Omics advances in Ecotoxicology

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Omics advances in Ecotoxicology Xiaowei Zhang, Pu Xia, Pingping Wang, Jianghua Yang, and Donald Baird Environ. Sci. Technol., Just Accepted Manuscript • DOI: 10.1021/acs.est.7b06494 • Publication Date (Web): 26 Feb 2018 Downloaded from http://pubs.acs.org on February 27, 2018

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Environmental Science & Technology

Omics advances in Ecotoxicology Xiaowei Zhang†*, Pu Xia†, Pingping Wang†, Jianghu Yang†, Donald J. Baird‡

†State Key Laboratory of Pollution Control & Resource Reuse, School of the Environment, Nanjing University, Nanjing 210023, China; ‡Environment & Climate Change Canada @ Canadian Rivers Institute, Department of Biology, University of New Brunswick, New Brunswick, Canada.

Corresponding author: Xiaowei Zhang, PhD, Prof. School of Environment, Nanjing University 163 Xianlin Avenue, Nanjing, 210023, China Tel: (86)-25-89680623 E-mail address: [email protected] [email protected]

ACS Paragon Plus Environment


Abstract Toxic substances in the environment generate adverse effects at all levels of biological organization from the molecular level to community and ecosystem. Given this complexity, it is not surprising that ecotoxicologists have struggled to address the full consequences of toxic substance release at ecosystem level, due to the limits of observational and experimental tools to reveal the changes in deep structure at different levels of organization. -Omics technologies, consisting of genomics and ecogenomics, have the power to reveal, in unprecedented detail, the cellular processes of an individual or biodiversity of a community in response to environmental change with high sample/observation throughput. This represents a historic opportunity to transform the way we study toxic substances in ecosystems, through direct linkage of ecological effects with the systems biology of organisms. Three recent examples of -omics advance in the assessment of toxic substances are explored here: 1) the use of functional genomics in the discovery of novel molecular mechanisms of toxicity of chemicals in the environment; 2) the development of laboratory pipelines of dose-dependent, reduced transcriptomics to support high-throughput chemical testing at the biological pathway level; and 3) the use of eDNA metabarcoding approaches for assessing chemical effects on biological communities in mesocosm experiments and through direct observation in field monitoring. -Omics advances in ecotoxicological studies not only generate new knowledge regarding mechanisms of toxicity and environmental effect, improving the relevance and immediacy of laboratory toxicological assessment, but can provide a wholly new paradigm for ecotoxicology by linking ecological models to mechanism-based, systems biology approaches.


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Box 1: Terms and Definitions: -Omics: -Omics is defined as the collective characterization and quantification of all of the specific biological molecules, such as genes, metabolites, proteins, and RNA which can be associated with the structure, function, and dynamics of an organism or organisms. For example, “transcriptomics” is the study of all of the mRNA transcripts and “proteomics” is the study of the proteins. In the field of toxicology, toxicogenomics applies -omics technologies (e.g. transcriptomics, metabolomics, proteomics, lipidomics, functional genomics) to study the molecular mechanism of action (e.g. global mRNA, protein and metabolite) of toxic substances. Functional genomics: In toxicology, functional genomics describes the high-throughput screening of genes whose loss of function by knockout (i.e. deletion) or knockdown (i.e. suppression) increases or decreases susceptibility of phenotypes to adverse effects (e.g. reduced survival). Ecogenomics: Ecogenomics applies the DNA-based technology like metabarcoding, metagenomics, metatranscriptomics, metaproteomics, and metametabolomics to analyze phylogenetic, functional and metabolic diversity of species within naturally-occurring assemblages of organisms (e.g. communities). Specifically, metatranscriptomics, metaproteomics, and metametabolomics are the aggregated, multispecies equivalents of individual organism transcriptomics, proteomics and metabolomics, respectively. CRISPR-Cas9: CRISPR (Clustered Regularly Interspaced Short Palindrome Repeats)associated nuclease Cas9 represents a genome editing technology derived from a prokaryotic immune system capable of cleaving foreign nucleic acids. The CRISPR-Cas9 system contains a Cas9 nuclease guided by a 20-base pair single-guide RNA (sgRNA) to precisely cleave DNA.



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Reduced transcriptome analysis: As an alternative strategy to sequencing of the whole transcriptome, reduced transcriptome analysis targets a reduced set of genes to focus on key toxic response genes and associated pathways to facilitate testing a wide-range of chemical concentrations. A key supporting principle is that a subset of representative genes in a network may function as surrogates for all genes of that network. DNA Barcoding: Is an identification method that uses single or multiple short genetic barcoding makers (e.g., COI, 18S, rbcL) to assign taxonomic identity to tissue samples from unidentified organisms. Environmental DNA (eDNA): The DNA directly extracted from environmental samples (e.g. soil, sediment, water or air) without enrichment. Environmental DNA in samples may originate from whole organisms, or from faeces, mucus, skin cells, organelles, gametes or even extracellular DNA. Bulk or trace amounts of eDNA can be analyzed by PCR amplification and subsequent quantification, including high-throughput DNA sequencing methods. High-throughput

sequencing

(HTS):

Sequencing

techniques

that

allow

for

simultaneous analysis of millions of sequences in a single instrument run (e.g., Illumina, Ion Torrent platforms). DNA Metabarcoding: Taxonomic identification of multiple species from an eDNA sample based on DNA barcoding of sequences generated from high-throughput sequencing. DNA metabarcoding can be used in biodiversity monitoring, and food wed re-construction etc. Species sensitivity distribution (SSD): The relationship generated by ranking species sensitivity to a particular toxic substance as measured by a standard endpoint (e.g. LC50). SSDs are widely used in single and multi-substance risk assessment, e.g. in the setting of water or sediment ecological quality guidelines.


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Operational Taxonomic Unit (OTU): Originally derived from numerical taxonomy, OTUs are taxonomic constructs based on aggregated clusters of a specific taxonomic marker gene e.g. cytochrome oxidase (COI), based on sequence similarity. Field-based OTUs sensitivity distribution (f-OSD): The sensitivity distribution of Operational Taxonomic Units (OTUs) in a community to a specific toxicant.



1. The challenge & opportunity of -omics technologies for ecotoxicology By integrating knowledge from toxicology and ecology, ecotoxicologists study the effects of toxic substances or other harmful agents such as algal toxins or nanoparticles, on the health of ecological systems and their constituent species, populations and individuals.1 As an applied science, the purpose of ecotoxicology can be prospective, i.e. to predict the effects of pollution, or retrospective, e.g. to diagnose the stressors of polluted ecosystems. The knowledge gained can be used to guide decision making on the mitigative or remediative measure required to ensure the protection of ecosystem goods and services. To fulfill this goal, ecotoxicological studies by necessity draw evidence from across a wide range of biological organisation, from the molecular, cellular, tissue, organs, individuals, all the way to the population, community, ecosystem and, ultimately, the biosphere.2 Such approaches have been used very successfully in the ecological risk assessment of legacy environmental chemicals such as DDT and certain PCBs. However, it has been widely recognized that a tendency for ecotoxicologists to focus on individuals (or lower levels of biological organization), following the phenomenological model of 'effect of chemical A on species B under conditions C' limits our ability to carry out environmental assessment of a wide range of emerging pollution issues, including the very large number of untested chemicals and the ubiquity of the presence of multiple stressors in damaged ecosystems. 3, 4 Toxic substances in the environment generate adverse effects at all levels of biological organization from the molecular level to community and ecosystem. To support chemical risk assessment, analytical frameworks such as the use of Adverse Outcome Pathways (AOPs), or Source-To-Outcome pathways (STOs) have been adopted to describe cascading chains of causal events occurring at different levels of biological organization that result in a measurable ecotoxicological effect.5 In particular, the AOP framework has gained traction in regulatory science as it offers an efficient and effective means for linking toxicological


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mechanisms with the standardised toxicity end points required for regulatory assessments, increasing their relevance as predictors of ecosystem effects.6 Nevertheless, among the many challenges and limitations that must be addressed to realize the full potential of the AOP framework in regulatory decision-making, one prominent task is the development of appropriate in vitro bioassays to capture all possible Molecular Initiating Events (MIEs) and/or Key Events (KEs) that could be generated by thousands of untested chemicals.6 Perhaps the greatest challenge facing contemporary ecotoxicology is how to reliably assess community and ecosystem impacts of chemical pollutants. Current ecotoxicological approaches are rooted in laboratory tests or field observations which focus at the individual level or below. This can largely be attributed to the lack of an efficient technology to measure the responses at community level or higher, in turn reflecting the failure of ecologists to address this pressing issue, coupled with a regulatory process dominated by a demand for highly standardized laboratory tests.3 A major shortcoming in current AOP frameworks is that the assessment of adverse outcomes is remains focused on toxicity endpoints at the individual level or below, indicating its strong linkage to the perceived needs of regulatory programs. Although there are promising examples of the use of population modeling employing endpoint information generated from endpoints measured at or below individual level to predict population or community responses, an inability to predict indirect effects (i.e. species interactions), and the co-occurrence of multiple stressors remain significant obstacles to extrapolating modelling results to the field.3,

4

In terms of direct

effects on communities, the most successful model in ecotoxicology is the Species Sensitivity Distribution (SSD) model, which has been widely used by regulators to derive environmental quality standards. However, input data in current SSD modeling are necessarily based on the standardized laboratory toxicity testing of individual species, as this remains the only consistently observed information available on differential sensitivity among species.



Moreover, it has been acknowledged that an ongoing failure to generate relevant regulatory monitoring results remains a key weakness in nature management and wildlife protection in developed and developing countries.4, 7 Omics technologies, if well applied, have the potential to enable qualitative and quantitative measurement of changes from the molecular, cell, tissue, individual to population, to community level, and thus provide a historic opportunity to transform our knowledge of the consequences of toxic substance release to the environment. Omics technologies have the ability to provide a global view of the cellular processes of an individual and also the compositional changes occurring in natural communities of organisms in response to environmental change, and to do so in a high throughput manner with the advance of bioinformatics (Figure 1). Therefore, the widespread adoption of -omics can 1) increase the efficacy, efficiency and timeliness of environmental assessment, 2) generate new knowledge on the underlying mechanisms contributing to environmental degradation, and 3) transform the entire regulatory process by permitting areas currently viewed as intractable to be directly studied (e.g. multi-substance impacts on biodiverse target groups based on an understanding of causal mechanisms). Covering all aspects of -omics application in regulatory ecotoxicology would be beyond the scope of this article, however we will employ three recent examples to show how -omics advances are creating a paradigm-shift in the ecotoxicological study of chemicals, 1) the use of functional genomics to discover novel molecular mechanisms of toxicity; 2) the creation of pipelines of dose-dependent reduced transcriptomes which permit high-throughput chemical tests at biological pathway level; and 3) metabarcoding for assessing the effects of toxic substances on natural communities.

2 Using functional genomics to study novel molecular mechanisms of toxicity


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Contrasting

with

toxicogenomics

approaches

based

on

the

expressome

(e.g.

transcriptomics, proteomics and metabolomics) that indirectly correlate chemical exposure with gene products (e.g. mRNA, protein, metabolites), functional genomics can directly link genes and their functions to tolerance to substance exposure, providing unprecedented mechanistic insight into the critical genes and/or pathways modulated by chemicals.8-10 Functional genomics involves genome-scale knockout or knockdown, followed by the screening of genes whose loss-of-function confers resistance or sensitivity to toxicity at the phenotype level (e.g. cell death) following toxic substance exposure. In unicellular organisms, functional genomic screening has been conducted on various species, such as bacteria,11 yeast12. In more complex eukaryotes, represented, for example, by mammalian cells, functional genomic screening is supported by genome-editing technology such as RNA interference (RNAi) and CRISPR-Cas9. The RNAi method works by degrading gene-specific mRNA, and has been widely used in functional genomic screening for drug discovery.13,

14

However,

RNAi is confined to incomplete gene knockdown and off-target effects, which may itself cause unexpected alterations of the phenotype being tested. As an alternative, CRISPR-Cas9, a leading-edge genome-editing biotechnology, can complete gene knockout in mammalian cells more efficiently.15 The CRISPR-Cas9 system utilizes a short (20 base pair) single guide RNA (sgRNA) to permit Cas9 nuclease to create a permanent mutation at a specific genomic locus. CRISPR-Cas9 functional genomic screening can be conducted by lentivirus transduction of a CRISPR-Cas9 knockout library in mammalian cells, where each sgRNA works as a unique barcode that can be distinguished and counted by high-throughput sequencing (HTS) (Figure 2).15, 16 CRISPR-Cas9 functional genomic screening provides an efficient untargeted approach to identify genome alteration arising from toxic substance exposure, and can be performed in



two manners: positive or negative selection. Positive selection utilizes a cytotoxic concentration (e.g. the concentration causing a 50% reduction in cell viability, IC50) to screen genes whose loss indicates a reduction in cellular survival under toxic exposure. Negative selection applies a non-cytotoxic concentration (e.g. IC10) to investigate genes whose loss sensitizes cells to specific substance exposure. A recent study in our lab has performed both CRISPR-Cas9 positive and negative selection in human HepG2 cells on triclosan, a widely used antimicrobial.17 By positive selection screening at the IC50 of triclosan, genes involved in MAPK signaling pathway and PPAR signaling pathway were identified to play potentially critical role in triclosan-induced cytotoxicity. We evaluated the top-ranked resistant genes, FTO and MAP2K3, whose individual knockout supported the hypothesis that their loss enhanced resistance to triclosan. By negative selection screening at IC10 and IC20 of TCS, we identified genes mainly involved in pathways of immune response which were consistent with the transcriptomic profiles of triclosan at low concentration ranges (

Omics advances in Ecotoxicology

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