The effect of fampridine on working memory: a randomized controlled trial based on a genome-guided repurposing approach.

Working memory (WM), a key component of cognitive functions, is often impaired in psychiatric disorders such as schizophrenia. Through a genome-guided drug repurposing approach, we identified fampridine, a potassium channel blocker used to improve walking in multiple sclerosis, as a candidate for modulating WM. In a subsequent double-blind, randomized, placebo-controlled, crossover trial in 43 healthy young adults (ClinicalTrials.

The Iranian Corona Stress Study: Psychological Impacts of COVID-19 Pandemic in an Iranian Population.

Background: To assess the psychological consequences of changes during the coronavirus 2019 (COVID-19) pandemic in the Iranian population.

Methods: We performed an anonymous online survey in the first 3 weeks of March 2020. Individuals older than 14 who could read Persian, and lived in Iran, were eligible for the study.

Dynamics of gene regulatory networks and their dependence on network topology and quantitative parameters - the case of phage λ.

Background: Gene regulatory networks can be modelled in various ways depending on the level of detail required and biological questions addressed. One of the earliest formalisms used for modeling is a Boolean network, although these models cannot describe most temporal aspects of a biological system. Differential equation models have also been used to model gene regulatory networks, but these frameworks tend to be too detailed for large models and many quantitative parameters might not be deducible in practice.

IL-17A inhibition by secukinumab induces early clinical, histopathologic, and molecular resolution of psoriasis.

Background: Hyperactivity of the IL-23/IL-17 axis is central to plaque psoriasis pathogenesis. Secukinumab, a fully human mAb that selectively inhibits IL-17A, is approved for treatment of psoriasis, psoriatic arthritis, and ankylosing spondylitis. Secukinumab improves the complete spectrum of psoriasis manifestations, with durable clinical responses beyond 5 years of treatment.

Extremophiles in an Antarctic Marine Ecosystem.

Recent attempts to explore marine microbial diversity and the global marine microbiome have indicated a large proportion of previously unknown diversity. However, sequencing alone does not tell the whole story, as it relies heavily upon information that is already contained within sequence databases. In addition, microorganisms have been shown to present small-to-large scale biogeographical patterns worldwide, potentially making regional combinations of selection pressures unique.

Network-Informed Gene Ranking Tackles Genetic Heterogeneity in Exome-Sequencing Studies of Monogenic Disease.

Genetic heterogeneity presents a significant challenge for the identification of monogenic disease genes. Whole-exome sequencing generates a large number of candidate disease-causing variants and typical analyses rely on deleterious variants being observed in the same gene across several unrelated affected individuals. This is less likely to occur for genetically heterogeneous diseases, making more advanced analysis methods necessary.

Pooled sequencing of 531 genes in inflammatory bowel disease identifies an associated rare variant in BTNL2 and implicates other immune related genes.

The contribution of rare coding sequence variants to genetic susceptibility in complex disorders is an important but unresolved question. Most studies thus far have investigated a limited number of genes from regions which contain common disease associated variants. Here we investigate this in inflammatory bowel disease by sequencing the exons and proximal promoters of 531 genes selected from both genome-wide association studies and pathway analysis in pooled DNA panels from 474 cases of Crohn's disease and 480 controls.

Predicting the functional consequences of non-synonymous DNA sequence variants--evaluation of bioinformatics tools and development of a consensus strategy.

The study of DNA sequence variation has been transformed by recent advances in DNA sequencing technologies. Determination of the functional consequences of sequence variant alleles offers potential insight as to how genotype may influence phenotype. Even within protein coding regions of the genome, establishing the consequences of variation on gene and protein function is challenging and requires substantial laboratory investigation.

Approaches to modeling gene regulatory networks: a gentle introduction.

This chapter is split into two main sections; first, I will present an introduction to gene networks. Second, I will discuss various approaches to gene network modeling which will include some examples for using different data sources. Computational modeling has been used for many different biological systems and many approaches have been developed addressing the different needs posed by the different application fields.

BioGranat-IG: a network analysis tool to suggest mechanisms of genetic heterogeneity from exome-sequencing data.

Motivation: Recent exome-sequencing studies have successfully identified disease-causing sequence variants for several rare monogenic diseases by examining variants common to a group of patients. However, the current data analysis strategies are only insufficiently able to deal with confounding factors such as genetic heterogeneity, incomplete penetrance, individuals lacking data and involvement of several genes.

Results: We introduce BioGranat-IG, an analysis strategy that incorporates the information contained in biological networks to the analysis of exome-sequencing data.

HSM - a hybrid system based approach for modelling intracellular networks.

The paper proposes a hybrid system based approach for modelling of intracellular networks and introduces a restricted subclass of hybrid systems - HSM - with an objective of still being able to provide sufficient power for the modelling of biological systems, while imposing some restrictions that facilitate analysis of systems described by such models. The use of hybrid system based models has become increasingly popular, likely due to the facts that: 1) they provide sufficiently powerful mathematical formalism to describe biological processes of interest and do it in a 'natural way' from the biological perspective; 2) there are well established mathematical techniques as well as supporting software tools for analysing such models. However often these models are very dependent on the quantitative parameters of the system (concentrations of proteins, their growth functions etc.

Breaking free from the chains of pathway annotation: de novo pathway discovery for the analysis of disease processes.

Interpreting the biological implications of high-throughput experiments such as gene-expression studies, genome-wide association studies and large-scale sequencing studies is not trivial. Gene-set and pathway analyses are useful tools to support the interpretation of such experiments, but rely on curated pathways or gene sets. The recent development of de novo pathway discovery methods aims to overcome this limitation.

From SNPs to genes: disease association at the gene level.

Interpreting Genome-Wide Association Studies (GWAS) at a gene level is an important step towards understanding the molecular processes that lead to disease. In order to incorporate prior biological knowledge such as pathways and protein interactions in the analysis of GWAS data it is necessary to derive one measure of association for each gene. We compare three different methods to obtain gene-wide test statistics from Single Nucleotide Polymorphism (SNP) based association data: choosing the test statistic from the most significant SNP; the mean test statistics of all SNPs; and the mean of the top quartile of all test statistics.

Exome localization of complex disease association signals.

Background: Genome-wide association studies (GWAS) of common diseases have had a tremendous impact on genetic research over the last five years; the field is now moving from microarray-based technology towards next-generation sequencing. To evaluate the potential of association studies for complex diseases based on exome sequencing we analysed the distribution of association signal with respect to protein-coding genes based on GWAS data for seven diseases from the Wellcome Trust Case Control Consortium.

Results: We find significant concentration of association signal in exons and genes for Crohn's Disease, Type 1 Diabetes and Bipolar Disorder, but also observe enrichment from up to 40 kilobases upstream to 40 kilobases downstream of protein-coding genes for Crohn's Disease and Type 1 Diabetes; the exact extent of the distribution is disease dependent.

Protein-protein interaction databases: keeping up with growing interactomes.

Over the past few years, the number of known protein-protein interactions has increased substantially. To make this information more readily available, a number of publicly available databases have set out to collect and store protein-protein interaction data. Protein-protein interactions have been retrieved from six major databases, integrated and the results compared.

Learning about gene regulatory networks from gene deletion experiments.

Gene regulatory networks are a major focus of interest in molecular biology. A crucial question is how complex regulatory systems are encoded and controlled by the genome. Three recent publications have raised the question of what can be learned about gene regulatory networks from microarray experiments on gene deletion mutants.

Current approaches to gene regulatory network modelling.

Many different approaches have been developed to model and simulate gene regulatory networks. We proposed the following categories for gene regulatory network models: network parts lists, network topology models, network control logic models, and dynamic models. Here we will describe some examples for each of these categories.

Modelling in molecular biology: describing transcription regulatory networks at different scales.

Approaches to describe gene regulation networks can be categorized by increasing detail, as network parts lists, network topology models, network control logic models or dynamic models. We discuss the current state of the art for each of these approaches. We study the relationship between different topology models, and give examples how they can be used to infer functional annotations for genes of unknown function.

Modelling gene networks at different organisational levels.

Approaches to modelling gene regulation networks can be categorized, according to increasing detail, as network parts lists, network topology models, network control logic models, or dynamic models. We discuss the current state of the art for each of these approaches. There is a gap between the parts list and topology models on one hand, and control logic and dynamic models on the other hand.

From microarray data to results. Workshop on genomic approaches to microarray data analysis.

Workshop on Genomic Approaches to Microarray Data Analysis

From gene networks to gene function.

We propose a novel method to identify functionally related genes based on comparisons of neighborhoods in gene networks. This method does not rely on gene sequence or protein structure homologies, and it can be applied to any organism and a wide variety of experimental data sets. The character of the predicted gene relationships depends on the underlying networks;they concern biological processes rather than the molecular function.

Alien/CSN2 gene expression is regulated by thyroid hormone in rat brain.

Alien has been described as a corepressor for the thyroid hormone receptor (TR). Corepressors are coregulators that mediate gene silencing of DNA-bound transcriptional repressors. We describe here that Alien gene expression in vivo is regulated by thyroid hormone both in the rat brain and in cultured cells.