Antisense RNA: the new favorite in genetic research

Xu, Jianzhong; Zhang, Jun-lan; Zhang, Weiguo

doi:10.1631/jzus.b1700594

Cited by 70 publications

(49 citation statements)

References 67 publications

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“…At present, molecular biology techniques are good options for the control of ripening, like the antisense RNA technology, which signifies a single type of DNA transcript of 19–23 nucleotides and is complementary to mRNA (Xu et al, 2018). This RNA technology has been used to regulate gene expressions during the replication, transcription, and translation.…”

Section: Changes In Nutritional and Phytochemical Components During Pmentioning

confidence: 99%

“…For example, antisense RNA of 1-aminocyclopropane-1-carboxylate oxidase inhibits the expression of rate-limiting enzymes involved in the biosynthesis of ethylene in tomatoes, delaying its maturation (Oeller et al, 1991). At present, ongoing studies have been applied to mangoes, among other fruits (Xu et al, 2018).…”

Section: Changes In Nutritional and Phytochemical Components During Pmentioning

confidence: 99%

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Chemical Composition of Mango (Mangifera indica L.) Fruit: Nutritional and Phytochemical Compounds

et al. 2019

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Mango fruit has a high nutritional value and health benefits due to important components. The present manuscript is a comprehensive update on the composition of mango fruit, including nutritional and phytochemical compounds, and the changes of these during development and postharvest. Mango components can be grouped into macronutrients (carbohydrates, proteins, amino acids, lipids, fatty, and organic acids), micronutrients (vitamins and minerals), and phytochemicals (phenolic, polyphenol, pigments, and volatile constituents). Mango fruit also contains structural carbohydrates such as pectins and cellulose. The major amino acids include lysine, leucine, cysteine, valine, arginine, phenylalanine, and methionine. The lipid composition increases during ripening, particularly the omega-3 and omega-6 fatty acids. The most important pigments of mango fruit include chlorophylls (a and b) and carotenoids. The most important organic acids include malic and citric acids, and they confer the fruit acidity. The volatile constituents are a heterogeneous group with different chemical functions that contribute to the aromatic profile of the fruit. During development and maturity stages occur important biochemical, physiological, and structural changes affecting mainly the nutritional and phytochemical composition, producing softening, and modifying aroma, flavor, and antioxidant capacity. In addition, postharvest handling practices influence total content of carotenoids, phenolic compounds, vitamin C, antioxidant capacity, and organoleptic properties.

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Section: Changes In Nutritional and Phytochemical Components During Pmentioning

confidence: 99%

Section: Changes In Nutritional and Phytochemical Components During Pmentioning

confidence: 99%

Chemical Composition of Mango (Mangifera indica L.) Fruit: Nutritional and Phytochemical Compounds

et al. 2019

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“…Other non-coding genes with a potential role in obesity identified in the present study are NONMUT00716(3-5), NONMUT01574(5-7), and KnownTID_00007994. They represent non-coding antisense and linc RNAs that are often assumed to have regulatory functions similar to transcription factors (56,57).…”

Section: Figure 3 |mentioning

confidence: 99%

Developmentally Driven Changes in Adipogenesis in Different Fat Depots Are Related to Obesity

Breitfeld

Kehr

Müller³

et al. 2020

Front. Endocrinol.

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Subcutaneous (sc) and visceral (vis) adipose tissue (AT) contribute to the variability in pathophysiological consequences of obesity and adverse fat distribution. To gain insights into the molecular mechanisms distinguishing vis and sc fat, we compared the transcriptome during differentiation of immortalized adipocytes from murine epididymal (epi) and inguinal (ing) AT. RNA was extracted on different days of adipogenesis (−2, 0, 2, 4, 6, 8) and analyzed using Clariom TM D mouse assays (Affymetrix) covering >214,900 transcripts in >66,100 genes. Transcript Time Course Analysis revealed 137 differentially expressed genes. The top genes with most divergent expression dynamics included developmental genes like Alx1, Lhx8, Irx1/2, Hoxc10, Hoxa5/10, and Tbx5/15. According to pathway analysis the majority of the genes were enriched in pathways related to AT development. Finally, in paired samples of human vis and sc AT (N = 63), several of these genes exhibited depot-specific variability in expression which correlated closely with body mass index and/or waist-to-hip ratio. In conclusion, intrinsically programmed differences in gene expression patterns during adipogenesis suggest that fat depot specific regulation of adipogenesis contributes to individual risk of obesity.

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“…(Vav Guanine Nucleotide Exchange Factor 3) is important to the migration of smooth muscle cells, which suggests that it has a role in vascular proliferation 29. VAV3-AS1 is an RNA gene coding for anti-sense VAV3, which might regulate expression of VAV3 30. VAV3-AS1 rs3747945 has not been previously associated with cardiovascular disease related outcomes, but further supports the role for vasculogenesis in risk for MACE.…”

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confidence: 87%

Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and Selection

Feroze

Nguyen

et al. 2020

Preprint

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Background Predicting risk for major adverse cardiovascular events (MACE) is an evidence-based practice that incorporates lifestyle, history, and other risk factors. Statins reduce risk for MACE by decreasing lipids, but it is difficult to stratify risk following initiation of a statin. Genetic risk determinants for on-statin MACE are low-effect size and impossible to generalize. Our objective was to determine high-level epistatic risk factors for on-statin MACE with GWAS-scale data. Methods Controlled-access data for 5,980 subjects taking a statin collected from Vanderbilt University Medical Center's BioVU were obtained from dbGaP. We used Random Forest Iterative Feature Reduction and Selection (RF-IFRS) to select highly informative genetic and environmental features from a GWAS-scale dataset of patients taking statin medications. Variant-pairs were distilled into overlapping networks and assembled into individual decision trees to provide an interpretable set of variants and associated risk. Results 1,718 cases who suffered MACE and 4,172 controls were obtained from dbGaP. Pathway analysis showed that variants in genes related to vasculogenesis (FDR=0.024), angiogenesis (FDR=0.019), and carotid artery disease (FDR=0.034) were related to risk for on-statin MACE. We identified six gene-variant networks that predicted odds of on-statin MACE. The most elevated risk was found in a small subset of patients carrying variants in COL4A2, TMEM178B, SZT2, and TBXAS1 (OR=4.53, p<0.001). Conclusions The RF-IFRS method is a viable method for interpreting complex "black-box" findings from machine-learning. In this study, it identified epistatic networks that could be applied to risk estimation for on-statin MACE. Further study will seek to replicate these findings in other populations.

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Antisense RNA: the new favorite in genetic research

Cited by 70 publications

References 67 publications

Chemical Composition of Mango (Mangifera indica L.) Fruit: Nutritional and Phytochemical Compounds

Chemical Composition of Mango (Mangifera indica L.) Fruit: Nutritional and Phytochemical Compounds

Developmentally Driven Changes in Adipogenesis in Different Fat Depots Are Related to Obesity

Genome Wide Epistasis Study of On-Statin Cardiovascular Events with Iterative Feature Reduction and Selection

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