miRNA Datasets Research Articles

Comprehensive Analysis of Key Genes Associated with ceRNA Networks in Nasopharyngeal Carcinoma Based on Bioinformatics Analysis

Nasopharyngeal carcinoma (NPC) is an epithelial malignancy that has high morbidity rates in the east and southeast Asia, especially in South China. However, the molecular mechanisms of NPC remain largely unknown. Therefore, it is urgent to explore the pathogenesis of NPC and search for the potential biomarkers and the prognostic indicators for NPC precision treatment. We comprehensively analyzed the mRNAs, long non-coding RNAs (lncRNAs), and microRNA (miRNAs) from the full transcriptome sequencing data of our hospital (5 normal vs. 5 NPC tissues) and from six microarray datasets (62 normal vs. 334 NPC tissues) downloaded from the Gene Expression Omnibus (GEO) dataset (GSE12452, GSE13597, GSE95166, GSE126683, GSE70970, GSE43039) to see gene expression clustering by using principal components analysis (PCA). Differentially expressed genes (DEGs) were identified using the Limma package of the R software and visualized through volcano plot and Venn diagram. The gene ontology (GO) enrichment, Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analysis and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Furthermore, we constructed mRNA-miRNA-lncRNA (ceRNA) networks using miRanda and TargetScan and obtained the protein-protein interaction (PPI) network of DEGs via STRING database. The hub genes were identified by MCODE, NetworkAnalyzer and CytoHubba plugin. In total, 61 mRNAs, 14miRNAs and 10 lncRNAs were discovered to be DEGs after the intersection of three mRNAs datasets, two miRNAs datasets and three lncRNAs datasets. Based on the GO analysis, which includes annotation of biological processes (BPs), molecular functions (MFs) and cellular components (CCs), we found that changes in NPC were mainly enriched in the chromosomal region, sister chromatid segregation, nuclear chromosome segregation, and DNA replication. The KEGG pathway enrichment analysis indicated that the DEGs were mainly concentrated in DNA replication and cell cycle. Moreover, the GSEA suggested that the MAPK signaling pathway, PI3K-AKT signaling pathway, and apoptotic pathway were largely involved in the initiation and development of NPC. Finally, using the PPI network, a total of 20 hub genes (NUSAP1, RACGAP1, PRC1, KIF4A, TOP2A, PBK, KIF2C, TPX2, CENPU, OIP5, TTK, MAD2L1, NDC80, BIRC5, MELK, CENPF, FOXM1, TYMS, CDK1 and CEP55) were uncovered. Our findings may provide additional insights into the biological characteristics of NPC via integrative analysis of the microarray datasets and the total transcriptome sequencing, offering potential therapeutic targets, biomarkers and prognostic indicators for NPC.

Increased MicroRNA Levels in Women With Polycystic Ovarian Syndrome but Without Insulin Resistance: A Pilot Prospective Study.

BackgroundSmall noncoding microRNA (miRNA) have regulatory functions in polycystic ovary syndrome (PCOS) that differ to those in women without PCOS. However, little is known about miRNA expression in women with PCOS who are not insulin resistant (IR).MethodsCirculating miRNAs were measured using quantitative polymerase chain reaction (qPCR) in 24 non-obese BMI and age matched women with PCOS and 24 control women. A miRNA data set was used to determine miRNA levels.ResultsWomen with PCOS showed a higher free androgen index (FAI) and anti-mullerian hormone (AMH) but IR did not differ. Four miRNAs (miR-1260a, miR-18b-5p, miR-424-5p, and miR let-7b-3p) differed between control and PCOS women that passed the false discovery rate (FDR) out of a total of 177 circulating miRNAs that were detected. MiRNA let-7b-3p correlated with AMH in PCOS (p < 0.05). When the groups were combined, miR-1260a correlated with FAI and let-7b-3p correlated with body mass index (BMI) (p < 0.05). There was no correlation to androgen levels. Ingenuity pathway analysis showed that nine of the top 10 miRNAs reported were associated with inflammatory pathways.ConclusionWhen IR did not differ between PCOS and control women, only four miRNA differed significantly suggesting that IR may be a driver for many of the miRNA changes reported. Let-7b-3p was related to AMH in PCOS, and to BMI as a group, whilst miR-1260a correlated with FAI. Androgen levels, however, had no effect upon circulating miRNA profiles. The expressed miRNAs were associated with the inflammatory pathway involving TNF and IL6.

Quantitative and time-resolved miRNA pattern of early human T cell activation

T cells are central to the immune response against various pathogens and cancer cells. Complex networks of transcriptional and post-transcriptional regulators, including microRNAs (miRNAs), coordinate the T cell activation process. Available miRNA datasets, however, do not sufficiently dissolve the dynamic changes of miRNA controlled networks upon T cell activation. Here, we established a quantitative and time-resolved expression pattern for the entire miRNome over a period of 24 h upon human T-cell activation. Based on our time-resolved datasets, we identified central miRNAs and specified common miRNA expression profiles. We found the most prominent quantitative expression changes for miR-155-5p with a range from initially 40 molecules/cell to 1600 molecules/cell upon T-cell activation. We established a comprehensive dynamic regulatory network of both the up- and downstream regulation of miR-155. Upstream, we highlight IRF4 and its complexes with SPI1 and BATF as central for the transcriptional regulation of miR-155. Downstream of miR-155-5p, we verified 17 of its target genes by the time-resolved data recorded after T cell activation. Our data provide comprehensive insights into the range of stimulus induced miRNA abundance changes and lay the ground to identify efficient points of intervention for modifying the T cell response.

In silico transcriptional analysis of mRNA and miRNA reveals unique biosignatures that characterizes different types of diabetes.

Diabetes (DM) has a significant impact on public health. We performed an in silico study of paired datasets of messenger RNA (mRNA) micro-RNA (miRNA) transcripts to delineate potential biosignatures that could distinguish prediabetes (pre-DM), type-1DM (T1DM) and type-2DM (T2DM). Two publicly available datasets containing expression values of mRNA and miRNA obtained from individuals diagnosed with pre-DM, T1DM or T2DM, and normoglycemic controls (NC), were analyzed using systems biology approaches to define combined signatures to distinguish different clinical groups. The mRNA profile of both pre-DM and T2DM was hallmarked by several differentially expressed genes (DEGs) compared to NC. Nevertheless, T1DM was characterized by an overall low number of DEGs. The miRNA signature profiles were composed of a substantially lower number of differentially expressed targets. Gene enrichment analysis revealed several inflammatory pathways in T2DM and fewer in pre-DM, but with shared findings such as Tuberculosis. The integration of mRNA and miRNA datasets improved the identification and discriminated the group composed by pre-DM and T2DM patients from that constituted by normoglycemic and T1DM individuals. The integrated transcriptomic analysis of mRNA and miRNA expression revealed a unique biosignature able to characterize different types of DM.

Integrative analysis of lncRNAs, miRNAs, and mRNAs-associated ceRNA network in a neonatal mouse model of bronchopulmonary dysplasia

Objective To elucidate the potential roles of the lncRNA-mediated competitive endogenous RNA (ceRNA) network in the pathogenesis of bronchopulmonary dysplasia (BPD), we performed an integrated bioinformatics analysis based on miRNA and mRNA microarray datasets between BPD and normal samples. Study design The mRNA and miRNA expression profiles of BPD were downloaded from the Gene Expression Omnibus (GEO) database to perform an integrated analysis. The limma package was used to identify differentially expressed genes (DEGs) and differentially expressed miRNA (DEmiRs), followed by functional enrichment analysis of DEGs. DEmiR–DEG and DEmiRNA–lncRNA interactions were predicted. Subsequently, the lncRNA-related ceRNA network was structured. Finally, a newborn BPD mouse model was established, and quantitative real-time PCR (qPCR) was used to validate the expression of the selected mRNAs, miRNAs, and lncRNAs. Results A total of 445 DEGs and 155 DEmiRs were obtained by comparing BPD samples and normal samples. Functional enrichment analysis showed that DEGs were primarily enriched in GO terms such as cell division and inflammatory response; and DEGs were mainly involved in the p53 signaling pathway. The miR17hg-miR-130b-3p-roundabout guidance receptor 2 (Robo2) and GM20455-miR-34a-5p-BMP/retinoic acid-inducible neural specific 1 (Brinp1) ceRNA axes were obtained by constructing the ceRNA network. In addition, the upregulation of Robo2 and miR17hg while the downregulation of miR-130b-3p; as well as the upregulation of Brinp1 and GM20455 but the downregulation of miR-34a-5p were validated by qPCR. Conclusion The miR17hg-miR-130b-3p-Robo2 and GM20455-miR-34a-5p-Brinp1 axes may serve important role in the development of BPD. These findings might provide novel insight for a comprehensive understanding of molecular mechanisms in BPD, and genes in the ceRNA network might be considered as potential biomarkers and therapeutic targets against BPD.

Identification and expression profile of microRNA in seven tissues of the Golden snub-nosed monkey (Rhinopithecus roxellanae).

MicroRNAs (miRNAs) are key in the post-transcriptional regulation of gene expression and thus characterization of miRNAs and investigation of the relative abundance and specificity of tissue expression are essential for understanding gene expression in the golden snub-nosed monkey (GSM, Rhinopithecus roxellanae). Here, we report the first dataset of GSM miRNAs where we identified 460 miRNAs in seven tissues, with 246 conserved known mature miRNAs and 214 novel mature miRNAs. We determined miRNA abundance and expression in the seven tissues using a Tissue Specificity Index score and found that most novel GSM miRNAs showed a highly tissue-specific expression pattern. In particular, 67 novel miRNAs and the miR-34 family were expressed in abundance only in the lung. Five known miRNAs were highly abundant in digestive organs such as the pancreas and liver, and four novel miRNAs were highly expressed in the heart and muscle. Annotation of target genes of GSM miRNAs indicated that target genes were enriched in many important pathways, such as the HIF-1 signaling pathway and xenobiotic biodegradation-related pathways. Collectively, these results emphasize that miRNAs play important roles in GSM diet and high-elevation adaptation regulation. In summary, this study provides essential information on GSM miRNAs and will benefit further investigations of the function and mechanism of miRNAs in controlling gene expression in the GSM.

Abstract 278: Deciphering circular RNA axes in early-onset breast cancer using a breast epithelial risk-progression 3D culture model

Abstract One of the known circular RNAs (circRNAs) functions is to sponge microRNAs (miRNAs). Studies reported dysregulated mRNA-circRNA-miRNAs axes as biomarker signatures in breast cancer (BC). Signatures in early-onset BC have not been characterized, while its incidence increased drastically worldwide. To address this gap, we performed circRNAs microarrays and miRNA-Sequencing on 3D breast epithelial risk-progression HMT-3522 S1 (S1) culture model to identify dysregulated post-transcriptional axes. S1 (non-neoplastic) cells form fully-polarized epithelium and closely mimic normal in vivo mammary epithelial morphology, while (pre-tumorigenic) Cx43KO-S1 (S1 silenced with shRNA for gap junction Cx43) lose epithelial polarity, multi-layer and mimic tumor-initiated in vivo mammary epithelial morphology. We detected through circRNA microarray (Arraystar Human circRNA Array V2) 75 significantly upregulated and 46 significantly downregulated circRNAs (Fold Change &amp;gt; 2, p-value &amp;lt; 0.05) in 6 samples of Cx43KO-S1 versus S1 cells in 3D. We then selected 10 upregulated and 8 downregulated circRNAs for validation. Of these, hsa_circ_0007961 and hsa_circ_0081481 were most significantly upregulated while hsa_circ_0060055, hsa_circ_0005185, hsa_circ_0083442 and hsa_circ_0077755 were most significantly downregulated by RT-qPCR. We focus on “possible” sponging activity of validated circRNAs to their predicted (in silico) target miRNAs, and cross-compared their dysregulation to our validated miRNA datasets (from young patients and 3D cell line). When circRNAs are upregulated, they downregulate the function of their sponged miRNAs, hence preventing them from deregulating their target mRNAs. For upregulated hsa_circ_0007961 (originating from SPRED2 mRNA), its predicted miR-99a-3p was indeed downregulated in young Lebanese and matched U.S. BC patient tissues (and in Cx43KO-S1 versus S1 microarrays) and predicted miR-653 possessed treatment potential in BC cells. For the second upregulated hsa_circ_0081481 (originating from FBXO24 mRNA), its predicted miR-3960 was exclusively dysregulated in young Lebanese BC patients. For downregulated hsa_circ_0077755 (originating from Cx43mRNA), its predicted miR-182 was commonly upregulated in young Lebanese and matched US patients and predicted miR-203a-3p was reported as promising prognostic biomarker in triple negative BC. Additionally, all predicted miRNAs targeted by all detected circRNAs were predominantly enriched in pathways in cancer. Thus, we suggest that our 3D risk-progression culture model might help delineate post-transcriptional axes to better understand early-onset BC initiation. We reveal three validated axes: i) SPRED2/hsa_circ_0007961/miR-99a-3p or miR-653-5p, ii) FBXO24/hsa_circ_0081481/miR-3960 and iii) Cx43/hsa_circ_0077755/miR-182 or miR-203, which could serve as potential biomarker signatures in early-onset BC. Citation Format: Nataly Naser Al Deen, Nadia Atallah Lanman, Shirisha Chittiboyina, Sophie Lelièvre, Heinrich Zu Dohna, Mounir AbouHaidar, Rabih Talhouk. Deciphering circular RNA axes in early-onset breast cancer using a breast epithelial risk-progression 3D culture model [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 278.

Comprehensive Analysis of circRNA-miRNA-mRNA Network in Cervical Squamous Cell Carcinoma by Integrated Analysis.

PurposeCervical squamous cell carcinoma (CSCC) seriously affects women’s health worldwide, and it is of great significance to illuminate the specific role of circRNAs in CSCC.Materials and MethodsThree mRNA datasets, two miRNA datasets and one circRNA dataset of CSCC, downloaded from GEO, were utilized in this study. Differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs) and circRNAs (DEcircRNAs) were identified, and a ceRNA (DEcircRNA-DEmiRNA-DEmRNA) regulatory network was constructed. GO and pathway analyses of DEcircRNAs and DEmRNAs in the ceRNA regulatory network were performed. Quantitative real-time polymerase chain reaction (qRT-PCR) validation of the expression of the selected DEmRNAs, DEmiRNAs and DEcircRNAs was performed.ResultsA total of 1356 DEmRNAs, 13 DEmiRNAs and 77 DEcircRNAs were obtained. The ceRNA network contained 3 circRNA-miRNA pairs and 158 miRNA-mRNA pairs, including 3 circRNAs, 3 miRNAs, and 138 mRNAs. Functional annotation of DEmRNAs in the ceRNA regulatory network revealed that these DEmRNAs were significantly enriched in cell cycle, p53 signalling pathway and DNA replication. The qRT-PCR results were generally consistent with those of our integrated analysis.ConclusionIn conclusion, we speculate that the regulation of the hsa_circ_0000069/hsa-miR-125b-5p/CDKN2A and hsa_circ_0020594/hsa-let-7c-5p/CCNB2 axes may be involved in CSCC.

Circulating miRNA-3552 as a Potential Biomarker for Ischemic Stroke in Rats.

Objective With the growing incidence of ischemic stroke worldwide, there is an urgent need to identify blood biomarkers for ischemic stroke patients. Thus, our aim was to identify potential circulating microRNA (miRNA) as a potential biomarker and to explore its potential mechanism for ischemic stroke in rats. Methods The mRNA dataset GSE97537 and miRNA dataset GSE97532 were downloaded from the Gene Expression Omnibus (GEO) GSE97537 including 7 middle cerebral artery occlusion (MCAO) rat brain tissues and 5 sham-operated rat brain tissues GSE97532 including 6 MCAO rat blood samples and 3 sham-operated rat blood samples. Differentially expressed mRNAs and miRNAs with corrected p value ≤ 0.01 and fold change ≥2 or ≤0.05 were identified. To explore potential biological processes and pathways of differentially expressed mRNAs, functional enrichment analysis was performed. The target mRNAs of differentially expressed miRNAs were predicted using DNA Intelligent Analysis (DIANA)-microT tools. The target mRNAs and differentially expressed mRNAs were intersected. Results 1228 differentially expressed mRNAs in MCAO rat brain tissues were identified. Highly expressed mRNAs were mainly enriched in the inflammatory responses. Nine differentially expressed miRNAs were identified in MCAO rat blood samples. A total of 673 target mRNAs were predicted to significantly bind these differentially expressed miRNAs. Among them, 54 target mRNAs were differentially expressed in MCAO rat blood samples. Enrichment analysis results showed that these 54 target mRNAs were closely related to neurological diseases and immune responses. Among all miRNA-mRNA relationship, miR-3552-CASP3 interaction was identified, indicating that CASP3 might be mediated by miR-3552. Functional enrichment analysis revealed that CASP3 was involved in the apoptosis pathway, indicating that miR-3552 might participate in apoptosis by CASP3. Conclusion Our findings reveal that circulating miR-3552 shows promise as a potential biomarker for ischemic stroke in rats.

Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification.

Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. The proposed approach is first tested on a tumor classification problem in order to separate 10 different types of cancer, with samples collected over 10 different clinical trials, and later is assessed on a cancer subtype classification task, with the aim to distinguish triple negative breast cancer from other subtypes of breast cancer. Overall, the presented methodology proves to be effective and compares favorably to other state-of-the-art feature selection methods.

Principal component analysis of blood microRNA datasets facilitates diagnosis of diverse diseases.

Early, ideally pre-symptomatic, recognition of common diseases (e.g., heart disease, cancer, diabetes, Alzheimer’s disease) facilitates early treatment or lifestyle modifications, such as diet and exercise. Sensitive, specific identification of diseases using blood samples would facilitate early recognition. We explored the potential of disease identification in high dimensional blood microRNA (miRNA) datasets using a powerful data reduction method: principal component analysis (PCA). Using Qlucore Omics Explorer (QOE), a dynamic, interactive visualization-guided bioinformatics program with a built-in statistical platform, we analyzed publicly available blood miRNA datasets from the Gene Expression Omnibus (GEO) maintained at the National Center for Biotechnology Information at the National Institutes of Health (NIH). The miRNA expression profiles were generated from real time PCR arrays, microarrays or next generation sequencing of biologic materials (e.g., blood, serum or blood components such as platelets). PCA identified the top three principal components that distinguished cohorts of patients with specific diseases (e.g., heart disease, stroke, hypertension, sepsis, diabetes, specific types of cancer, HIV, hemophilia, subtypes of meningitis, multiple sclerosis, amyotrophic lateral sclerosis, Alzheimer’s disease, mild cognitive impairment, aging, and autism), from healthy subjects. Literature searches verified the functional relevance of the discriminating miRNAs. Our goal is to assemble PCA and heatmap analyses of existing and future blood miRNA datasets into a clinical reference database to facilitate the diagnosis of diseases using routine blood draws.

Integrated analysis of miRNA and mRNA expression in the blood of patients with Alzheimer's disease.

Alzheimer's disease (AD) is a progressive neurodegenerative disease, which is considered the most common type of dementia worldwide. The aim of the present study was to identify key microRNAs (miRNAs/miRs) and mRNAs affecting the pathogenesis of AD, which may be developed as promising biomarkers for the early diagnosis or targeted therapy of patients with AD. Integrative analysis was performed on 12 representative miRNA datasets and three mRNA datasets of the blood from patients with AD, in order to identify differentially expressed (DE)miRNAs and DEmRNAs. Subsequently, the miRWalk database was used to identify the potential miRNA‑mRNA interactions among DEmiRNAs and DEmRNAs, and an AD‑specific miRNA‑mRNA network was constructed using Cytoscape software. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed to assess the target mRNAs of DEmiRNAs. A total of 37 DEmiRNAs and 2,011 DEmRNAs were identified between AD and normal control samples. In addition, 853 high confidence miRNA‑mRNA interactions were identified and subsequently used to construct the AD specific miRNA‑mRNA network. A total of five miRNAs, including hsa‑miR‑93, hsa‑miR‑26b, hsa‑miR‑34a, hsa‑miR‑98‑5p and hsa‑miR‑15b‑5p were identified as the key nodes in the miRNA‑mRNA network by topological analysis. Functional enrichment analysis demonstrated that the target mRNAs of DEmiRNAs were enriched in AD‑associated pathways, such as the 'neurotrophin signaling pathway' and 'insulin signaling pathway'. Taken together, the results of the present study provide novel insights into the molecular mechanisms underlying AD and contribute to the identification of biomarkers and novel strategies for drug design for AD treatment.

MiRViz: a novel webserver application to visualize and interpret microRNA datasets.

MicroRNAs (miRNAs) are small non-coding RNAs that are involved in the regulation of major pathways in eukaryotic cells through their binding to and repression of multiple mRNAs. With high-throughput methodologies, various outcomes can be measured that produce long lists of miRNAs that are often difficult to interpret. A common question is: after differential expression or phenotypic screening of miRNA mimics, which miRNA should be chosen for further investigation? Here, we present miRViz (http://mirviz.prabi.fr/), a webserver application designed to visualize and interpret large miRNA datasets, with no need for programming skills. MiRViz has two main goals: (i) to help biologists to raise data-driven hypotheses and (ii) to share miRNA datasets in a straightforward way through publishable quality data representation, with emphasis on relevant groups of miRNAs. MiRViz can currently handle datasets from 11 eukaryotic species. We present real-case applications of miRViz, and provide both datasets and procedures to reproduce the corresponding figures. MiRViz offers rapid identification of miRNA families, as demonstrated here for the miRNA-320 family, which is significantly exported in exosomes of colon cancer cells. We also visually highlight a group of miRNAs associated with pluripotency that is particularly active in control of a breast cancer stem-cell population in culture.

An efficient gene bigdata analysis using machine learning algorithms

Bioinformatics is one of the emerging and rapidly developing research areas that is predominantly used for genetic data analysis and processing. Bioinformatics is characterized by its huge and voluminous data that is growing in nature which in turn complicates data analysis. In most cases, Bioinformatics data analysis and processing involve big data analytics due to the complex nature of the data. Previous research works handled data analytics using traditional tools and conventional big data analytical methods. However, it can be proved that machine learning algorithms and approaches can be effectively deployed to perform parallel, distributed and incremental processing of complex big data analytics especially in the case of gene big data analytics to enhance the efficiency in processing this large chunk of Bioinformatics-based gene big data. This paper provides a Machine Learning algorithm-based Convolution Neural Network (ML-CNN) approach for the process of identifying potential target genes, predicting miRNAs, visualizing the unique miRNA patterns, and validating genomes. The proposed approach has experimented with MATLAB software using deep learning toolbox on the pre - miRNA dataset. Experimental results indicate that machine learning algorithms certainly increases the efficiency of Bioinformatics-based methods of processing gene data in terms of prediction accuracy and reduced processing time. The mean performance of ML-CNN is improved 7% high than the existing system.

Small molecule supplementation potentiates miRNA mediated direct reprogramming of human fibroblasts into functional hepatocytes

BackgroundHepatic transcription factors such as Hnf4α, Foxa1, Foxa2 or Foxa3 and Gata4 have successfully been used for directly reprogram the fibroblasts into hepatic lineages (iHeps). Although it has several advantages such as devoid of teratoma formation, robust conversion rate and potential in vivo reprograming to repair injured tissues, majority of the direct reprograming that employed genetic manipulations to modify the expression of tissue‐specific transcription factors have limitation in downstream clinical applications. Thus, it is critical to develop a new method for reprograming fibroblasts to hepatocytes.HypothesisWe hypothesized that the combination of small molecules such as epigenetic modifiers and GSK‐inhibitor, and miRNAs could successfully be used in direct differentiation of fibroblasts to iHeps.AimThe aim of this study was to directly reprogram the human fibroblasts into functional iHeps using small molecules that regulate reprogramming of somatic cells and miRNAs.MethodsHepatocyte specific miRNAs were selected from liver fibrosis, regulation of mesenchymal to epithelial transition (MET) and regulation of liver specific transcription factors HNF4α and FOXa2 miRNA datasets. Primary human fibroblasts grown to 80% confluent were transfected with either miRNA cocktail and/or small molecules. Transfected cells were cultured in hepatocyte growth media to generate iHeps. Albumin, E‐cad, HNF4α and FOXa2 expressions were detected by flow cytometry, immunostaining and qPCR analyses to establish iHeps. The iHeps mixed with human vascular endothelial cells (HUVEC) in collagen were implanted in partial‐hepatectomized immune‐compromised mice. Explants collected were characterized 4 weeks following implantation. Human albumin level was estimated by ELISA in iHeps implanted mouse serum.ResultsmiRNA cocktail alone significantly downregulated Snail1 and vimentin, and upregulation E‐cad specific mRNA abundance. Although small molecules do not exert any effect, its supplementation along with miRNA cocktail drastically downregulated Snail1 and vimentin, and upregulated E‐cad abundance. Albumin, E‐cad, HNF4α and FOXa2 expression established iHeps lineage. Integration of iHeps with vascular structures were present in liver (shown by arrows in Figure ), while human albumin (control vs iHeps: 0.78 ± 0.01 vs 63.8 ± 0.2 ng/ml) was detected in serum of mice implanted with iHeps.ConclusionsWe conclude that: 1) the down‐regulation of Snail1 and vimentin, and upregulation E‐cad establish MET; 2) the small molecule supplementation potentiate the miRNA cocktail induced MET; and 3) the direct reprograming of human fibroblast using miRNA along with small molecule supplementation generate a viable and functional iHeps.SpeculationWe speculate that small molecules along with miRNA cocktail could potentially be used to treat liver fibrosis in vivo.Support or Funding InformationFunding was provided by the Institute of Bioengineering and Nanotechnology(Biomedical Research Council, Agency for Science, Technology and Research, Singapore). VMR was supported by NIH grants DK112085 and DK104791.H&amp;E staining of sections of explants harvested from partially hepatectomized mouse modelFigure 1

Identification of novel biomarkers in ischemic stroke: a genome-wide integrated analysis

BackgroundIschemic Stroke (IS) is the most common neurological emergency disease and has become the second most frequent cause of death after coronary artery disease in 2015. Owing to its high fatality rate and narrow therapeutic time window, early identification and prevention of potential stroke is becoming increasingly important.MethodsWe used meta-analysis and bioinformatics mining to explore disease-related pathways and regulatory networks after combining messengerRNA (mRNA) and miRNA expression analyses. The purpose of our study was to screen for candidate target genes and microRNA(miRNA) for early diagnosis of potential stroke.ResultsFive datasets were collected from the Gene Expression Omnibus (GEO) database by systematical retrieval, which contained three mRNA datasets (102 peripheral blood samples in total) and two miRNA dataset (59 peripheral blood samples). Approximately 221 different expression(DE) mRNAs (155 upregulated and 66 downregulated mRNAs) and 185 DE miRNAs were obtained using the metaDE package and GEO2R tools. Further functional enrichments of DE-mRNA, DE-miRNA and protein-protein interaction (PPI) were performed and visualized using Cytoscape.ConclusionOur study identified six core mRNAs and two regulated miRNAs in the pathogenesis of stroke, and we elaborated the intrinsic role of systemic lupus erythematosus (SLE) and atypical infections in stroke, which may aid in the development of precision medicine for treating ischemic stroke. However, the role of these novel biomarkers and the underlying molecular mechanisms in IS require further fundamental experiments and further clinical evidence.

Integrated analysis of miRNA and mRNA expression profiles in p53-edited PFF cells

ABSTRACT p53 is the most frequently mutated gene in human cancers, with over half of all tumors harboring mutation at this locus. R248 and R249 (corresponding to porcine R241 and R242), are among the hotspot mutations frequently mutated in liver, lung, breast, and some other cancers. In this study, p53 gene was knocked out or point-edited (R241 and R242 were converted to 241W and 242S) in porcine fetal fibroblast (PFF) cells via CRISPR-Cas9 technique. High throughput sequencing of miRNA and mRNA uncovered a total of 225 differentially expressed miRNAs (DEMs) and 738 differentially expressed genes (DEGs) in the p53 knockout (p53-KO) cells, and a total of 211 DEMs and 722 DEGs in the point-modified (p53-241W242S) cells. Totally 28 annotated DEMs were found to overlap between p53-KO/p53-WT and p53-241W242S/p53-WT miRNAs datasets, of which miR-34 c, miR-218, miR-205, miR-105-1, miR-105-2, miR-206, miR-224 and miR-429 play important roles in p53 regulatory network. Among the top 10 DEGs in p53-KO and p53-241W242S cells, most genes were reported to be involved in tumors, cell proliferation or cell migration. p53-KO and p53-241W242S cells showed a significantly higher (P < 0.01) proliferation rate compared with p53-WT cells. In conclusion, genetic modifications of p53 gene significantly affect the expression levels of a large number of genes and miRNAs in the PFF cells. The p53-edited PFF cells could be used as non-tumor cell models for investigating the p53 signaling network, and as donor cells for somatic nuclear transfer, with the aim to develop porcine models with the corresponding p53 mutations. Abbreviations: CRISPR-Cas9: Clustered regularly interspaced short palindromic repeats-associated protein 9; PFF: porcine fetal fibroblasts; SCNT: somatic cell nuclear transfer; RNA sequencing: small RNA sequencing and mRNA sequencing; DEGs: differentially expressed mRNAs; DEMs: differentially expressed miRNAs.

Identification of the differential expression of genes and upstream microRNAs in small cell lung cancer compared with normal lung based on bioinformatics analysis.

Small cell lung cancer (SCLC) is one of the most lethal cancer, mainly attributing to its high tendency to metastasis. Mounting evidence has demonstrated that genes and microRNAs (miRNAs) are related to human cancer onset and progression including invasion and metastasis.An eligible gene dataset and an eligible miRNA dataset were downloaded from the Gene Expression Omnibus (GEO) database based our screening criteria. Differentially expressed genes (DE-genes) or DE-miRNAs for each dataset obtained by the R software package. The potential target genes of the top 10 DE-miRNAs were predicted by multiple databases. For annotation, visualization and integrated discovery, Metascape 3.0 was introduced to perform enrichment analysis for the DE-genes and the predicted target genes of the selected top 10 DE-miRNAs, including Pathway and Process Enrichment Analysis or protein–protein interaction enrichment analysis. The intersection of predicted target genes and DE-genes was taken as the final DE-genes. Then apply the predicted miRNAs-targets relationship of top 10 DE-miRNAs to the final DE-genes to gain more convinced DE-miRNAs, DE-genes and their one to one relationship.GSE19945 (miRNA microarray) and GSE40275 (gene microarray) datasets were selected and downloaded. 56 DE-miRNAs and 861 DE-genes were discovered. 297 miRNAs-targets relationships (284 unique genes) were predicted as the target of top 10 upregulating DE-miRNAs. 245 miRNAs-targets relationships (238 unique genes) were identified as the target of top 10 downregulating DE-miRNAs. The key results of enrichment analysis include protein kinase B signaling, transmembrane receptor protein tyrosine kinase signaling pathway, negative regulation of cell differentiation, response to growth factor, cellular response to lipid, muscle structure development, response to growth factor, signaling by Receptor Tyrosine Kinases, epithelial cell migration, cellular response to organic cyclic compound, Cell Cycle (Mitotic), DNA conformation change, cell division, DNA replication, cell cycle phase transition, blood vessel development, inflammatory response, Staphylococcus aureus infection, leukocyte migration, and myeloid leukocyte activation. Differential expression of genes-upstream miRNAs (RBMS3-hsa-miR-7–5p, NEDD9-hsa-miR-18a-5p, CRIM1-hsa-miR-18a-5p, TGFBR2-hsa-miR-9–5p, MYO1C-hsa-miR-9–5p, KLF4-hsa-miR-7–5p, EMP2-hsa-miR-1290, TMEM2-hsa-miR-18a-5p, CTGF-hsa-miR-18a-5p, TNFAIP3-hsa-miR-18a-5p, THBS1-hsa-miR-182–5p, KPNA2-hsa-miR-144–3p, GPR137C-hsa-miR-1–3p, GRIK3-hsa-miR-144–3p, and MTHFD2-hsa-miR-30a-3p) were identified in SCLC.RBMS3, NEDD9, CRIM1, KPNA2, GPR137C, GRIK3, hsa-miR-7–5p, hsa-miR-18a-5p, hsa-miR-144–3p, hsa-miR-1–3p along with the pathways included protein kinase B signaling, muscle structure development, Cell Cycle (Mitotic) and blood vessel development may gain a high chance to play a key role in the prognosis of SCLC, but more studies should be conducted to reveal it more clearly.

Screening and identification of microRNAs during larval metamorphic development of Chinese mitten crab Eriocheir sinensis

MicroRNAs (miRNAs) represent a class of endogenous small non-coding RNAs with 18–25 nucleotides that are involved in diverse biological processes. In the present study, differentially expressed miRNAs in four different metamorphic stages of Eriocheir sinensis were comprehensively detected and validated from high-throughput sequencing miRNA data sets. We identified and characterized 597 known miRNAs and 901 novel miRNAs from the four small RNA libraries. We also demonstrated the evolutionary conservation of miRNAs from Chinese mitten crab with those from other species. Analyses of miRNA expression profiles during brachyurization development of the Chinese mitten crab revealed that 86 mature miRNAs were dysregulated, 47 of which were significantly differentially expressed. The results of real-time quantitative PCR analyses for a few selected miRNAs were similar to those of deep sequencing analyses. Intriguingly, the results of Gene Ontology annotation and KEGG pathway analyses indicated that miRNA gene clusters and families were consistently dysregulated at four different metamorphic stages, although they were enriched at various levels. Functional enrichment analysis revealed the versatile physiological roles (over six signal pathways) of these miRNAs. The results suggest that these miRNAs might play potential key roles in the regulation of gene expression in the brachyurization development of the Chinese mitten crab. Particularly, miR-2a-3p, miR-305-5p, miR-263a-5p and miR-7-5p may play major regulatory roles in the metamorphosis of E. sinensis.

MiR477 targets the phenylalanine ammonia-lyase gene and enhances the susceptibility of the tea plant (Camellia sinensis) to disease during Pseudopestalotiopsis species infection.

MAIN CONCLUSION: miR477 acts as a negative regulator in tea plant immunity against Pseudopestalotiopsis infection by repressing the expression of its target gene PAL. MicroRNA (miRNA)-mediated post-transcriptional regulation plays a fundamental role in various plant physiological processes, including responses to pathogens. Our previous research revealed that miR477 might be involved in the tea plant-Pseudopestalotiopsis interaction (data not shown). In the present study, the accumulation of miR477 significantly decreased in tea plants during Pseudopestalotiopsis species infection. Using miRNA and degradome data sets, the targeting of phenylalanine ammonia-lyase (PAL) by miR477 was validated by 5' RLM-RACE. GUS assay showed that the expression of PAL was post-transcriptionally regulated by miR477 and silenced by mRNA cleavage. A negative correlation between the expression of miR477 and PAL was found in tea plants infected by the pathogen. The transgenic lines overexpressing Csn-miR477 exhibited increased susceptibility to Pseudopestalotiopsis species, which was associated with reduced expression of PAL during infection. The degree of severity of the leaf lesions and the results of trypan blue staining showed that the plants overexpressing Csn-miR477 exhibited more severe damage upon pathogen infection than wild-type plants. In addition, more H2O2 and O2-, higher malondialdehyde (MDA) contents and less superoxide dismutase (SOD) and peroxidase (POD) activities were detected in the transgenic plants than in the wild-type plants after inoculation with Pseudopestalotiopsis species. Taken together, our results implied that Csn-miR477 might act as a negative regulator in pathogen-infected tea plants by inhibiting the expression of its target, PAL, and that Csn-miR477 is a candidate miRNA for improving the adaptation of tea plant to disease.