scholarly journals Identification and functional analysis of diet-responsive genes in Spodoptera litura (Fabricius)

2020 ◽  
Author(s):  
Li Wang ◽  
Peng Zhao ◽  
Junyu Luo ◽  
Chunyi Wang ◽  
Xiangzhen Zhu ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Spodoptera Litura ◽  
Host Plants ◽  
Glutathione Metabolism ◽  
Larval Performance ◽  
Rna Seq ◽  
Plant Host ◽  
Cytochrome P450 Genes ◽  
Wide Range ◽  
Plant Hosts

Abstract Spodoptera litura is one of the most devastating agricultural pests with a wide range of host plants. To study larval performance on different diets and midgut adaptation at transcriptional levels, feeding assay and RNA-Seq experiments were conducted. RNA interference technology was used to explore the detoxification and metabolism of two cytochrome P450 genes. The bioassay data showed that S. litura larvae developed more quickly when fed on cabbage than when fed on soybean, corn and cotton, tannin can inhibit the growth of S. litura . The result of RNA-Seq indicated that S. litura midgut modified gene expression levels to accommodate different diets, and the most differentially expressed genes were detoxification-related and digestion-related genes . Further analysis showed that the glutathione metabolism pathway was the common detoxification pathway in S. litura. The expression of cytochrome P450 genes showed a clear response to different plant hosts, and these differences may play key functions in primary detoxification of secondary metabolites from host plants. Meanwhile, the digestive enzymes of proteinases, lipases, and carbohydrases in midgut showed special responses to different plant hosts. After injection of dsRNA of CYP321A19 and CYP6AB60 , the expression level of target gene were decreased, and the sensitivity of insect to plant allelochemicals increased and the weight increase significantly slowed. In this study, genes involved in detoxification were identified, and the results demonstrate the genes and pathways S. litura utlize to detoxify specific plant-host allelochemicals. These results may also provide a theoretical basis for S. litura management.

scholarly journals Identification and functional analysis of diet-responsive genes in Spodoptera litura (Fabricius)

2020 ◽  
Author(s):  
Li Wang ◽  
Peng Zhao ◽  
Junyu Luo ◽  
Chunyi Wang ◽  
Xiangzhen Zhu ◽  
...  
Keyword(s):  
Spodoptera Litura ◽  
Host Plants ◽  
Glutathione Metabolism ◽  
Larval Performance ◽  
Rna Seq ◽  
Agricultural Pests ◽  
Plant Host ◽  
Wide Range ◽  
Plant Hosts ◽  
Bioassay Data

Abstract Background: Spodoptera litura is one of the most devastating agricultural pests with a wide range of host plants. To study larval performance on different diets and midgut adaptation at transcriptional levels, feeding assay and RNA-Seq experiments were conducted. RNA interference technology was used to explore the detoxification and metabolism of two cytochrome P450 genes. Results: The bioassay data showed that Spodoptera litura larvae developed more quickly when fed on cabbage than when fed on soybean, corn and cotton, tannin can inhibit the growth of Spodoptera litura. The result of RNA-Seq indicated that Spodoptera litura midgut modified gene expression levels to accommodate different diets, and the most differentially expressed genes were detoxification-related and digestion-related genes. Further analysis showed that the glutathione metabolism pathway was the common detoxification pathway in Spodoptera litura. The expression of cytochrome P450 genes showed a clear response to different plant hosts, and these differences may play key functions in primary detoxification of secondary metabolites from host plants. Meanwhile, the digestive enzymes of proteinases, lipases, and carbohydrases in midgut showed special responses to different plant hosts. After injection of dsRNA of CYP321A19 and CYP6AB60, the expression level of target gene were decreased, and the sensitivity of insect to plant allelochemicals increased and the weight increase significantly slowed. Conclusion: In this study, genes involved in detoxification were identified, and the results demonstrate the genes and pathways Spodoptera litura utlize to detoxify specific plant-host allelochemicals. These results may also provide a theoretical basis for Spodoptera litura management.

scholarly journals Escalation of Pyrethroid Resistance in the Malaria Vector Anopheles funestus Induces a Loss of Efficacy of Piperonyl Butoxide–Based Insecticide-Treated Nets in Mozambique

2019 ◽  
Vol 220 (3) ◽  
pp. 467-475 ◽  
Author(s):  
Jacob M Riveron ◽  
Silvie Huijben ◽  
Williams Tchapga ◽  
Magellan Tchouakui ◽  
Murielle J Wondji ◽  
...  
Keyword(s):  
Cytochrome P450 ◽  
Pyrethroid Resistance ◽  
Anopheles Funestus ◽  
Resistance Mechanisms ◽  
Piperonyl Butoxide ◽  
Insecticide Treated Nets ◽  
Molecular Analyses ◽  
Cytochrome P450 Genes ◽  
High Degree ◽  
Long Lasting Insecticidal Nets

Abstract Background Insecticide resistance poses a serious threat to insecticide-based interventions in Africa. There is a fear that resistance escalation could jeopardize malaria control efforts. Monitoring of cases of aggravation of resistance intensity and its impact on the efficacy of control tools is crucial to predict consequences of resistance. Methods The resistance levels of an Anopheles funestus population from Palmeira, southern Mozambique, were characterized and their impact on the efficacy of various insecticide-treated nets established. Results A dramatic loss of efficacy of all long-lasting insecticidal nets (LLINs), including piperonyl butoxide (PBO)–based nets (Olyset Plus), was observed. This An. funestus population consistently (2016, 2017, and 2018) exhibited a high degree of pyrethroid resistance. Molecular analyses revealed that this resistance escalation was associated with a massive overexpression of the duplicated cytochrome P450 genes CYP6P9a and CYP6P9b, and also the fixation of the resistance CYP6P9a_R allele in this population in 2016 (100%) in contrast to 2002 (5%). However, the low recovery of susceptibility after PBO synergist assay suggests that other resistance mechanisms could be involved. Conclusions The loss of efficacy of pyrethroid-based LLINs with and without PBO is a concern for the effectiveness of insecticide-based interventions, and action should be taken to prevent the spread of such super-resistance.

scholarly journals Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model

Genes ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 311
Author(s):  
Zhenqiu Liu
Keyword(s):  
Single Cell ◽  
Free Parameter ◽  
Graphical Model ◽  
Expression Patterns ◽  
Information Criterion ◽  
Log P ◽  
Rna Seq ◽  
Clustering Methods ◽  
Wide Range ◽  
Free Parameters

Single-cell RNA-seq (scRNA-seq) is a powerful tool to measure the expression patterns of individual cells and discover heterogeneity and functional diversity among cell populations. Due to variability, it is challenging to analyze such data efficiently. Many clustering methods have been developed using at least one free parameter. Different choices for free parameters may lead to substantially different visualizations and clusters. Tuning free parameters is also time consuming. Thus there is need for a simple, robust, and efficient clustering method. In this paper, we propose a new regularized Gaussian graphical clustering (RGGC) method for scRNA-seq data. RGGC is based on high-order (partial) correlations and subspace learning, and is robust over a wide-range of a regularized parameter λ. Therefore, we can simply set λ=2 or λ=log(p) for AIC (Akaike information criterion) or BIC (Bayesian information criterion) without cross-validation. Cell subpopulations are discovered by the Louvain community detection algorithm that determines the number of clusters automatically. There is no free parameter to be tuned with RGGC. When evaluated with simulated and benchmark scRNA-seq data sets against widely used methods, RGGC is computationally efficient and one of the top performers. It can detect inter-sample cell heterogeneity, when applied to glioblastoma scRNA-seq data.

scholarly journals The Coevolution of Plants and Microbes Underpins Sustainable Agriculture

2021 ◽  
Vol 9 (5) ◽  
pp. 1036
Author(s):  
Dongmei Lyu ◽  
Levini A. Msimbira ◽  
Mahtab Nazari ◽  
Mohammed Antar ◽  
Antoine Pagé ◽  
...  
Keyword(s):  
Microbial Community ◽  
Plant Growth ◽  
Stress Resistance ◽  
Mycorrhizal Fungi ◽  
Plant Tissues ◽  
Terrestrial Plants ◽  
Plant Host ◽  
Symbiotic Relationships ◽  
Wide Range ◽  
Terrestrial Environments

Terrestrial plants evolution occurred in the presence of microbes, the phytomicrobiome. The rhizosphere microbial community is the most abundant and diverse subset of the phytomicrobiome and can include both beneficial and parasitic/pathogenic microbes. Prokaryotes of the phytomicrobiome have evolved relationships with plants that range from non-dependent interactions to dependent endosymbionts. The most extreme endosymbiotic examples are the chloroplasts and mitochondria, which have become organelles and integral parts of the plant, leading to some similarity in DNA sequence between plant tissues and cyanobacteria, the prokaryotic symbiont of ancestral plants. Microbes were associated with the precursors of land plants, green algae, and helped algae transition from aquatic to terrestrial environments. In the terrestrial setting the phytomicrobiome contributes to plant growth and development by (1) establishing symbiotic relationships between plant growth-promoting microbes, including rhizobacteria and mycorrhizal fungi, (2) conferring biotic stress resistance by producing antibiotic compounds, and (3) secreting microbe-to-plant signal compounds, such as phytohormones or their analogues, that regulate aspects of plant physiology, including stress resistance. As plants have evolved, they recruited microbes to assist in the adaptation to available growing environments. Microbes serve themselves by promoting plant growth, which in turn provides microbes with nutrition (root exudates, a source of reduced carbon) and a desirable habitat (the rhizosphere or within plant tissues). The outcome of this coevolution is the diverse and metabolically rich microbial community that now exists in the rhizosphere of terrestrial plants. The holobiont, the unit made up of the phytomicrobiome and the plant host, results from this wide range of coevolved relationships. We are just beginning to appreciate the many ways in which this complex and subtle coevolution acts in agricultural systems.

scholarly journals MUREN: a robust and multi-reference approach of RNA-seq transcript normalization

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yance Feng ◽  
Lei M. Li
Keyword(s):  
Biological Significance ◽  
Housekeeping Genes ◽  
R Package ◽  
Data Sets ◽  
Statistical Regression ◽  
Rna Seq ◽  
Least Trimmed Squares ◽  
Standard Data ◽  
Wide Range ◽  
Multiple References

Abstract Background Normalization of RNA-seq data aims at identifying biological expression differentiation between samples by removing the effects of unwanted confounding factors. Explicitly or implicitly, the justification of normalization requires a set of housekeeping genes. However, the existence of housekeeping genes common for a very large collection of samples, especially under a wide range of conditions, is questionable. Results We propose to carry out pairwise normalization with respect to multiple references, selected from representative samples. Then the pairwise intermediates are integrated based on a linear model that adjusts the reference effects. Motivated by the notion of housekeeping genes and their statistical counterparts, we adopt the robust least trimmed squares regression in pairwise normalization. The proposed method (MUREN) is compared with other existing tools on some standard data sets. The goodness of normalization emphasizes on preserving possible asymmetric differentiation, whose biological significance is exemplified by a single cell data of cell cycle. MUREN is implemented as an R package. The code under license GPL-3 is available on the github platform: github.com/hippo-yf/MUREN and on the conda platform: anaconda.org/hippo-yf/r-muren. Conclusions MUREN performs the RNA-seq normalization using a two-step statistical regression induced from a general principle. We propose that the densities of pairwise differentiations are used to evaluate the goodness of normalization. MUREN adjusts the mode of differentiation toward zero while preserving the skewness due to biological asymmetric differentiation. Moreover, by robustly integrating pre-normalized counts with respect to multiple references, MUREN is immune to individual outlier samples.

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