KENeV: A web-application for the automated reconstruction and visualization of the enriched metabolic and signaling super-pathways deriving from genomic experiments

PHENSIM: Phenotype Simulator

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. Here we propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. Our tool’s applications include predicting the outcome of drug administration, knockdown experiments, gene transduction, and exposure to exosomal cargo. Importantly, PHENSIM enables the user to make inferences on well-defined cell lines and includes pathway maps from three different model organisms. To assess our approach’s reliability, we built a benchmark from transcriptomics data gathered from NCBI GEO and performed four case studies on known biological experiments. Our results show high prediction accuracy, thus highlighting the capabilities of this methodology. PHENSIM standalone Java application is available at https://github.com/alaimos/phensim, along with all data and source codes for benchmarking. A web-based user interface is accessible at https://phensim.tech/.

Despite the unprecedented growth in our understanding of cell biology, it still remains challenging to connect it to experimental data obtained with cells and tissues’ physiopathological status under precise circumstances. This knowledge gap often results in difficulties in designing validation experiments, which are usually labor-intensive, expensive to perform, and hard to interpret. In this context, ’in silico’ simulations can be extensively applied in massive scales, testing thousands of hypotheses under various conditions, which is usually experimentally infeasible. At present, many simulation models have become available. However, complex biological networks might pose challenges to their performance.

We propose PHENSIM, a computational tool using a systems biology approach to simulate how cell phenotypes are affected by the activation/inhibition of one or multiple biomolecules, and it does so by exploiting signaling pathways. We implemented our tool as a freely accessible web application, hoping to allow ’in silico’ simulations to play a more central role in the modeling and understanding of biological phenomena.

Control of anterior GRadient 2 (AGR2) dimerization links endoplasmic reticulum proteostasis to inflammation

Anterior gradient 2 (AGR2) is a dimeric protein disulfide isomerase family member involved in the regulation of protein quality control in the endoplasmic reticulum (ER). Mouse AGR2 deletion increases intestinal inflammation and promotes the development of inflammatory bowel disease (IBD). Although these biological effects are well established, the underlying molecular mechanisms of AGR2 function toward inflammation remain poorly defined. Here, using a protein-protein interaction screen to identify cellular regulators of AGR2 dimerization, we unveiled specific enhancers, including TMED2, and inhibitors of AGR2 dimerization, that control AGR2 functions. We demonstrate that modulation of AGR2 dimer formation, whether enhancing or inhibiting the process, yields pro-inflammatory phenotypes, through either autophagy-dependent processes or secretion of AGR2, respectively. We also demonstrate that in IBD and specifically in Crohn's disease, the levels of AGR2 dimerization modulators are selectively deregulated, and this correlates with severity of disease. Our study demonstrates that AGR2 dimers act as sensors of ER homeostasis which are disrupted upon ER stress and promote the secretion of AGR2 monomers. The latter might represent systemic alarm signals for pro-inflammatory responses.

PANEV: an R package for a pathway-based network visualization

Background

During the last decade, with the aim to solve the challenge of post-genomic and transcriptomic data mining, a plethora of tools have been developed to create, edit and analyze metabolic pathways. In particular, when a complex phenomenon is considered, the creation of a network of multiple interconnected pathways of interest could be useful to investigate the underlying biology and ultimately identify functional candidate genes affecting the trait under investigation.

Results

PANEV (PAthway NEtwork Visualizer) is an R package set for gene/pathway-based network visualization. Based on information available on KEGG, it visualizes genes within a network of multiple levels (from 1 to n) of interconnected upstream and downstream pathways. The network graph visualization helps to interpret functional profiles of a cluster of genes.

Conclusions

The suite has no species constraints and it is ready to analyze genomic or transcriptomic outcomes. Users need to supply the list of candidate genes, specify the target pathway(s) and the number of interconnected downstream and upstream pathways (levels) required for the investigation. The package is available at https://github.com/vpalombo/PANEV.

Integrative analysis of time course metabolic data and biomarker discovery

Background

Metabolomics time-course experiments provide the opportunity to understand the changes to an organism by observing the evolution of metabolic profiles in response to internal or external stimuli. Along with other omic longitudinal profiling technologies, these techniques have great potential to uncover complex relations between variations across diverse omic variables and provide unique insights into the underlying biology of the system. However, many statistical methods currently used to analyse short time-series omic data are i) prone to overfitting, ii) do not fully take into account the experimental design or iii) do not make full use of the multivariate information intrinsic to the data or iv) are unable to uncover multiple associations between different omic data. The model we propose is an attempt to i) overcome overfitting by using a weakly informative Bayesian model, ii) capture experimental design conditions through a mixed-effects model, iii) model interdependencies between variables by augmenting the mixed-effects model with a conditional auto-regressive (CAR) component and iv) identify potential associations between heterogeneous omic variables by using a horseshoe prior.

Results

We assess the performance of our model on synthetic and real datasets and show that it can outperform comparable models for metabolomic longitudinal data analysis. In addition, our proposed method provides the analyst with new insights on the data as it is able to identify metabolic biomarkers related to treatment, infer perturbed pathways as a result of treatment and find significant associations with additional omic variables. We also show through simulation that our model is fairly robust against inaccuracies in metabolite assignments. On real data, we demonstrate that the number of profiled metabolites slightly affects the predictive ability of the model.

Conclusions

Our single model approach to longitudinal analysis of metabolomics data provides an approach simultaneously for integrative analysis and biomarker discovery. In addition, it lends better interpretation by allowing analysis at the pathway level. An accompanying R package for the model has been developed using the probabilistic programming language Stan. The package offers user-friendly functions for simulating data, fitting the model, assessing model fit and postprocessing the results. The main aim of the R package is to offer freely accessible resources for integrative longitudinal analysis for metabolomics scientists and various visualization functions easy-to-use for applied researchers to interpret results.

A Computational Pipeline for the Extraction of Actionable Biological Information From NGS-Phage Display Experiments

Phage Display is a powerful method for the identification of peptide binding to targets of variable complexities and tissues, from unique molecules to the internal surfaces of vessels of living organisms. Particularly for in vivo screenings, the resulting repertoires can be very complex and difficult to study with traditional approaches. Next Generation Sequencing (NGS) opened the possibility to acquire high resolution overviews of such repertoires and thus facilitates the identification of binders of interest. Additionally, the ever-increasing amount of available genome/proteome information became satisfactory regarding the identification of putative mimicked proteins, due to the large scale on which partial sequence homology is assessed. However, the subsequent production of massive data stresses the need for high-performance computational approaches in order to perform standardized and insightful molecular network analysis. Systems-level analysis is essential for efficient resolution of the underlying molecular complexity and the extraction of actionable interpretation, in terms of systemic biological processes and pathways that are systematically perturbed. In this work we introduce PepSimili, an integrated workflow tool, which performs mapping of massive peptide repertoires on whole proteomes and delivers a streamlined, systems-level biological interpretation. The tool employs modules for modeling and filtering of background noise due to random mappings and amplifies the biologically meaningful signal through coupling with BioInfoMiner, a systems interpretation tool that employs graph-theoretic methods for prioritization of systemic processes and corresponding driver genes. The current implementation exploits the Galaxy environment and is available online. A case study using public data is presented, with and without a control selection.

Re-curation and rational enrichment of knowledge graphs in Biological Expression Language

The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich KGs. We have developed two workflows: one for re-curating a given KG to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the KGs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full-text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment/bel-enrichment.

Genome-wide Methyl-Seq analysis of blood-brain targets of glucocorticoid exposure

Chronic exposure to glucocorticoids (GCs) can lead to psychiatric complications through epigenetic mechanisms such as DNA methylation (DNAm). We sought to determine whether epigenetic changes in a peripheral tissue can serve as a surrogate for those in a relatively inaccessible tissue such as the brain. DNA extracted from the hippocampus and blood of mice treated with GCs or vehicle solution was assayed using a genome-wide DNAm platform (Methyl-Seq) to identify differentially methylated regions (DMRs) induced by GC treatment. We observed that ∼70% of the DMRs in both tissues lost methylation following GC treatment. Of the 3,095 DMRs that mapped to the same genes in both tissues, 1,853 DMRs underwent DNAm changes in the same direction. Interestingly, only 209 DMRs (<7%) overlapped in genomic coordinates between the 2 tissues, suggesting tissue-specific differences in GC-targeted loci. Pathway analysis showed that the DMR-associated genes were members of pathways involved in metabolism, immune function, and neurodevelopment. Also, changes in cell type composition of blood and brain were examined by fluorescence-activated cell sorting. Separation of the cortex into neuronal and non-neuronal fractions and the leukocytes into T-cells, B-cells, and neutrophils showed that GC-induced methylation changes primarily occurred in neurons and T-cells, with the blood tissue also undergoing a shift in the proportion of constituent cell types while the proportion of neurons and glia in the brain remained stable. From the current pilot study, we found that despite tissue-specific epigenetic changes and cellular heterogeneity, blood can serve as a surrogate for GC-induced changes in the brain.

Using web2py Python framework for creating data-driven web applications in the academic library

Purpose

– Many libraries have a need to develop their own data-driven web applications, but their technical staff often lacks the required specialized training – which includes knowledge of SQL, a web application language like PHP, JavaScript, CSS, and jQuery. The web2py framework greatly reduces the learning curve for creating data-driven websites by focussing on three main goals: ease of use; rapid development; and security. web2py follows a strict MVC framework where the controls and web templates are all written in pure Python. No additional templating language is required. The paper aims to discuss these issues.

Design/methodology/approach

– There are many frameworks available for creating database-driven web applications. The author had used ColdFusion for many years but wanted to move to a more complete web framework which was also open source.

Findings

– After evaluating a number of Python frameworks, web2py was found to provide the best combination of functionality and ease of use. This paper focusses on the strengths of web2py and not the specifics of evaluating the different frameworks.

Practical implications

– Librarians who feel that they do not have the skills to create data-driven websites in other frameworks might find that they can develop them in web2py. It is a good web application framework to start with, which might also provide a gateway to other frameworks.

Originality/value

– web2py is an open source framework that could have great benefit for those who may have struggled to create database-driven websites in other frameworks or languages.

TGF-β1 prevents rat retinal insult induced by amyloid-β (1-42) oligomers

To set up a retinal degenerative model in rat that mimics pathologic conditions such as age-related macular degeneration (AMD) using amyloid-β (Aβ) oligomers, and assess the effect of TGF-β1. Sprague-Dawley male rats were used. Human Aβ1-42 oligomers were intravitreally (ITV) injected (10µM) in the presence or in the absence of recombinant human TGF-β1 (1ng/μl ITV injected). After 48h, the animals were sacrificed and the eyes removed and dissected. The apoptotic markers Bax and Bcl-2 were assessed by western blot analysis in retina lysates. Gene-pathway network analysis was carried out in order to identify pathways involved in AMD. Treatment with Aβ oligomers induced a strong increase in Bax protein level (about 4-fold; p<0.01) and a significant reduction in Bcl-2 protein level (about 2-fold; p<0.05). Co-injection of TGF-β1 triggered a significant reduction of Bax protein induced by Aβ oligomers. Bioinformatic analysis revealed that Bcl-2 and PI3K-Akt are the most connected nodes, for genes and pathways respectively, in the enriched gene-pathway network common to AMD and Alzheimer disease (AD). Overall, these data indicate that ITV injection of Aβ1-42 oligomers in rat induces molecular changes associated with apoptosis in rat retina, highlighting a potential pathogenetic role of Aβ oligomers in AMD. Bioinformatics analysis confirms that apoptosis pathways can take part in AMD. Furthermore, these findings suggest that human recombinant TGF-β1 can prevent retinal damage elicited by Aβ oligomers.