53
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: not found

      From cohorts to molecules: Adverse impacts of endocrine disrupting mixtures

      1 , 2 , 3 , 4 , 5 , 1 , 1 , 2 , 3 , 6 , 6 , 7 , 8 , 9 , 1 , 1 , 1 , 9 , 1 , 2 , 1 , 1 , 1 , 2 , 1 , 1 , 6 , 10 , 11 , 6 , 12 , 7 , 13 , 14 , 15 , 16 , 6 , 17 , 15 , 10 , 18 , 19 , 20 , 4 , 4 , 20 , 6 , 7 , 5 , 1 , 2 , 3
      Science
      American Association for the Advancement of Science (AAAS)

      Read this article at

      ScienceOpenPublisherPubMed
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Convergent evidence associates exposure to endocrine disrupting chemicals (EDCs) with major human diseases, even at regulation-compliant concentrations. This might be because humans are exposed to EDC mixtures, whereas chemical regulation is based on a risk assessment of individual compounds. Here, we developed a mixture-centered risk assessment strategy that integrates epidemiological and experimental evidence. We identified that exposure to an EDC mixture in early pregnancy is associated with language delay in offspring. At human-relevant concentrations, this mixture disrupted hormone-regulated and disease-relevant regulatory networks in human brain organoids and in the model organisms Xenopus leavis and Danio rerio , as well as behavioral responses. Reinterrogating epidemiological data, we found that up to 54% of the children had prenatal exposures above experimentally derived levels of concern, reaching, for the upper decile compared with the lowest decile of exposure, a 3.3 times higher risk of language delay.

          Mixed chemicals’ potential risks

          Exposure to endocrine-disrupting chemicals in the environment can cause a variety of potential health problems in human patients. Such exposures are not isolated, however, and environmental safety regulations that only consider doses of individual chemicals do not take multisubstance exposures into account. Caporale et al . examined chemical exposure data in a large cohort of human mother-child pairs and identified a set of common endocrine-disrupting chemicals (see the Perspective by Liew and Guo). The authors then showed that a mixture of these substances could cause abnormalities in two model organisms and may also be associated with language delay in humans. These findings suggest a need for further research into the effects of chemical mixtures and regulations that take such combinatorial effects into account. —YN

          Abstract

          Exposure to mixed endocrine-disrupting chemicals is common in pregnant women and may be associated with language delay in their children.

          Abstract

          INTRODUCTION

          Endocrine disrupting chemicals (EDCs) are compounds that interfere with physiological hormonal regulation. Humans are pervasively exposed to many different EDCs, and a growing body of evidence indicates that early life exposure to such EDC mixtures can induce changes in the human organism that underlie increased susceptibility to diseases throughout the life span, including neurodevelopmental disorders. Chemical regulation is, however, entirely based on the risk assessment of individual compounds, leaving the real-life impact of chemical mixtures unexamined and unregulated. This is relevant insofar as cumulative exposure to multiple compounds may be associated with adverse health outcomes even when the concentrations of individual chemicals fall below the regulatory dose.

          RATIONALE

          We set out to make the epidemiological associations between exposure to mixtures and health outcomes experimentally tractable, defining molecular pathways and dose responses that could be translated back to actual human exposures and thereby refine current risk assessment practices. As opposed to previous studies that focused on single compounds, we identified and tested an EDC mixture associated with adverse neurodevelopmental outcomes in the Swedish Environmental Longitudinal, Mother and child, Asthma and allergy (SELMA) pregnancy cohort (MIX N) by integrating epidemiological data with experimental toxicology and characterized real life–relevant exposure.

          RESULTS

          We used weighted quantile sum (WQS) regression to identify chemicals associated with language delay in children and included those chemicals in MIX N. MIX N was synthesized following the relative proportions and total concentrations found in the SELMA cohort. It was then tested in both in vitro and in vivo models. In human fetal primary neural stem cells and three-dimensional cortical brain organoids differentiated from human pluripotent stem cells, transcriptomic analysis showed that MIX N interferes with hormonal pathways and dysregulates expression of genes and biological pathways that are causally linked to autism spectrum disorders. Data from experiments in Xenopus leavis and Danio rerio , in vivo models validated by the Organisation for Economic Co-operation and Development (OECD), confirmed thyroid function as one of the key and unifying points of vulnerability to MIX N and linked thyroid disruption to neurodevelopmental effects measured as alterations in locomotor activity. The resulting dose-response relationships were then used to estimate a point of departure (POD), which is the toxicological measure to estimate no-effect concentration. This enabled us to apply a similar mixture approach (SMACH) where we (i) identified individuals in the SELMA study who were sufficiently similarly exposed compared with the experimental mixtures and (ii) determined the proportion of the SELMA children with exposure ranges of concern using the POD as reference.

          CONCLUSION

          Integrating experimental and epidemiological evidence, we established mechanistic and correlative evidence for neurodevelopmental adversities of an EDC mixture associated with language delay. Using the generated experimental data in a risk assessment concept, we found increased odds of language delay in offspring of up to 54% of pregnant women. These results emphasize the need to take mixtures into account during chemical testing and risk assessment and provide an integrative framework to guide risk assessment strategies.

          Related collections

          Most cited references122

          • Record: found
          • Abstract: found
          • Article: not found

          Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method.

          The two most commonly used methods to analyze data from real-time, quantitative PCR experiments are absolute quantification and relative quantification. Absolute quantification determines the input copy number, usually by relating the PCR signal to a standard curve. Relative quantification relates the PCR signal of the target transcript in a treatment group to that of another sample such as an untreated control. The 2(-Delta Delta C(T)) method is a convenient way to analyze the relative changes in gene expression from real-time quantitative PCR experiments. The purpose of this report is to present the derivation, assumptions, and applications of the 2(-Delta Delta C(T)) method. In addition, we present the derivation and applications of two variations of the 2(-Delta Delta C(T)) method that may be useful in the analysis of real-time, quantitative PCR data. Copyright 2001 Elsevier Science (USA).
            Bookmark
            • Record: found
            • Abstract: found
            • Article: found
            Is Open Access

            limma powers differential expression analyses for RNA-sequencing and microarray studies

            limma is an R/Bioconductor software package that provides an integrated solution for analysing data from gene expression experiments. It contains rich features for handling complex experimental designs and for information borrowing to overcome the problem of small sample sizes. Over the past decade, limma has been a popular choice for gene discovery through differential expression analyses of microarray and high-throughput PCR data. The package contains particularly strong facilities for reading, normalizing and exploring such data. Recently, the capabilities of limma have been significantly expanded in two important directions. First, the package can now perform both differential expression and differential splicing analyses of RNA sequencing (RNA-seq) data. All the downstream analysis tools previously restricted to microarray data are now available for RNA-seq as well. These capabilities allow users to analyse both RNA-seq and microarray data with very similar pipelines. Second, the package is now able to go past the traditional gene-wise expression analyses in a variety of ways, analysing expression profiles in terms of co-regulated sets of genes or in terms of higher-order expression signatures. This provides enhanced possibilities for biological interpretation of gene expression differences. This article reviews the philosophy and design of the limma package, summarizing both new and historical features, with an emphasis on recent enhancements and features that have not been previously described.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: found
              Is Open Access

              edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

              Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au
                Bookmark

                Author and article information

                Contributors
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                (View ORCID Profile)
                Journal
                Science
                Science
                American Association for the Advancement of Science (AAAS)
                0036-8075
                1095-9203
                February 18 2022
                February 18 2022
                : 375
                : 6582
                Affiliations
                [1 ]High Definition Disease Modelling Lab, Stem Cell and Organoid Epigenetics, IEO, European Institute of Oncology, IRCCS, 20141 Milan, Italy.
                [2 ]Department of Oncology and Hemato-oncology, University of Milan, 20122 Milan, Italy.
                [3 ]Human Technopole, V.le Rita Levi-Montalcini, 1, 20157 Milan, Italy.
                [4 ]UMR 7221, Phyma, CNRS–Muséum National d’Histoire Naturelle, Sorbonne Université, 75005 Paris, France.
                [5 ]Department of Biological and Environmental Sciences, University of Gothenburg, 41463 Gothenburg, Sweden.
                [6 ]Swedish Toxicology Sciences Research Center (SWETOX), Södertälje, Sweden.
                [7 ]Department of Organismal Biology, Environmental Toxicology, Uppsala University, SE-752 36 Uppsala, Sweden.
                [8 ]Division of Occupational and Environmental Medicine, Department of Laboratory Medicine, Lund University, SE-221 85 Lund, Sweden.
                [9 ]Medical Research Council Centre for Regenerative Medicine and Edinburgh Cancer Research UK Centre, University of Edinburgh, Edinburgh, UK.
                [10 ]Department of Environmental Science, Stockholm University, SE-10691 Stockholm, Sweden.
                [11 ]School of Science and Technology, Örebro University, SE-70182 Örebro, Sweden.
                [12 ]Division of Obstetrics and Gynecology, Department of Clinical Science, Intervention and Technology, Karolinska Institutet and Karolinska University Hospital, 141 86 Stockholm, Sweden.
                [13 ]Hospital for Children and Adolescents, Department of Women and Child Health, University Hospital, University of Leipzig, 04103 Leipzig, Germany.
                [14 ]Lab of Basic Sciences, Faculty of Dentistry, National and Kapodistrian University of Athens, 152 72 Athens, Greece.
                [15 ]Department of Health Security, Finnish Institute for Health and Welfare (THL), Kuopio 70210, Finland.
                [16 ]School of Health Sciences, Örebro University, SE-70182 Örebro, Sweden.
                [17 ]Institute of Environmental Medicine, Karolinska Institutet, SE-17177 Stockholm, Sweden.
                [18 ]Department of Women’s and Children’s Health, Pediatric Endocrinology Division, Karolinska Institutet and University Hospital, SE-17176 Stockholm, Sweden.
                [19 ]Faculty of Health, Science and Technology, Department of Health Sciences, Karlstad University, SE- 651 88 Karlstad, Sweden.
                [20 ]Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA.
                Article
                10.1126/science.abe8244
                35175820
                872cc92f-3538-4dbd-83ca-e46b1ce8a609
                © 2022
                History

                Comments

                Comment on this article