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This study aimed to evaluate the response of HepaRG cells after co-exposure to phthalates and heavy metals, using a high-dimensional biology paradigm (HDB). Liver is the main metabolism site for the majority of xenobiotics. For this reason, the HepaRG cell line was used as an in vitro model, and cells were exposed to two characteristic mixtures of phthalates and heavy metals containing phthalates (DEHP, DiNP, BBzP) and metals (lead, methylmercury, total mercury) in a concentration-dependent manner. The applied chemical mixtures were selected as the most abundant pollutants in the REPRO_PL and PHIME cohorts, which were studied using the exposome-wide approach in the frame of the EU project HEALS. These studies investigated the environmental causation of neurodevelopmental disorders in neonates and across Europe. The INTEGRA computational platform was used for the calculation of the effective concentrations of the chemicals in the liver through extrapolation from human biomonitoring data and this dose (and a ten-times higher one) was applied to the hepatocyte model. Multi-omics analysis was performed to reveal the genes, proteins, and metabolites affected by the exposure to these chemical mixtures. By extension, we could detect the perturbed metabolic pathways. The generated data were analyzed using advanced bioinformatic tools following the HEALS connectivity paradigm for multi-omics pathway analysis. Co-mapped transcriptomics and proteomics data showed that co-exposure to phthalates and heavy metals leads to perturbations of the urea cycle due to differential expression levels of arginase-1 and -2, argininosuccinate synthase, carbamoyl-phosphate synthase, ornithine carbamoyltransferase, and argininosuccinate lyase. Joint pathway analysis of proteomics and metabolomics data revealed that the detected proteins and metabolites, choline phosphate cytidylyltransferase A, phospholipase D3, group XIIA secretory phospholipase A2, α-phosphatidylcholine, and the a 1,2-diacyl-sn-glycero-3-phosphocholine, are responsible for the homeostasis of the metabolic pathways phosphatidylcholine biosynthesis I, and phospholipases metabolism. The urea, phosphatidylcholine biosynthesis I and phospholipase metabolic pathways are of particular interest since they have been identified also in human samples from the REPRO_PL and PHIME cohorts using untargeted metabolomics analysis and have been associated with impaired psychomotor development in children at the age of two. In conclusion, this study provides the mechanistic evidence that co-exposure to phthalates and metals disturb biochemical processes related to mitochondrial respiration during critical developmental stages, which are clinically linked to neurodevelopmental perturbations.
Pubmed ID: 32949613
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Open and collaborative platform dedicated to curation of biological pathways. Each pathway has dedicated wiki page, displaying current diagram, description, references, download options, version history, and component gene and protein lists. Database of biological pathways maintained by and for scientific community.
View all literature mentionsA collection of Pathway/Genome Databases which describes the genome and metabolic pathways of a single organism. The BioCyc collection of Pathway/Genome Databases (PGDBs) provides an electronic reference source on the genomes and metabolic pathways of sequenced organisms. BioCyc PGDBs are generated by software that predicts the metabolic pathway complements of completely sequenced organisms from their genome sequences. They also include the results of a number of other computational inference procedures applied to these genomes, including predictions of which genes code for missing enzymes in metabolic pathways, and predicted operons. The BioCyc Web site provides a suite of software tools for database searching and visualization, for omics data analysis, and for comparative genomics and comparative pathway questions. The databases within the BioCyc collection are organized into tiers according to the amount of manual review and updating they have received. Tier 1 PGDBs have been created through intensive manual efforts, and receive continuous updating. Tier 2 PGDBs were computationally generated by the PathoLogic program, and have undergone moderate amounts of review and updating. Tier 3 PGDBs were computationally generated by the PathoLogic program, and have undergone no review and updating. There are 967 DBs in Tier 3. The downloadable version of BioCyc that includes the Pathway Tools software provides more speed and power than the BioCyc Web site.
View all literature mentionsProject exploring the spectrum of genomic changes involved in more than 20 types of human cancer that provides a platform for researchers to search, download, and analyze data sets generated. As a pilot project it confirmed that an atlas of changes could be created for specific cancer types. It also showed that a national network of research and technology teams working on distinct but related projects could pool the results of their efforts, create an economy of scale and develop an infrastructure for making the data publicly accessible. Its success committed resources to collect and characterize more than 20 additional tumor types. Components of the TCGA Research Network: * Biospecimen Core Resource (BCR); Tissue samples are carefully cataloged, processed, checked for quality and stored, complete with important medical information about the patient. * Genome Characterization Centers (GCCs); Several technologies will be used to analyze genomic changes involved in cancer. The genomic changes that are identified will be further studied by the Genome Sequencing Centers. * Genome Sequencing Centers (GSCs); High-throughput Genome Sequencing Centers will identify the changes in DNA sequences that are associated with specific types of cancer. * Proteome Characterization Centers (PCCs); The centers, a component of NCI's Clinical Proteomic Tumor Analysis Consortium, will ascertain and analyze the total proteomic content of a subset of TCGA samples. * Data Coordinating Center (DCC); The information that is generated by TCGA will be centrally managed at the DCC and entered into the TCGA Data Portal and Cancer Genomics Hub as it becomes available. Centralization of data facilitates data transfer between the network and the research community, and makes data analysis more efficient. The DCC manages the TCGA Data Portal. * Cancer Genomics Hub (CGHub); Lower level sequence data will be deposited into a secure repository. This database stores cancer genome sequences and alignments. * Genome Data Analysis Centers (GDACs) - Immense amounts of data from array and second-generation sequencing technologies must be integrated across thousands of samples. These centers will provide novel informatics tools to the entire research community to facilitate broader use of TCGA data. TCGA is actively developing a network of collaborators who are able to provide samples that are collected retrospectively (tissues that had already been collected and stored) or prospectively (tissues that will be collected in the future).
View all literature mentionsA quantitative proteomics software package for analyzing large-scale mass-spectrometric data sets. It is a set of algorithms that include peak detection and scoring of peptides, mass calibration, database searches for protein identification, protein quantification, and provides summary statistics.
View all literature mentionsA software which acquires and processes data sets, primarily through the Xcalibur system.
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