Network Analysis of Microarray Data

DNA microarrays are widely used to investigate gene expression. Even though the classical analysis of microarray data is based on the study of differentially expressed genes, it is well known that genes do not act individually. Network analysis can be applied to study association patterns of the genes in a biological system. Moreover, it finds wide application in differential coexpression analysis between different systems. Network based coexpression studies have for example been used in (complex) disease gene prioritization, disease subtyping, and patient stratification.Peer reviewe

VOLTA : adVanced mOLecular neTwork Analysis

Motivation: Network analysis is a powerful approach to investigate biological systems. It is often applied to study gene co-expression patterns derived from transcriptomics experiments. Even though co-expression analysis is widely used, there is still a lack of tools that are open and customizable on the basis of different network types and analysis scenarios (e.g. through function accessibility), but are also suitable for novice users by providing complete analysis pipelines. Results: We developed VOLTA, a Python package suited for complex co-expression network analysis. VOLTA is designed to allow users direct access to the individual functions, while they are also provided with complete analysis pipelines. Moreover, VOLTA offers when possible multiple algorithms applicable to each analytical step (e.g. multiple community detection or clustering algorithms are provided), hence providing the user with the possibility to perform analysis tailored to their needs. This makes VOLTA highly suitable for experienced users who wish to build their own analysis pipelines for a wide range of networks as well as for novice users for which a 'plug and play' system is provided.Peer reviewe

Designing Inherently Photodegradable Cell‐Adhesive Hydrogels for 3D Cell Culture

Light-based microfabrication techniques constitute an indispensable approach to fabricate tissue assemblies, benefiting from noncontact spatially and temporarily controlled manipulation of soft matter. Light-triggered degradation of soft materials, such as hydrogels, is important in tissue engineering, bioprinting, and related fields. The photoresponsiveness of hydrogels is generally not intrinsic and requires complex synthetic procedures wherein photoresponsive crosslinking groups are incorporated into the hydrogel. This paper demonstrates a novel biocompatible and inherently photodegradable poly(ethylene glycol) methacrylate (PEGMA)-based gelatin-methacryloyl (GelMA)-containing hydrogel that can be used to culture cells in 3D for at least 14 d. These gels are conveniently and quickly degraded via UV irradiation for 10 min to produce structured hydrogels of various geometries, sizes, and free-standing cell-laden hydrogel particles. These structures can be flexibly produced on demand. In particular, photodegradation can be temporarily delayed from photopolymerization, offering an alternative to hydrogel array production via photopolymerization with a photomask. The paper investigates the influences of hydrogel composition and swelling liquid on both its photodegradability and biocompatibility

The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design

Big Data pervades nearly all areas of life sciences, yet the analysis of large integrated data sets remains a major challenge. Moreover, the field of life sciences is highly fragmented and, consequently, so is its data, knowledge, and standards. This, in turn, makes integrated data analysis and knowledge gathering across sub-fields a demanding task. At the same time, the integration of various research angles and data types is crucial for modelling the complexity of organisms and biological processes in a holistic manner. This is especially valid in the context of drug development and chemical safety assessment where computational methods can provide solutions for the urgent need of fast, effective, and sustainable approaches. At the same time, such computational methods require the development of methodologies suitable for an inte-grated and data centred Big Data view. Here we discuss Knowledge Graphs (KG) as a solution to a data centred analysis approach for drug and chemical development and safety assessment. KGs are knowledge bases, data analysis engines, and knowledge discovery systems all in one, allowing them to be used from simple data retrieval, over meta-analysis to complex predictive and knowledge discovery systems. Therefore, KGs have immense potential to advance the data centred approach, the re-usability, and infor-mativity of data. Furthermore, they can improve the power of analysis, and the complexity of modelled processes, all while providing knowledge in a natively human understandable network data model. (c) 2022 The Author(s). Published by Elsevier B.V. on behalf of Research Network of Computational and Structural Biotechnology. This is an open access article under the CC BY-NC-ND license (http://creative-commons.org/licenses/by-nc-nd/4.0/).Peer reviewe

Unsupervised Algorithms for Microarray Sample Stratification

The amount of data made available by microarrays gives researchers the opportunity to delve into the complexity of biological systems. However, the noisy and extremely high-dimensional nature of this kind of data poses significant challenges. Microarrays allow for the parallel measurement of thousands of molecular objects spanning different layers of interactions. In order to be able to discover hidden patterns, the most disparate analytical techniques have been proposed. Here, we describe the basic methodologies to approach the analysis of microarray datasets that focus on the task of (sub)group discovery.Peer reviewe

Integrated network analysis reveals new genes suggesting COVID-19 chronic effects and treatment

The COVID-19 disease led to an unprecedented health emergency, still ongoing worldwide. Given the lack of a vaccine or a clear therapeutic strategy to counteract the infection as well as its secondary effects, there is currently a pressing need to generate new insights into the SARS-CoV-2 induced host response. Biomedical data can help to investigate new aspects of the COVID-19 pathogenesis, but source heterogeneity represents a major drawback and limitation. In this work, we applied data integration methods to develop a Unified Knowledge Space (UKS) and used it to identify a new set of genes associated with SARS-CoV-2 host response, both in vitro and in vivo. Functional analysis of these genes reveals possible long-term systemic effects of the infection, such as vascular remodelling and fibrosis. Finally, we identified a set of potentially relevant drugs targeting proteins involved in multiple steps of the host response to the virus.Peer reviewe

Toxicogenomics Data for Chemical Safety Assessment and Development of New Approach Methodologies : An Adverse Outcome Pathway-Based Approach

Mechanistic toxicology provides a powerful approach to inform on the safety of chemicals and the development of safe-by-design compounds. Although toxicogenomics supports mechanistic evaluation of chemical exposures, its implementation into the regulatory framework is hindered by uncertainties in the analysis and interpretation of such data. The use of mechanistic evidence through the adverse outcome pathway (AOP) concept is promoted for the development of new approach methodologies (NAMs) that can reduce animal experimentation. However, to unleash the full potential of AOPs and build confidence into toxicogenomics, robust associations between AOPs and patterns of molecular alteration need to be established. Systematic curation of molecular events to AOPs will create the much-needed link between toxicogenomics and systemic mechanisms depicted by the AOPs. This, in turn, will introduce novel ways of benefitting from the AOPs, including predictive models and targeted assays, while also reducing the need for multiple testing strategies. Hence, a multi-step strategy to annotate AOPs is developed, and the resulting associations are applied to successfully highlight relevant adverse outcomes for chemical exposures with strong in vitro and in vivo convergence, supporting chemical grouping and other data-driven approaches. Finally, a panel of AOP-derived in vitro biomarkers for pulmonary fibrosis (PF) is identified and experimentally validated.Peer reviewe

Marrying chemistry with biology by combining on-chip solution-based combinatorial synthesis and cellular screening

International audienceDrug development often relies on high-throughput cell-based screening oflarge compound libraries. However, the lack of miniaturized andparallelized methodologies in chemistry as well as strict separation andincompatibility of the synthesis of bioactive compounds from theirbiological screenings makes this process expensive and inefficient.Here, we demonstrate an on-chip platform that combines solution-basedsynthesis of compound libraries with high-throughput biologicalscreenings (chemBIOS). The chemBIOS platform is compatible with bothorganic solvents required for the synthesis and aqueous solutionsnecessary for biological screenings. We use the chemBIOS platform toperform 75 parallel, three-component reactions to synthesize a libraryof lipidoids, followed by characterization via MALDI-MS, on-chipformation of lipoplexes, and on-chip cell screening. The entire processfrom the library synthesis to cell screening takes only 3 days and about1 mL of total solutions, demonstrating the potential of the chemBIOStechnology to increase efficiency and accelerate screenings and drugdevelopment