Transcriptome Analysis Reveals Dynamic Cultivar-Dependent Patterns of Gene Expression in Potato Spindle Tuber Viroid-Infected Pepper

Potato spindle tuber viroid (PSTVd) infects various plants. PSTVd pathogenesis is associated with interference with the cellular metabolism and defense signaling pathways via direct interaction with host factors or via the transcriptional or post-transcriptional modulation of gene expression. To better understand host defense mechanisms to PSTVd infection, we analyzed the gene expression in two pepper cultivars, Capsicum annuum Kurtovska kapia (KK) and Djulunska shipka (DS), which exhibit mild symptoms of PSTVd infection. Deep sequencing-based transcriptome analysis revealed differential gene expression upon infection, with some genes displaying contrasting expression patterns in KK and DS plants. More genes were downregulated in DS plants upon infection than in KK plants, which could underlie the more severe symptoms seen in DS plants. Gene ontology enrichment analysis revealed that most of the downregulated differentially expressed genes in both cultivars were enriched in the gene ontology term photosynthesis. The genes upregulated in DS plants fell in the biological process of gene ontology term defense response. We validated the expression of six overlapping differentially expressed genes that are involved in photosynthesis, plant hormone signaling, and defense pathways by quantitative polymerase chain reaction. The observed differences in the responses of the two cultivars to PSTVd infection expand the understanding of the fine-tuning of plant gene expression that is needed to overcome the infection.

PFP-WGAN: Protein function prediction by discovering Gene Ontology term correlations with generative adversarial networks

Understanding the functionality of proteins has emerged as a critical problem in recent years due to significant roles of these macro-molecules in biological mechanisms. However, in-laboratory techniques for protein function prediction are not as efficient as methods developed and processed for protein sequencing. While more than 70 million protein sequences are available today, only the functionality of around one percent of them are known. These facts have encouraged researchers to develop computational methods to infer protein functionalities from their sequences. Gene Ontology is the most well-known database for protein functions which has a hierarchical structure, where deeper terms are more determinative and specific. However, the lack of experimentally approved annotations for these specific terms limits the performance of computational methods applied on them. In this work, we propose a method to improve protein function prediction using their sequences by deeply extracting relationships between Gene Ontology terms. To this end, we construct a conditional generative adversarial network which helps to effectively discover and incorporate term correlations in the annotation process. In addition to the baseline algorithms, we compare our method with two recently proposed deep techniques that attempt to utilize Gene Ontology term correlations. Our results confirm the superiority of the proposed method compared to the previous works. Moreover, we demonstrate how our model can effectively help to assign more specific terms to sequences.

Targeting CLK3 inhibits the progression of cholangiocarcinoma by reprogramming nucleotide metabolism

CDC-like kinase 3 (CLK3) is a dual specificity kinase that functions on substrates containing serine/threonine and tyrosine. But its role in human cancer remains unknown. Herein, we demonstrated that CLK3 was significantly up-regulated in cholangiocarcinoma (CCA) and identified a recurrent Q607R somatic substitution that represented a gain-of-function mutation in the CLK3 kinase domain. Gene ontology term enrichment suggested that high CLK3 expression in CCA patients mainly was associated with nucleotide metabolism reprogramming, which was further confirmed by comparing metabolic profiling of CCA cells. CLK3 directly phosphorylated USP13 at Y708, which promoted its binding to c-Myc, thereby preventing Fbxl14-mediated c-Myc ubiquitination and activating the transcription of purine metabolic genes. Notably, the CCA-associated CLK3-Q607R mutant induced USP13-Y708 phosphorylation and enhanced the activity of c-Myc. In turn, c-Myc transcriptionally up-regulated CLK3. Finally, we identified tacrine hydrochloride as a potential drug to inhibit aberrant CLK3-induced CCA. These findings demonstrate that CLK3 plays a crucial role in CCA purine metabolism, suggesting a potential therapeutic utility.

Automated exploration of gene ontology term and pathway networks with ClueGO-REST

Summary Large scale technologies produce massive amounts of experimental data that need to be investigated. To improve their biological interpretation we have developed ClueGO, a Cytoscape App that selects representative Gene Onology terms and pathways for one or multiple lists of genes/proteins and visualizes them into functionally organized networks. Because of its reliability, userfriendliness and support of many species ClueGO gained a large community of users. To further allow scientists programmatic access to ClueGO with R, Python, JavaScript etc., we implemented the cyREST API into ClueGO. In this article we describe this novel, complementary way of accessing ClueGO via REST, and provide R and Phyton examples to demonstrate how ClueGO workflows can be integrated into bioinformatic analysis pipelines. Availability and implementation ClueGO is available in the Cytoscape App Store (http://apps.cytoscape.org/apps/cluego). Supplementary information Supplementary data are available at Bioinformatics online.