Improving biocuration of microRNAs in diseases: a case study in idiopathic pulmonary fibrosis

The function of non-coding RNAs in idiopathic pulmonary fibrosis

Non-coding ribonucleic acids (ncRNAs) are a diverse group of RNA molecules that are mostly not translated into proteins after transcription, including long non-coding RNAs (lncRNAs) with longer than 200 nucleotides non-coding transcripts and microRNAs (miRNAs) which are only 18–22 nucleotides. As families of evolutionarily conserved ncRNAs, lncRNAs activate and repress genes via a variety of mechanisms at both transcriptional and translational levels, whereas miRNAs regulate protein-coding gene expression mainly through mRNA silencing. ncRNAs are widely involved in biological functions, such as proliferation, differentiation, migration, angiogenesis, and apoptosis. Idiopathic pulmonary fibrosis (IPF) is a progressive lung disease with a poor prognosis. The etiology of IPF is still unclear. Increasing evidence shows the close correlations between the development of IPF and aberrant expressions of ncRNAs than thought previously. In this study, we provide an overview of ncRNAs participated in pathobiology of IPF, seeking the early diagnosis biomarker and aiming for potential therapeutic applications for IPF.

Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature

The detection of MicroRNA (miRNA) mentions in scientific literature facilitates researchers with the ability to find relevant and appropriate literature based on queries formulated using miRNA information. Considering most published biological studies elaborated on signal transduction pathways or genetic regulatory information in the form of figure captions, the extraction of miRNA from both the main content and figure captions of a manuscript is useful in aggregate analysis and comparative analysis of the studies published. In this study, we present a statistical principle-based miRNA recognition and normalization method to identify miRNAs and link them to the identifiers in the Rfam database. As one of the core components in the text mining pipeline of the database miRTarBase, the proposed method combined the advantages of previous works relying on pattern, dictionary and supervised learning and provided an integrated solution for the problem of miRNA identification. Furthermore, the knowledge learned from the training data was organized in a human-interpretable manner to understand the reason why the system considers a span of text as a miRNA mention, and the represented knowledge can be further complemented by domain experts. We studied the ambiguity level of miRNA nomenclature to connect the miRNA mentions to the Rfam database and evaluated the performance of our approach on two datasets: the BioCreative VI Bio-ID corpus and the miRNA interaction corpus by extending the later corpus with additional Rfam normalization information. Our study highlights and also proposes a better understanding of the challenges associated with miRNA identification and normalization in scientific literature and the research gap that needs to be further explored in prospective studies.

MicroRNA‑326 inhibits endometrial fibrosis by regulating TGF‑β1/Smad3 pathway in intrauterine adhesions

Intrauterine adhesion (IUA), characterized by endometrial fibrosis, may lead to infertility and recurrent pregnancy loss. At present, there is no ideal therapy for IUA. Recent findings have revealed that microRNAs (miRNAs) have a decisive role in the regulation of fibrosis. The aim of the present study was to investigate the molecular mechanism of miRNAs in endometrial fibrosis. The present study compared the expression profiles of miRNAs between endometrial tissues from patients with IUA and normal endometrial tissues using microarray analysis. Validation of miR‑326 level in endometrial tissues was performed using reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). Subsequently, the effects of miR‑326 on fibrotic markers including α‑smooth muscle actin (α‑SMA), collagen type I α 1 chain (COL1A1), transforming growth factor‑β1 (TGF‑β1) and fibronectin (FN), were evaluated in endometrial tissues and endometrial stromal cells (ESCs) from patients with IUA. Additional bioinformatics analysis, luciferase reporter assays, RT‑qPCR and western blotting were performed to identify target genes. Additionally, the expression levels of TGF‑β1, p‑Smad3 and Smad3 were quantified to determine whether the anti‑fibrotic role of miR‑326 was associated with the activity of the TGF‑β1/Smad3 signaling pathway. The present study determined that miR‑326 was downregulated in endometrial tissues from patients with IUA and miR‑326 levels were inversely correlated with the expression of TGF‑β1, α‑SMA, COL1A1 and FN. Additional findings revealed that overexpression of miR‑326 inhibited endometrial fibrosis by downregulating these pro‑fibrotic genes. TGF‑β1, an important pro‑fibrogenic mediator, was identified as a direct target of miR‑326. Additionally, overexpression of miR‑326 blocked the activation of the TGF‑β1/SMAD family member 3 (Smad3) signaling pathway by suppressing the expression of TGF‑β1 in ESCs from patients with IUA. The findings of the present study indicated that miR‑326 inhibited endometrial fibrosis by suppressing the TGF‑β1/Smad3 signaling pathway, suggesting that miR‑326 may be a prognostic biomarker and therapeutic target for IUA.

Strategies towards digital and semi-automated curation in RegulonDB

Experimentally generated biological information needs to be organized and structured in order to become meaningful knowledge. However, the rate at which new information is being published makes manual curation increasingly unable to cope. Devising new curation strategies that leverage upon data mining and text analysis is, therefore, a promising avenue to help life science databases to cope with the deluge of novel information. In this article, we describe the integration of text mining technologies in the curation pipeline of the RegulonDB database, and discuss how the process can enhance the productivity of the curators. Specifically, a named entity recognition approach is used to pre-annotate terms referring to a set of domain entities which are potentially relevant for the curation process. The annotated documents are presented to the curator, who, thanks to a custom-designed interface, can select sentences containing specific types of entities, thus restricting the amount of text that needs to be inspected. Additionally, a module capable of computing semantic similarity between sentences across the entire collection of articles to be curated is being integrated in the system. We tested the module using three sets of scientific articles and six domain experts. All these improvements are gradually enabling us to obtain a high throughput curation process with the same quality as manual curation.