Exogenous Target Research Articles

Two “turn on” fluorescence probes based on nitroso recognition group for detecting hydrogen sulfide

Hydrogen sulfide is a vital signaling molecule which holds a pivotal position in numerous biological functions. In this research, two novel “OFF-ON” fluorescence probes named YNO and TNO were designed based on the nitroso recognition group to detect H2S. Both YNO and TNO performed outstanding response rate and linear relationship between the fluorescence intensity and the concentration of H2S. YNO possessed larger Stokes shift and longer emission wavelength. TNO had lower limit of detection. In addition, YNO was successful applied to sense endogenous and exogenous H2S and target endoplasmic reticulum (ER) in Hela cells.

Predictive Models for Aggregate Available Capacity Prediction in Vehicle-to-Grid Applications

The integration of vehicle-to-grid (V2G) technology into smart energy management systems represents a significant advancement in the field of energy suppliers for Industry 4.0. V2G systems enable a bidirectional flow of energy between electric vehicles and the power grid and can provide ancillary services to the grid, such as peak shaving, load balancing, and emergency power supply during power outages, grid faults, or periods of high demand. In this context, reliable prediction of the availability of V2G as an energy source in the grid is fundamental in order to optimize both grid stability and economic returns. This requires both an accurate modeling framework that includes the integration and pre-processing of readily accessible data and a prediction phase over different time horizons for the provision of different time-scale ancillary services. In this research, we propose and compare two data-driven predictive modeling approaches to demonstrate their suitability for dealing with quasi-periodic time series, including those dealing with mobility data, meteorological and calendrical information, and renewable energy generation. These approaches utilize publicly available vehicle tracking data within the floating car data paradigm, information about meteorological conditions, and fuzzy weekend and holiday information to predict the available aggregate capacity with high precision over different time horizons. Two data-driven predictive modeling approaches are then applied to the selected data, and the performance is compared. The first approach is Hankel dynamic mode decomposition with control (HDMDc), a linear state-space representation technique, and the second is long short-term memory (LSTM), a deep learning method based on recurrent nonlinear neural networks. In particular, HDMDc performs well on predictions up to a time horizon of 4 h, demonstrating its effectiveness in capturing global dynamics over an entire year of data, including weekends, holidays, and different meteorological conditions. This capability, along with its state-space representation, enables the extraction of relationships among exogenous inputs and target variables. Consequently, HDMDc is applicable to V2G integration in complex environments such as smart grids, which include various energy suppliers, renewable energy sources, buildings, and mobility data.

MRNA vaccine development and applications: A special focus on tumors (Review).

Cancer is characterized by unlimited proliferation and metastasis, and traditional therapeutic strategies usually result in the acquisition of drug resistance, thus highlighting the need for more personalized treatment. mRNA vaccines transfer the gene sequences of exogenous target antigens into human cells through transcription and translation to stimulate the body to produce specific immune responses against the encoded proteins, so as to enable the body to obtain immune protection against said antigens; this approach may be adopted for personalized cancer therapy. Since the recent coronavirus pandemic, the development of mRNA vaccines has seen substantial progress and widespread adoption. In the present review, the development of mRNA vaccines, their mechanisms of action, factors influencing their function and the current clinical applications of the vaccine are discussed. A focus is placed on the application of mRNA vaccines in cancer, with the aim of highlighting unique advances and the remaining challenges of this novel and promising therapeutic approach.

Comparison and optimization of different CRISPR/Cas9 donor-adapting systems for gene editing.

Gene knock-in in mammalian cells usually uses homology-directed repair (HDR) mechanism to integrate exogenous DNA template into the target genome site. However, HDR efficiency is often low, and the co-localization of exogenous DNA template and target genome site is one of the key limiting factors. To improve the efficiency of HDR mediated by CRISPR/Cas9 system, our team and previous studies fused different adaptor proteins with SpCas9 protein and expressed them. By using their characteristics of binding to specific DNA sequences, many different CRISPR/SpCas9 donor adapter gene editing systems were constructed. In this study, we used them to knock-in eGFP gene at the 3'-end of the terminal exon of GAPDH and ACTB genes in HEK293T cells to facilitate a comparison and optimization of these systems. We utilized an optimized donor DNA template design method, validated the knock-in accuracy via PCR and Sanger sequencing, and assessed the efficiency using flow cytometry. The results showed that the fusion of yGal4BD, hGal4BD, hLacI, hTHAP11 as well as N57 and other adaptor proteins with the C-terminus of SpCas9 protein had no significant effect on its activity. At the GAPDH site, the donor adapter systems of SpCas9 fused with yGal4BD, hGal4BD, hLacI and hTHAP11 significantly improved the knock-in efficiency. At the ACTB site, SpCas9 fused with yGal4BD and hGal4BD significantly improved the knock-in efficiency. Furthermore, increasing the number of BS in the donor DNA template was beneficial to enhance the knock-in efficiency mediated by SpCas9-hTHAP11 system. In conclusion, this study compares and optimizes multiple CRISPR/Cas9 donor adapter gene editing systems, providing valuable insights for future gene editing applications.

Abstract 6919: Cell line panel with HIBIT-tagged endogenous proteins to accelerate PROTAC drug discovery

Abstract Small molecular drugs have been an important part of disease treatment, but there are also some problems along with time and intrinsic disadvantages of small molecular drugs. In recent years, the emergence of proteolysis targeting chimeras (PROTACs) is expected to overcome some defeats of traditional small molecules. PROTACs, also known as hetero-bifunctional compounds, are a new kind of therapeutic, that consists of a specific ligand to bind the target protein, a suitable linker, and an E3 ubiquitin ligase substrate. After binding to target proteins, PROTACs recruit E3 ligase and induce degradation of target proteins through the ubiquitin-proteasome system. Therefore, PROTACs are a good choice for some protein targets that have small molecular binding ligands but poor small molecular efficacy, which expands druggable targets. Meanwhile, the protein degradation induced by PROTACs destroys not only the enzymatic but also the non-enzymatic functions of targets, rather than only inhibiting the protein activity, which reduces drug resistance. In this study, in order to detect the degradation of intracellular target proteins by PROTACs, Kyinno has designed and constructed several HiBiT-tagged cell line models for different target proteins and their mutations, including KRA, CDK2, LDHA, and HPK. HiBiT is a small tag with 11 amino acids that don’t influence the expression, folding, and localization of target proteins, and luminesces with a detection buffer. All these cell lines with endogenous protein mutations and HiBiT knock-ins are constructed by CRISPR-Cas9 technology and validated by siRNA and PROTACs. Compared with overexpressed exogenous HiBiT-tagged target proteins, endogenous proteins can more accurately and stably reflect the efficacy of PROTACs. Our cell models can be used for high-throughput screening not only for PROTAC drugs but also for molecular glues, lysosome-targeting chimeras (LYTACs), and antibody-based PROTACs (AbTACs). Citation Format: Yunpeng Zhai, Jiayi Ma, Yu Wang, Meng Liang, Yao Peng, Jinying Ning, Feng Hao. Cell line panel with HIBIT-tagged endogenous proteins to accelerate PROTAC drug discovery [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6919.

Expanding the DNA editing toolbox: Novel lambda integrase variants targeting microalgal and human genome sequences.

Recombinase enzymes are extremely efficient at integrating very large DNA fragments into target genomes. However, intrinsic sequence specificities curtail their use to DNA sequences with sufficient homology to endogenous target motifs. Extensive engineering is therefore required to broaden applicability and robustness. Here, we describe the directed evolution of novel lambda integrase variants capable of editing exogenous target sequences identified in the diatom Phaeodactylum tricornutum and the algae Nannochloropsis oceanica. These microorganisms hold great promise as conduits for green biomanufacturing and carbon sequestration. The evolved enzyme variants show >1000-fold switch in specificity towards the non-natural target sites when assayed in vitro. A single-copy target motif in the human genome with homology to the Nannochloropsis oceanica site can also be efficiently targeted using an engineered integrase, both in vitro and in human cells. The developed integrase variants represent useful additions to the DNA editing toolbox, with particular application for targeted genomic insertion of large DNA cargos.

PASTE, Don't Cut: Genome Editing Tool Looks Beyond CRISPR and Prime

PASTE, Don't Cut: Genome Editing Tool Looks Beyond CRISPR and Prime

Viral Capsid Change upon Encapsulation of Double-Stranded DNA into an Infectious Hypodermal and Hematopoietic Necrosis Virus-like Particle.

In this study, we aimed to encapsulate the sizable double-stranded DNA (dsDNA, 3.9 kbp) into a small-sized infectious hypodermal and hematopoietic necrosis virus-like particle (IHHNV-VLP; T = 1) and compared the changes in capsid structure between dsDNA-filled VLP and empty VLP. Based on our encapsulation protocol, IHHNV-VLP was able to load dsDNA at an efficiency of 30-40% (w/w) into its cavity. Structural analysis revealed two subclasses of IHHNV-VLP, so-called empty and dsDNA-filled VLPs. The three-dimensional (3D) structure of the empty VLP produced in E. coli was similar to that of the empty IHHNV-VLP produced in Sf9 insect cells. The size of the dsDNA-filled VLP was slightly bigger (50 Å) than its empty VLP counterpart; however, the capsid structure was drastically altered. The capsid was about 1.5-fold thicker due to the thickening of the capsid interior, presumably from DNA-capsid interaction evident from capsid protrusions or nodules on the interior surface. In addition, the morphological changes of the capsid exterior were particularly observed in the vicinity of the five-fold axes, where the counter-clockwise twisting of the "tripod" structure at the vertex of the five-fold channel was evident, resulting in a widening of the channel's opening. Whether these capsid changes are similar to virion capsid maturation in the host cells remains to be investigated. Nevertheless, the ability of IHHNV-VLP to encapsulate the sizable dsDNA has opened up the opportunity to package a dsDNA vector that can insert exogenous genes and target susceptible shrimp cells in order to halt viral infection.

Limited commitment, endogenous credibility and the challenges of price‐level targeting

AbstractThis paper studies the cost of limited commitment when a central bank commits to a price‐level target path but retains the discretion to re‐optimize it whenever the cost of honouring its commitment exceeds a tolerance threshold. Such discretion undermines the credibility of the price‐level target path and weakens its effectiveness to stabilize the economy. Endogenous target resets are more costly than exogenous target re‐optimizations and can give rise to multiple equilibria, including a low credibility equilibrium with frequent target resets and high volatility. Canadian data suggest that price‐level target resets could be quite frequent in practice.

Deep learning hybrid predictions for the amount of municipal solid waste: A case study in Shanghai

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology.

Inequality assessment in a dynamic framework with heterogenous agents

We propose a method to carry out an inequality assessment in a dynamic and cross-sectional framework, by applying the dynamic version of a suitable inequality index, such as the Gini coefficient, as a function of time. We use our methodology to a setup where the optimal value functions is the individuals’ income flows while the initial conditions characterize their level of wealth. When the Hamilton–Jacobi–Bellman system of equations can be solved in closed form, the monotone path of the income distribution is established. Extending the model according to a government intervention gives the possibility to study, first policy for reducing income inequality under a specific exogenous target, and second to minimise income inequality across individuals.

Generation and Identification of the Number of Copies of Exogenous Genes and the T-DNA Insertion Site in SCN-Resistance Transformation Event ZHs1-2.

Soybean cyst nematode (SCN, Heterodera glycines Ichinohe) causes an estimated economic loss of about USD 3 billion each year in soybean (Glycine max L.) production worldwide. Overexpression of resistance genes against SCN provides a powerful approach to develop SCN resistance cultivars in soybean. The clarification of molecular characterization in transformation events is a prerequisite for ecological risk assessment, food safety, and commercial release of genetically modified crops. Here, we generated transgenic events harboring the BCN (beet cyst nematode) resistance Hs1pro−1 gene using the Agrobacterium-mediated method in soybean, evaluated their resistance to SCN infection, and clarified the molecular characterization of one of the transformation events. Five independent and stable inheritable transformation events were generated by an Agrobacterium-mediated transformation method. SCN resistance tests showed the average number of developed females per plant and female index (FI) in T4 ZHs1-1, ZHs1-2, ZHs1-3, ZHs1-4, and ZHs1-5 transformation events were significantly lower than that in the nontransgenic control. Among these, the ZHs1-2 transformation event had the lowest number of developed females per plant and FI. Southern hybridization showed the exogenous target Hs1pro−1 gene was inserted in one copy and the Bar gene was inserted two copies in the ZHs1-2 transformation event. The exogenous T-DNA fragment was integrated in the reverse position of Chr02: 5351566–5231578 (mainly the Bar gene expression cassette) and in the forward position of Chr03: 17083358–17083400 (intact T-DNA, including Hs1pro−1 and Bar gene expression cassette) using a whole genome sequencing method (WGS). The results of WGS method and Southern hybridization were consistent. All the functional elements of exogenous T-DNA fragments were verified by PCR using specific primer pairs in the T5 and T6 ZHs1-2 transformation events. These results demonstrated that the overexpression of Hs1pro−1 gene enhanced SCN resistance, and provide an important reference for the biosafety assessment and the labeling detection in transformation event ZHs1-2.

Hierarchical attention network for multivariate time series long-term forecasting.

Multivariate time series long-term forecasting has always been the subject of research in various fields such as economics, finance, and traffic. In recent years, attention-based recurrent neural networks (RNNs) have received attention due to their ability of reducing error accumulation. However, the existing attention-based RNNs fail to eliminate the negative influence of irrelevant factors on prediction, and ignore the conflict between exogenous factors and target factor. To tackle these problems, we propose a novel Hierarchical Attention Network (HANet) for multivariate time series long-term forecasting. At first, HANet designs a factor-aware attention network (FAN) and uses it as the first component of the encoder. FAN weakens the negative impact of irrelevant exogenous factors on predictions by assigning small weights to them. Then HANet proposes a multi-modal fusion network (MFN) as the second component of the encoder. MFN employs a specially designed multi-modal fusion gate to adaptively select how much information about the expression of current time come from target and exogenous factors. Experiments on two real-world datasets reveal that HANet not only outperforms state-of-the-art methods, but also provides interpretability for prediction.

Nigeria's Perpetual Underdevelopment and Theory of Development Policy

The paper revealed how Nigeria has remained perpetually underdeveloped by various development indicators. To explain Nigeria’s perpetual underdevelopment, the paper presented a Structural Theory of Development Policy. The theory postulates an interrelationship among four components of any economic system, comprising policy instruments (vector x), external exogenous variables (vector u), target and non-target endogenous variables (vectors y and z), impacting welfare function w(y), as illustrated below: The theory elucidates perpetual underdevelopment by the following obstacles: (i) Perverted articulation of development objectives (ii) Deficient knowledge of the economic system (iii) Excessive vulnerability to external factors (iv) Limited capacity for policy implementation The resolution of these obstacles was discussed. However, the deficient knowledge of the economic system was considered the critical factor for extensive empirical analysis. A classical illustration is the foreign-exchange excess-demand theory, empirically proven to be a false paradigm, inapplicable to the Nigerian economy characterized by overwhelming dependence on primary commodity exports, price and income inelastic demand for goods and services, double-digit inflation rate, severe political instability, and unbridled corruption fuelling capital flight. Comprehensive econometric analysis for Nigeria negated the foreign exchange excess demand theory in all its ramifications, justifying the need for foreign exchange market regulation.

Structure and function encoding of a bidirectional activatable synergetic DNA machine for speeded and ultrasensitive determination of microRNAs

This work describes the unique design of a bidirectional activatable synergetic DNA machine (BAS-DNA machine) for speeded and ultrasensitive determination of microRNA-21 (miR-21), a well-known biomarker for biomedical research and early diagnosis of lung cancer. The BAS-DNA machine is composed by a pair of track strands (Track 1 and Track 2) encoding with two regions in the opposite direction for miR-21 recognition. Introduction of miR-21 can hybridize either with Track 1 or with Track 2 to activate the BAS-DNA machine with a synergistic effect for speeded amplifying the fluorescence signal. Moreover, compared with common DNA machine with only one switch for exogenous target recognition, the BAS-DNA machine with two switches for miR-21 binding allows the speeded and strong operation of the autonomous strand scission, replication, and displacement on Track 1 and Track 2 simultaneously. This behavior makes the BAS-DNA machine powerful for ultrasensitive, specific, and fast screening of miR-21 even from real biological samples, and the fluorescence signal was found to be linear from 1 pM to 10 nM with a detection limit of 703.6 fM. We envision this BAS-DNA machine with its superior assay performance will provide a new avenue for simple, sensitive, and affordable biomedical assays.

Monitoring and traceability of genetically modified soya bean event GTS 40-3-2 during soya bean protein concentrate and isolate preparation.

To evaluate DNA fragmentation and GMO quantification during soya bean protein concentrate and isolate preparation, genetically modified soya bean event GTS 40-3-2 (Roundup ReadyTM soya bean, RRS) was blended with conventional soya beans at mass percentages of 0.9%, 2%, 3%, 5% and 10%. Qualitative PCR and real-time PCR were used to monitor the taxon-specific lectin and exogenous cp4 epsps target levels in all of the main products and by-products, which has practical significance for RRS labelling threshold and traceability. Along the preparation chain, the majority of DNA was distributed in main products, and the DNA degradation was noticed. From a holistic perspective, the lectin target degraded more than cp4 epsps target during both of the two soya bean proteins preparations. Therefore, the transgenic contents in the final protein products were higher than the actual mass percentages of RRS in raw materials. Our results are beneficial to the improvement of GMO labelling legislation and the protection of consumer rights.

Fine-tune control of targeted RNAi efficacy by plant artificial small RNAs.

Eukaryotic RNA interference (RNAi) results in gene silencing upon the sequence-specific degradation of target transcripts by complementary small RNAs (sRNAs). In plants, RNAi-based tools have been optimized for high efficacy and high specificity, and are extensively used in gene function studies and for crop improvement. However, efficient methods for finely adjusting the degree of induced silencing are missing. Here, we present two different strategies based on artificial sRNAs for fine-tuning targeted RNAi efficacy in plants. First, the degree of silencing induced by synthetic-trans-acting small interfering RNAs (syn-tasiRNAs) can be adjusted by modifying the precursor position from which the syn-tasiRNA is expressed. The accumulation and efficacy of Arabidopsis TAS1c-based syn-tasiRNAs progressively decrease as the syn-tasiRNA is expressed from positions more distal to the trigger miR173 target site. And second, syn-tasiRNA activity can also be tweaked by modifying the degree of base-pairing between the 3′ end of the syn-tasiRNA and the 5′ end of the target RNA. Both strategies were used to finely modulate the degree of silencing of endogenous and exogenous target genes in Arabidopsis thaliana and Nicotiana benthamiana. New high-throughput syn-tasiRNA vectors were developed and functionally analyzed, and should facilitate the precise control of gene expression in multiple plant species.

Targeted delivery and enhanced gene-silencing activity of centrally modified folic acid-siRNA conjugates.

One of the major hurdles in RNAi research has been the development of safe and effective delivery systems for siRNAs. Although various chemical modifications have been proposed to improve their pharmacokinetic behaviour, their delivery to target cells and tissues presents many challenges. In this work, we implemented a receptor-targeting strategy to selectively deliver siRNAs to cancer cells using folic acid as a ligand. Folic acid is capable of binding to cell-surface folate receptors with high affinity. These receptors have become important molecular targets for cancer research as they are overexpressed in numerous cancers despite being expressed at low levels in normal tissues. Employing a post-column copper-catalyzed alkyne–azide cycloaddition (CuAAC), we report the synthesis of siRNAs bearing folic acid modifications at different positions within the sense strand. In the absence of a transfection carrier, these siRNAs were selectively taken up by cancer cells expressing folate receptors. We show that centrally modified folic acid–siRNAs display enhanced gene-silencing activity against an exogenous gene target (∼80% knockdown after 0.75 μM treatment) and low cytotoxicity. In addition, these siRNAs achieved potent dose-dependent knockdown of endogenous Bcl-2, an important anti-apoptotic gene.

DSTP-RNN: A dual-stage two-phase attention-based recurrent neural network for long-term and multivariate time series prediction

Long-term prediction of multivariate time series is still an important but challenging problem. The key to solve this problem is capturing (1) the spatial correlations at the same time, (2) the spatio-temporal relationships at different times, and (3) long-term dependency of the temporal relationships between different series. Attention-based recurrent neural networks (RNN) can effectively represent and learn the dynamic spatio-temporal relationships between exogenous series and target series, but they only perform well in one-step time prediction and short-term time prediction. In this paper, inspired by human attention mechanism including the dual-stage two-phase (DSTP) model and the influence mechanism of target information and non-target information, we propose DSTP-based RNN (DSTP-RNN) and DSTP-RNN-Ⅱ respectively for long-term time series prediction. Specifically, we first propose the DSTP-based structure to enhance the spatial correlations between exogenous series. The first phase produces violent but decentralized response weight, while the second phase leads to stationary and concentrated response weight. Then, we employ multiple attentions on target series to boost the long-term dependency. Finally, we study the performance of deep spatial attention mechanism and provide interpretation. Experimental results demonstrate that the present work can be successfully used to develop expert or intelligent systems for a wide range of applications, with state-of-the-art performances superior to nine baseline methods on four datasets in the fields of energy, finance, environment and medicine, respectively. Overall, the present work carries a significant value not merely in the domain of machine intelligence and deep learning, but also in the fields of many applications.

Different Components of the RNA Interference Machinery Are Required for Conidiation, Ascosporogenesis, Virulence, Deoxynivalenol Production, and Fungal Inhibition by Exogenous Double-Stranded RNA in the Head Blight Pathogen Fusarium graminearum.

In filamentous fungi, gene silencing through RNA interference (RNAi) shapes many biological processes, including pathogenicity. We explored the requirement of key components of fungal RNAi machineries, including DICER-like 1 and 2 (FgDCL1, FgDCL2), ARGONAUTE 1 and 2 (FgAGO1, FgAGO2), AGO-interacting protein FgQIP (QDE2-interacting protein), RecQ helicase (FgQDE3), and four RNA-dependent RNA polymerases (FgRdRP1, FgRdRP2, FgRdRP3, FgRdRP4), in the ascomycete mycotoxin-producing fungal pathogen Fusarium graminearum (Fg) for sexual and asexual multiplication, pathogenicity, and its sensitivity to double-stranded (ds)RNA. We corroborate and extend earlier findings that conidiation, ascosporogenesis, and Fusarium head blight (FHB) symptom development require an operable RNAi machinery. The involvement of RNAi in conidiation is dependent on environmental conditions as it is detectable only under low light (<2 μmol m−2 s−1). Although both DCLs and AGOs partially share their functions, the sexual ascosporogenesis is mediated primarily by FgDCL1 and FgAGO2, while FgDCL2 and FgAGO1 contribute to asexual conidia formation and germination. FgDCL1 and FgAGO2 also account for pathogenesis as their knockout (KO) results in reduced FHB development. Apart from KO mutants Δdcl2 and Δago1, mutants Δrdrp2, Δrdrp3, Δrdrp4, Δqde3, and Δqip are strongly compromised for conidiation, while KO mutations in all RdPRs, QDE3, and QIP strongly affect ascosporogenesis. Analysis of trichothecenes mycotoxins in wheat kernels showed that the relative amount of deoxynivalenol (DON), calculated as [DON] per amount of fungal genomic DNA was reduced in all spikes infected with RNAi mutants, suggesting the possibility that the fungal RNAi pathways affect Fg’s DON production. Moreover, silencing of fungal genes by exogenous target gene-specific double-stranded RNA (dsRNA) (spray-induced gene silencing, SIGS) is dependent on DCLs, AGOs, and QIP, but not on QDE3. Together these data show that in F. graminearum, different key components of the RNAi machinery are crucial in different steps of fungal development and pathogenicity.