Suppression of Gluconeogenic Gene Expression by LSD1-Mediated Histone Demethylation

Aberrant gluconeogenic gene expression is associated with diabetes, glycogen storage disease, and liver cancer. However, little is known how these genes are regulated at the chromatin level. In this study, we investigated in HepG2 cells whether histone demethylation is a potential mechanism. We found that knockdown or pharmacological inhibition of histone demethylase LSD1 causes remarkable transcription activation of two gluconeogenic genes, FBP1 and G6Pase, and consequently leads to increased de novo glucose synthesis and decreased intracellular glycogen content. Mechanistically, LSD1 occupies the promoters of FBP1 and G6Pase, and modulates their H3K4 dimethylation levels. Thus, our work identifies an epigenetic pathway directly governing gluconeogenic gene expression, which might have important implications in metabolic physiology and diseases

Temperature and Development Impacts on Housekeeping Gene Expression in Cowpea Aphid, \u3cem\u3eAphis craccivora\u3c/em\u3e (Hemiptera: Aphidiae)

Quantitative real-time PCR (qRT-PCR) is a powerful technique to quantify gene expression. To standardize gene expression studies and obtain more accurate qRT-PCR analysis, normalization relative to consistently expressed housekeeping genes (HKGs) is required. In this study, ten candidate HKGs including elongation factor 1 α (EF1A), ribosomal protein L11 (RPL11), ribosomal protein L14 (RPL14), ribosomal protein S8 (RPS8), ribosomal protein S23 (RPS23), NADH-ubiquinone oxidoreductase (NADH), vacuolar-type H+-ATPase (ATPase), heat shock protein 70 (HSP70), 18S ribosomal RNA (18S), and 12S ribosomal RNA (12S) from the cowpea aphid, Aphis craccivora Koch were selected. Four algorithms, geNorm, Normfinder, BestKeeper, and the ΔCt method were employed to evaluate the expression profiles of these HKGs as endogenous controls across different developmental stages and temperature regimes. Based on RefFinder, which integrates all four analytical algorithms to compare and rank the candidate HKGs, RPS8, RPL14, and RPL11 were the three most stable HKGs across different developmental stages and temperature conditions. This study is the first step to establish a standardized qRT-PCR analysis in A. craccivora following the MIQE guideline. Results from this study lay a foundation for the genomics and functional genomics research in this sap-sucking insect pest with substantial economic impact

Stably Expressed Housekeeping Genes across Developmental Stages in the Two-Spotted Spider Mite, \u3cem\u3eTetranychus urticae\u3c/em\u3e

Quantitative real-time PCR (qRT-PCR) is a reliable and reproducible technique for measuring mRNA expression. To facilitate gene expression studies and obtain more accurate qRT-PCR analysis, normalization relative to stable housekeeping genes is mandatory. In this study, ten housekeeping genes, including beta-actin (Actin), elongation factor 1 α (EF1A), glyceralde hyde-3-phosphate dehydrogenase (GAPDH), ribosomal protein L13 (RPL13), ribosomal protein 49 (RP49), α-tubulin (Tubulin), vacuolar-type H+-ATPase (v-ATPase), succinate dehydrogenase subunit A (SDHA) , 28S ribosomal RNA (28S), and 18S ribosomal RNA (18S) from the two-spotted spider mite, Tetranychus urticae, were selected as the candidate reference genes. Four algorithms, geNorm, Normfinder, BestKeeper, and the ΔCt method, were used to evaluate the performance of these candidates as endogenous controls across different developmental stages. In addition, RefFinder, which integrates the above-mentioned software tools, provided the overall ranking of the stability/suitability of these candidate reference genes. Among them, PRL13 and v-ATPase were the two most stable housekeeping genes across different developmental stages. This work is the first step toward establishing a standardized qRT-PCR analysis in T. urticae following the MIQE guideline. With the recent release of the T. urticae genome, results from this study provide a critical piece for the subsequent genomics and functional genomics research in this emerging model system

Selection of Reference Genes for Expression Analysis Using Quantitative Real-Time PCR in the Pea Aphid, \u3cem\u3eAcyrthosiphon pisum\u3c/em\u3e (Harris) (Hemiptera, Aphidiae)

To facilitate gene expression study and obtain accurate qRT-PCR analysis, normalization relative to stable expressed housekeeping genes is required. In this study, expression profiles of 11 candidate reference genes, including actin (Actin), elongation factor 1 α (EF1A), TATA-box-binding protein (TATA), ribosomal protein L12 (RPL12), β-tubulin (Tubulin), NADH dehydrogenase (NADH), vacuolar-type H+-ATPase (v-ATPase), succinate dehydrogenase B (SDHB), 28S ribosomal RNA (28S), 16S ribosomal RNA (16S), and 18S ribosomal RNA (18S) from the pea aphid Acyrthosiphon pisum, under different developmental stages and temperature conditions, were investigated. A total of four analytical tools, geNorm, Normfinder, BestKeeper, and the ΔCt method, were used to evaluate the suitability of these genes as endogenous controls. According to RefFinder, a web-based software tool which integrates all four above-mentioned algorithms to compare and rank the reference genes, SDHB, 16S, and NADH were the three most stable house-keeping genes under different developmental stages and temperatures. This work is intended to establish a standardized qRT-PCR protocol in pea aphid and serves as a starting point for the genomics and functional genomics research in this emerging insect model

Red recombination enables a wide variety of markerless manipulation of porcine epidemic diarrhea virus genome to generate recombinant virus

Porcine epidemic diarrhea virus (PEDV) is a member of the genera Alphacoronavirus that has been associated with acute watery diarrhea and vomiting in swine. Unfortunately, no effective vaccines and antiviral drugs for PEDV are currently available. Reverse genetics systems are crucial tools for these researches. Here, a PEDV full-length cDNA clone was constructed. Furtherly, three PEDV reporter virus plasmids containing red fluorescent protein (RFP), Nano luciferase (Nluc), or green fluorescence protein (GFP) were generated using Red recombination with the GS1783 E. coli strain. These reporter-expressing recombinant (r) PEDVs showed similar growth properties to the rPEDV, and the foreign genes were stable to culture up to P9 in Vero cells. Using the Nluc-expressing rPEDV, the replication of PEDV was easily quantified, and a platform for rapid anti-PEDV drug screening was constructed. Among the three drugs, Bergenin, Umifenovir hydrochloride (Arbidol), and Ganoderma lucidum triterpenoids (GLTs), we found that GLTs inhibited PEDV replication mainly after the stage of virus “Entry”. Overall, this study will broaden insight into the method for manipulating the PEDV genome and provide a powerful tool for screening anti-PEDV agents

Genome-scale MicroRNA target prediction through clustering with Dirichlet process mixture model

Background: MicroRNA regulation is fundamentally responsible for fine-tuning the whole gene network in human and has been implicated in most physiological and pathological conditions. Studying regulatory impact of microRNA on various cellular and disease processes has resulted in numerous computational tools that investigate microRNA-mRNA interactions through the prediction of static binding site highly dependent on sequence pairing. However, what hindered the practical use of such target prediction is the interplay between competing and cooperative microRNA binding that complicates the whole regulatory process exceptionally. Results: We developed a new method for improved microRNA target prediction based on Dirichlet Process Gaussian Mixture Model (DPGMM) using a large collection of molecular features associated with microRNA, mRNA, and the interaction sites. Multiple validations based on microRNA-mRNA interactions reported in recent large-scale sequencing analyses and a screening test on the entire human transcriptome show that our model outperformed several state-of-the-art tools in terms of promising predictive power on binding sites specific to transcript isoforms with reduced false positive prediction. Last, we illustrated the use of predicted targets in constructing conditional microRNA-mediated gene regulation networks in human cancer. Conclusion: The probability-based binding site prediction provides not only a useful tool for differentiating microRNA targets according to the estimated binding potential but also a capability highly important for exploring dynamic regulation where binding competition is involved

Selection of Reference Genes for RT-qPCR Analysis in \u3cem\u3eCoccinella septempunctata\u3c/em\u3e\u3c to Assess Un-intended Effects of RNAi Transgenic Plants

The development of genetically engineered plants that employ RNA interference (RNAi) to suppress invertebrate pests opens up new avenues for insect control. While this biotechnology shows tremendous promise, the potential for both non-target and off-target impacts, which likely manifest via altered mRNA expression in the exposed organisms, remains a major concern. One powerful tool for the analysis of these un-intended effects is reverse transcriptase-quantitative polymerase chain reaction, a technique for quantifying gene expression using a suite of reference genes for normalization. The seven-spotted ladybeetle Coccinella septempunctata, a commonly used predator in both classical and augmentative biological controls, is a model surrogate species used in the environmental risk assessment (ERA) of plant incorporated protectants (PIPs). Here, we assessed the suitability of eight reference gene candidates for the normalization and analysis of C. septempunctata v-ATPase A gene expression under both biotic and abiotic conditions. Five computational tools with distinct algorisms, geNorm, Normfinder, BestKeeper, the ΔCtmethod, and RefFinder, were used to evaluate the stability of these candidates. As a result, unique sets of reference genes were recommended, respectively, for experiments involving different developmental stages, tissues, and ingested dsRNAs. By providing a foundation for standardized RT-qPCR analysis in C. septempunctata, our work improves the accuracy and replicability of the ERA of PIPs involving RNAi transgenic plants

Selection of Reference Genes for RT-qPCR Analysis in Coccinella septempunctata to Assess Un-intended Effects of RNAi Transgenic Plants

The development of genetically engineered plants that employ RNA interference (RNAi) to suppress invertebrate pests opens up new avenues for insect control. While this biotechnology shows tremendous promise, the potential for both non-target and off-target impacts, which likely manifest via altered mRNA expression in the exposed organisms, remains a major concern. One powerful tool for the analysis of these un-intended effects is reverse transcriptase-quantitative polymerase chain reaction, a technique for quantifying gene expression using a suite of reference genes for normalization. The seven-spotted ladybeetle Coccinella septempunctata, a commonly used predator in both classical and augmentative biological controls, is a model surrogate species used in the environmental risk assessment (ERA) of plant incorporated protectants (PIPs). Here, we assessed the suitability of eight reference gene candidates for the normalization and analysis of C. septempunctata v-ATPase A gene expression under both biotic and abiotic conditions. Five computational tools with distinct algorisms, geNorm, Normfinder, BestKeeper, the ΔCt method, and RefFinder, were used to evaluate the stability of these candidates. As a result, unique sets of reference genes were recommended, respectively, for experiments involving different developmental stages, tissues, and ingested dsRNAs. By providing a foundation for standardized RT-qPCR analysis in C. septempunctata, our work improves the accuracy and replicability of the ERA of PIPs involving RNAi transgenic plants

Selection of Reference Genes for RT-qPCR Analysis in a Predatory Biological Control Agent, \u3cem\u3eColeomegilla maculata\u3c/em\u3e (Coleoptera: Coccinellidae)

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for quantifying gene expression across various biological processes, of which requires a set of suited reference genes to normalize the expression data. Coleomegilla maculata (Coleoptera: Coccinellidae), is one of the most extensively used biological control agents in the field to manage arthropod pest species. In this study, expression profiles of 16 housekeeping genes selected from C. maculata were cloned and investigated. The performance of these candidates as endogenous controls under specific experimental conditions was evaluated by dedicated algorithms, including geNorm, Normfinder, BestKeeper, and ΔCt method. In addition, RefFinder, a comprehensive platform integrating all the above-mentioned algorithms, ranked the overall stability of these candidate genes. As a result, various sets of suitable reference genes were recommended specifically for experiments involving different tissues, developmental stages, sex, and C. maculate larvae treated with dietary double stranded RNA. This study represents the critical first step to establish a standardized RT-qPCR protocol for the functional genomics research in a ladybeetle C. maculate. Furthermore, it lays the foundation for conducting ecological risk assessment of RNAi-based gene silencing biotechnologies on non-target organisms; in this case, a key predatory biological control agent

Selection of Reference Genes for the Normalization of RT-qPCR Data in Gene Expression Studies in Insects: A Systematic Review

Reverse transcriptase-quantitative polymerase chain reaction (RT-qPCR) is a reliable technique for quantifying expression levels of targeted genes during various biological processes in numerous areas of clinical and biological research. Selection of appropriate reference genes for RT-qPCR normalization is an elementary prerequisite for reliable measurements of gene expression levels. Here, by analyzing datasets published between 2008 and 2017, we summarized the current trends in reference gene selection for insect gene expression studies that employed the most widely used SYBR Green method for RT-qPCR normalization. We curated 90 representative papers, mainly published in 2013–2017, in which a total of 78 insect species were investigated in 100 experiments. Furthermore, top five journals, top 10 frequently used reference genes, and top 10 experimental factors have been determined. The relationships between the numbers of the reference genes, experimental factors, analysis tools on the one hand and publication date (year) on the other hand was investigated by linear regression. We found that the more recently the paper was published, the more experimental factors it tended to explore, and more analysis tools it used. However, linear regression analysis did not reveal a significant correlation between the number of reference genes and the study publication date. Taken together, this meta-analysis will be of great help to researchers that plan gene expression studies in insects, especially the non-model ones, as it provides a summary of appropriate reference genes for expression studies, considers the optimal number of reference genes, and reviews the average number of experimental factors and analysis tools per study