRF-guided gene regulation by hijacking the
host RNAi machinery.
A) Abundance of artificial miRNAs
measured by stem-loop-qRT-PCR in aMIR-tRF001 (2) and aMIR-
tRF003 transgenic roots (3) and respective empty-vector
transgenic roots (1) 28 dpi. (
) Expression of the putative
tRF/artificial miRNA targets measured by qRT-PCR in the same
samples as described in (A). Values in (A) and (B), with one set as
“1” and the others adjusted accordingly, are shown as means ± s.e.
from three biological replicates. Asterisks indicate the significance
level at P < 0.01 (t-test). (
) Nodule numbers in the same samples
as described in (A), with all data points represented by dots, are
shown as box and whisker plots displaying 95%-5% interval from
three biological replicates (12 plants per replicate). (
activity in transgenic roots of “GFP-tRF target site” fusion genes
(W1-W3) and “GFP-mutated tRF target site” fusion genes (M1-M3)
24 hours post inoculation with USDA110. Bj- and Bj+ indicate
uninoculated and inoculated roots, respectively. (
) Association of
the three tRFs with soybean GmAGO1b in 28-dpi nodules detected
from three experimental replicates. “+” and “−” indicate the
GmAGO1b-Myc fusion protein-
associated fraction
immunoprecipitated by the Myc antibody and the nodule lysate
without Myc antibody incubation, respectively.
on July 25, 2019 from
First release: 25 July 2019 (Page numbers not final at time of first release) 8
Fig. 4. Modulation of early-stage
rhizobial infection by rhizobial tRFs
and their targets in soybean.
and (
B) Morphological differences
between the root hairs
overexpressing the rhizobial tRF
targets and the negative control, and
between the STTM root hairs
inhibiting the rhizobial tRF function
and the negative control. (
Quantitation of deformed root hairs
and curled root hairs with infection
foci in samples as exemplified in (A)
and (B). The values are shown as
means ± s.d. from three biological
replicates (n = 25 per replicates).
Asterisks indicate the significant level
at P < 0.05 (t-test).
on July 25, 2019 from
Rhizobial tRNA-derived small RNAs are signal molecules regulating plant nodulation
Bo Ren, Xutong Wang, Jingbo Duan and Jianxin Ma
published online July 25, 2019
This article cites 42 articles, 11 of which you can access for free
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... This phenomenon has since been observed later in many different interacting organisms. For example, other plant fungal pathogens (such as Fusarium oxysporum, Verticillium dahliae), oomycete pathogens (such as Hyaloperonospora arabidopsidis), the parasitic plant Cuscuta campestris, and even the symbiotic ectomycorrhizal fungus Pisolithus microcarpus and the bacterial symbiont Rhizobium (Bradyrhizobium japonicum) can send a set of sRNAs into the host plant to silence the expression of host target genes (Dunker et al., 2020, Shahid et al., 2018, Ren et al., 2019, Wong-Bajracharya et al., 2022, Ji et al., 2021, Zhang et al., 2022. Furthermore, the mode of action of transferred microbial sRNAs seems also to be conserved. ...
... Furthermore, the mode of action of transferred microbial sRNAs seems also to be conserved. The sRNAs from F. oxysporum (Ji et al., 2021), V. dahliae (Wang et al., 2016), H. arabidopsidis (Dunker et al., 2020) and the Rhizobium (Ren et al., 2019) also utilize host AGO proteins for silencing host target genes. Excitingly, cross-kingdom or cross-species RNAi has also been observed in animal-pathogen or parasite interaction systems. ...
... Bc-siR3.2 and Bc-siR5 were detected by a stem-loop RT-PCR method. The same molecular mechanism was also found in plant pathogens F. oxysporum, V. dahliae, H. arabidopsidis, animal fungal pathogen Beauveria bassiana and even in beneficial bacterial symbiont Rhizobium during cross-kingdom RNAi with their hosts (Ji et al., 2021, Zhang et al., 2022, Dunker et al., 2020, Ren et al., 2019. The sRNAs from these microbes are loaded into host AGO proteins to silence host target genes. ...
Full-text available
Cross-kingdom or cross-species RNA interference (RNAi) is broadly present in many interacting systems between microbes/parasites and their plant and animal hosts. Recent study by Qin et al. (2022) performed correlation analysis using global sRNA- and mRNA-deep sequencing data of cultured B. cinerea and B. cinerea-infected tomato leaves and claimed that cross kingdom RNAi may not occur in B. cinerea tomato interaction (Qin et al., 2022). In this response letter, we use experimental evidence and additional bioinformatics analysis of the datasets produced by Qin et al. (2022) to identify the key reasons why a discrepancy with previously published findings occurred. Here, we also provided additional experimental evidence to support the presence of cross-kingdom RNAi between tomato and B. cinerea. We believe it is important to clarify the basic concept and mechanism of cross-kingdom/cross-species sRNA trafficking and illustrate proper bioinformatics analyses in this regard for all the scientists and researchers in this field.
... The functional conservation of tsRNAs between plants and animals could be partially due to their similarity in biogenesis processes, inferring their key role in evolution. Interestingly, cross-kingdom regulation by tsRNAs has also been discovered in plant recently (Ren et al., 2019;Cao et al., 2022) (Figure 1). ...
... Remarkably, the crosskingdom communication of tsRNAs was observed between rhizobial and its host soybean. Rhizobial tRFs can transfer to soybean roots and hijack the host RNAi machinery to silence key host genes, thus enhancing nodulation in soybean (Ren et al., 2019). ...
Full-text available
tRNA-derived small RNAs (tsRNAs) represent a novel category of small non-coding RNAs and serve as a new regulator of gene expression at both transcriptional and post-transcriptional levels. Growing evidence indicates that tsRNAs can be induced by diverse stimuli and regulate stress-responsive target genes, allowing plants to adapt to unfavorable environments. Here, we discuss the latest developments about the biogenesis and classification of tsRNAs and highlight the expression regulation and potential function of tsRNAs in plant biotic and abiotic stress responses. Of note, we also collect useful bioinformatics tools and resources for tsRNAs study in plants. Finally, we propose current limitations and future directions for plant tsRNAs research. These recent discoveries have refined our understanding of whether and how tsRNAs enhance plant stress tolerance.
... The expression of extracellular RNase T2 in response to stress correlates with the accumulation of tRFs (Alves et al., 2017;Megel et al., 2019), suggesting a regulatory role of extracellularly produced tRFs in this process. Like siRNAs and miRNAs, tRNA-derived fragments are also loaded into AGO proteins and regulate gene expression in plants, oomycetes and animals (Alves et al., 2017;Kumar et al., 2014;Loss-Morais et al., 2013;Ren et al., 2019;Wang et al., 2016b). For instance, tRFs have been shown to mediate trans-kingdom gene silencing between Rhizobia and soybean. ...
... For instance, tRFs have been shown to mediate trans-kingdom gene silencing between Rhizobia and soybean. Rhizobial tRFs produced by proccesing of tRNA Gly , tRNA Gln and tRNA Val associate with soybean AGO1 to catalyze tRF-guided cleavage of target mRNAs in soybean to promote nodulation (Ren et al., 2019). Although direct evidence of a role for plant extracellular tRFs in cross-kingdom gene silencing is lacking, the high accumulation of tRFs in the leaf apoplast tempt us to speculate that this is likely. ...
Full-text available
Extracellular RNA (exRNA) has long been considered as cellular waste that plants can degrade and utilize to recycle nutrients. However, recent findings highlight the need to reconsider the biological significance of RNAs found outside of plant cells. A handful of studies suggest that the exRNA repertoire, which turns out to be an extremely heterogenous group of non-coding RNAs, comprises species as small as a dozen nucleotides to hundreds of nucleotides long. They are found mostly in free form or associated with RNA-binding proteins, while very few are found inside extracellular vesicles (EVs). Despite their low abundance, small RNAs associated with EVs have been a focus of exRNA research due to their putative role in mediating transkingdom RNA interference. Therefore, non-vesicular exRNAs have remained completely under the radar until very recently. Here we summarize our current knowledge of the RNA species that constitute the extracellular RNAome and discuss mechanisms that could explain the diversity of exRNAs, focusing not only on the potential mechanisms involved in RNA secretion but also on post-release processing of exRNAs. We will also share our thoughts on the putative roles of vesicular and extravesicular exRNAs in plant-pathogen interactions, intercellular communication, and other physiological processes in plants.
... Based on scattered genetic and molecular biological evidence, a general picture of extensive recruitment of core and accessory functions, in addition to key nodulation and nitrogen fixation genes, in optimizing symbiotic efficiency has been proposed earlier [16,17]. The list of core and accessory functions contributing to rhizobial fitness, from rhizosphere to rhizoplane to nodules, is ever-increasing (above 900 genes) and usually in a strain-host-dependent manner [52], e.g., motility and chemotaxis [124], surface polysaccharides [125][126][127][128][129], outer membrane vesicles [130], quorum sensing [131][132][133][134][135], T1SS, T3SS, T4SS and T6SS [136][137][138][139][140][141][142], dicarboxylate transport [143], poly-3-hydroxybutyrate [144][145][146], transporters of branched amino acids [147][148][149], uptake of ions (phosphorus [150][151][152], potassium [153,154], molybdenum [155], iron [156,157], sulfur [155], zinc [94] and manganese [158]), nitrate reduction [40], NO modulation [159,160], oxygen limitation responses [40,[161][162][163], cell cycle [164][165][166], peptide importers [167][168][169][170][171], regulatory non-coding RNAs (e.g., globally acting trans-small RNA AbcR1/2 and those fragments derived from transfer RNA) [172,173] and carbon-nitrogen metabolism coordination by nitrogen-related phosphotransferase system [153,174,175]. These efforts indicate that the successful integration of key nodulation and nitrogen fixation circuits in various bacterial recipients involves systematic and dynamic coordination with other functions during the establishment of symbiosis. ...
Full-text available
There are ubiquitous variations in symbiotic performance of different rhizobial strains associated with the same legume host in agricultural practices. This is due to polymorphisms of symbiosis genes and/or largely unexplored variations in integration efficiency of symbiotic function. Here, we reviewed cumulative evidence on integration mechanisms of symbiosis genes. Experimental evolution, in concert with reverse genetic studies based on pangenomics, suggests that gain of the same circuit of key symbiosis genes through horizontal gene transfer is necessary but sometimes insufficient for bacteria to establish an effective symbiosis with legumes. An intact genomic background of the recipient may not support the proper expression or functioning of newly acquired key symbiosis genes. Further adaptive evolution, through genome innovation and reconstruction of regulation networks, may confer the recipient of nascent nodulation and nitrogen fixation ability. Other accessory genes, either co-transferred with key symbiosis genes or stochastically transferred, may provide the recipient with additional adaptability in ever-fluctuating host and soil niches. Successful integrations of these accessory genes with the rewired core network, regarding both symbiotic and edaphic fitness, can optimize symbiotic efficiency in various natural and agricultural ecosystems. This progress also sheds light on the development of elite rhizobial inoculants using synthetic biology procedures.
... arabidopsidis 16 , an animal fungal pathogen Beauveria bassiana of mosqiuito 17 , a bacterial pathogen 19 and even symbiotic microbes, such as ectomycorrhizal fungus Pisolithus microcarpus 20 and Rhizobium (Bradyrhizobium japonicum) 21 , also utilize host Argonaute proteins for crosskingdom RNAi. The biogenesis of these pathogen sRNAs involved in cross-kingdom RNAi are also largely dependent on pathogen DCL proteins [7][8]10,15,[22][23][24][25][26] . ...
Full-text available
Spray-Induced Gene Silencing (SIGS) is an innovative and eco-friendly technology where topical application of pathogen gene-targeting RNAs to plant material can enable disease control. SIGS applications remain limited because of the instability of dsRNA, which can be rapidly degraded when exposed to various environmental conditions. Inspired by the natural mechanism of crosskingdom RNAi through extracellular vesicle trafficking, we describe herein the use of artificial nanovesicles (AVs) for dsRNA encapsulation and control against the fungal pathogen, Botrytis cinerea. AVs were synthesized using three different cationic lipid formulations, DOTAP + PEG, DOTAP, and DODMA, and examined for their ability to protect and deliver dsRNA. All three formulations enabled dsRNA delivery and uptake by B. cinerea. Further, encapsulating dsRNA in AVs provided strong protection from nuclease degradation and from removal by leaf washing. This improved stability led to prolonged RNAi-mediated protection against B. cinerea both on pre- and post-harvest plant material using AVs. Specifically, the AVs extended the protection duration conferred by dsRNA to 10 days on tomato and grape fruits and to 21 days on grape leaves. The results of this work demonstrate how AVs can be used as a new nanocarrier to overcome dsRNA instability in SIGS for crop protection.
... arabidopsidis 16 , an animal fungal pathogen Beauveria bassiana of mosqiuito 17 , a bacterial pathogen 19 and even symbiotic microbes, such as ectomycorrhizal fungus Pisolithus microcarpus 20 and Rhizobium (Bradyrhizobium japonicum) 21 , also utilize host Argonaute proteins for crosskingdom RNAi. The biogenesis of these pathogen sRNAs involved in cross-kingdom RNAi are also largely dependent on pathogen DCL proteins [7][8]10,15,[22][23][24][25][26] . ...
Full-text available
Spray‐Induced Gene Silencing (SIGS) is an innovative and eco‐friendly technology where topical application of pathogen gene‐targeting RNAs to plant material can enable disease control. SIGS applications remain limited because of the instability of dsRNA, which can be rapidly degraded when exposed to various environmental conditions. Inspired by the natural mechanism of cross‐kingdom RNAi through extracellular vesicle trafficking, we describe herein the use of artificial nanovesicles (AVs) for dsRNA encapsulation and control against the fungal pathogen, Botrytis cinerea. AVs were synthesized using three different cationic lipid formulations, DOTAP + PEG, DOTAP, and DODMA, and examined for their ability to protect and deliver dsRNA. All three formulations enabled dsRNA delivery and uptake by B. cinerea. Further, encapsulating dsRNA in AVs provided strong protection from nuclease degradation and from removal by leaf washing. This improved stability led to prolonged RNAi‐mediated protection against B. cinerea both on pre‐ and post‐harvest plant material using AVs. Specifically, the AVs extended the protection duration conferred by dsRNA to 10 days on tomato and grape fruits and to 21 days on grape leaves. The results of this work demonstrate how AVs can be used as a new nanocarrier to overcome dsRNA instability in SIGS for crop protection.
Extracellular vesicles (EVs) are lipid bilayer-enclosed nanoparticles that deliver bioactive proteins, nucleic acids, lipids, and other small molecules from donor to recipient cells. They have attracted significant interest recently due to their important roles in regulating plant-microbe interaction. During microbial infection, plant EVs play a prominent role in defense by delivering small regulatory RNA into pathogens, resulting in the silencing of pathogen virulence genes. Pathogens also deliver small RNAs into plant cells to silence host immunity genes. Recent evidence indicates that microbial EVs may be involved in pathogenesis and host immunity modulation by transporting RNAs and other biomolecules. However, the biogenesis and function of microbial EVs in plant-microbe interaction remain ill-defined. In this review, we discuss various aspects of microbial EVs, with a particular focus on current methods for EV isolation, composition, biogenesis, and their roles in plant-microbe interaction. We also discussed the potential role of microbial EVs in cross-kingdom RNA trafficking from pathogens to plants, as it is a highly likely possibility to explore in the future.
Full-text available
Soybean (Glycine max) is a major source of plant protein and oil. Soybean breeding has benefitted from advances in functional genomics. In particular, the release of soybean reference genomes has advanced our understanding of soybean adaptation to soil nutrient deficiencies, the molecular mechanism of symbiotic nitrogen fixation, biotic and abiotic stress tolerance, and the roles of flowering time in regional adaptation, plant architecture, and seed yield and quality. Nevertheless, many challenges remain for soybean functional genomics and molecular breeding, mainly related to improving grain yield through high-density planting, maize-soybean intercropping, taking advantage of wild resources, utilization of heterosis, genomic prediction and selection breeding, and precise breeding through genome editing. This review summarizes the current progress in soybean functional genomics and directs future challenges for molecular breeding of soybean. This article is protected by copyright. All rights reserved.
Transfer RNAs (tRNAs) are well-known for their essential function as adapters in delivering amino acids to ribosomes and making the link between mRNA and protein according to the genetic code. Besides this central role in protein synthesis, other functions are attributed to these macromolecules or their genes in all living organisms. Here, this review focuses on these extra functions in photosynthetic organisms. For example, tRNAs are implicated in Tetrapyrrole biosynthesis, mRNA stabilization or transport, priming reverse transcription of viral RNAs, and tRNA-like structures play important roles in RNA viral genomes. Another important function of tRNAs in regulating gene expression is related to their cleavage allowing the production of small non-coding RNAs named tDRs for tRNA-derived RNAs. Here, we examine in more detail the biogenesis of tDRs and their emerging functions in plants.
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Plant regulatory small RNAs (sRNAs), which include most microRNAs (miRNAs) and a subset of small interfering RNAs (siRNAs), such as the phased siRNAs (phasiRNAs), play important roles in regulating gene expression. Although generated from genetically distinct biogenesis pathways, these regulatory sRNAs share the same mechanisms for post-translational gene silencing and translational inhibition. psRNATarget was developed to identify plant sRNA targets by (i) analyzing complementary matching between the sRNA sequence and target mRNA sequence using a predefined scoring schema and (ii) by evaluating target site accessibility. This update enhances its analytical performance by developing a new scoring schema that is capable of discovering miRNA-mRNA interactions at higher 'recall rates' without significantly increasing total prediction output. The scoring procedure is customizable for the users to search both canonical and non-canonical targets. This update also enables transmitting and analyzing 'big' data empowered by (a) the implementation of multi-threading chunked file uploading, which can be paused and resumed, using HTML5 APIs and (b) the allocation of significantly more computing nodes to its back-end Linux cluster. The updated psRNATarget server has clear, compelling and user-friendly interfaces that enhance user experiences and present data clearly and concisely. The psRNATarget is freely available at
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Many leguminous species have adapted their seed coat with a layer of powdery bloom that contains hazardous allergens and makes the seeds less visible, offering duel protection against potential predators 1 . Nevertheless, a shiny seed surface without bloom is desirable for human consumption and health, and is targeted for selection under domestication. Here we show that seed coat bloom in wild soybeans is mainly controlled by Bloom1 (B1), which encodes a transmembrane transporter-like protein for biosynthesis of the bloom in pod endocarp. The transition from the 'bloom' to 'no-bloom' phenotypes is associated with artificial selection of a nucleotide mutation that naturally occurred in the coding region of B1 during soybean domestication. Interestingly, this mutation not only 'shined' the seed surface, but also elevated seed oil content in domesticated soybeans. Such an elevation of oil content in seeds appears to be achieved through b1-modulated upregulation of oil biosynthesis in pods. This study shows pleiotropy as a mechanism underlying the domestication syndrome 2 , and may pave new strategies for development of soybean varieties with increased seed oil content and reduced seed dust.
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Background Soybean (Glycine max [L.] Merr.) is one of the most important oil and protein crops. Ever-increasing soybean consumption necessitates the improvement of varieties for more efficient production. However, both correlations among different traits and genetic interactions among genes that affect a single trait pose a challenge to soybean breeding. Results To understand the genetic networks underlying phenotypic correlations, we collected 809 soybean accessions worldwide and phenotyped them for two years at three locations for 84 agronomic traits. Genome-wide association studies identified 245 significant genetic loci, among which 95 genetically interacted with other loci. We determined that 14 oil synthesis-related genes are responsible for fatty acid accumulation in soybean and function in line with an additive model. Network analyses demonstrated that 51 traits could be linked through the linkage disequilibrium of 115 associated loci and these links reflect phenotypic correlations. We revealed that 23 loci, including the known Dt1, E2, E1, Ln, Dt2, Fan, and Fap loci, as well as 16 undefined associated loci, have pleiotropic effects on different traits. Conclusions This study provides insights into the genetic correlation among complex traits and will facilitate future soybean functional studies and breeding through molecular design. Electronic supplementary material The online version of this article (doi:10.1186/s13059-017-1289-9) contains supplementary material, which is available to authorized users.
Full-text available
Background ImageJ is an image analysis program extensively used in the biological sciences and beyond. Due to its ease of use, recordable macro language, and extensible plug-in architecture, ImageJ enjoys contributions from non-programmers, amateur programmers, and professional developers alike. Enabling such a diversity of contributors has resulted in a large community that spans the biological and physical sciences. However, a rapidly growing user base, diverging plugin suites, and technical limitations have revealed a clear need for a concerted software engineering effort to support emerging imaging paradigms, to ensure the software’s ability to handle the requirements of modern science. Results We rewrote the entire ImageJ codebase, engineering a redesigned plugin mechanism intended to facilitate extensibility at every level, with the goal of creating a more powerful tool that continues to serve the existing community while addressing a wider range of scientific requirements. This next-generation ImageJ, called “ImageJ2” in places where the distinction matters, provides a host of new functionality. It separates concerns, fully decoupling the data model from the user interface. It emphasizes integration with external applications to maximize interoperability. Its robust new plugin framework allows everything from image formats, to scripting languages, to visualization to be extended by the community. The redesigned data model supports arbitrarily large, N-dimensional datasets, which are increasingly common in modern image acquisition. Despite the scope of these changes, backwards compatibility is maintained such that this new functionality can be seamlessly integrated with the classic ImageJ interface, allowing users and developers to migrate to these new methods at their own pace. Conclusions Scientific imaging benefits from open-source programs that advance new method development and deployment to a diverse audience. ImageJ has continuously evolved with this idea in mind; however, new and emerging scientific requirements have posed corresponding challenges for ImageJ’s development. The described improvements provide a framework engineered for flexibility, intended to support these requirements as well as accommodate future needs. Future efforts will focus on implementing new algorithms in this framework and expanding collaborations with other popular scientific software suites. Electronic supplementary material The online version of this article (doi:10.1186/s12859-017-1934-z) contains supplementary material, which is available to authorized users.
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A genome-wide analysis identified the set of small RNAs (sRNAs) from the agronomical important legume Phaseolus vulgaris (common bean), including novel P. vulgaris-specific microRNAs (miRNAs) potentially important for the regulation of the rhizobia-symbiotic process. Generally, novel miRNAs are difficult to identify and study because they are very lowly expressed in a tissue- or cell-specific manner. In this work, we aimed to analyze sRNAs from common bean root hairs (RH), a single-cell model, induced with pure Rhizobium etli nodulation factors (NF), a unique type of signal molecule. The sequence analysis of samples from NF-induced and control libraries led to the identity of 132 mature miRNAs, including 63 novel miRNAs and 1984 phasiRNAs. From these, six miRNAs were significantly differentially expressed during NF induction, including one novel miRNA: miR-RH82. A parallel degradome analysis of the same samples revealed 29 targets potentially cleaved by novel miRNAs specifically in NF-induced RH samples; however, these novel miRNAs were not differentially accumulated in this tissue. This study reveals Phaseolus vulgaris-specific novel miRNA candidates and their corresponding targets that meet all criteria to be involved in the regulation of the early nodulation events, thus setting the basis for exploring miRNA-mediated improvement of the common bean-rhizobia symbiosis.
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Evolview is an online visualization and management tool for customized and annotated phylogenetic trees. It allows users to visualize phylogenetic trees in various formats, customize the trees through built-in functions and user-supplied datasets and export the customization results to publication-ready figures. Its ‘dataset system’ contains not only the data to be visualized on the tree, but also ‘modifiers’ that control various aspects of the graphical annotation. Evolview is a single-page application (like Gmail); its carefully designed interface allows users to upload, visualize, manipulate and manage trees and datasets all in a single webpage. Developments since the last public release include a modern dataset editor with keyword highlighting functionality, seven newly added types of annotation datasets, collaboration support that allows users to share their trees and datasets and various improvements of the web interface and performance. In addition, we included eleven new ‘Demo’ trees to demonstrate the basic functionalities of Evolview, and five new ‘Showcase’ trees inspired by publications to showcase the power of Evolview in producing publication-ready figures. Evolview is freely available at:
tRNA fragments (tRFs) are a class of small, regulatory RNAs with diverse functions. 3'-Derived tRFs perfectly match long terminal repeat (LTR)-retroelements which use the 3'-end of tRNAs to prime reverse transcription. Recent work has shown that tRFs target LTR-retroviruses and -transposons for the RNA interference (RNAi) pathway and also inhibit mobility by blocking reverse transcription. The highly conserved tRNA primer binding site (PBS) in LTR-retroelements is a unique target for 3'-tRFs to recognize and block abundant but diverse LTR-retrotransposons that become transcriptionally active during epigenetic reprogramming in development and disease. 3'-tRFs are processed from full-length tRNAs under so far unknown conditions and potentially protect many cell types. tRFs appear to be an ancient link between RNAi, transposons, and genome stability.
Global demand to increase food production and simultaneously reduce synthetic nitrogen‐fertiliser inputs in agriculture are underpinning the need to intensify the use of legume crops. The symbiotic relationship that legume plants establish with nitrogen‐fixing rhizobia bacteria is central to their advantage. This plant‐microbe interaction results in newly developed root organs, called nodules, where the rhizobia convert atmospheric nitrogen gas into forms of nitrogen the plant can use. However, the process of developing and maintaining nodules is resource‐intensive; hence, the plant tightly controls the number of nodules forming. A variety of molecular mechanisms are used to regulate nodule numbers under both favourable and stressful growing conditions, enabling the plant to conserve resources and optimise development in response to a range of circumstances. Using genetic and genomic approaches, many components acting in the regulation of nodulation have now been identified. Discovering and functionally characterising these components can provide genetic targets and polymorphic markers that aid in the selection of superior legume cultivars and rhizobia strains that benefit agricultural sustainability and food security. This review addresses recent findings in nodulation control, presents detailed models of the molecular mechanisms driving these processes and identifies gaps in these processes that are not yet fully explained.
Transposon reactivation is an inherent danger in cells that lose epigenetic silencing during developmental reprogramming. In the mouse, long terminal repeat (LTR)-retrotransposons, or endogenous retroviruses (ERV), account for most novel insertions and are expressed in the absence of histone H3 lysine 9 trimethylation in preimplantation stem cells. We found abundant 18 nt tRNA-derived small RNA (tRF) in these cells and ubiquitously expressed 22 nt tRFs that include the 3′ terminal CCA of mature tRNAs and target the tRNA primer binding site (PBS) essential for ERV reverse transcription. We show that the two most active ERV families, IAP and MusD/ETn, are major targets and are strongly inhibited by tRFs in retrotransposition assays. 22 nt tRFs post-transcriptionally silence coding-competent ERVs, while 18 nt tRFs specifically interfere with reverse transcription and retrotransposon mobility. The PBS offers a unique target to specifically inhibit LTR-retrotransposons, and tRF-targeting is a potentially highly conserved mechanism of small RNA–mediated transposon control.