Stress-related transcriptomic changes associated with GFP transgene expression and active transgene silencing in plants.

Plants respond to biotic and abiotic stress by activating and interacting with multiple defense pathways, allowing for an efficient global defense response. RNA silencing is a conserved mechanism of regulation of gene expression directed by small RNAs important in acquired plant immunity and especially virus and transgene repression. Several RNA silencing pathways in plants are crucial to control developmental processes and provide protection against abiotic and biotic stresses as well as invasive nucleic acids such as viruses and transposable elements. Various notable studies have shed light on the genes, small RNAs, and mechanisms involved in plant RNA silencing. However, published research on the potential interactions between RNA silencing and other plant stress responses is limited. In the present study, we tested the hypothesis that spreading and maintenance of systemic post-transcriptional gene silencing (PTGS) of a GFP transgene are associated with transcriptional changes that pertain to non-RNA silencing-based stress responses. To this end, we analyzed the structure and function of the photosynthetic apparatus and conducted whole transcriptome analysis in a transgenic line of Nicotiana benthamiana that spontaneously initiates transgene silencing, at different stages of systemic GFP-PTGS. In vivo analysis of chlorophyll a fluorescence yield and expression levels of key photosynthetic genes indicates that photosynthetic activity remains unaffected by systemic GFP-PTGS. However, transcriptomic analysis reveals that spreading and maintenance of GFP-PTGS are associated with transcriptional reprogramming of genes that are involved in abiotic stress responses and pattern- or effector-triggered immunity-based stress responses. These findings suggest that systemic PTGS may affect non-RNA-silencing-based defense pathways in N. benthamiana, providing new insights into the complex interplay between different plant stress responses.

PSTVd infection in Nicotiana benthamiana plants has a minor yet detectable effect on CG methylation.

Viroids are small circular RNAs infecting a wide range of plants. They do not code for any protein or peptide and therefore rely on their structure for their biological cycle. Observed phenotypes of viroid infected plants are thought to occur through changes at the transcriptional/translational level of the host. A mechanism involved in such changes is RNA-directed DNA methylation (RdDM). Till today, there are contradictory works about viroids interference of RdDM. In this study, we investigated the epigenetic effect of viroid infection in Nicotiana benthamiana plants. Using potato spindle tuber viroid (PSTVd) as the triggering pathogen and via bioinformatic analyses, we identified endogenous gene promoters and transposable elements targeted by 24 nt host siRNAs that differentially accumulated in PSTVd-infected and healthy plants. The methylation status of these targets was evaluated following digestion with methylation-sensitive restriction enzymes coupled with PCR amplification, and bisulfite sequencing. In addition, we used Methylation Sensitive Amplification Polymorphism (MSAP) followed by sequencing (MSAP-seq) to study genomic DNA methylation of 5-methylcytosine (5mC) in CG sites upon viroid infection. In this study we identified a limited number of target loci differentially methylated upon PSTVd infection. These results enhance our understanding of the epigenetic host changes as a result of pospiviroid infection.

Complete genome sequence of a carlavirus identified in grapevine (Vitis sp) in Greece.

Using high-throughput sequencing, we identified a novel carlavirus sequence in a 28-year-old 'Kotsifali' grapevine sample collected in Heraklion (Crete, Greece). Using RT-PCR and 5'/3' RACE together with Sanger sequencing, the complete genome sequence of 8299 nt was confirmed and found to contain five open reading frames (ORFs) but to lack an ORF6, which is present in some members of the genus Carlavirus. The novel sequence is most similar to those of two carlaviruses infecting caper, and taking into account the ICTV nomenclature, we propose the name "grapevine carlavirus 1" for this new virus.

GABAA receptor-mediated seizure liabilities: a mixed-methods screening approach.

GABAA receptors, members of the pentameric ligand-gated ion channel superfamily, are widely expressed in the central nervous system and mediate a broad range of pharmaco-toxicological effects including bidirectional changes to seizure threshold. Thus, detection of GABAA receptor-mediated seizure liabilities is a big, partly unmet need in early preclinical drug development. This is in part due to the plethora of allosteric binding sites that are present on different subtypes of GABAA receptors and the critical lack of screening methods that detect interactions with any of these sites. To improve in silico screening methods, we assembled an inventory of allosteric binding sites based on structural data. Pharmacophore models representing several of the binding sites were constructed. These models from the NeuroDeRisk IL Profiler were used for in silico screening of a compiled collection of drugs with known GABAA receptor interactions to generate testable hypotheses. Amoxapine was one of the hits identified and subjected to an array of in vitro assays to examine molecular and cellular effects on neuronal excitability and in vivo locomotor pattern changes in zebrafish larvae. An additional level of analysis for our compound collection is provided by pharmacovigilance alerts using FAERS data. Inspired by the Adverse Outcome Pathway framework, we postulate several candidate pathways leading from specific binding sites to acute seizure induction. The whole workflow can be utilized for any compound collection and should inform about GABAA receptor-mediated seizure risks more comprehensively compared to standard displacement screens, as it rests chiefly on functional data.

Revisiting the Non-Coding Nature of Pospiviroids.

Viroids are small, circular, highly structured pathogens that infect a broad range of plants, causing economic losses. Since their discovery in the 1970s, they have been considered as non-coding pathogens. In the last few years, the discovery of other RNA entities, similar in terms of size and structure, that were shown to be translated (e.g., cirRNAs, precursors of miRNA, RNA satellites) as well as studies showing that some viroids are located in ribosomes, have reignited the idea that viroids may be translated. In this study, we used advanced bioinformatic analysis, in vitro experiments and LC-MS/MS to search for small viroid peptides of the PSTVd. Our results suggest that in our experimental conditions, even though the circular form of PSTVd is found in ribosomes, no produced peptides were identified. This indicates that the presence of PSTVd in ribosomes is most probably not related to peptide production but rather to another unknown function that requires further study.

Molecular basis of mood and cognitive adverse events elucidated via a combination of pharmacovigilance data mining and functional enrichment analysis.

Drug-induced Mood- and Cognition-related adverse events (MCAEs) are often only detected during the clinical trial phases of drug development, or even after marketing, thus posing a major safety concern and a challenge for both pharmaceutical companies and clinicians. To fill some gaps in the understanding and elucidate potential biological mechanisms of action frequently associated with MCAEs, we present a unique workflow linking observational population data with the available knowledge at molecular, cellular, and psychopharmacology levels. It is based on statistical analysis of pharmacovigilance reports and subsequent signaling pathway analyses, followed by evidence-based expert manual curation of the outcomes. Our analysis: (a) ranked pharmaceuticals with high occurrence of such adverse events (AEs), based on disproportionality analysis of the FDA Adverse Event Reporting System (FAERS) database, and (b) identified 120 associated genes and common pathway nodes possibly underlying MCAEs. Nearly two-thirds of the identified genes were related to immune modulation, which supports the critical involvement of immune cells and their responses in the regulation of the central nervous system function. This finding also means that pharmaceuticals with a negligible central nervous system exposure may induce MCAEs through dysregulation of the peripheral immune system. Knowledge gained through this workflow unravels putative hallmark biological targets and mediators of drug-induced mood and cognitive disorders that need to be further assessed and validated in experimental models. Thereafter, they can be used to substantially improve in silico/in vitro/in vivo tools for predicting these adversities at a preclinical stage.

From depression to neurodegeneration and heart failure: re-examining the potential of MAO inhibitors.

Initially introduced in the 1950s for treating depression, monoamine oxidase (MAO) inhibitors were gradually abandoned, mainly owing to their potential for drug-drug and drug-food interactions, the most widely known being with tyramine-containing food (the 'cheese' effect). Since then, more selective MAO-A or MAO-B inhibitors have been developed with substantially reduced risks, and have been approved for the treatment of depression and Parkinson's disease, respectively. Recent research suggests that some of these drugs also have neuroprotective properties, while preclinical evidence expands the spectrum of potential indications to heart failure, renal diseases and multiple sclerosis. In this article, the authors review the relevance of MAO isoforms to disease, and they also outline current research and development efforts in this class of drugs, including newer multipotent compounds.

Literature mining, ontologies and information visualization for drug repurposing.

The immense growth of MEDLINE coupled with the realization that a vast amount of biomedical knowledge is recorded in free-text format, has led to the appearance of a large number of literature mining techniques aiming to extract biomedical terms and their inter-relations from the scientific literature. Ontologies have been extensively utilized in the biomedical domain either as controlled vocabularies or to provide the framework for mapping relations between concepts in biology and medicine. Literature-based approaches and ontologies have been used in the past for the purpose of hypothesis generation in connection with drug discovery. Here, we review the application of literature mining and ontology modeling and traversal to the area of drug repurposing (DR). In recent years, DR has emerged as a noteworthy alternative to the traditional drug development process, in response to the decreased productivity of the biopharmaceutical industry. Thus, systematic approaches to DR have been developed, involving a variety of in silico, genomic and high-throughput screening technologies. Attempts to integrate literature mining with other types of data arising from the use of these technologies as well as visualization tools assisting in the discovery of novel associations between existing drugs and new indications will also be presented.

Drug repurposing and adverse event prediction using high-throughput literature analysis.

Drug repurposing is the process of using existing drugs in indications other than the ones they were originally designed for. It is an area of significant recent activity due to the mounting costs of traditional drug development and scarcity of new chemical entities brought to the market by bio-pharmaceutical companies. By selecting drugs that already satisfy basic toxicity, ADME and related criteria, drug repurposing promises to deliver significant value at reduced cost and in dramatically shorter time frames than is normally the case for the drug development process. The same process that results in drug repurposing can also be used for the prediction of adverse events of known or novel drugs. The analytics method is based on the description of the mechanism of action of a drug, which is then compared to the molecular mechanisms underlying all known adverse events. This review will focus on those approaches to drug repurposing and adverse event prediction that are based on the biomedical literature. Such approaches typically begin with an analysis of the literature and aim to reveal indirect relationships among seemingly unconnected biomedical entities such as genes, signaling pathways, physiological processes, and diseases. Networks of associations of these entities allow the uncovering of the molecular mechanisms underlying a disease, better understanding of the biological effects of a drug and the evaluation of its benefit/risk profile. In silico results can be tested in relevant cellular and animal models and, eventually, in clinical trials.

CIRCADIAN CLOCK ASSOCIATED1 and LATE ELONGATED HYPOCOTYL function synergistically in the circadian clock of Arabidopsis.

The circadian clock is an endogenous mechanism that coordinates biological processes with daily and seasonal changes in the environment. Heterodimerization of central clock components is an important way of controlling clock function in several different circadian systems. CIRCADIAN CLOCK ASSOCIATED1 (CCA1) and LATE ELONGATED HYPOCOTYL (LHY) are Myb-related proteins that function in or close to the central oscillator in Arabidopsis (Arabidopsis thaliana). Single mutants of cca1 and lhy have a phenotype of short-period rhythms. cca1 lhy double mutants show an even shorter period phenotype than the cca1 single mutant, suggesting that CCA1 and LHY are only partially functionally redundant. To determine whether CCA1 and LHY act in parallel or synergistically in the circadian clock, we examined their expression in both light-grown and etiolated seedlings. We have shown that LHY and CCA1 bind to the same region of the promoter of a Light-harvesting chlorophyll a/b protein (Lhcb, also known as CAB). CCA1 and LHY can form homodimers, and they also colocalize in the nucleus and heterodimerize in vitro and in vivo. In Arabidopsis, CCA1 and LHY physically interact in a manner independent of photoperiod. Moreover, results from gel filtration chromatography indicate that CCA1 and LHY are present in the same large complex in plants. Taken together, these results imply that CCA1 and LHY function synergistically in regulating circadian rhythms of Arabidopsis.

The clock protein CCA1 and the bZIP transcription factor HY5 physically interact to regulate gene expression in Arabidopsis.

The circadian clock regulates the expression of an array of Arabidopsis genes such as those encoding the LIGHT-HARVESTING CHLOROPHYLL A/B (Lhcb) proteins. We have previously studied the promoters of two of these Arabidopsis genes--Lhcb1*1 and Lhcb1*3--and identified a sequence that binds the clock protein CIRCADIAN CLOCK ASSOCIATED 1 (CCA1). This sequence, designated CCA1-binding site (CBS), is necessary for phytochrome and circadian responsiveness of these genes. In close proximity to this sequence, there exists a G-box core element that has been shown to bind the bZIP transcription factor HY5 in other light-regulated plant promoters. In the present study, we examined the importance of the interaction of transcription factors binding the CBS and the G-box core element in the control of normal circadian rhythmic expression of Lhcb genes. Our results show that HY5 is able to specifically bind the G-box element in the Lhcb promoters and that CCA1 can alter the binding activity of HY5. We further show that CCA1 and HY5 can physically interact and that they can act synergistically on transcription in a yeast reporter gene assay. An absence of HY5 leads to a shorter period of Lhcb1*1 circadian expression but does not affect the circadian expression of CATALASE3 (CAT3), whose promoter lacks a G-box element. Our results suggest that interaction of the HY5 and CCA1 proteins on Lhcb promoters is necessary for normal circadian expression of the Lhcb genes.

Mining of relations between proteins over biomedical scientific literature using a deep-linguistic approach.

OBJECTIVE: The amount of new discoveries (as published in the scientific literature) in the biomedical area is growing at an exponential rate. This growth makes it very difficult to filter the most relevant results, and thus the extraction of the core information becomes very expensive. Therefore, there is a growing interest in text processing approaches that can deliver selected information from scientific publications, which can limit the amount of human intervention normally needed to gather those results. MATERIALS AND METHODS: This paper presents and evaluates an approach aimed at automating the process of extracting functional relations (e.g. interactions between genes and proteins) from scientific literature in the biomedical domain. The approach, using a novel dependency-based parser, is based on a complete syntactic analysis of the corpus. RESULTS: We have implemented a state-of-the-art text mining system for biomedical literature, based on a deep-linguistic, full-parsing approach. The results are validated on two different corpora: the manually annotated genomics information access (GENIA) corpus and the automatically annotated arabidopsis thaliana circadian rhythms (ATCR) corpus. CONCLUSION: We show how a deep-linguistic approach (contrary to common belief) can be used in a real world text mining application, offering high-precision relation extraction, while at the same time retaining a sufficient recall.

Identification of echocardiographic indices for the early detection of left-ventricular systolic dysfunction in beta-thalassaemia via Self-Organizing Maps: a data-exploration study.

Congestive heart failure (CHF) remains the primary cause of death in patients suffering from beta-thalassaemia major. Its early detection allows the prompt initiation of aggressive chelation therapy, when the condition can still be reversed. We aimed at identifying echocardiographic indices for the early detection of left ventricular (LV) systolic dysfunction, the physiological abnormality underlying CHF, in these patients. We used Self-Organizing Maps (SOMs)--an artificial neural network--for identifying novel correlations within our Electronic Healthcare Record (EHCR) database on beta-thalassaemia. We sought echocardiographic parameters that are correlated to future deterioration of the LV ejection fraction and therefore constitute early signs of LV systolic dysfunction. At the same time, we evaluated SOMs as tools for exploring clinical datasets and make recommendations on the setup of the SOM algorithm that is appropriate for such tasks. We found that high values of the LV end-systolic diameter index and of the E/A ratio are early indications of LV systolic dysfunction. From a technical point of view, zero-mean unit-variance normalization of the input data, a large initial neighbourhood radius and a rectangular SOM grid produced optimal maps for the purpose of detecting clinical correlations. We have successfully used SOMs for exploring a clinical dataset and for creating novel medical hypotheses. A clinical study has been launched to confirm these hypotheses, and initial results are encouraging.

Novel biological networks modulated by complement.

The almost complete deciphering of the human genome has paved the way for the application of new technology platforms in understanding the contribution of complex biological pathways to human pathophysiology and disease. In the post-genomic era, the concept of systems biology has gained significant momentum and biomedical research is now being conducted on an integrated and cross-disciplinary platform that pulls together its resources from diverse fields such as computational biology, bioinformatics, functional genomics, structural biology, and proteomics. In this perspective, the identity of established biologic systems is being re-examined in the light of novel findings that suggest novel associations between otherwise unrelated pathways and individual proteins. Complement exemplifies such a system that, transcending its innate immune identity, has forged functional associations with multiple pathways and networks in modulating basic biologic processes. In the present article, we provide a global overview of these unusual system associations of complement with the aid of a powerful and high-throughput bioinformatics platform. Using a novel approach called systems literature analysis that allows the rapid extraction of text-based associations between genes and pathways from the ever expanding scientific article database, we have selected a broad range of biologic processes modulated by complement proteins and have constructed an integrated map of complement-mediated networks that incorporates well over 85 diverse biologic pathways. Expanding the complement cascade beyond its approximately 35 designated components, we discuss protein-protein interactions involving novel ligands and associations with signaling cascades and cellular networks that affect both inflammatory and non-inflammatory processes. This integrated consideration of complement within a unified 'systems biology' framework underscores the concept that innate immunity goes well beyond the protection of 'self' extending links to critical developmental, homeostatic, and metabolic processes.