A glance at DNA microarray technology and applications

Analysis of Microarray-Identified Genes and MicroRNAs Associated with Idiopathic Pulmonary Fibrosis

The aim of this study was to identify potential microRNAs and genes associated with idiopathic pulmonary fibrosis (IPF) through web-available microarrays. The microRNA microarray dataset GSE32538 and the mRNA datasets GSE32537, GSE53845, and GSE10667 were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed miRNAs (DE-miRNAs)/genes (DEGs) were screened with GEO2R, and their associations with IPF were analyzed by comprehensive bioinformatic analyses. A total of 45 DE-microRNAs were identified between IPF and control tissues, whereas 67 common DEGs were determined to exhibit the same expression trends in all three microarrays. Furthermore, functional analysis indicated that microRNAs in cancer and ECM-receptor interaction were the most significant pathways and were enriched by the 45 DE-miRNAs and 67 common DEGs. Finally, we predicted potential microRNA-target interactions between 17 DE-miRNAs and 17 DEGs by using at least three online programs. A microRNA-mediated regulatory network among the DE-miRNAs and DEGs was constructed that might shed new light on potential biomarkers for the prediction of IPF progression.

Differential expression of microRNA in the lungs of rats with pulmonary arterial hypertension

Pulmonary arterial hypertension (PAH) is a chronic disorder of the small pulmonary arteries, and the efficacy of the therapies and the prognosis remain poor. The pathobiology of PAH is complex, and needs to be elucidated by multiple approaches. The present study used a monocrotaline-induced PAH rat model to perform a comprehensive microRNA (miRNA) microarray screening in the lungs and identified 16 downregulated miRNAs in the lungs from PAH rats. High-enrichment gene ontology (GO) analysis identified several sets of genes, and established the miRNA-mRNA network by outlining the interactions of miRNA and GO-associated genes. Three downregulated miRNAs [miRNA 125-3p (miR-125-3p), miR-148-3p and miR-193] displayed the most marked regulatory function, and miR-148-3p and miR-193 were observed to have the highest number of target mRNAs. Signaling pathway analysis demonstrated 26 signal transduction pathways, with MAPK, TGF-β and cell cycle signaling as the most prominent. In addition, 342 genes were identified as the potential targets of these 16 miRNAs. Thus, a set of miRNAs in the lungs from rats with PAH and novel associations between biological events and PAH pathogenesis were identified, providing potential therapeutic targets for this disorder.

Computerized techniques pave the way for drug-drug interaction prediction and interpretation

INTRODUCTION

Health care industry also patients penalized by medical errors that are inevitable but highly preventable. Vast majority of medical errors are related to adverse drug reactions, while drug-drug interactions (DDIs) are the main cause of adverse drug reactions (ADRs). DDIs and ADRs have mainly been reported by haphazard case studies. Experimental in vivo and in vitro researches also reveals DDI pairs. Laboratory and experimental researches are valuable but also expensive and in some cases researchers may suffer from limitations.

METHODS

In the current investigation, the latest published works were studied to analyze the trend and pattern of the DDI modelling and the impacts of machine learning methods. Applications of computerized techniques were also investigated for the prediction and interpretation of DDIs.

RESULTS

Computerized data-mining in pharmaceutical sciences and related databases provide new key transformative paradigms that can revolutionize the treatment of diseases and hence medical care. Given that various aspects of drug discovery and pharmacotherapy are closely related to the clinical and molecular/biological information, the scientifically sound databases (e.g., DDIs, ADRs) can be of importance for the success of pharmacotherapy modalities.

CONCLUSION

A better understanding of DDIs not only provides a robust means for designing more effective medicines but also grantees patient safety.

Screening of potential biomarkers for chemoresistant ovarian carcinoma with miRNA expression profiling data by bioinformatics approach

The aim of the present study was to screen out the biomarkers associated with chemoresistance in ovarian carcinomas and to investigate the molecular mechanisms. microRNA (miRNA) expression data was obtained from published microarray data of the GSE43867 dataset from Gene Expression Omnibus (GEO), including the data of 86 chemotherapy-treated patients with serous epithelial ovarian carcinomas (response group, 36 complete response cases and 12 partial response cases; non-response group, 10 stable cases and 28 progressive disease cases), and identification of differentially-expressed miRNAs were conducted with a GEO2R online tool based on R language. TargetScan 6.2 was used to predict the targets of differentially-expressed miRNAs. Protein-protein interaction network analysis was conducted by STRING 9.1, while functional enrichment [Gene Ontology (GO) biological process terms] and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted by GeneCodis3 for the target genes. A total of 6 differentially-expressed miRNAs were screened out, with 317 target genes obtained. It was found that 67 interactions existed among 76 genes/proteins through the PPI network analysis, and that 6 of these were potential key genes (PIK3R5, MAPK3, PTEN, S1PR3, BDKRB2 and NCBP2). The main biological processes involved in chemoresistant ovarian carcinoma were apoptosis, programmed cell death, cell migration, cell death and cell motility. The miRNA target genes were found to be associated with the ErbB signaling pathway, the gonadotropin-releasing hormone signaling pathway and other pathways in cancer. IK3R5, MAPK3 and PIK3R5 are involved in the majority of GO terms and KEGG pathways associated with chemoresistance in ovarian carcinoma.

Impacts of quantum dots in molecular detection and bioimaging of cancer

Introduction: A number of assays have so far been exploited for detection of cancer biomarkers in various malignancies. However, the expression of cancer biomarker(s) appears to be extremely low, therefore accurate detection demands sensitive optical imaging probes. While optical detection using conventional fluorophores often fail due to photobleaching problems, quantum dots (QDs) offer stable optical imaging in vitro and in vivo.

Methods: In this review, we briefly overview the impacts of QDs in biology and its applications in bioimaging of malignancies. We will also delineate the existing obstacles for early detection of cancer and the intensifying use of QDs in advancement of diagnostic devices.

Results: Of the QDs, unlike the II-VI type QDs (e.g., cadmium (Cd), selenium (Se) or tellurium (Te)) that possess inherent cytotoxicity, the I-III-VI 2 type QDs (e.g., AgInS₂, CuInS₂, ZnS-AgInS₂) appear to be less toxic bioimaging agents with better control of band-gap energies. As highly-sensitive bioimaging probes, advanced hybrid QDs (e.g., QD-QD, fluorochrome-QD conjugates used for sensing through fluorescence resonance energy transfer (FRET), quenching, and barcoding techniques) have also been harnessed for the detection of biomarkers and the monitoring of delivery of drugs/genes to the target sites. Antibody-QD (Ab-QD) and aptamer- QD (Ap-QD) bioconjugates, once target the relevant biomarker, can provide highly stable photoluminescence (PL) at the target sites. In addition to their potential as nanobiosensors, the bioconjugates of QDs with homing devices have successfully been used for the development of smart nanosystems (NSs) providing targeted bioimaging and photodynamic therapy (PDT).

Conclusion: Having possessed great deal of photonic characteristics, QDs can be used for development of seamless multifunctional nanomedicines, theranostics and nanobiosensors.

Intrinsic bio-signature of gene delivery nanocarriers may impair gene therapy goals

Non-viral lipid/polymeric vectors have widely been used as nanocarriers (NCs) for gene delivery. They possess large surface area to volume ratio and are able to interact with biomolecules through functional moieties, resulting in inadvertent biological impacts, in particular at genomic level. Thus, their genomic bio-signature needs to be investigated prior to use in vivo. Using high-throughput microarray and qPCR gene expression profiling techniques, we have reported the genomic impacts of lipid/polymeric NCs. Given the fact that the ultimate objectives of gene therapy may inevitably be impaired by nonspecific intrinsic genomic impacts of these NCs, here, we highlight their nonspecific genomic bio-signature. We envision that better understanding on the genotoxicity of gene delivery NCs, as guiding premise, will help us to develop much safer NCs and also to accelerate their translation into clinical use and to provide pivotal information on safety liabilities early in discovery and developments process prior to its inevitable consequences in vivo.

Targeted Gene Therapy of Cancer: Second Amendment toward Holistic Therapy

It seems solid tumors are developing smart organs with specialized cells creating specified bio-territory, the so called "tumor microenvironment (TME)", in which there is reciprocal crosstalk among cancer cells, immune system cells and stromal cells. TME as an intricate milieu also consists of cancer stem cells (CSCs) that can resist against chemotherapies. In solid tumors, metabolism and vascularization appears to be aberrant and tumor interstitial fluid (TIF) functions as physiologic barrier. Thus, chemotherapy, immunotherapy and gene therapy often fail to provide cogent clinical outcomes. It looms that it is the time to accept the fact that initiation of cancer could be generation of another form of life that involves a cluster of thousands of genes, while we have failed to observe all aspects of it. Hence, the current treatment modalities need to be re-visited to cover all key aspects of disease using combination therapy based on the condition of patients. Perhaps personalized cluster of genes need to be simultaneously targeted.