Improving miRNA-mRNA interaction predictions

Species-specific microRNA discovery and target prediction in the soybean cyst nematode

The soybean cyst nematode (SCN) is a devastating pathogen for economic and food security considerations. Although the SCN genome has recently been sequenced, the presence of any miRNA has not been systematically explored and reported. This paper describes the development of a species-specific SCN miRNA discovery pipeline and its application to the SCN genome. Experiments on well-documented model nematodes (Caenorhabditis elegans and Pristionchus pacificus) are used to tune the pipeline's hyperparameters and confirm its recall and precision. Application to the SCN genome identifies 3342 high-confidence putative SCN miRNA. Prediction specificity within SCN is confirmed by applying the pipeline to RNA hairpins from known exonic regions of the SCN genome (i.e., sequences known to not be miRNA). Prediction recall is confirmed by building a positive control set of SCN miRNA, based on a limited deep sequencing experiment. Interestingly, a number of novel miRNA are predicted to be encoded within the intronic regions of effector genes, known to be involved in SCN parasitism, suggesting that these miRNA may also be involved in the infection process or virulence. Beyond miRNA discovery, gene targets within SCN are predicted for all high-confidence novel miRNA using a miRNA:mRNA target prediction system. Lastly, cross-kingdom miRNA targeting is investigated, where putative soybean mRNA targets are identified for novel SCN miRNA. All predicted miRNA and gene targets are made available in appendix and through a Borealis DataVerse open repository ( https://borealisdata.ca/dataset.xhtml?persistentId=doi:10.5683/SP3/30DEXA ).

P-TarPmiR accurately predicts plant-specific miRNA targets

microRNAs (miRNAs) are small non-coding ribonucleic acids that post-transcriptionally regulate gene expression through the targeting of messenger RNA (mRNAs). Most miRNA target predictors have focused on animal species and prediction performance drops substantially when applied to plant species. Several rule-based miRNA target predictors have been developed in plant species, but they often fail to discover new miRNA targets with non-canonical miRNA-mRNA binding. Here, the recently published TarDB database of plant miRNA-mRNA data is leveraged to retrain the TarPmiR miRNA target predictor for application on plant species. Rigorous experiment design across four plant test species demonstrates that animal-trained predictors fail to sustain performance on plant species, and that the use of plant-specific training data improves accuracy depending on the quantity of plant training data used. Surprisingly, our results indicate that the complete exclusion of animal training data leads to the most accurate plant-specific miRNA target predictor indicating that animal-based data may detract from miRNA target prediction in plants. Our final plant-specific miRNA prediction method, dubbed P-TarPmiR, is freely available for use at http://ptarpmir.cu-bic.ca . The final P-TarPmiR method is used to predict targets for all miRNA within the soybean genome. Those ranked predictions, together with GO term enrichment, are shared with the research community.

Molecular Classification of Colorectal Cancer by microRNA Profiling: Correlation with the Consensus Molecular Subtypes (CMS) and Validation of miR-30b Targets

Colorectal cancer consensus molecular subtypes (CMSs) are widely accepted and constitutes the basis for patient stratification to improve clinical practice. We aimed to find whether miRNAs could reproduce molecular subtypes, and to identify miRNA targets associated to the High-stroma/CMS4 subtype. The expression of 939 miRNAs was analyzed in tumors classified in CMS. TALASSO was used to find gene-miRNA interactions. A miR-mRNA regulatory network was constructed using Cytoscape. Candidate gene-miR interactions were validated in 293T cells. Hierarchical-Clustering identified three miRNA tumor subtypes (miR-LS; miR-MI; and miR-HS) which were significantly associated (p < 0.001) to the reported mRNA subtypes. miR-LS correlated with the low-stroma/CMS2; miR-MI with the mucinous-MSI/CMS1 and miR-HS with high-stroma/CMS4. MicroRNA tumor subtypes and association to CMSs were validated with TCGA datasets. TALASSO identified 1462 interactions (p < 0.05) out of 21,615 found between 176 miRs and 788 genes. Based on the regulatory network, 88 miR-mRNA interactions were selected as candidates. This network was functionally validated for the pair miR-30b/SLC6A6. We found that miR-30b overexpression silenced 3′-UTR-SLC6A6-driven luciferase expression in 293T-cells; mutation of the target sequence in the 3′-UTR-SLC6A6 prevented the miR-30b inhibitory effect. In conclusion CRC subtype classification using a miR-signature might facilitate a real-time analysis of the disease course and treatment response.

Bioinformatics analysis and experimental verification of Notch signalling pathway-related miRNA-mRNA subnetwork in extracellular vesicles during Echinococcus granulosus encystation

Background

Encystation of the protoscoleces (PSCs) of Echinococcus granulosus is the main cause of secondary hydatid dissemination in the intermediate host. Extracellular vesicles (EVs) can transfer miRNAs into parasite cells to regulate mRNA expression. However, loading of developmental pathway-related miRNAs, such as those related to the Notch signalling pathway in EVs is unclear. Thus, we screened the miRNA-mRNA subnetwork involved in the Notch pathway during E. granulosus encystation in vitro and assessed changes in expression in the parasite and EVs.

Methods

mRNAs and miRNAs differentially expressed (DE) between PSCs and microcysts (MCs) were screened using high-throughput sequencing. DE mRNAs obtained from transcriptome analysis were intersected with mRNAs predicted to be targets of the conserved DE miRNAs of a small RNA library. DE miRNA functions were analysed using public databases, and a miRNA–mRNA subnetwork related to the Notch pathway was established. Notch pathway-related mRNA and miRNA expression of worms and EVs at different times was verified.

Results

In total, 1445 DE mRNAs between MCs and PSCs were screened after the intersection between 1586 DE mRNAs from the transcriptome and 9439 target mRNAs predicted using 39 DE miRNAs from the small RNA library. The DE mRNAs were clustered into 94 metabolic pathways, including the Notch pathway. Five DE miRNAs, including the most significantly expressed new DE miRNA, egr-new-mir0694-3p, corresponding to four target mRNAs (EgrG_000892700, EgrG_001029400, EgrG_001081400 and EgrG_000465800) were all enriched in the Notch pathway. The expression of the above mRNAs and miRNAs was consistent with the results of high-throughput sequencing, and the expression of each miRNA in EVs was verified. Annotated as ADAM17/TACE in the Notch pathway, EgrG_000892700 was down-regulated during PSC encystation. egr-miR-4989-3p and egr-miR-277a-3p expression in EVs after encystation was nearly five times that in EVs before encystation, which might regulate the expression of EgrG_000892700.

Conclusions

Five miRNAs corresponding to four target mRNAs may be involved in regulating the Notch pathway during the PSC encystation. EVs may regulate the expression of EgrG_000892700 in PSCs because of continuous targeting of egr-miR-4989-3p and egr-miR-277a-3p and participate in the regulation the Notch pathway. The study might expand new ideas for blocking the secondary infection of E. granulosus PSCs via EVs miRNAs.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-022-05391-8.

Prediction methods for microRNA targets in bilaterian animals: Toward a better understanding by biologists

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression at the posttranscriptional level. Because of their wide network of interactions, miRNAs have become the focus of many studies over the past decade, particularly in animal species. To streamline the number of potential wet lab experiments, the use of miRNA target prediction tools is currently the first step undertaken. However, the predictions made may vary considerably depending on the tool used, which is mostly due to the complex and still not fully understood mechanism of action of miRNAs. The discrepancies complicate the choice of the tool for miRNA target prediction. To provide a comprehensive view of this issue, we highlight in this review the main characteristics of miRNA-target interactions in bilaterian animals, describe the prediction models currently used, and provide some insights for the evaluation of predictor performance.

DisCoVering potential candidates of RNAi-based therapy for COVID-19 using computational methods

The ongoing pandemic of a novel coronavirus (SARS-CoV-2) leads to international concern; thus, emergency interventions need to be taken. Due to the time-consuming experimental methods for proposing useful treatments, computational approaches facilitate investigating thousands of alternatives simultaneously and narrow down the cases for experimental validation. Herein, we conducted four independent analyses for RNA interference (RNAi)-based therapy with computational and bioinformatic methods. The aim is to target the evolutionarily conserved regions in the SARS-CoV-2 genome in order to down-regulate or silence its RNA. miRNAs are denoted to play an important role in the resistance of some species to viral infections. A comprehensive analysis of the miRNAs available in the body of humans, as well as the miRNAs in bats and many other species, were done to find efficient candidates with low side effects in the human body. Moreover, the evolutionarily conserved regions in the SARS-CoV-2 genome were considered for designing novel significant siRNA that are target-specific. A small set of miRNAs and five siRNAs were suggested as the possible efficient candidates with a high affinity to the SARS-CoV-2 genome and low side effects. The suggested candidates are promising therapeutics for the experimental evaluations and may speed up the procedure of treatment design. Materials and implementations are available at: https://github.com/nrohani/SARS-CoV-2.

Computational Methods and Software Tools for Functional Analysis of miRNA Data

miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.

Mammary tissue transcriptomic analysis for construction of integrated regulatory networks involved in lactogenesis of Ovis aries

The mammary gland experiences vast changes between the onset of lactation and pregnancy. This remodeling involves different functions such as lactation that is controlled by innumerable regulators and various gene networks which are still not completely understood. MicroRNAs (miRNAs) are one of the important non-coding gene regulators which control an extensive range of biological processes. Thus, exploring miRNAs functions is important for solving gene regulation complexity. The main purpose in the present study is to identify the various gene regulative integrated networks involved in lactation progress in mammary gland. We analyzed ovine mammary tissue data sets which included expression profiles of mRNA (genes) and miRNAs related to six ewes in different days of lactation and nutritional treatments. We combined two different types of information: the network that is module inference by mRNAs (RNA-seq data), miRNAs and transcription factors (TFs) expression matrix and prediction of targets via computational methods. To discover the miRNAs regulatory function, 134 modules were predicted by using gene expression data and 14 TFs and 20 miRNAs were allocated to these predicted modules. By applying this integrated computation-based method, 38 miRNA-modules and 35 TF-module interactions were identified from ovine mammary tissue data during lactogenesis. A lot of these modules were involved in lipid and protein metabolism, as well as steroids and vitamin biosynthesis, which would play key roles in mammary tissue and lactation development. These results present new information about the regulatory procedures at the miRNAs and TF levels throughout lactation.

RPmirDIP: Reciprocal Perspective improves miRNA targeting prediction

MicroRNAs (miRNAs) are short, non-coding RNAs that interact with messenger RNA (mRNA) to accomplish critical cellular activities such as the regulation of gene expression. Several machine learning methods have been developed to improve classification accuracy and reduce validation costs by predicting which miRNA will target which gene. Application of these predictors to large numbers of unique miRNA-gene pairs has resulted in datasets comprising tens of millions of scored interactions; the largest among these is mirDIP. We here demonstrate that miRNA target prediction can be significantly improved ([Formula: see text]) through the application of the Reciprocal Perspective (RP) method, a cascaded, semi-supervised machine learning method originally developed for protein-protein interaction prediction. The RP method, aptly named RPmirDIP, augments the original mirDIP prediction scores by leveraging local thresholds from the two complimentary views available to each miRNA-gene pair, rather than apply a traditional global decision threshold. Application of this novel RPmirDIP predictor promises to help identify new, unexpected miRNA-gene interactions. A dataset of RPmirDIP-scored interactions are made available to the scientific community at cu-bic.ca/RPmirDIP and https://doi.org/10.5683/SP2/LD8JKJ.

miRNA target identification and prediction as a function of time in gene expression data

The understanding of miRNA target interactions is still limited due to conflicting data and the fact that high-quality validation of targets is a time-consuming process. Faster methods like high-throughput screens and bioinformatics predictions are employed but suffer from several problems. One of these, namely the potential occurrence of downstream (i.e. secondary) effects in high-throughput screens has been only little discussed so far. However, such effects limit usage for both the identification of interactions and for the training of bioinformatics tools. In order to analyse this problem more closely, we performed time-dependent microarray screening experiments overexpressing human miR-517a-3p, and, together with published time-dependent datasets of human miR-17-5p, miR-135b and miR-124 overexpression, we analysed the dynamics of deregulated genes. We show that the number of deregulated targets increases over time, whereas seed sequence content and performance of several miRNA target prediction algorithms actually decrease over time. Bioinformatics recognition success of validated miR-17 targets was comparable to that of data gained only 12 h post-transfection. We therefore argue that the timing of microarray experiments is of critical importance for detecting direct targets with high confidence and for the usability of these data for the training of bioinformatics prediction tools.

Differential expression of miRNAs and their target genes: Exploring a new perspective of acquired aplastic anemia pathogenesis

INTRODUCTION

MicroRNAs (miRNAs) play a critical role in orchestrating T cell differentiation and activation and may thus play a vital role in acquired aplastic anemia (aAA). The study aimed to evaluate the differential expression of selected miRNAs and their relevant target genes in bone marrow samples of aAA patients.

METHODS

Differential expression of 8 miRNAs viz; hsa-miR-126-3p, miR-145-5p, miR-155-5p, miR-150-5p, miR-146b-5p, miR-34a, miR-29a, and miR-29b was evaluated in the bone marrow mononuclear cells of aAA patients. TaqMan microRNA assay was performed for preparing the cDNA of specific miRNA, followed by expression analysis using qRT-PCR. Data were normalized using two endogenous controls, RNU6B and RNU48. Delta-delta CT method was used to calculate the fold change (FC) of miRNA expression in individual samples, and a FC of >1.5 was taken as significant. Target genes of these miRNAs were evaluated by qRT-PCR.

RESULTS

Thirty five samples of aAA patients and 20 controls were evaluated. Irrespective of the disease severity, five miRNAs were found to be deregulated; miR-126 (FC-0.348; P-value-.0001) and miR-145 (FC-0.31; P-value-.0001) were downregulated, while miR-155 (FC-3.50; P-value-.0067), miR-146 (FC-3.13; P-value-.0105), and miR-150 (FC-5.78; P-value-.0001) were upregulated. Target gene study revealed an upregulation of PIK3R2, MYC, SOCS1, and TRAF-6, and downregulation of MYB.

CONCLUSION

This is the first study from the Indian subcontinent demonstrating the presence of altered miRNA expression in the bone marrow samples of aAA patients, suggesting their role in the pathogenesis of the disease. A comprehensive study focusing on the effect of these miRNA-mRNA interactions is likely to open new avenues of management.

Associating lncRNAs with small molecules via bilevel optimization reveals cancer-related lncRNAs

Long noncoding RNA (lncRNA) transcripts have emerging impacts in cancer studies, which suggests their potential as novel therapeutic agents. However, the molecular mechanism behind their treatment effects is still unclear. Here, we designed a computational model to Associate LncRNAs with Anti-Cancer Drugs (ALACD) based on a bilevel optimization model, which optimized the gene signature overlap in the upper level and imputed the missing lncRNA-gene association in the lower level. ALACD predicts genes coexpressed with lncRNAs mean while matching drug’s gene signatures. This model allows us to borrow the target gene information of small molecules to understand the mechanisms of action of lncRNAs and their roles in cancer. The ALACD model was systematically applied to the 10 cancer types in The Cancer Genome Atlas (TCGA) that had matched lncRNA and mRNA expression data. Cancer type-specific lncRNAs and associated drugs were identified. These lncRNAs show significantly different expression levels in cancer patients. Follow-up functional and molecular pathway analysis suggest the gene signatures bridging drugs and lncRNAs are closely related to cancer development. Importantly, patient survival information and evidence from the literature suggest that the lncRNAs and drug-lncRNA associations identified by the ALACD model can provide an alternative choice for cancer targeting treatment and potential cancer pognostic biomarkers. The ALACD model is freely available at https://github.com/wangyc82/ALACD-v1.

LncRNAs are RNA transcripts that are longer than 200 bp and do not encode proteins. Recent experimental studies have indicated the crucial role of lncRNAs in cancer. We proposed a computational model, ALACD, to understand a lncRNA’s molecular mechanism by associating it with a drug through the drug’s target genes. ALACD reveals lncRNAs, the associated anti-cancer drug, and the induced gene signatures that are involved in the regulation of cancer. Furthermore, these cancer-related lncRNAs are differentially expressed in cancer patients and closely associated with patient survival.

Identification and expression of microRNAs in european eels Anguilla anguilla from two natural sites with different pollution levels

MicroRNAs (miRNAs) are a class of small non-coding RNA that control multiple biological processes through negative post-transcriptional regulation of gene expression. Recently a role of miRNAs in the response of aquatic organisms to environmental toxicants emerged. Toxicant-induced changes in miRNA expression might then represent novel biomarkers to evaluate the health status of these organisms. In this study, we aimed to identify the miRNA repertoire in the liver of the European eel Anguilla anguilla and to compare their differential expression between a polluted site located in the Gironde Estuary and a pristine site in Arcachon Bay (France). A total of 299 mature miRNAs were identified. In polluted water, 19 miRNAs were up-regulated and 22 were down-regulated. We predicted that these differentially expressed miRNAs could target 490 genes that were involved in ribosome biogenesis, response to hormones, response to chemical and chromatin modification. Moreover, we observed only few examples (29) of negative correlation between the expression levels of miRNAs and their targets suggesting that, in the system studied, miRNAs might not only regulate gene expression directly by degrading mRNA but also by inhibiting protein translation or by regulating other epigenetic processes. This study is the first example of in situ investigation of the role of miRNAs in the response of a fish species to water quality. Our findings provide new insights into the involvement of epigenetic mechanisms in the response of animals chronically exposed to pollution and pave the way for the utilization of miRNAs in aquatic ecotoxicology.

The long non-coding RNA Cerox1 is a post transcriptional regulator of mitochondrial complex I catalytic activity

To generate energy efficiently, the cell is uniquely challenged to co-ordinate the abundance of electron transport chain protein subunits expressed from both nuclear and mitochondrial genomes. How an effective stoichiometry of this many constituent subunits is co-ordinated post-transcriptionally remains poorly understood. Here we show that Cerox1, an unusually abundant cytoplasmic long noncoding RNA (lncRNA), modulates the levels of mitochondrial complex I subunit transcripts in a manner that requires binding to microRNA-488-3p. Increased abundance of Cerox1 cooperatively elevates complex I subunit protein abundance and enzymatic activity, decreases reactive oxygen species production, and protects against the complex I inhibitor rotenone. Cerox1 function is conserved across placental mammals: human and mouse orthologues effectively modulate complex I enzymatic activity in mouse and human cells, respectively. Cerox1 is the first lncRNA demonstrated, to our knowledge, to regulate mitochondrial oxidative phosphorylation and, with miR-488-3p, represent novel targets for the modulation of complex I activity.

MiR-29b-1-5p is altered in BRCA1 mutant tumours and is a biomarker in basal-like breast cancer

Depletion of BRCA1 protein in mouse mammary glands results in defects in lactational development and increased susceptibility to mammary cancer. Extensive work has focussed on the role of BRCA1 in the normal breast and in the development of breast cancer, the cell of origin for BRCA1 tumours and the protein-coding genes altered in BRCA1 deficient cells. However, the role of non-coding RNAs in BRCA1-deficient cells is poorly understood. To evaluate miRNA expression in BRCA1 deficient mammary cells, RNA sequencing was performed on the mammary glands of Brca1 knockout mice. We identified 140 differentially expressed miRNAs, 9 of which were also differentially expressed in human BRCA1 breast tumours or familial non-BRCA1 patients and during normal gland development. We show that BRCA1 binds to putative cis-elements in promoter regions of the miRNAs with the potential to regulate their expression, and that four miRNAs (miR-29b-1-5p, miR-664, miR-16-2 and miR-744) significantly stratified the overall survival of basal-like tumours. Importantly the prognostic value of miR-29b-1-5p was higher in significance than several commonly used clinical biomarkers. These results emphasise the role of Brca1 in modulating expression of miRNAs and highlights the potential for BRCA1 regulated miRNAs to be informative biomarkers associated with BRCA1 loss and survival in breast cancer.

Integrated regulatory network reveals novel candidate regulators in the development of negative energy balance in cattle

Negative energy balance (NEB) is an altered metabolic state in modern high-yielding dairy cows. This metabolic state occurs in the early postpartum period when energy demands for milk production and maintenance exceed that of energy intake. Negative energy balance or poor adaptation to this metabolic state has important effects on the liver and can lead to metabolic disorders and reduced fertility. The roles of regulatory factors, including transcription factors (TFs) and micro RNAs (miRNAs) have often been separately studied for evaluating of NEB. However, adaptive response to NEB is controlled by complex gene networks and still not fully understood. In this study, we aimed to discover the integrated gene regulatory networks involved in NEB development in liver tissue. We downloaded data sets including mRNA and miRNA expression profiles related to three and four cows with severe and moderate NEB, respectively. Our method integrated two independent types of information: module inference network by TFs, miRNAs and mRNA expression profiles (RNA-seq data) and computational target predictions. In total, 176 modules were predicted by using gene expression data and 64 miRNAs and 63 TFs were assigned to these modules. By using our integrated computational approach, we identified 13 TF-module and 19 miRNA-module interactions. Most of these modules were associated with liver metabolic processes as well as immune and stress responses, which might play crucial roles in NEB development. Literature survey results also showed that several regulators and gene targets have already been characterized as important factors in liver metabolic processes. These results provided novel insights into regulatory mechanisms at the TF and miRNA levels during NEB. In addition, the method described in this study seems to be applicable to construct integrated regulatory networks for different diseases or disorders.

miRNA profiling during antigen-dependent T cell activation: A role for miR-132-3p

microRNAs (miRNAs) are tightly regulated during T lymphocyte activation to enable the establishment of precise immune responses. Here, we analyzed the changes of the miRNA profiles of T cells in response to activation by cognate interaction with dendritic cells. We also studied mRNA targets common to miRNAs regulated in T cell activation. pik3r1 gene, which encodes the regulatory subunits of PI3K p50, p55 and p85, was identified as target of miRNAs upregulated after T cell activation. Using 3′UTR luciferase reporter-based and biochemical assays, we showed the inhibitory relationship between miR-132-3p upregulation and expression of the pik3r1 gene. Our results indicate that specific miRNAs whose expression is modulated during T cell activation might regulate PI3K signaling in T cells.

MicroRNA expression and gene regulation drive breast cancer progression and metastasis in PyMT mice

Background

MicroRNAs (miRNAs) are small non-coding RNA molecules of about 22 nucleotides which function to silence the expression of their target genes. Numerous studies have shown that miRNAs are not only key regulators in important cellular processes but are also drivers in the development of many diseases, especially cancer. Estrogen receptor positive luminal B is the second most common but the least studied subtype of breast cancer. Only a few studies have examined the expression profiles of miRNAs in luminal B breast cancer, and their regulatory roles in cancer progression have yet to be investigated.

Methods

In this study, using polyoma middle T antigen (PyMT) mice, a widely used luminal B breast cancer model, we profiled microRNA (miRNA) expression at four time points that represent different key developmental stages of cancer progression. We considered the expression of both miRNAs and messenger RNAs (mRNAs) at these time points to improve the identification of regulatory targets of miRNAs. By combining gene functional and pathway annotation with miRNA-mRNA interactions, we created a PyMT-specific tripartite miRNA-mRNA-pathway network and identified novel functional regulatory programs (FRPs).

Results

We identified 151 differentially expressed miRNAs with a strict dual nature of either upregulation or downregulation during the whole course of disease progression. Among 82 newly discovered breast-cancer-related miRNAs, 35 can potentially regulate 271 protein-coding genes based on their sequence complementarity and expression profiles. We also identified miRNA-mRNA regulatory modules driving specific cancer-related biological processes.

Conclusions

In this study we profiled the expression of miRNAs during breast cancer progression in the PyMT mouse model. By integrating miRNA and mRNA expression profiles, we identified differentially expressed miRNAs and their target genes involved in several hallmarks of cancer. We applied a novel clustering method to an annotated miRNA-mRNA regulatory network and identified network modules involved in specific cancer-related biological processes.

Electronic supplementary material

The online version of this article (doi:10.1186/s13058-016-0735-z) contains supplementary material, which is available to authorized users.

MicroRNA-206 is differentially expressed in Brca1-deficient mice and regulates epithelial and stromal cell compartments of the mouse mammary gland

Depletion of Brca1 leads to defects in mouse mammary gland development and mammary tumors in humans and mice. To explore the role of microRNAs (miRNAs) in this process, we examined the mammary glands of MMTV-Cre Brca1^Co/Co mice for differential miRNA expression using a candidate approach. Several miRNAs were differentially expressed in mammary tissue at day 1 of lactation and in mammary epithelial cell lines in which Brca1 messenger RNA (mRNA) levels have been reduced. Functional studies revealed that several of these miRNAs regulate mammary epithelial cell function in vitro, including miR-206. Creation and analysis of MMTV-miR-206 transgenic mice showed no effect on lactational mammary development and no tumors, but indicates a role in mammary tissue remodeling in mature mice, potentially involving Igf-1 and Sfrp1. These results indicate the potential of miRNAs to mediate the consequences of Brca1 loss and suggest a novel function for miR-206.

MicroRNAs Circulate in the Hemolymph of Drosophila and Accumulate Relative to Tissue microRNAs in an Age-Dependent Manner

In mammals, extracellular miRNAs circulate in biofluids as stable entities that are secreted by normal and diseased tissues, and can enter cells and regulate gene expression. Drosophila melanogaster is a proven system for the study of human diseases. They have an open circulatory system in which hemolymph (HL) circulates in direct contact with all internal organs, in a manner analogous to vertebrate blood plasma. Here, we show using deep sequencing that Drosophila HL contains RNase-resistant circulating miRNAs (HL-miRNAs). Limited subsets of body tissue miRNAs (BT-miRNAs) accumulated in HL, suggesting that they may be specifically released from cells or particularly stable in HL. Alternatively, they might arise from specific cells, such as hemocytes, that are in intimate contact with HL. Young and old flies accumulated unique populations of HL-miRNAs, suggesting that their accumulation is responsive to the physiological status of the fly. These HL-miRNAs in flies may function similar to the miRNAs circulating in mammalian biofluids. The discovery of these HL-miRNAs will provide a new venue for health and disease-related research in Drosophila.