Covid-19 acute responses and possible long term consequences: what nanotoxicology can teach us

Non 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

TinderMIX : Time-dose integrated modelling of toxicogenomics data

Background: Omics technologies have been widely applied in toxicology studies to investigate the effects of different substances on exposed biological systems. A classical toxicogenomic study consists in testing the effects of a compound at different dose levels and different time points. The main challenge consists in identifying the gene alteration patterns that are correlated to doses and time points. The majority of existing methods for toxicogenomics data analysis allow the study of the molecular alteration after the exposure (or treatment) at each time point individually. However, this kind of analysis cannot identify dynamic (time-dependent) events of dose responsiveness. Results: We propose TinderMIX, an approach that simultaneously models the effects of time and dose on the transcriptome to investigate the course of molecular alterations exerted in response to the exposure. Starting from gene log fold-change, TinderMIX fits different integrated time and dose models to each gene, selects the optimal one, and computes its time and dose effect map; then a user-selected threshold is applied to identify the responsive area on each map and verify whether the gene shows a dynamic (time-dependent) and dose-dependent response; eventually, responsive genes are labelled according to the integrated time and dose point of departure. Conclusions: To showcase the TinderMIX method, we analysed 2 drugs from the Open TG-GATEs dataset, namely, cyclosporin A and thioacetamide. We first identified the dynamic dose-dependent mechanism of action of each drug and compared them. Our analysis highlights that different time- and dose-integrated point of departure recapitulates the toxicity potential of the compounds as well as their dynamic dose-dependent mechanism of action.Peer reviewe

Modeling of facade leaching in urban catchments

Building facades are protected from microbial attack by incorporation of biocides within them. Flow over facades leaches these biocides and transports them to the urban environment. A parsimonious water quantity/quality model applicable for engineered urban watersheds was developed to compute biocide release from facades and their transport at the urban basin scale. The model couples two lumped submodels applicable at the basin scale, and a local model of biocide leaching at the facade scale. For the facade leaching, an existing model applicable at the individual wall scale was utilized. The two lumped models describe urban hydrodynamics and leachate transport. The integrated model allows prediction of biocide concentrations in urban rivers. It was applied to a 15 km2 urban hydrosystem in western Switzerland, the Vuachere river basin, to study three facade biocides (terbutryn, carbendazim, diuron). The water quality simulated by the model matched well most of the pollutographs at the outlet of the Vuachere watershed. The model was then used to estimate possible ecotoxicological impacts of facade leachates. To this end, exceedance probabilities and cumulative pollutant loads from the catchment were estimated. Results showed that the considered biocides rarely exceeded the relevant predicted no-effect concentrations for the riverine system. Despite the heterogeneities and complexity of (engineered) urban catchments, the model application demonstrated that a computationally ‘‘light’’ model can be employed to simulate the hydrograph and pollutograph response within them. It thus allows catchment-scale assessment of the potential ecotoxicological impact of biocides on receiving waters

Parsimonious hydrological modeling of urban sewer and river catchments

A parsimonious model of flow capable of simulating flow in natural/engineered catchments and at WWTP (Wastewater Treatment Plant) inlets was developed. The model considers three interacting, dynamic storages that account for transfer of water within the system. One storage describes the ``flashy'' response of impervious surfaces, another pervious areas and finally one storage describes subsurface flow. The sewerage pipe network is considered as an impervious surface and is thus included in the impervious surface storage. In addition, the model assumes that water discharged from several CSOs (combined sewer overflows) can be accounted for using a single, characteristic CSO. The model was calibrated on, and validated for, the Vidy Bay WWTP, which receives effluent from Lausanne, Switzerland (population about 200,000), as well as for an overlapping urban river basin. The results indicate that a relatively simple approach is suitable for predicting the responses of interacting engineered and natural hydrosystems

Toxicogenomics analysis of dynamic dose-response in macrophages highlights molecular alterations relevant for multi-walled carbon nanotube-induced lung fibrosis

Toxicogenomics approaches are increasingly used to gain mechanistic insight into the toxicity of engineered nanomaterials (ENMs). These emerging technologies have been shown to aid the translation of in vitro experimentation into relevant information on real-life exposures. Furthermore, integrating multiple layers of molecular alteration can provide a broader understanding of the toxicological insult. While there is growing evidence of the immunotoxic effects of several ENMs, the mechanisms are less characterized, and the dynamics of the molecular adaptation of the immune cells are still largely unknown. Here, we hypothesized that a multi-omics investigation of dynamic dose-dependent (DDD) molecular alterations could be used to retrieve relevant information concerning possible long-term consequences of the exposure. To this end, we applied this approach on a model of human macrophages to investigate the effects of rigid multi-walled carbon nanotubes (rCNTs). THP-1 macrophages were exposed to increasing concentrations of rCNTs and the genome-wide transcription and gene promoter methylation were assessed at three consecutive time points. The results suggest dynamic molecular adaptation with a rapid response in the gene expression and contribution of DNA methylation in the long-term adaptation. Moreover, our analytical approach is able to highlight patterns of molecular alteration in vitro that are relevant for the pathogenesis of pulmonary fibrosis, a known long-term effect of rCNTs exposure in vivo.Peer reviewe

Microarray Data Preprocessing: From Experimental Design to Differential Analysis

DNA microarray data preprocessing is of utmost importance in the analytical path starting from the experimental design and leading to a reliable biological interpretation. In fact, when all relevant aspects regarding the experimental plan have been considered, the following steps from data quality check to differential analysis will lead to robust, trustworthy results. In this chapter, all the relevant aspects and considerations about microarray preprocessing will be discussed. Preprocessing steps are organized in an orderly manner, from experimental design to quality check and batch effect removal, including the most common visualization methods. Furthermore, we will discuss data representation and differential testing methods with a focus on the most common microarray technologies, such as gene expression and DNA methylation.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