A Cancer-Related microRNA Signature Shows Biomarker Utility in Multiple Myeloma

Multiple myeloma (MM) is the second most common hematological malignancy, arising from terminally differentiated B cells, namely plasma cells. miRNAs are small non-coding RNAs that participate in the post-transcriptional regulation of gene expression. In this study, we investigated the role of nine miRNAs in MM. CD138+ plasma cells were selected from bone marrow aspirates from MM and smoldering MM (sMM) patients. Total RNA was extracted and in vitro polyadenylated. Next, first-strand cDNA synthesis was performed using an oligo-dT-adapter primer. For the relative quantification of the investigated miRNAs, an in-house real-time quantitative PCR (qPCR) assay was developed. A functional in silico analysis of the miRNAs was also performed. miR-16-5p and miR-155-5p expression was significantly lower in the CD138+ plasma cells of MM patients than in those of sMM patients. Furthermore, lower levels of miR-15a-5p, miR-16-5p, and miR-222-3p were observed in the CD138+ plasma cells of MM patients with osteolytic bone lesions, compared to those without. miR-125b-5p was also overexpressed in the CD138+ plasma cells of MM patients with bone disease that presented with skeletal-related events (SREs). Furthermore, lower levels of miR-223-3p were associated with significantly worse overall survival in MM patients. In conclusion, we propose a miRNA signature with putative clinical utility in MM.

Improving Bioinformatics Prediction of microRNA Targets by Ranks Aggregation

microRNAs are noncoding RNAs which downregulate a large number of target mRNAs and modulate cell activity. Despite continued progress, bioinformatics prediction of microRNA targets remains a challenge since available software still suffer from a lack of accuracy and sensitivity. Moreover, these tools show fairly inconsistent results from one another. Thus, in an attempt to circumvent these difficulties, we aggregated all human results of four important prediction algorithms (miRanda, PITA, SVmicrO, and TargetScan) showing additional characteristics in order to rerank them into a single list. Instead of deciding which prediction tool to use, our method clearly helps biologists getting the best microRNA target predictions from all aggregated databases. The resulting database is freely available through a webtool called miRabel¹ which can take either a list of miRNAs, genes, or signaling pathways as search inputs. Receiver operating characteristic curves and precision-recall curves analysis carried out using experimentally validated data and very large data sets show that miRabel significantly improves the prediction of miRNA targets compared to the four algorithms used separately. Moreover, using the same analytical methods, miRabel shows significantly better predictions than other popular algorithms such as MBSTAR, miRWalk, ExprTarget and miRMap. Interestingly, an F-score analysis revealed that miRabel also significantly improves the relevance of the top results. The aggregation of results from different databases is therefore a powerful and generalizable approach to many other species to improve miRNA target predictions. Thus, miRabel is an efficient tool to guide biologists in their search for miRNA targets and integrate them into a biological context.

Comparative Identification of MicroRNAs in Apis cerana cerana Workers' Midguts in Responseto Nosema ceranae Invasion

Here, the expression profiles and differentially expressed miRNAs (DEmiRNAs) in the midguts of Apis cerana cerana workers at 7 d and 10 d post-inoculation (dpi) with N. ceranae were investigated via small RNA sequencing and bioinformatics. Five hundred and twenty nine (529) known miRNAs and 25 novel miRNAs were identified in this study, and the expression of 16 predicted miRNAs was confirmed by Stem-loop RT-PCR. A total of 14 DEmiRNAs were detected in the midgut at 7 dpi, including eight up-regulated and six down-regulated miRNAs, while 12 DEmiRNAs were observed in the midgut at 10 dpi, including nine up-regulated and three down-regulated ones. Additionally, five DEmiRNAs were shared, while nine and seven DEmiRNAs were specifically expressed in midguts at 7 dpi and 10 dpi. Gene ontology analysis suggested some DEmiRNAs and corresponding target mRNAs were involved in various functions including immune system processes and response to stimulus. KEGG pathway analysis shed light on the potential functions of some DEmiRNAs in regulating target mRNAs engaged in material and energy metabolisms, cellular immunity and the humoral immune system. Further investigation demonstrated a complex regulation network between DEmiRNAs and their target mRNAs, with miR-598-y, miR-252-y, miR-92-x and miR-3654-y at the center. Our results can facilitate future exploration of the regulatory roles of miRNAs in host responses to N. ceranae, and provide potential candidates for further investigation of the molecular mechanisms underlying eastern honeybee-microsporidian interactions.

Preliminary functional inquiry of lncRNA ENST00000433673 in embryo implantation using bioinformatics analysis

Long non-coding RNAs (lncRNAs), a class of non-coding RNA, have been shown to be essential in many diseases, such as infertility. Here, we found three candidate lncRNAs, ENST00000414116, ENST00000433673, and ENST00000448179, that are highly expressed in the uterus endometrial tissues of normal patients compared to the tissues of patients with adenomyosis, endometriosis, and recurrent implantation failure. lncRNAs ENST00000414116 and ENST00000433673 showed high expression in endometrial stromal cells (ESCs) and endometrial epithelial cells (EECs), respectively, and lncRNA ENST00000448179 was specifically expressed in ESCs. The bioinformatics analysis results indicated that the target mRNAs of lncRNA ENST00000433673 were related to biological adhesion. Interestingly, intercellular adhesion molecule 1 (ICAM1), an interacting mRNA of the target mRNA integrin subunit alpha L (ITGAL), has been reported be an important regulator of embryo implantation. Further studies found that the target mRNA ITGAL and the interacting mRNA ICAM1 were highly expressed in the uterus endometrial tissues and EECs of normal patients. Based on our results, our study indicates that lncRNA ENST00000433673 might mediate the high expression of the target mRNA ITGAL, thereby promoting the expression of the interacting mRNA ICAM1 and the adhesion of EECs, which facilitates adhesion and implantation between the embryo and the mater. Abbreviations: AMs: adenomyosis; EMs: endometriosis; RIF: recurrent implantation failure; miRNAs: microRNAs; lncRNAs: Long non-coding RNAs; RT-qPCR: real-time quantitative PCR; ESCs: endometrial stromal cells; EECs: endometrial epithelial cells; BFE: free binding energy; PCDHB9: protocadherin beta 9; PARVG: parvin gamma; MAPK6: mitogen-activated protein kinase 6; LAF1: lymphocyte function-associated antigen 1.

The Small Regulatory RNAs LhrC1-5 Contribute to the Response of Listeria monocytogenes to Heme Toxicity

The LhrC family of small regulatory RNAs (sRNAs) is known to be induced when the foodborne pathogen Listeria monocytogenes is exposed to infection-relevant conditions, such as human blood. Here we demonstrate that excess heme, the core component of hemoglobin in blood, leads to a strong induction of the LhrC family members LhrC1–5. The heme-dependent activation of lhrC1–5 relies on the response regulator LisR, which is known to play a role in virulence and stress tolerance. Importantly, our studies revealed that LhrC1–5 and LisR contribute to the adaptation of L. monocytogenes to excess heme. Regarding the regulatory function of the sRNAs, we demonstrate that LhrC1–5 act to down-regulate the expression of known LhrC target genes under heme-rich conditions: oppA, tcsA, and lapB, encoding surface exposed proteins with virulence functions. These genes were originally identified as targets for LhrC-mediated control under cell envelope stress conditions, suggesting a link between the response to heme toxicity and cell envelope stress in L. monocytogenes. We also investigated the role of LhrC1–5 in controlling the expression of genes involved in heme uptake and utilization: lmo2186 and lmo2185, encoding the heme-binding proteins Hbp1 and Hbp2, respectively, and lmo0484, encoding a heme oxygenase-like protein. Using in vitro binding assays, we demonstrated that the LhrC family member LhrC4 interacts with mRNAs encoded from lmo2186, lmo2185, and lmo0484. For lmo0484, we furthermore show that LhrC4 uses a CU-rich loop for basepairing to the AG-rich Shine–Dalgarno region of the mRNA. The presence of a link between the response to heme toxicity and cell envelope stress was further underlined by the observation that LhrC1–5 down-regulate the expression of lmo0484 in response to the cell wall-acting antibiotic cefuroxime. Collectively, this study suggests a role for the LisR-regulated sRNAs LhrC1–5 in a coordinated response to excess heme and cell envelope stress in L. monocytogenes.

Systematic characterization of artificial small RNA-mediated inhibition of Escherichia coli growth

A new screening system for artificial small RNAs (sRNAs) that inhibit the growth of Escherichia coli was constructed. In this system, we used a plasmid library to express RNAs of ∼120 nucleotides, each with a random 30-nucleotide sequence that can recognize its target mRNA(s). After approximately 60,000 independent colonies were screened, several plasmids that inhibited bacterial growth were isolated. To understand the inhibitory mechanism, we focused on one sRNA, S-20, that exerted a strong inhibitory effect. A time-course analysis of the proteome of S-20-expressing E. coli and a bioinformatic analysis were used to identify potential S-20 target mRNAs, and suggested that S-20 binds the translation initiation sites of several mRNAs encoding enzymes such as peroxiredoxin (osmC), glycyl-tRNA synthetase α subunit (glyQ), uncharacterized protein ygiM, and tryptophan synthase β chain (trpB). An in vitro translation analysis of chimeric luciferase-encoding mRNAs, each containing a potential S-20 target sequence, indicated that the translation of these mRNAs was inhibited in the presence of S-20. A gel shift analysis combined with the analysis of a series of S-20 mutants suggested that S-20 targets multiple mRNAs that are responsible for inhibiting E. coli growth. These data also suggest that S-20 acts like an endogenous sRNA and that E. coli can utilize artificial sRNAs.