academic.oup.com Open in urlscan Pro
52.224.90.245  Public Scan

Form analysis
1 forms found in the DOM

GET /Citation/Download

<form action="/Citation/Download" method="get" id="citationModal">
  <input type="hidden" name="resourceId" value="266726">
  <input type="hidden" name="resourceType" value="3">
  <label for="selectFormat" class="hide js-citation-format-label">Select Format</label>
  <select required="" name="citationFormat" class="citation-download-format js-citation-format" id="selectFormat">
    <option selected="" disabled="">Select format</option>
    <option value="0">.ris (Mendeley, Papers, Zotero)</option>
    <option value="1">.enw (EndNote)</option>
    <option value="2">.bibtex (BibTex)</option>
    <option value="3">.txt (Medlars, RefWorks)</option>
  </select>
  <button class="btn citation-download-link disabled" type="submit">Download citation</button>
</form>

Text Content

We use cookies to enhance your experience on our website. By clicking 'continue'
or by continuing to use our website, you are agreeing to our use of cookies. You
can change your cookie settings at any time.
 * Continue
 * Find out more



We use cookies to enhance your experience on our website.By continuing to use
our website, you are agreeing to our use of cookies. You can change your cookie
settings at any time. Find out more Skip to Main Content
Advertisement
Journals
Books
 * Search Menu
 * 
 * 
 * Menu
 * 
 * 


Navbar Search Filter BioScienceThis issue EnvironmentScience and
MathematicsBooksJournalsOxford Academic Mobile Microsite Search Term Search
 * Sign In
   * 

 * Issues
 * More Content
   * Advance articles
   * Editor's Choice
 * Submit
   * Author Guidelines
   * Submission Site
   * Open Access
 * Purchase
 * Alerts
 * About
   * About BioScience
   * About AIBS
   * Journals Career Network
   * Editorial Board
   * Advertising and Corporate Services
   * Self-Archiving Policy
   * Potentially Offensive Content
   * Terms and Conditions
 * Journals on Oxford Academic
 * Books on Oxford Academic


 * Issues
 * More Content
   * Advance articles
   * Editor's Choice
 * Submit
   * Author Guidelines
   * Submission Site
   * Open Access
 * Purchase
 * Alerts
 * About
   * About BioScience
   * About AIBS
   * Journals Career Network
   * Editorial Board
   * Advertising and Corporate Services
   * Self-Archiving Policy
   * Potentially Offensive Content
   * Terms and Conditions

Close
Navbar Search Filter BioScienceThis issue EnvironmentScience and
MathematicsBooksJournalsOxford Academic Microsite Search Term Search
Advanced Search
Search Menu

Article Navigation
Close mobile search navigation
Article Navigation
Volume 63
Issue 8
August 2013


ARTICLE CONTENTS

 * Abstract
 * Risk posed by RNAi-based GM crops
 * Hazards posed by RNAi to nontarget organisms
 * Exposure to RNAi-based GM crops
 * Knowledge gaps in nontarget effects of RNAi-based crops and some potential
   solutions
 * Conclusions
 * Acknowledgments
 * References cited
 * Author notes

 * < Previous
 * Next >


Article Navigation
Article Navigation
Journal Article


RNAI-BASED INSECTICIDAL CROPS: POTENTIAL EFFECTS ON NONTARGET SPECIES

Jonathan G. Lundgren,
Jonathan G. Lundgren
Search for other works by this author on:
Oxford Academic
Google Scholar
Jian J. Duan
Jian J. Duan
Search for other works by this author on:
Oxford Academic
Google Scholar

Jonathan G. Lundgren (jonathan.lundgren@ars.usda.gov) and Jian J. Duan are
research entomologists with the US Department of Agriculture Agricultural
Research Service, in Washington, DC. Their expertise is in the ecological risk
of pest management technologies and developing biology-based management for
insects and weeds.

Author Notes
BioScience, Volume 63, Issue 8, August 2013, Pages 657–665,
https://doi.org/10.1525/bio.2013.63.8.8
Published:
01 August 2013

 * PDF
 * Split View
 * Views
     
   * Article contents
   * Figures & tables
 * Cite
   
   
   CITE
   
   Jonathan G. Lundgren, Jian J. Duan, RNAi-Based Insecticidal Crops: Potential
   Effects on Nontarget Species, BioScience, Volume 63, Issue 8, August 2013,
   Pages 657–665, https://doi.org/10.1525/bio.2013.63.8.8
   
   Select Format Select format .ris (Mendeley, Papers, Zotero) .enw (EndNote)
   .bibtex (BibTex) .txt (Medlars, RefWorks) Download citation
   Close
 * Permissions Icon Permissions
 * Share
     
   * Email
   * Twitter
   * Facebook
   * More
     

Navbar Search Filter BioScienceThis issue EnvironmentScience and
MathematicsBooksJournalsOxford Academic Mobile Microsite Search Term Search
 * Sign In
   * 


Close
Navbar Search Filter BioScienceThis issue EnvironmentScience and
MathematicsBooksJournalsOxford Academic Microsite Search Term Search
Advanced Search
Search Menu


ABSTRACT

The potential hazards posed by RNA interference (RNAi)–based pesticides and
genetically modified crops to nontarget organisms include off-target gene
silencing, silencing the target gene in unintended organisms, immune
stimulation, and saturation of the RNAi machinery. Nontarget organisms will vary
in their exposure to small RNAs produced by genetically modified crops, but
exposure to insecticidal small RNAs will probably occur at a previously
unrealized scale for many. Areas that warrant future work include the
persistence of insecticidal small RNAs in the environment, describing crop-based
food webs to understand those species that are most exposed, sequencing genomes
for species to proactively understand those that may be affected by RNAi, and
substantiating that laboratory toxicity testing can accurately predict the
field-level effects of this technology. The costs and benefits of pesticidal RNA
must be considered relative to current pest management options as pesticidal
RNAs move from a theoretical approach to being used as a practical tool.

Issue Section:
Forum

Modern pest management has evolved alongside recent developments in crop
production practices, and the speed at which new technologies for pest
management are advancing challenges our ability to predict and assess the
potential ecological risks associated with these technologies. Current
insect-resistant genetically modified (GM) crops are well tailored to fit within
modern crop production practices, but these technologies face challenges and
will need to adapt to accommodate increasing demands on crop production.
Additional pest management tools are needed to keep up with future agricultural
demands, and RNA interference (RNAi)–based insecticides and GM crops are one
response to this impending problem (box 1).



Box 1. What is RNAi?


RNA interference (RNAi) is a posttranscriptional technique for the
sequence-selective silencing of genes (Agrawal et al. 2003, Siomi and Siomi
2009). Fragments of small RNAs (small interfering RNAs [siRNA] or microRNAs)
bind to messenger RNAs (mRNAs) and promote cleavage by a complex of enzymes,
thereby reducing the expression of specific genes. For decades, RNAi was known
to occur in plants (as posttranscriptional gene silencing) and fungi (as
quelling) but was only first reported in animals (the nematode Caenorhabditis
elegans) in 1998 (Agrawal et al. 2003). A cell produces double-stranded RNAs
(dsRNAs) or microRNAs that target mRNAs from a specific gene, depending on
nucleotide sequence, or dsRNAs are taken into a cell from the exterior
environment (environmental RNAi; Huvenne and Smagghe 2010). The dsRNA (generally
fewer than 1000 nucleotides [nt] long) is then cleaved into much smaller siRNAs
(almost always 21–23 nt long), which are sometimes amplified intracellularly
(Siomi and Siomi 2009). It is noteworthy that this amplification has not been
widely found in insects (a primary target of RNAi-based GM crops; an exception
is embryonic Drosophila melanogaster) or mammals (Agrawal et al. 2003, Dillin
2003). The siRNAs are incorporated into an RNA-induced silencing complex (RISC),
where mRNAs are cleaved with an enzyme in the Argonaute family, and their
translation is silenced. Silencing in the absence of cleavage may result if the
RISC unit simply binds to an mRNA, thereby restricting its translation (Alemán
et al. 2007). RNAi is not a way to knock out gene expression, only a way to
suppress it, and sometimes only temporarily.

There is a growing interest in using RNAi for insect control, both as a
traditionally applied insecticide and within GM plants. RNAi-based GM plants
targeting insects have been developed in three independent research programs,
although additional GM crops are in development. Baum and colleagues (2007)
developed GM corn plants that resisted the western corn rootworm (Diabrotica
virgifera; Coleoptera: Chrysomelidae). By reducing translation of vacuolar
H+-ATPase subunit A (v-ATPase A) in the pest, the plant increased pest mortality
and larval stunting and experienced less root damage as a result. Zha and
colleagues (2011) transformed rice plants to suppress the expression of several
genes in Nilaparvata lugens (Hemiptera: Delphacidae), a major pest. Although
gene expression was suppressed, the insects were not killed by feeding on the GM
rice plants. In a different approach to pest management, Mao and colleagues
(2011) transformed cotton plants to produce double-stranded RNA (dsRNA) that
reduced the expression of the P450 gene CYP6AE14 in cotton bollworms
(Helicoverpa armigera; Lepidoptera: Noctuidae). This P450 degrades gossypol, an
antiherbivore phytochemical produced by cotton. GM cotton plants experienced
less damage than the conventional plants did, and the larvae that were fed the
GM cotton had reduced growth but were not killed. These examples illustrate that
the creation of RNAi-based GM crops that are lethal to pests or that
deleteriously affect interactions of the pests with other organisms (including
the crop) is a very real technology that has potential for limiting the impact
of pests on crops.


RISK POSED BY RNAI-BASED GM CROPS

There are similarities and differences in the risks associated with insecticidal
RNAi relative to those posed by chemical and microbial pesticides and Bt crops,
which have pesticidal effects derived from the bacteria Bacillus thuringiensis
(Heinemann et al. 2013). Risk is often assessed for pesticides and Bt crops
using a tiered approach that relies on a maximum-hazard dose testing regimen
targeted at indicator species. Laboratory toxicity assays involve administering
nontarget species a maximum-hazard dose (1–20 times the dose) of the known
environmental exposure concentration (Harrap 1982, USEPA 2002, 2003). The tests
are often focused on six to eight indicator species (such as honeybees,
springtails, earthworms, daphnia, predatory beetles or pirate bugs, and
parasitoid wasps), which represent different functional guilds (e.g.,
pollinators, predators, parasitoids, detritivores; USEPA 1994, 1998). A good
example of this approach involves Bt crops.

Since the early 1990s, the maximum-hazard dose regimen has been used to
characterize the level of risk of several classes of insecticidal Cry proteins
produced by the entomopathogen B. thuringiensis that are expressed in Bt crops.
To date, this testing regimen has revealed no toxicity of Cry proteins to the
selected indicator species (O'Callaghan et al. 2005, Romeis et al. 2006, Duan et
al. 2008, 2010). In part, this is because Bt crops have a very narrow and
predictable activity spectrum. This specificity is related to the physiological
conditions of the insect gut, especially the presence of specific receptor sites
on the midgut epithelium (van Frankenhuyzen 2009, Jurat-Fuentes and Jackson
2012). The long historical use of Bt as a microbial pesticide provided crucial
baseline information on the mode of action of Bt against susceptible insects,
which has helped scientists understand the results of maximum-hazard dose
testing involving Bt crops. The effects of Bt toxins on the field abundance of
nontarget organisms is often (but not always) predictable using the
maximum-hazard dose regimen (Duan et al. 2010), and, arguably, the
commercialization of Bt crops has had few if any consistent direct effects on
the abundance of nontarget organisms under field conditions (Marvier et al.
2007, Duan et al. 2008, Wolfenbarger et al. 2008, Lundgren et al. 2009, Naranjo
2009, Peterson et al. 2011). For chemical and microbial pesticides and Bt crops,
the modes of action are well described, and laboratory nontarget toxicity assays
can be focused and optimized on the basis of predictable effects. Although many
aspects of the risk assessment of RNAi are similar to those used to assess the
risks of other GM crops and pesticides, there is a crucial difference between
these technologies that pertains to the mode of action of small RNAs. Small RNAs
often have off-target binding elsewhere in a nontarget species' genome that
makes predicting toxic effects and designing maximum-hazard dose assays
challenging for the wide range of species potentially exposed. This conclusion
is in contrast to that of McLean (2011), which was that the maximum-hazard dose
paradigm would sufficiently address the risk of RNAi-based technologies. Some
potential hazards of small RNAs and exposure pathways are presented in detail
below.

The risk posed by RNAi used by plants and other organisms to regulate gene
expression, cellular development, and to combat transposon or viral invaders
differs from that of insecticidal small RNAs. Evidence suggests that RNAi may
have originally evolved within eukaryotes as a way to combat infections from
viruses and transposons (Agrawal et al. 2003). GM RNAi-based plants that resist
viral phytopathogens are currently commercially available (Mansoor et al. 2006,
Auer and Frederick 2009). However, insecticidal RNAi differs from RNAi used in
plants to combat viral pathogens in that—to the best of our knowledge—RNAi is
not used by plants in the natural world to silence critical gene functions in
herbivores. Insecticidal small RNAs are specifically selected or designed to
overcome cellular defenses and barriers to small RNAs in order to kill a higher
organism. With barriers overcome, genes in higher organisms may be more exposed
to insecticidal small RNAs than they are to antiviral small RNAs.


HAZARDS POSED BY RNAI TO NONTARGET ORGANISMS

Although small interfering RNAs (siRNAs) were originally believed to be
extremely specific (Dillin 2003), recent experience with RNAi in functional
genomics has revealed that siRNAs often silence unintended genes (Davidson and
McCray 2011). Moreover, the process of RNAi can affect organisms in ways that
transcend the effects of gene silencing. The hazards of siRNAs within nontargets
can be categorized as off-target gene silencing, silencing the target gene in
nontarget organisms, immune stimulation, and saturation of the RNAi machinery
(this list is adapted from Jackson and Linsley 2010). Knowledge gaps in the
genomics and physiologies of highly exposed nontarget organisms currently
preclude our ability to assess the activity spectrum of RNAi, determine whether
toxicity assays will be sufficient in predicting the risks of RNAi-based crops,
and explain how these risks may affect food webs associated with agroecosystems.
This last knowledge gap is not unique to RNAi-based technologies.

The specificity of siRNAs for a specific messenger RNA (mRNA) is linked to a
certain minimal level of sequence homology. Perfect sequence homology between an
mRNA and the dsRNA expectedly results in suppression of the targeted mRNA
(Elbashir et al. 2002) but represses the phenotype to varying degrees, depending
on the mRNA selected. Substantial sequence divergence between the two molecules
does not preclude gene silencing (Saxena et al. 2003). In part, this is because
the dsRNA is cleaved into numerous, very short (21–23 nucleotides [nt]) siRNAs
that have abundant direct sequence matches throughout the genomes of most
organisms. This consistent size of siRNAs optimizes the specificity of the siRNA
for the target mRNA relative to the likelihood of off-target binding (Qiu et al.
2005) but does not preclude off-target effects for nontarget organisms (Jackson
and Linsley 2010). Quite the contrary, it appears that RNAi operates within
cells using a certain level of redundancy among targets (Jackson et al. 2006).
One way to reduce potential nontarget effects may be to engineer plants to
produce siRNA or microRNA of a known sequence rather than dsRNAs that are
subsequently cleaved, but this may reduce the likelihood of silencing the target
gene, as well. Recent research has shown that sequence identity in the final 2–8
nt of the 5′ end of the guide strand of siRNA (dubbed the seed region; this
corresponds to the 3′ untranscribed region [UTR] of the mRNA) is the only
homology necessary for some level of silencing of both target and off-target
genes (Jackson et al. 2006, Jackson and Linsley 2010). Once this requisite seed
region sequence is matched, additional sequence homology and characteristics can
encourage the fidelity of the reaction with the target. But even the most
rational dsRNA design does not preclude some level of off-target sequence
matching and potential off-target gene suppression in nontarget organisms.


OFF-TARGET GENE SILENCING.

One conclusion from the recent advances in functional genomics that has
important implications for risk assessment of RNAi-based GM crops is that siRNAs
commonly have off-target effects within a targeted cell or organism (Davidson
and McCray 2011). The first evidence of this comes through in silico comparisons
of sequence homologies between siRNAs and sequences present in the targeted
organism. One in silico examination of sequence homologies between siRNA
sequences and three transcriptomes from diverse organisms revealed that
off-target effects were observed in as few as 5% and up to 80% of the siRNAs
assessed (Qiu et al. 2005). Another study showed that 17% of siRNAs had complete
sequence homologies with off-target binding sites in the Drosophila melanogaster
genome (Kulkarni et al. 2006). Designing siRNA to reduce off-target binding
still produced an average off-target binding rate of 10% or greater (Qiu et al.
2005). Given the small sizes of siRNAs, it is not surprising that off-target
binding sites are prevalent within the genomes of all organisms evaluated to
date. Although off-target binding would not appear to be a concern in target
organisms, off-target binding in nontarget organisms is a real hazard posed by
RNAi if the nontargets are sufficiently exposed to the RNAi.

Increasing rates of mRNA and protein suppression are often correlated with
increasing rates of off-target binding predicted by in silico searches for
sequence homologies between siRNAs and mRNAs, especially when the sequences of
the seed region, rather than the complete sequence of the siRNA (Birmingham et
al. 2006), are considered. Suppression of mRNA by off-target binding reduces
some phenotypes (Saxena et al. 2003), although RNAi effects on off-target
protein levels tend to be less studied than mRNA regulation. Federov and
colleagues (2006) found that 29% of off-target suppression of mRNAs resulted in
reduced viability of transfected cells and that sequence characteristics of the
dsRNA affected these viability rates. Off-target binding of siRNAs resulted in
reduced protein production in 7 of 30 cases involving a cell culture; this
off-target suppression of genes was not accompanied by mRNA cleavage but by
binding of the siRNA and the RNA-induced silencing complex (RISC) unit with the
targeted mRNA (Alemán et al. 2007). Therefore, considering only mRNA levels may
overlook some off-target gene silencing (Saxena et al. 2003, Alemán et al.
2007). These studies indicate that off-target effects of siRNAs used in RNAi are
probably more common than was initially believed; these effects could have
implications for nontarget effects of GM crops if off-target gene suppression
occurs in nontarget organisms and if these organisms are exposed to RNAi to a
sufficient degree.


SILENCING GENES IN NONTARGET ORGANISMS.

Most of the work on off-target silencing is related to functional genomics
within a single organism, and so the question of how dsRNAs affect target and
off-target genes in nontarget organisms has received very little attention.
Nevertheless, this is a critical factor in the commercialization of insecticide
RNAi. Jackson and Linsley (2010) suggested that off-target silencing appears to
be more common within the target organism than in nontarget organisms, but this
suggestion was based solely on a comparison between humans and mice. A recent
study showed that plant-produced microRNAs constitute 5%–10% of human microRNAs
and that these are likely taken in with food (Zhang et al. 2012). The amount of
plant microRNA found in rat serum increased when the rats were fed diets
containing specific plant microRNAs from rice, even when the rice diet was
cooked. One specific plant-produced microRNA examined, miR168, was complementary
to mRNAs within rat liver cells and reduced the production of proteins involved
in regulating levels of low-density lipoprotein in the rat circulatory system.
This work indicates that interspecies nontarget binding of siRNAs and microRNAs
taken into an animal through food may occur more often than is commonly thought
and may influence gene expression in nontarget organisms that ingest siRNAs
within plant tissues. In developing GM maize plants resistant to D. virgifera,
Baum and colleagues (2007) also examined the effects of a few of the dsRNAs
identified for plant transformation on several other beetle species. They found
that the dsRNAs that targeted D. virgifera v-ATPase A and E also reduced
survival of Diabrotica undecimpunctata and Leptinotarsa decemlineata
significantly, even though these pests shared only 79% and 83%, respectively,
sequence homologies in these genes with D. virgifera. Off-target binding of
dsRNAs that targeted the v-ATPase A and E genes was not examined in this study.
In laboratory feeding assays, Whyard and colleagues (2009) did not find
increased mortality when Drosophila spp. ingested dsRNAs designed to suppress a
congener's tubulin gene. These results were echoed when more phylogenetically
distant insect taxa ingested dsRNAs aimed at repressing other species' γ-tubulin
or v-ATPase expression, although mRNA knockdown for the latter gene was minimal
even for the targeted insect species. Additional research in this area will shed
light on the potential nontarget effects of insecticidal dsRNAs and will
hopefully address whether a focus on toxicity (the focus of published studies
thus far) is sufficient for predicting the nontarget effects of RNAi-based crops
under field conditions.


IMMUNE STIMULATION.

The innate immune systems of higher organisms rely on pattern recognition
proteins and other factors to identify potentially pathogenic invaders, and
these defenses recognize and eliminate dsRNAs that are potential pathogens.
Recently, it was found that the injection of small fragments (fewer than 30 nt)
of RNA could stimulate an immune reaction in mammals (Robbins et al. 2009). In
this group, some Toll-like receptors recognize and respond to the sequence,
length, and structure of siRNAs. This has been studied most intensively in
mammals, and in mice, the immunostimulation by RNAi led to reduced lymphocytes
and platelet cells, largely correlated with cytokine response to the siRNA
(Judge et al. 2005). Although there are some similarities in the innate immune
response of insects and mammals (Lundgren and Jurat-Fuentes 2012), it is unclear
how the immune systems of other organisms will react to an influx of small RNAs.
Nor is it known how this immunostimulation will affect the fitness of nontarget
organisms. Indeed, the risk of immunostimulation by dsRNAs may be one reason for
which the enzyme RNA-dependent RNA polymerase (RdRP), which is responsible for
amplifying the abundance of siRNAs in some organisms, has yet to be found in
mammals and insects (Agrawal et al. 2003, Dillin 2003). Although slight changes
in nucleotide sequence can mitigate many immunostimulatory effects in a given
organism (Jackson and Linsley 2010), substantial research will be required if we
are to determine the effects of RNAi inputs on the immune responses of members
of entire biological communities associated with agroecosystems.


SATURATION OF THE RNAI MACHINERY.

High levels of exogenous siRNAs can saturate a cell's RNAi machinery and thereby
reduce the efficiency at which a cell regulates endogenous gene expression
(Agrawal et al. 2003, Dillin 2003). Essentially, there is a limited number of
RISCs present within a cell, and if the augmented siRNAs saturate these
complexes, the health and performance of the cell may be compromised (Kahn et
al. 2009). Jackson and Lindley (2010) found evidence that small RNAs could have
“global effects on the expression of genes predicted to be under the control of
endogenous microRNAs” (p. 64). This process of saturation is better documented
with small hairpin RNA (a type of siRNA that targets a specific place on the
mRNA), although it is known from siRNA as well (Jackson and Linsley 2010). The
degree to which a nontarget species is exposed to a specific pesticidal small
RNA needs to be considered when saturation potentials are discussed. Suffice it
to say that it is unclear how dsRNAs produced by plants could affect the RNAi
machinery used by both target and nontarget organisms and whether there will be
sufficient small RNA produced by GM plants to saturate an organism's cellular
machinery.


EXPOSURE TO RNAI-BASED GM CROPS

Even the most toxic pesticides pose no risk to nontarget organisms if the
organisms are not exposed physically or physiologically to these toxins in the
environment. There is a substantial number of nontarget species that will be
exposed to RNAi-based crops if planting is widespread, but the exposure level
for each of the myriad species remains difficult to predict. This exposure
includes physical exposure to the toxin (i.e., being in the right place at the
right time), but it also involves the organism's having the correct
physiological characteristics (e.g., receptor sites, genetic sequences, cellular
machinery) to allow the toxin to work if it is physically exposed.


PHYSICAL EXPOSURE TO INSECTICIDAL RNAI.

A large number of nontarget organisms will likely be physically exposed to
insecticidal small RNAs if RNAi-based crops and RNAi-based insecticidal sprays
are commercialized and widely used, but this physical exposure would be similar
to that experienced by current GM crops and systemic chemical and endophytic
microbial pesticides. Physical exposure is also constrained to those organisms
that consume the toxin. However, much of the non- and off-target work on RNAi
has been conducted in a Petri dish, so understanding the physical exposure to
small RNAs at this greatly amplified scale is important. In 2011, nearly 10%
(735,000 square kilometers) of the land surface in the continental United States
was planted with three plant species (corn, soybean, and cotton), each of which
is currently genetically modified to facilitate pest management (insects or
weeds; see figure 1; www.nass.usda.gov/index.asp). Although biological
inventories of these agroecosystems have been pursued by biologists for more
than 100 years, we still have a poor resolution of the large number of
eukaryotic species (most of which possess the machinery for and use RNAi) that
reside within these habitats. Nevertheless, the numbers available suggest that
each community has several hundred species. For example, hundreds of arthropod
species reside within cornfields, and these dynamic communities change over the
season and vary by region (Bhatti et al. 2005, Dively 2005, Lundgren and Fergen
2010). Add to this inventory fungi, noninsect animals, and noncrop plants that
use RNAi, and the list of species in this habitat expands substantially. The
roles that most of these species play in healthy ecosystem functioning are
entirely unknown. Considering the current footprint of GM crops on the
terrestrial landscape and the number of species residing in those crop habitats,
a significant number of species will be exposed to RNAi-based crops if this
technology becomes adopted at a level comparable to that of current GM crops.

Figure 1.
Open in new tabDownload slide

In 2011, approximately 26% of the land surface (558,000 square kilometers) in 12
Midwestern states (Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota,
Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin) was planted
almost exclusively with two plant species: corn and soybean, both of which are
genetically modified for weed and insect pest management. In the map, red marks
cotton fields, green is corn, and blue is soybean. The data, from 2011, were
generated using the US Department of Agriculture National Agriculture Statistics
Service's Cropland Data Layer Program data set.


PHYSIOLOGICAL EXPOSURE TO INSECTICIDAL RNAI.

Many nontarget organisms have characteristics that will allow them to be
physiologically exposed to insecticidal small RNAs; this differs from narrow
spectrum insecticides such as Bt. If possessing the correct 23-nt gene sequence
were all that dictated the physiological exposure to insecticidal RNAs, nearly
all physically exposed organisms would be considered physiologically exposed
(these short nucleotide sequences are randomly present in many genomes);
clearly, this is not the case. Higher organisms also present numerous barriers
(e.g., physiological gut conditions, specificity of RNAi enzymatic machinery) to
restrict unwanted gene silencing by ingested small RNAs. Understanding the
physiological basis of RNAi reveals several levels of physiological
characteristics that will winnow the number of nontarget species ultimately
exposed to unintentional gene silencing by insecticidal RNAs.

Organisms ingest small RNAs with every meal, and this obviously does not appear
to silence gene functions. Environmental and physiological conditions in the gut
probably destroy many small RNAs taken in with food (Wang J et al. 2010, O'Neill
et al. 2011). Those small RNAs that survive must be adapted to function within
an organism. Different organisms have slight deviations in the receptors that
allow transmembrane movement of dsRNAs (SID1, SID2) and in enzymes that direct
RNAi (e.g., Dicer, Argonaute, RdRP, RNA and DNA helicases; Agrawal et al. 2003,
Siomi and Siomi 2009). These enzymes often have similar or identical functional
domains, and knowledge gaps make it unclear how dsRNAs that target a pest will
function within the RNAi pathways of other organisms (especially
phylogenetically divergent ones). What is clear is that insecticidal small RNAs
are selected or designed to suppress genes within arthropods after ingestion
and, therefore, possess mechanisms that allow them to overcome the restrictions
that prohibit the function of the myriad other small RNAs ingested with every
meal. We hypothesize that nontarget taxa that are phylogenetically close to the
targeted pest will be most likely to have similar RNAi pathways and suggest that
these taxa are most likely to be affected by RNAi; additional information on
other species is necessary to substantiate this assumption.

In a sense, mRNAs are in an arms race with RNAi, and the nucleotide sequences of
both drive which genes might be affected by a particular RNAi. Genes whose
regulation is tied to RNAi tend to have longer 3′ UTRs with more potential seed
regions that facilitate binding of siRNAs and microRNAs; those mRNAs that are
not targeted by RNAi have shorter 3′ UTRs (Jackson et al. 2006). These
untargeted mRNAs can also regulate their expression so that coexpression with
mRNA targets is avoided (Qiu et al. 2005, Jackson et al. 2006). The structure of
the siRNA and mRNA in question also has important effects on the outcome of
off-target RNAi. Modifying the second position of the seed region of an siRNA by
substituting it with O-methyl ribosyl can reduce but not eliminate off-target
binding within a target organism (at least in cell lines; Jackson and Linsley
2010). The concentration of the small RNA and the level of gene expression
dictate which genes will be suppressed in specific tissues and at what level
(Elbashir et al. 2002, Jackson and Linsley 2010). Much of the focus in
off-target studies also centers on the sequences of the siRNA and the
corresponding region of the mRNA, but sequences in the mRNA that surround the
homologous region also affect whether a specific mRNA will be bound to a RISC.
Although the knowledge gained from each study improves our ability to predict
the outcome of RNAi, we still do not fully understand all of the reasons that
RNAi functions only some of the time (Jackson and Linsley 2010). Suffice it to
say that knowledge gaps reduce our ability to predict when the fitness and
performance of nontarget organisms will be affected, even when in silico
comparisons between siRNAs and nontarget genomes suggest that binding is likely.


KNOWLEDGE GAPS IN NONTARGET EFFECTS OF RNAI-BASED CROPS AND SOME POTENTIAL
SOLUTIONS

Not all of the hundreds of species living in agroecosystems will be equally
exposed to the pesticidal RNAs or will have measurable levels of hazard. Before
we are able to weigh the risks to nontarget species against the benefits gained
from protecting crops from herbivory, there are a number of knowledge gaps
regarding the nontarget effects of RNAi-based insecticides that merit further
study, not all of which are unique to RNAi-based technologies.

The persistence of dsRNAs and siRNAs in the environment and the movement of
these molecules throughout the landscape are largely unknown, but their
persistence will affect the degree to which nontarget organisms are exposed
(Auer and Frederick 2009, McLean 2011). Methods have been developed for
detecting the degradation of nucleic acids in various soils (Levy-Booth et al.
2007), and it is feasible that some of the technologies developed for studying
DNA degradation in the soil could be adapted to small RNAs (Wang Y et al. 2009).
Key considerations in RNA degradation rates include the biological, chemical,
and physical aspects of the soil (Levy-Booth et al. 2007, Pietramellara et al.
2009). Nucleic acids—DNA has been studied the most in this regard—in the soil
can persist by binding to humic substances and minerals, can be degraded by
microbes or extracellular deoxyribonucleases, or can be incorporated into
microbial genomes (Levy-Booth et al. 2007, Pietramellara et al. 2009). DNA from
crop plants can persist in the soil for as little as 7 days but for as long as
many years (Levy-Booth et al. 2007, Nielsen et al. 2007). For good evolutionary
reasons, RNA seems to degrade more quickly than DNA in the soil, but structural
aspects of the RNA molecule (e.g., hairpins) and the degradation rates of plant
tissues harboring the RNAi may facilitate the persistence of these molecules in
the environment. If transgenes or small RNA products are taken into microbial
genomes, this will have implications for which species are trophically exposed
to plant-derived RNAi. Environmental persistence of insecticidal toxins—be they
chemical, microbial, or nucleic acids—depends on various aspects of the soil,
environment, and biological community within a habitat. Understanding the
relative degradation rates of these myriad compounds will be important in
assessing the costs and benefits of RNAi-based technologies relative to those of
other insecticides.

Poor resolution of crop-based food webs prohibits knowing which species will be
exposed to crop-expressed dsRNAs. The primary route of exposure to pesticidal
RNAi is trophic in nature, as it is in current insecticidal GM crops. Recent
advances in tracking foods through food webs offer a good opportunity for
quantitatively and empirically narrowing the list of species that may be exposed
to RNAi technology. Gut content analysis (searching for a food-specific marker
within the stomachs or feces of field-collected animals; e.g., Weber and
Lundgren 2009) can identify which species directly consume a crop species (or a
crop's DNA) in the field (Harwood et al. 2005, Zwahlen and Andow 2005). The
reliability of these linkages is based on the technique's specificity for a
food-associated marker. Although there are limitations to the technique (Weber
and Lundgren 2009), polymerase chain reaction–based gut analysis has rapidly
become the tool of choice for many within this field because of its cost
effectiveness and the specificity possible with primer sequences (Weber and
Lundgren 2009). The relative frequencies of consumption of the GM crop can be
used to develop a focused list of species that can be further examined under
various hazard scenarios.

The activity spectrum of RNAi is ultimately sequence based, and genomic
information on most species is sparse (Auer and Frederick 2009). Recent advances
in next-generation sequencing technologies (Metzker 2010) will permit studies
that proactively identify targets for pesticidal RNAi on larger numbers of
species within an exposed community. The costs of sequencing entire genomes are
low enough that it is now feasible to sequence multiple species from members of
a biological community that may be exposed to the small RNAs (determined on the
basis of food-web analysis). Coordinated efforts to sequence entire genomes of
10,000 vertebrate species (Haussler 2009) and 5000 insect species (Robinson et
al. 2011) are currently under way. The sequence homologies between small RNAs
expressed by a GM plant and key species in a crop habitat could then be compared
in silico using modern algorithms designed for searching for RNAi off-target
homologies (McLean 2011).

It is unclear whether traditional toxicity assays are appropriately attuned to
determine the effects of RNAi on the fitness and performance of nontarget
organisms. The physiological effects of RNAi on nontarget organisms are
difficult to predict without some knowledge of which genes are at risk of being
silenced by specific small RNAs. Given the mechanism of RNAi (gene suppression),
it is possible that the nontarget effects experienced would be sublethal; it is
unlikely that these effects would be measurable by looking at survival over time
in the laboratory. Some evaluations of the nontarget effects of Bt crops and
traditional insecticides advocate the use of the intrinsic rate of population
growth, which can be measured in the laboratory and which integrates survival,
development time, reproduction, and sex ratio to generate a clearer picture of
how a plant-incorporated toxin affects a nontarget species (Bøhn et al. 2010, Li
and Romeis 2010). Assessments that provide a comprehensive view of various
life-history traits may be justified, given the mode of action posed by RNAi.

Are laboratory assays sufficient for assessing the effects of RNAi? As was
discussed at length earlier, RNAi suppresses phenotypes by prohibiting the
translation of mRNAs, and it may be challenging to predict which target or
off-target genes will be suppressed in nontarget organisms solely on the basis
of sequence homologies. Since mRNAs are transcribed as they are needed by the
organism, it is important to recognize that the environment plays a significant
role in gene expression and, therefore, in which genes will be exposed to the
inhibitory small RNAs (Smith and Kruglyak 2008). As a result, conducting hazard
assays under controlled conditions within a laboratory may change or reduce the
expression of genes that are potential off-targets in nontarget organisms. If
gene expression is reduced or altered under laboratory conditions, it may be
appropriate to conduct field-based assessments of RNAi-based, insect-resistant
crops against nontarget organisms, regardless of the outcome of laboratory-based
hazard testing.


CONCLUSIONS

The rapid development of RNAi applications has challenged scientists to identify
and fill key knowledge gaps that underlie the environmental implications of
large-scale, pesticidal RNAi-based crops. Much of what we know regarding RNAi
comes from the field of functional genomics and the development of gene
therapies within individual organisms or even within a specific tissue. How
specific small RNAs affect diverse nontarget communities merits further
attention, especially in light of the frequent off-target effects of siRNAs
within a single target organism. New technologies involved in food web analysis
and next-generation sequencing are likely to facilitate the development of
risk-assessment frameworks for RNAi-based crops, particularly by honing the
relative exposure levels experienced by members of the nontarget community.
Because RNAi effects are sequence-based, proactive identification of species
with sequences homologous with putative small RNAs for use in pest control could
expedite the selection of small RNAs that balance the maximum effects on the
target pests with the minimal effects on nontarget organisms. For example, if an
organism does not have the genetic sequences that small RNAs can affect, even
maximum exposure doses will not result in hazard. Therefore, targeting genes for
pest management that are inherently tied to a single species' biology (e.g.,
detoxification pathways, developmental regulatory hormones, or mate-finding
signals) may reduce the likelihood of silencing a target gene in a nontarget
organism. The flexibility, adaptability, and demonstrated effectiveness of RNAi
technology indicate that it will have an important place in the future of pest
management, but these benefits should be viewed in light of the relative
environmental risks that the technology poses.


ACKNOWLEDGMENTS

We thank Tatyana Rand of the US Department of Agriculture Agricultural Research
Service (USDA-ARS) for creating the distribution map of genetically modified
crops. Jimmy Becnel, Keith Hopper, and Don Weber from USDA-ARS; John Turner of
the USDA Animal and Plant Health Inspection Service; Robert Wiedenmann of the
University of Arkansas; and Chris Wozniak of the US Environmental Protection
Agency provided helpful comments on earlier drafts of the manuscript. Mention of
trade names or commercial products in this publication is solely for the purpose
of providing specific information and does not imply recommendation or
endorsement by the US Department of Agriculture.


REFERENCES CITED

Agrawal
N
Dasaradhi
PVN
Mohmmed
A
Malhotra
P
Bhatnagar
RK
Mukherjee
SK
.
2003
.
RNA interference: Biology, mechanism, and applications
.
Microbiology and Molecular Biology Reviews
67
:
657
–
685
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Alemán
LM
Doench
J
Sharp
PA
.
2007
.
Comparison of siRNA-induced off-target RNA and protein effects
.
RNA
13
:
385
–
395
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Auer
C
Frederick
R
.
2009
.
Crop improvement using small RNAs: Applications and predictive ecological risk
assessments
.
Trends in Biotechnology
27
:
644
–
651
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Baum
JA
et al.  .
2007
.
Control of coleopteran insect pests through RNA interference
.
Nature Biotechnology
25
:
1322
–
1326
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Bhatti
MA
Duan
J[J]
Head
GP
Jiang
C
McKee
MJ
Nickson
TE
Pilcher
CL
Pilcher
CD
.
2005
.
Field evaluation of the impact of corn rootworm (Coleoptera:
Chrysomelidae)–protected Bt corn on foliage-dwelling arthropods
.
Environmental Entomology
34
:
1336
–
1345
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Birmingham
A
et al.  .
2006
.
3′ UTR seed matches, but not overall identity, are associated with RNAi
off-targets
.
Nature Methods
3
:
199
–
204
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Bøhn
T
Traavik
T
Primicerio
R
.
2010
.
Demographic responses of Daphnia magna fed transgenic Bt-maize
.
Ecotoxicology
19
:
419
–
430
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Davidson
BL
McCray
PB
.
2011
.
Current prospects for RNA interference–based therapies
.
Nature Reviews Genetics
12
:
329
–
340
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Dillin
A
.
2003
.
The specifics of small interfering RNA specificity
.
Proceedings of the National Academy of Sciences
100
:
6289
–
6291
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Dively
GP
.
2005
.
Impact of transgenic VIP3a × Cry1Ab lepidopteran-resistant field corn on the
nontarget arthropod community
.
Environmental Entomology
34
:
1267
–
1291
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Duan
JJ
Marvier
M
Huesing
J
Dively
G[P]
Huang
ZY
.
2008
.
A meta-analysis of effects of Bt crops on honey bees (Hymenoptera: Apidae)
.
PLOS ONE
3
(art. e1415)
.



Google Scholar

OpenURL Placeholder Text

WorldCat

 

Duan
JJ
Lundgren
JG
Naranjo
S[E]
Marvier
M
.
2010
.
Extrapolating non-target risk of Bt crops from laboratory to field
.
Biology Letters
6
:
74
–
77
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Elbashir
SM
Harborth
J
Weber
K
Tuschl
T
.
2002
.
Analysis of gene function in somatic mammalian cells using small interfering
RNAs
.
Methods
26
:
199
–
213
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Federov
Y
Anderson
EM
Birmingham
A
Reynolds
A
Karpilow
J
Robinson
K
Leake
D
Marshall
WS
Khvorova
A
.
2006
.
Off-target effects by siRNA can induce toxic phenotype
.
RNA
12
:
1188
–
1196
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Harrap
KA
.
1982
.
Assessment of the human and ecological hazards of microbial insecticides
.
Parasitology
84
:
269
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Harwood
JD
Wallin
WG
Obrycki
JJ
.
2005
.
Uptake of Bt-endotoxins by non-target herbivores and higher order arthropod
predators: Molecular evidence from a transgenic corn agroecosystem
.
Molecular Ecology
14
:
2815
–
2823
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Haussler
D
et al.  .
2009
.
Genome 10K: A proposal to obtain whole-genome sequence for 10,000 vertebrate
species
.
Journal of Heredity
100
:
659
–
674
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Heinemann
JA
Agapito-Tenfen
SZ
Carman
JA
.
2013
.
A comparative evaluation of the regulation of GM crops or products containing
dsRNA and suggested improvements to risk assessments
.
Environment International
55
:
43
–
55
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Huvenne
H
Smagghe
G
.
2010
.
Mechanisms of dsRNA uptake in insects and potential of RNAi for pest control: A
review
.
Journal of Insect Physiology
56
:
227
–
235
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Jackson
AL
Linsley
PS
.
2010
.
Recognizing and avoiding siRNA off-target effects for target identification and
therapeutic application
.
Nature Reviews Drug Discovery
9
:
57
–
67
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Jackson
AL
Burchard
J
Schelter
J
Chau
BN
Cleary
M
Lim
L
Linsley
PS
.
2006
.
Widespread siRNA “off-target” transcript silencing mediated by seed region
sequence complementarity
.
RNA
12
:
1179
–
1187
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Judge
AD
Sood
V
Shaw
JR
Fang
D
McClintock
K
MacLachlan
I
.
2005
.
Sequence-dependent stimulation of the mammalian innate immune response by
synthetic siRNA
.
Nature Biotechnology
23
:
457
–
462
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Jurat-Fuentes
JL
Jackson
TA
.
2012
.
Bacterial entomopathogens
. Pages
265
–
350
in
Vega
FE
Kaya
HK
, eds.
Insect Pathology
, 2nd ed.
Academic Press
.





Google Scholar

Crossref
Search ADS


Google Preview

WorldCat

COPAC
 

Kahn
AA
Betel
D
Miller
ML
Sander
C
Leslie
CS
Marks
DS
.
2009
.
Transfection of small RNAs globally perturbs gene regulation by endogenous
microRNAs
.
Nature Biotechnology
27
:
549
–
555
.





Google Scholar

PubMed
OpenURL Placeholder Text

WorldCat

 

Kulkarni
MM
Booker
M
Silver
SJ
Friedman
A
Hong
P
Perrimon
N
Mathey-Prevot
B
.
2006
.
Evidence of off-target effects associated with long dsRNAs in Drosophila
melanogaster cell-based assays
.
Nature Methods
3
:
833
–
838
.





Google Scholar

PubMed
OpenURL Placeholder Text

WorldCat

 

Levy-Booth
DJ
Campbell
RG
Gulden
RH
Hart
MM
Powell
JR
Klironomos
JN
Pauls
KP
Swanton
CJ
Trevors
JT
Dunfield
KE
.
2007
.
Cycling of extracellular DNA in the soil environment
.
Soil Biology and Biochemistry
39
:
2977
–
2991
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Li
Y
Romeis
J
.
2010
.
Bt maize expressing Cry3Bb1 does not harm the spider mite, Tetranychus urticae,
or its ladybird beetle predator, Stethorus punctillum
.
Biological Control
53
:
337
–
344
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Lundgren
JG
Fergen
JK
.
2010
.
The effects of a winter cover crop on Diabrotica virgifera (Coleoptera:
Chrysomelidae) populations and beneficial arthropod communities in no-till maize
.
Environmental Entomology
39
:
1816
–
1828
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Lundgren
JG
Jurat-Fuentes
JL
.
2012
.
Physiology and ecology of host defense against microbial invaders
. Pages
461
–
480
in
Vega
FE
Kaya
HK
, eds.
Insect Pathology
, 2nd ed.
Academic Press
.





Google Scholar

Crossref
Search ADS


Google Preview

WorldCat

COPAC
 

Lundgren
JG
Gassmann
AJ
Bernal
J
Duan
J[J]
Ruberson
J
.
2009
.
Ecological compatibility of GM crops and biological control
.
Crop Protection
28
:
1017
–
1030
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Mansoor
S
Amin
I
Hussain
M
Zafar
Y
Briddon
RW
.
2006
.
Engineering novel traits in plants through RNA interference
.
Trends in Plant Science
11
:
559
–
565
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Mao
Y-B
Tao
X-Y
Xue
X-Y
Wang
L-J
Chen
X-Y
.
2011
.
Cotton plants expressing CYP6AE14 double-stranded RNA show enhanced resistance
to bollworms
.
Transgenic Research
20
:
665
–
673
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Marvier
M
McCreedy
C
Regetz
J
Kareiva
P
.
2007
.
A meta-analysis of effects of Bt cotton and maize on nontarget invertebrates
.
Science
316
:
1475
–
1477
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

McLean
MA
ed.
2011
.
Problem Formulation for the Environmental Risk Assessment of RNAi Plants,
Conference Proceedings (June 1–3, 2011)
.
Center for Environmental Risk Assessment, International Life Sciences Institute
Research Foundation
.



Google Scholar

Google Preview

OpenURL Placeholder Text

WorldCat

COPAC
 

Metzker
ML
.
2010
.
Sequencing technologies: The next generation
.
Nature Reviews Genetics
11
:
31
–
46
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Naranjo
SE
.
2009
.
Impacts of Bt crops on non-target organisms and insecticide use patterns
.
CAB Reviews: Perspectives in Agriculture, Veterinary Science, Nutrition, and
Natural Resources
4
:
1
–
23
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Nielsen
KM
Johnsen
PJ
Bensasson
D
Daffonchio
D
.
2007
.
Release and persistence of extracellular DNA in the environment
.
Environmental Biosafety Research
6
:
37
–
53
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

O'Callaghan
M
Glare
TR
Burgess
EPJ
Malone
LA
.
2005
.
Effects of plants genetically modified for insect resistance on nontarget
organisms
.
Annual Review of Entomology
50
:
271
–
292
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

O'Neill
MJ
Bourre
L
Melgar
S
O'Driscoll
CM
.
2011
.
Intestinal delivery of non-viral gene therapeutics: Physiological barriers and
preclinical models
.
Drug Discovery Today
16
:
203
–
218
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Peterson
JA
Lundgren
JG
Harwood
JD
.
2011
.
Interactions of transgenic Bacillus thuringiensis insecticidal crops with
spiders (Araneae)
.
Journal of Arachnology
39
:
1
–
21
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Pietramellara
G
Ascher
J
Borgogni
F
Ceccherini
MT
Guerri
G
Nannipieri
P
.
2009
.
Extracellular DNA in soil and sediment: Fate and ecological relevance
.
Biology and Fertility of Soils
45
:
219
–
235
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Qiu
S
Adema
CM
Lane
T
.
2005
.
A computational study of off-target effects of RNA interference
.
Nucleic Acids Research
33
:
1834
–
1847
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Robbins
M
Judge
A
MacLachlan
I
.
2009
.
siRNA and innate immunity
.
Oligonucleotides
19
:
89
–
102
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Robinson
GE
Hackett
KJ
Purcell-Miramontes
M
Brown
SJ
Evans
JD
Goldsmith
MR
Lawson
D
Okamuro
J
Robertson
HM
Schneider
DJ
.
2011
.
Creating a buzz about insect genomes
.
Science
331
:
1386
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Romeis
J
Meissle
M
Bigler
F
.
2006
.
Transgenic crops expressing Bacillus thuringiensis toxins and biological control
.
Nature Biotechnology
24
:
63
–
71
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Saxena
S
Jónsson
ZO
Dutta
A
.
2003
.
Small RNAs with imperfect match to endogenous mRNA repress translation
.
Journal of Biological Chemistry
278
:
44312
–
44319
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Siomi
H
Siomi
MC
.
2009
.
On the road to reading the RNA-interference code
.
Nature
457
:
396
–
404
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Smith
EN
Kruglyak
L
.
2008
.
Gene–environment interaction in yeast gene expression
.
PLOS Biology
6
(art. e83)
.



Google Scholar

OpenURL Placeholder Text

WorldCat

 

[USEPA] US Environmental Protection Agency
.
1994
.
Proposed policy: Plant-pesticides subject to the federal insecticide fungicide,
and rodenticide act and the Federal Food, Drug, and Cosmetic Act
.
Federal Register
59
:
60496
–
60518
.



Google Scholar

OpenURL Placeholder Text

WorldCat

[USEPA] US Environmental Protection Agency
.
1998
.
Guidelines for Ecological Risk Assessment
.
USEPA
.



Google Scholar

Google Preview

OpenURL Placeholder Text

WorldCat

COPAC

[USEPA] US Environmental Protection Agency
.
2002
.
Guidance on Cumulative Risk Assessment of Pesticide Chemicals That Have a Common
Mechanism of Toxicity
.
Office of Pesticide Programs, USEPA
.
(14 May 2013; www.epa.gov/oppfead1/trac/science/cumulative_guidance.pdf )



Google Scholar

Google Preview

OpenURL Placeholder Text

WorldCat

COPAC

[USEPA] US Environmental Protection Agency
.
2003
.
Biopesticides Registration Action Document: Event MON 863 Bacillus thuringiensis
Cry3Bb1 corn
.
Office of Pesticide Programs, USEPA
.



Google Scholar

Google Preview

OpenURL Placeholder Text

WorldCat

COPAC

Van Frankenhuyzen
K
.
2009
.
Insecticidal activity of B. thuringiensis crystal proteins
.
Journal of Invertebrate Pathology
101
:
1
–
16
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Wang
J
Lu
Z
Wientjes
MG
Au
JL-S
.
2010
.
Delivery of siRNA therapeutics: Barriers and carriers
.
AAPS Journal
12
:
492
–
503
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Wang
Y
Morimoto
S
Ogawa
N
Oomori
T
Fujii
T
.
2009
.
An improved method to extract RNA from soil with efficient removal of humic
acids
.
Journal of Applied Microbiology
107
:
1168
–
1177
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Weber
DC
Lundgren
JG
.
2009
.
Assessing the trophic ecology of the Coccinellidae: Their roles as predators and
prey
.
Biological Control
51
:
199
–
214
.





Google Scholar

Crossref
Search ADS


WorldCat

 

Whyard
S
Singh
AD
Wong
S
.
2009
.
Ingested double-stranded RNAs can act as species-specific insecticides
.
Insect Biochemistry and Molecular Biology
39
:
824
–
832
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Wolfenbarger
LL
Naranjo
SE
Lundgren
JG
Bitzer
RJ
Watrud
LS
.
2008
.
Bt crop effects on functional guilds of non-target arthropods: A meta-analysis
.
PLOS ONE
3
(art. e2118)
.



Google Scholar

OpenURL Placeholder Text

WorldCat

 

Zha
W
Peng
X
Chen
R
Du
B
Zhu
L
He
G
.
2011
.
Knockdown of midgut genes by dsRNA-transgenic plant-mediated RNA interference in
the hemipteran insect Nilaparvata lugens
.
PLOS ONE
6
(art. e20504)
.



Google Scholar

OpenURL Placeholder Text

WorldCat

 

Zhang
L
et al.  .
2012
.
Exogenous plant MIR168a specifically targets mammalian LDLRAP1: Evidence of
cross-kingdom regulation by microRNA
.
Cell Research
22
:
107
–
126
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 

Zwahlen
C
Andow
DA
.
2005
.
Field evidence for the exposure of ground beetles to Cry1Ab from transgenic corn
.
Environmental Biosafety Research
4
:
113
–
117
.





Google Scholar

Crossref
Search ADS

PubMed

WorldCat

 



AUTHOR NOTES

Jonathan G. Lundgren (jonathan.lundgren@ars.usda.gov) and Jian J. Duan are
research entomologists with the US Department of Agriculture Agricultural
Research Service, in Washington, DC. Their expertise is in the ecological risk
of pest management technologies and developing biology-based management for
insects and weeds.

© 2013 American Institute of Biological Sciences
© 2013 American Institute of Biological Sciences




Download all slides


Advertisement


CITATIONS

89
CITATIONS


VIEWS

6,286


ALTMETRIC


More metrics information
×


EMAIL ALERTS

Article activity alert
Advance article alerts
New issue alert
Receive exclusive offers and updates from Oxford Academic




RELATED ARTICLES IN

 * Web of Science
 * Google Scholar


CITING ARTICLES VIA

Web of Science (72)
Google Scholar
Crossref


 * LATEST


 * MOST READ


 * MOST CITED

Climate Change and Long-Term Ecological Research

The Impact of Field Courses on Undergraduate Knowledge, Affect, Behavior, and
Skills: A Scoping Review

Was Henry David Thoreau a Good Naturalist? An Approach for Assessing Data from
Historical Natural History Records

Principles for Leading, Learning, and Synthesizing in Inter- and
Transdisciplinary Research

Forest and Freshwater Ecosystem Responses to Climate Change and Variability at
US LTER Sites



More from Oxford Academic
Environment
Science and Mathematics
Social Sciences
Books
Journals
Advertisement

Advertisement
 * About BioScience
 * Editorial Board
 * Author Guidelines
 * Facebook
 * Twitter

 * Purchase
 * Recommend to your Library
 * Advertising and Corporate Services
 * Journals Career Network


 * Online ISSN 1525-3244
 * Print ISSN 0006-3568
 * Copyright © 2022 American Institute of Biological Sciences

 * About Oxford Academic
 * Publish journals with us
 * University press partners
 * What we publish
 * New features 

 * Authoring
 * Open access
 * Purchasing
 * Get help with access
 * Institutional account management

 * Accessibility
 * Contact us
 * Advertising
 * Media enquiries
 * Legal and policy

 * Oxford University Press
 * News
 * Oxford Languages
 * Epigeum
 * University of Oxford

Oxford University Press is a department of the University of Oxford. It furthers
the University's objective of excellence in research, scholarship, and education
by publishing worldwide

 * Copyright © 2022 Oxford University Press
 * Cookie policy
 * Privacy policy
 * Legal notice



Close

Close


THIS FEATURE IS AVAILABLE TO SUBSCRIBERS ONLY

Sign In or Create an Account

Close

This PDF is available to Subscribers Only

View Article Abstract & Purchase Options

For full access to this pdf, sign in to an existing account, or purchase an
annual subscription.

Close