Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug-gene interaction networks analysis.

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF-TG (transcription factors-target genes) network was drawn. Afterward, a list of drugs targeting TF-TG genes was obtained from the DGIdb V4.0 database, and two drug-gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF-TG, and drug-gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF-TG, and drug-gene interaction networks.

Alkaloid production and response to natural adverse conditions in Peganum harmala: in silico transcriptome analyses.

Peganum harmala is a valuable wild plant that grows and survives under adverse conditions and produces pharmaceutical alkaloid metabolites. Using different assemblers to develop a transcriptome improves the quality of assembled transcriptome. In this study, a concrete and accurate method for detecting stress-responsive transcripts by comparing stress-related gene ontology (GO) terms and public domains was designed. An integrated transcriptome for P. harmala including 42 656 coding sequences was created by merging de novo assembled transcriptomes. Around 35 000 transcripts were annotated with more than 90% resemblance to three closely related species of Citrus, which confirmed the robustness of the assembled transcriptome; 4853 stress-responsive transcripts were identified. CYP82 involved in alkaloid biosynthesis showed a higher number of transcripts in P. harmala than in other plants, indicating its diverse alkaloid biosynthesis attributes. Transcription factors (TFs) and regulatory elements with 3887 transcripts comprised 9% of the transcriptome. Among the TFs of the integrated transcriptome, cystein2/histidine2 (C2H2) and WD40 repeat families were the most abundant. The Kyoto Encyclopedia of Genes and Genomes (KEGG) MAPK (mitogen-activated protein kinase) signaling map and the plant hormone signal transduction map showed the highest assigned genes to these pathways, suggesting their potential stress resistance. The P. harmala whole-transcriptome survey provides important resources and paves the way for functional and comparative genomic studies on this plant to discover stress-tolerance-related markers and response mechanisms in stress physiology, phytochemistry, ecology, biodiversity, and evolution. P. harmala can be a potential model for studying adverse environmental cues and metabolite biosynthesis and a major source for the production of various alkaloids.

Rna-seq: a glanee at technologies and methodologies / RNA-Seq: un vistazo sobre las tecnologías y metodologías

RNA Sequencing (RNA-Seq) is a newly born tool that has revolutionized the post-genomic era. The data produced by RNA-Seq, sequencing technologies and use of bioinformatics are exploding rapidly. In recent years, RNA-Seq has been the method of choice for profiling dynamic transcriptome taking advantage of high throughput sequencing technologies. RNA-Seq studies have shown the transcriptome magnitude, notion and complexity. From 2008, as its introduction year, the relevant reports on RNA-Seq have been multiplied by more than 2822 times just in 6 years. RNA-Seq also contributes a more accurate gene expression and transcript isoform estimation than other methods. Furthermore, some of the potential applications for RNA-Seq cannot be conducted by other methods and as yet are unique to RNA-Seq. As RNA-Seq approaches increase in speed and decrease in cost, more distinct researches are applied and become more common and accurate. RNA-Seq is a cross and interdisciplinary method that interconnects biology to other scientific topics. This article describes RNA-Seq approach, technologies, methodologies, implementation, and methods done so far in characterizing and profiling transcriptomes.

En los últimos años, la técnica RNA-Seq ha tenido un desarrollo acelerado y se ha convertido en el método de elección para el estudio y la caracterización de los transcriptomas dinámicos, aprovechando las tecnologías de secuenciación de alto rendimiento. Estudios aplicando RNA-Seq han mostrado la magnitud, noción y complejidad del transcripotma. A partir de 2008, año de introducción de la técnica, los estudios con RNA-Seq se han multiplicados por más de 2822 veces sólo en 6 años. Al compararse con otros métodos, los estudios empleando RNA-Seq contribuyen a una estimación más precisa de la expresión génica y de las isoformas de los transcriptos. Además, algunas de las aplicaciones potenciales de RNA-Seq no se pueden llevar a cabo con otros métodos. El uso de RNA-Seq aumenta la velocidad de obtención de información y disminuye los costos, logrando con su uso, que investigaciones diversas se vuelvan más frecuentes y precisas. RNA-Seq es un método interdisciplinario que interconecta la biología a otros temas científicos. En este artículo se describe el planteamiento de la tecnología RNA-Seq, metodologías y los métodos realizados en la caracterización de transcriptomas.