Identification of novel miRNA‐mRNA regulatory networks in contact dermatitis by integrated microarray analysis

Abstract MicroRNAs (miRNAs) play crucial roles in the post-transcriptional control of messenger RNA (mRNA). These miRNA-mRNA regulatory networks are present in nearly all organisms and contribute to development, phenotypic divergence, and speciation. To examine the miRNA landscape of cichlid fishes, one of the most species-rich families of vertebrates, we profiled the expression of both miRNA and mRNA in a diverse set of cichlid lineages. Among these, we found that conserved miRNAs differ from recently arisen miRNAs (i.e. lineage specific) in average expression levels, number of target sites, sequence variability, and physical clustering patterns in the genome. Furthermore, conserved miRNA target sites tend to be enriched at the 5′ end of protein-coding gene 3′ UTRs. Consistent with the presumed regulatory role of miRNAs, we detected more negative correlations between the expression of miRNA-mRNA functional pairs than in random pairings. Finally, we provide evidence that novel miRNA targets sites are enriched in genes involved in protein synthesis pathways. Our results show how conserved and evolutionarily novel miRNAs differ in their contribution to the genomic landscape and highlight their particular evolutionary roles in the adaptive diversification of cichlids.

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Molecular Patterns of Neurodevelopmental Preconditioning: A Study of the Effects of Antenatal Steroid Therapy in a Protein-Restriction Mouse Model

ISRN Obstetrics and Gynecology ◽

10.1155/2014/193816 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 2

Author(s):

Clarissa Velayo ◽

Takuya Ito ◽

Yupeng Dong ◽

Miyuki Endo ◽

Rika Sugibayashi ◽

...

Keyword(s):

Mouse Model ◽

Microarray Analysis ◽

Steroid Therapy ◽

Regulatory Networks ◽

Harmful Effect ◽

High Stress ◽

Protein Restriction ◽

Late Gestation ◽

Treatment Protocols ◽

Antenatal Steroid

Introduction. Prenatal programming secondary to maternal protein restriction renders an inherent susceptibility to neural compromise in neonates and any addition of glucocorticosteroids results in further damage. This is an investigation of consequent global gene activity due to effects of antenatal steroid therapy on a protein restriction mouse model. Methods. C57BL/6N pregnant mice were administered control or protein restricted diets and subjected to either 100 μg/Kg of dexamethasone sodium phosphate with normosaline or normosaline alone during late gestation (E10–E17). Nontreatment groups were also included. Brain samples were collected on embryonic day 17 and analyzed by mRNA microarray analysis. Results. Microarray analyses presented 332 significantly regulated genes. Overall, neurodevelopmental genes were overrepresented and a subset of 8 genes allowed treatment segregation through the hierarchical clustering method. The addition of stress or steroids greatly affected gene regulation through glucocorticoid receptor and stress signaling pathways. Furthermore, differences between dexamethasone-administered treatments implied a harmful effect during conditions of high stress. Microarray analysis was validated using qPCR. Conclusion. The effects of antenatal steroid therapy vary in fetuses according to maternal-fetal factors and environmental stimuli. Defining the key regulatory networks that signal either beneficial or damaging corticosteroid action would result in valuable adjustments to current treatment protocols.

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Identification of crucial long non-coding RNAs and mRNAs along with related regulatory networks through microarray analysis in esophageal carcinoma

Functional & Integrative Genomics ◽

10.1007/s10142-021-00784-x ◽

2021 ◽

Author(s):

Yaowen Zhang ◽

Huitao Wang ◽

Fuyou Zhou ◽

Anlin Hao ◽

Ningtao Dai ◽

...

Keyword(s):

Esophageal Carcinoma ◽

Microarray Analysis ◽

Regulatory Networks ◽

Non Coding Rnas

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Microarray Analysis of Bacterial Gene Expression: Towards the Regulome

Comparative and Functional Genomics ◽

10.1002/cfg.193 ◽

2002 ◽

Vol 3 (4) ◽

pp. 352-354 ◽

Cited By ~ 3

Author(s):

Sharon L. Kendall ◽

Farahnaz Movahedzadeh ◽

Andreas Wietzorrek ◽

Neil G. Stoker

Keyword(s):

Gene Expression ◽

Microarray Analysis ◽

Type Strain ◽

Regulatory Networks ◽

Wild Type Strain ◽

Microarray Technology ◽

Wild Type ◽

Bacterial Gene ◽

Bacterial Gene Expression

Microarray technology allows co-regulated genes to be identified. In order to identify genes that are controlled by specific regulators, gene expression can be compared in mutant and wild-type bacteria. However, there are a number of pitfalls with this approach; in particular, the regulator may not be active under the conditions in which the wild-type strain is cultured. Once co-regulated genes have been identified, proteinbinding motifs can be identified. By combining these data with a map of promoters, or operons (the operome), the regulatory networks in the cell (the regulome) can start to be built up.

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Identification of transcription factors and construction of a novel miRNA regulatory network in primary osteoarthritis by integrated analysis

BMC Musculoskeletal Disorders ◽

10.1186/s12891-021-04894-2 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Ying Jiang ◽

Yi Shen ◽

Liyan Ding ◽

Shengli Xia ◽

Liying Jiang

Keyword(s):

Regulatory Networks ◽

Target Genes ◽

Therapeutic Targets ◽

Microarray Dataset ◽

Integrated Analysis ◽

Rna Seq ◽

Hub Genes ◽

Serum Samples ◽

Regulatory Pathways ◽

Novel Mirna

Abstract Backgrounds As osteoarthritis (OA) disease-modifying therapies are not available, novel therapeutic targets need to be discovered and prioritized. Here, we aim to identify miRNA signatures in patients to fully elucidate regulatory mechanism of OA pathogenesis and advance in basic understanding of the genetic etiology of OA. Methods Six participants (3 OA and 3 controls) were recruited and serum samples were assayed through RNA sequencing (RNA-seq). And, RNA-seq dataset was analysed to identify genes, pathways and regulatory networks dysregulated in OA. The overlapped differentially expressed microRNAs (DEMs) were further screened in combination with the microarray dataset GSE143514. The expression levels of candidate miRNAs were further validated by quantitative real-time PCR (qRT-PCR) based on the GEO dataset (GSE114007). Results Serum samples were sequenced interrogating 382 miRNAs. After screening of independent samples and GEO database, the two comparison datasets shared 19 overlapped candidate micRNAs. Of these, 9 up-regulated DEMs and 10 down-regulated DEMs were detected, respectively. There were 236 target genes for up-regulated DEMs and 400 target genes for those down-regulated DEMs. For up-regulated DEMs, the top 10 hub genes were KRAS, NRAS, CDC42, GDNF, SOS1, PIK3R3, GSK3B, IRS2, GNG12, and PRKCA; for down-regulated DEMs, the top 10 hub genes were NR3C1, PPARGC1A, SUMO1, MEF2C, FOXO3, PPP1CB, MAP2K1, RARA, RHOC, CDC23, and CREB3L2. Mir-584-5p-KRAS, mir-183-5p-NRAS, mir-4435-PIK3R3, and mir-4435-SOS1 were identified as four potential regulatory pathways by integrated analysis. Conclusions We have integrated differential expression data to reveal putative genes and detected four potential miRNA-target gene pathways through bioinformatics analysis that represent new mediators of abnormal gene expression and promising therapeutic targets in OA.

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