Impact of probe annotation on the integration of miRNA-mRNA expression profiles for miRNA target detection

MiR-223-3p Regulates Autophagy and Inflammation by Targeting ATG16L1 in Fusarium solani-Induced Keratitis

Purpose

Increasing evidence suggested that microRNAs (miRs) are implicated in the regulation of the inflammatory response and autophagy in multiple diseases. The present study aimed to explore the effect of miR-223-3p on inflammation and autophagy in fungal keratitis (FK).

Methods

An FK mouse model was established, and primary corneal stromal cells were isolated by inoculation with Fusarium solani. The expression of miR-223-3p was determined by quantitative RT-PCR. Subsequently, the target gene of miR-223-3p was identified by a dual-luciferase reporter assay. The levels of miR-223-3p were altered by transfecting miR agomir/antagomir to evaluate its effects. Slit-lamp biomicroscopy and hematoxylin and eosin staining were employed to detect corneal damage. The levels of autophagy were assessed by immunofluorescence, Western blotting, mRFP-GFP-LC3 fluorescence microscopy, and electron microscopy. In addition, inflammation was demonstrated by determining the proinflammatory mediators IL-1β and TNF-ɑ.

Results

Our data suggested that miR-223-3p was increased and that autophagic flux was impaired in mouse FK. Then, we confirmed that autophagy-related gene 16L1 (ATG16L1) was a potential target of miR-223-3p and that this miR negatively regulated the expression of ATG16L1. The inhibition of miR-223-3p attenuated inflammation in FK, reduced P62 expression, and increased the ratio of LC3-II/LC3-I, whereas the overexpression of miR-223-3p displayed the opposite results.

Conclusions

Taken together, miR-223-3p might regulate autophagy via targeting ATG16L1 in experimental F. solani keratitis and is associated with the inflammatory response. MiR-223-3p might be a potential therapeutic target for FK.

Comprehensive annotation of microRNA expression profiles

BACKGROUND

MicroRNAs (miRNAs) regulate many biological processes by post-translational gene silencing. Analysis of miRNA expression profiles is a reliable method for investigating particular biological processes due to the stability of miRNA and the development of advanced sequencing methods. However, this approach is limited by the broad specificity of miRNAs, which may target several mRNAs.

RESULT

In this study, we developed a method for comprehensive annotation of miRNA array or deep sequencing data for investigation of cellular biological effects. Using this method, the specific pathways and biological processes involved in Alzheimer's disease were predicted with high correlation in four independent samples. Furthermore, this method was validated for evaluation of cadmium telluride (CdTe) nanomaterial cytotoxicity. As a result, apoptosis pathways were selected as the top pathways associated with CdTe nanoparticle exposure, which is consistent with previous studies.

CONCLUSIONS

Our findings contribute to the validation of miRNA microarray or deep sequencing results for early diagnosis of disease and evaluation of the biological safety of new materials and drugs.

Network signatures of cellular immortalization in human lymphoblastoid cell lines

Human lymphoblastoid cell line (LCL) has been used as an in vitro cell model in genetic and pharmacogenomic studies, as well as a good model for studying gene expression regulatory machinery using integrated genomic analyses. In this study, we aimed to identify biological networks of LCL immortalization from transcriptomic profiles of microRNAs and their target genes in LCLs. We first selected differentially expressed genes (DEGs) and microRNAs (DEmiRs) between early passage LCLs (eLCLs) and terminally differentiated late passage LCLs (tLCLs). The in silico and correlation analysis of these DEGs and DEmiRs revealed that 1098 DEG-DEmiR pairs were found to be positively (n=591 pairs) or negatively (n=507 pairs) correlated with each other. More than 41% of DEGs are possibly regulated by miRNAs in LCL immortalizations. The target DEGs of DEmiRs were enriched for cellular functions associated with apoptosis, immune response, cell death, JAK-STAT cascade and lymphocyte activation while non-miRNA target DEGs were over-represented for basic cell metabolisms. The target DEGs correlated negatively with miR-548a-3p and miR-219-5p were significantly associated with protein kinase cascade, and the lymphocyte proliferation and apoptosis, respectively. In addition, the miR-106a and miR-424 clusters located in the X chromosome were enriched in DEmiR-mRNA pairs for LCL immortalization. In this study, the integrated transcriptomic analysis of LCLs could identify functional networks of biologically active microRNAs and their target genes involved in LCL immortalization.

Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.

Pleiotropic action of renal cell carcinoma-dysregulated miRNAs on hypoxia-related signaling pathways

The von Hippel-Lindau (VHL) gene is lost in ≈ 70% of all renal cell carcinomas (RCCs); however, increasing evidence supports the involvement of alternative mechanisms in the regulation of VHL expression, including suppression by microRNAs (miRNAs). miRNAs are small, noncoding RNA molecules that regulate gene expression through binding to target mRNAs. In this study, we found that miRNAs, which are dysregulated in cases of RCC, can target multiple members of RCC-related signaling pathways. Importantly, both VHL and the hypoxia-inducible factor 1-α gene are experimentally validated and are likely direct targets of miR-17-5p and miR-224, as shown by both luciferase assay and Western blot analysis. We found a negative correlation between miR-17-5p and its two predicted targets, VEGF-A and EGLN3, and between miR-224 and its targets SMAD4 and SMAD5 in RCC specimens, suggesting that downstream signaling pathways are also modulated by clear cell RCC-dysregulated miRs. Results from our bioinformatics analysis show that a single miRNA molecule can target multiple components of the same pathway and that multiple miRNAs can target the same molecule. Our results also indicate that miRNAs represent a mechanism for the inactivation of VHL in cases of RCC and can elucidate a new dimension in cancer pathogenesis. As such, miRNAs exemplify new potential therapeutic targets with a significant effect on both tumor growth and metastatic potential.

Up-regulation of microRNAs in brain of human alcoholics

BACKGROUND

MicroRNAs (miRNAs) are small, noncoding oligonucleotides with an important role in posttranscriptional regulation of gene expression at the level of translation and mRNA degradation. Recent studies have revealed that miRNAs play important roles in a variety of biological processes, such as cell proliferation, neuronal differentiation, developmental timing, synapse function, and neurogenesis. A single miRNA can target hundreds of mRNA transcripts for either translation repression or degradation, but the function of many human miRNAs is not known.

METHODS

miRNA array analysis was performed on the prefrontal cortex of 27 individual human cases (14 alcoholics and 13 matched controls). Target genes for differentially expressed miRNAs were predicted using multiple target prediction algorithms and a consensus approach, and predicted targets were matched against differentially expressed mRNAs from the same samples. Over- and under-representation analysis was performed using hypergeometric probability and z-score tests.

RESULTS

Approximately 35 miRNAs were significantly up-regulated in the alcoholic group compared with controls. Target prediction showed a large degree of overlap with our published cDNA microarray data. Functional classification of the predicted target genes of the regulated miRNAs includes apoptosis, cell cycle, cell adhesion, nervous system development, and cell-cell signaling.

CONCLUSIONS

These data suggest that the reduced expression of genes in human alcoholic cases may be because of the up-regulated miRNAs. Cellular processes fundamental to neuronal plasticity appear to represent major targets of the suggested miRNA regulation.

Statistical Test of Expression Pattern (STEPath): a new strategy to integrate gene expression data with genomic information in individual and meta-analysis studies

Background

In the last decades, microarray technology has spread, leading to a dramatic increase of publicly available datasets. The first statistical tools developed were focused on the identification of significant differentially expressed genes. Later, researchers moved toward the systematic integration of gene expression profiles with additional biological information, such as chromosomal location, ontological annotations or sequence features. The analysis of gene expression linked to physical location of genes on chromosomes allows the identification of transcriptionally imbalanced regions, while, Gene Set Analysis focuses on the detection of coordinated changes in transcriptional levels among sets of biologically related genes.

In this field, meta-analysis offers the possibility to compare different studies, addressing the same biological question to fully exploit public gene expression datasets.

Results

We describe STEPath, a method that starts from gene expression profiles and integrates the analysis of imbalanced region as an a priori step before performing gene set analysis. The application of STEPath in individual studies produced gene set scores weighted by chromosomal activation. As a final step, we propose a way to compare these scores across different studies (meta-analysis) on related biological issues. One complication with meta-analysis is batch effects, which occur because molecular measurements are affected by laboratory conditions, reagent lots and personnel differences. Major problems occur when batch effects are correlated with an outcome of interest and lead to incorrect conclusions. We evaluated the power of combining chromosome mapping and gene set enrichment analysis, performing the analysis on a dataset of leukaemia (example of individual study) and on a dataset of skeletal muscle diseases (meta-analysis approach).

In leukaemia, we identified the Hox gene set, a gene set closely related to the pathology that other algorithms of gene set analysis do not identify, while the meta-analysis approach on muscular disease discriminates between related pathologies and correlates similar ones from different studies.

Conclusions

STEPath is a new method that integrates gene expression profiles, genomic co-expressed regions and the information about the biological function of genes. The usage of the STEPath-computed gene set scores overcomes batch effects in the meta-analysis approaches allowing the direct comparison of different pathologies and different studies on a gene set activation level.