Conceived and designed the experiments: MJL SGG MRV KJC FF. Performed the experiments: TDW AMD HW CM BPL GDS IK. Analyzed the data: TDW NT KLB OH JKA JBT JMH. Wrote the paper: TDW FF.
The authors have declared that no competing interests exist.
The acquisition and analysis of datasets including multi-level
Understanding how living organisms adapt to changes in their natural habitats is of paramount importance particularly in respect to environmental stressors, such as pollution or climate. Computational models integrating the multi-level molecular responses with organism physiology are likely to be indispensable tools to address this challenge. However, because of the difficulties in acquiring and integrating data from non-model species and because of the intrinsic complexity of field studies, such an approach has not yet been attempted. Here we describe the first example of a global network reconstruction linking transcriptional and metabolic responses to physiology in the flatfish, European flounder, a species currently used to monitor coastal waters around Northern Europe. The model we developed has revealed a remarkable similarity between network modules predictive of chemical exposure in the environment and pathways involved in relevant aspects of human pathophysiology. Generally, the approach we have pioneered has important implications as it can be applied to model molecular mechanisms of compensatory adaptation to a wide range of scenarios in wild populations.
Modelling the responses and compensatory adaptations of living organisms to a changing environment is extremely important both in terms of scientific understanding and for its potential impact on global health. Although computational modelling of ecological systems has been utilised in ecotoxicology, the application of systems biology approaches to non-model organisms in general presents formidable difficulties, partly due to limited sequence information for environmentally relevant sentinel species. Moreover, the number of samples and the depth of information available are often limited and there may be a lack of truly relevant physiological endpoints. Thus, omics have proven effective in finding responses of aquatic organisms to model toxicants in laboratory-based experiments
Relatively few omics studies have focussed upon the ecotoxicology of environmentally sampled fish
The study integrated measurements representing a broad spectrum of samples characterized using transcriptomics, metabolomics, conventional biomarkers and analysis of chemicals in sediments from the sampling sites. Previous studies have shown both anthropogenic contamination and higher prevalence of pre-neoplastic and neoplastic lesions in flounder from the Elbe estuary
The networks we identified demonstrate a remarkable parallel between human liver carcinogenesis and environmental effects on fish liver as well as revealing potentially novel adaptation mechanisms. The broader application of network biology approaches to other non-model species sampled from the environment is therefore likely to profoundly change our understanding of how living systems are likely to adapt to complex environments.
An important assumption in many eco-toxicology studies is that the molecular states of organisms reflect their biological responses to complex chemical mixtures present within that environment. Indirect evidence suggests that this hypothesis may be correct. For example, consistent with previous studies
A. Fish Measurements | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
Length (cm) |
|
18.56+/−4.8 |
|
|
15.98+/−1.7 |
|
|
Weight (g) |
|
90.33+/−76.31 |
|
|
73.16+/−22.23 |
|
|
Condition Factor K |
|
|
0.79+/−0.07 | 0.82+/−0.07 |
|
|
|
Liver weight (g) |
|
1.53+/−1.33 |
|
|
1.05+/−0.38 |
|
1.15+/−0.75 |
HSI |
|
|
|
|
|
1.01+/−0.3 | 1.1+/−0.48 |
Gonad weight (g) |
nd | nd | nd | nd | 0.15+/−0.08 |
|
|
GSI |
nd | nd | nd | nd | 0.21+/−0.1 |
|
|
B. Sediment Metals | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
Al (%) | nd | 5.81 | 3.90 | 1.62 | 2.8 | 5.1 | 2.6 |
As (mg/kg) |
|
|
|
|
|
|
|
Cd (mg/kg) | <0.02 |
|
|
0.08 |
|
|
|
Cr (mg/kg) | 7.00 |
|
|
|
|
|
|
Cu (mg/kg) | 5.00 |
|
|
|
|
|
|
Fe (%) | nd | 3.33 | 2.43 | 4.12 | 3.1 | 4.8 | 2.9 |
Hg (mg/kg) | <0.01 |
|
|
0.02 |
|
|
|
Li (mg/kg) | nd | 63.98 | 34.80 | 16.60 | 36.3 | 57.4 | 30.1 |
Mn (mg/kg) | nd | 575.80 | 826.40 | 350.00 | 2009 | 1166 | 1388 |
Ni (mg/kg) |
|
|
|
|
|
|
|
Pb (mg/kg) |
|
|
|
|
|
|
|
Zn (mg/kg) |
|
|
|
33.30 |
|
|
|
C. Fish Liver Metals(mg/kg wet wt) | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
As | 7.5 | 2 | 12 | nd | 3.98 | 1.65 | 0.68 |
Cd | 0.31 | 0 | 0.19 | 0.161 | 0.1 | 0.07 | 0.16 |
Cr | 0 | 0.23 | 0 | nd | 1.2 | 0.35 | 0.33 |
Cu | 26 | 8 | 27 | nd | nd | 24.9 | nd |
Fe | 262 | 239 | 220 | nd | nd | nd | nd |
Hg | 0.1 | 0.02 | 0.73 | 0.009 | nd | 0.05 | nd |
Ni | 0.17 | 0.33 | 0.2 | nd | nd | nd | nd |
Pb | 0 | 0.35 | 0.94 | 0.53 | 0.1 | 0.34 | 0.1 |
Se | 2.6 | 2 | 2.5 | nd | nd | nd | nd |
Zn | 59 | 46 | 57 | nd | nd | nd | nd |
D. Sediment PAHs (mg/kg dry wt) | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
Anthracene | 1.4 |
|
|
<10 |
|
|
|
Benzo[a]anthracene | 6.0 |
|
|
3.1 |
|
31.0 |
|
Benzo[a]pyrene | 4.2 |
|
|
2.3 |
|
39.0 | 14.0 |
Benzo[ghi]perylene | 5.3 |
|
|
<10 |
|
57.0 |
|
Chrysene/Triphenylene | 3.3 |
|
|
3.6 |
|
30.0 | 51.0 |
Fluoranthrene | 1.5 |
|
|
4.2 |
|
59.0 | 77.0 |
Indeno[123-cd]pyrene | 4.3 |
|
|
<10 |
|
61.0 | 35.0 |
Napthalene | 6.0 |
|
nd | 18.0 |
|
30.0 | 7.0 |
Phenanthrene | 10.0 |
|
|
11.0 |
|
42.0 | 8.0 |
Pyrene | 7.4 |
|
|
3.8 |
|
|
5.6 |
Sum of Sediment PAHs | 49.4 | 23562.3 | 13996.8 | <76 | 5225.0 | 523.1 | 783.6 |
E. Fish Liver PAHs (mg/kg wet wt) | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
Acenaphthylene | nd | 12.7 | 4.9 | nd | nd | nd | nd |
acenaphthene | 1.59 | 150.7 | 17.5 | nd | nd | nd | nd |
Anthracene | nd | 13.5 | 4.3 | nd | nd | nd | nd |
benzo[a]anthracene | nd | 12.0 | 1.7 | nd | nd | nd | nd |
C1-Napthalene | 11.41 | 144.6 | 102.7 | nd | nd | nd | nd |
C1-Phenanthrene/anthracene | nd | 79.1 | 0.0 | nd | nd | nd | nd |
C2N-Napthalene | 30.22 | 217.8 | 221.8 | nd | nd | nd | nd |
C3N-Napthalene | 31.97 | 292.0 | 308.3 | nd | nd | nd | nd |
Chrysene | nd | 7.3 | 8.8 | nd | nd | nd | nd |
Fluoroanthrene | 1.65 | 43.0 | 4.6 | nd | nd | nd | nd |
Fluorene | 3.63 | 49.0 | 13.6 | nd | nd | nd | nd |
Napthalene | 1.23 | 108.0 | 11.0 | nd | nd | nd | nd |
Phenanthrene | 5.07 | 92.0 | 12.0 | nd | nd | nd | nd |
Pyrene | nd | 20.0 | 4.5 | nd | nd | nd | nd |
Sum of PAHs | 86.76 | 1241.77 | 715.71 | nd | nd | nd | nd |
F. Sediment PCBs (mg/kg dry wt) | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
CB28 | 0.02 |
|
|
<0.1 | <0.7 | <0.2 | <0.6 |
CB52 | 0.01 | 0.95 |
|
<0.1 | 0.95 | 0.3 | <0.4 |
CB101 | nd |
|
|
<0.1 | 0.80 | 0.4 | <0.6 |
CB118 | 0.00 |
|
|
<0.1 | nd | 0.5 | <0.1 |
CB138 | 0.01 |
|
|
<0.1 |
|
0.3 |
|
CB153 | 0.01 |
|
|
<0.1 |
|
0.2 | 0.99 |
CB180 | 0.00 | 0.60 |
|
<0.1 | <0.75 | 1 | 0.63 |
Sum of ICES 7 Sediment PCBs | 0.05 |
|
|
<0.7 |
|
|
|
Organic Carbon (%) | nd | 4.48 | 1.56 | 0.1 | 2.90 | 1.9 | 2.90 |
G. Fish Liver PCBs (mg/kg wet wt) | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
CB28 | 0.016 | nd | 0.015 | 0.005 | 0.037 | 0.005 | 0.021 |
CB52 | 0.013 | 0.009 | 0.021 | 0.007 | 0.066 | 0.004 | 0.051 |
CB101 | nd | nd | 0.11 | 0.011 | 0.037 | 0.008 | 0.009 |
CB118 | 0.004 | 0.011 | 0.033 | 0.01 | 0.081 | 0.007 | 0.007 |
CB138 | 0.009 | 0.019 | 0.045 | 0.02 | 0.257 | 0.01 | 0.01 |
CB153 | 0.011 | 0.025 | 0.065 | 0.024 | 0.374 | 0.18 | 0.291 |
CB180 | 0.004 | 0.015 | 0.027 | 0.01 | 0.165 | 0.01 | 0.241 |
Sum of ICES 7 Fish liver PCBs | 0.057 | 0.079 | 0.316 | 0.090 | 1.017 | 0.224 | 0.630 |
H. Histopathology | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
Lymphocystis (%) | 0 | 0 | 0 | 8 | nd | nd | nd |
Skin ulcer (%) | 5 | 0 | 0 | 0 | nd | nd | nd |
Liver nodules (%) | 0 | 0 | 6 | 0 | nd | nd | nd |
Multiple liver nodules (%) | 0 | 0 | 6 | 0 | nd | nd | nd |
75 | 13 | 56 | 88 | nd | nd | nd | |
70 | 6 | 56 | 79 | nd | nd | nd | |
5 | 44 | 28 | 4 | nd | nd | nd | |
5 | 0 | 6 | 8 | nd | nd | nd | |
Zonal differences on liver (%) | 0 | 6 | 6 | 0 | nd | nd | nd |
Phospholipoidosis (%) | 0 | 6 | 28 | 4 | nd | nd | nd |
Fibrillar Inclusions (%) | 0 | 31 | 50 | 4 | nd | nd | nd |
H&N pleomorphism (%) | 0 | 0 | 28 | 0 | nd | nd | nd |
Hydropic degeneration (%) | 0 | 0 | 6 | 4 | nd | nd | nd |
Clear-cell FCA (%) | 0 | 0 | 6 | 0 | nd | nd | nd |
Vacuolated FCA (%) | 5 | 0 | 11 | 0 | nd | nd | nd |
Eosinophillic FCA (%) | 5 | 0 | 28 | 21 | nd | nd | nd |
Basophillic FCA (%) | 0 | 0 | 6 | 0 | nd | nd | nd |
Hepatocellular adenoma (%) | 0 | 0 | 6 | 0 | nd | nd | nd |
Hepatocellular carcinoma (%) | 0 | 0 | 11 | 0 | nd | nd | nd |
Coagulative necrosis (%) | 10 | 19 | 17 | 8 | nd | nd | nd |
Apoptosis (%) | 0 | 0 | 6 | 0 | nd | nd | nd |
Lipoidosis (%) | 10 | 0 | 11 | 0 | nd | nd | nd |
Melanomacrophage centre (%) | 10 | 19 | 67 | 54 | nd | nd | nd |
Lymphocytic/monocytic infiltration (%) | 10 | 0 | 28 | 21 | nd | nd | nd |
Granuloma (%) | 0 | 6 | 0 | 0 | nd | nd | nd |
Regeneration (%) | 5 | 19 | 22 | 13 | nd | nd | nd |
Any liver abnormalites detected (%) | 40 | 75 | 89 | 67 | nd | nd | nd |
I. Biomarkers | Alde | Tyne | Mersey | Morecambe | Brunsbuttel | Helgoland | Cuxhaven |
VTG (mg/ml) | 0.03+/−0.04 | 0.07+/−0.11 | 87.55+/−369.97 |
|
|
nd | nd |
GR (nmol/mg) |
29.18+/−25.66 |
|
21.98+/−10.46 | 18.03+/−6.19 | 18.8+/−11.45 | nd | nd |
GST (umol/mg) |
|
|
0.98+/−0.39 | 0.78+/−0.29 |
|
nd | nd |
EROD (pmol/mg) |
21.6+/−17.3 | 161.37+/−230.29 |
|
|
|
nd | nd |
MT (ug/mg) |
2.49+/−1.52 | 3.1+/−1.36 |
|
2.24+/−2.15 |
|
nd | nd |
*significant by ANOVA at P<0.05.
Brunsbuttel | FDR | Mersey | FDR |
Perfluorooctane sulfonic acid* | 4.02E-03 | Lindane | 6.79E-02 |
Systhane | 6.42E-03 | Systhane | 6.79E-02 |
Chlorine | 6.92E-03 | Fluconazole | 7.15E-02 |
Endosulfan* | 1.15E-02 | Phenobarbital | 7.15E-02 |
Potassium dichromate* | 2.60E-02 | Dimethyl sulfoxide | 7.36E-02 |
2,4,5,2′,4′,5′-Hexachlorobiphenyl* | 3.90E-02 | Flavonoids | 7.36E-02 |
Chromium* | 3.92E-02 | Polyphenols | 7.49E-02 |
Polychlorinated biphenyls* | 3.92E-02 | Tobacco smoke pollution | 7.89E-02 |
Dieldrin* | 9.57E-02 | Ethinyl-estradiol* | 8.68E-02 |
Ethinyl-estradiol* | 9.90E-02 | 2,4,5,2′,4′,5′-Hexachlorobiphenyl* | 9.80E-02 |
Cuxhaven | FDR | Morecambe Bay | FDR |
Melatonin | 2.52E-02 | Paraquat | 1.12E-03 |
beta-Naphthoflavone | 2.54E-02 | Astemizole | 6.81E-02 |
Catechin | 2.54E-02 | Sodium arsenite* | 6.81E-02 |
Chromium* | 3.10E-02 | Cycloheximide | 7.13E-02 |
Epicatechin gallate | 3.65E-02 | Propiconazole | 7.13E-02 |
Nickel sulfate* | 4.77E-02 | Isoflavones | 7.20E-02 |
Lead* | 6.93E-02 | Corticosterone | 7.42E-02 |
Zinc* | 6.93E-02 | Estradiol* | 7.42E-02 |
Benzo(a)pyrene-7,8-dione* | 6.93E-02 | Benzo(a)pyrene* | 8.16E-02 |
Polychlorinated biphenyls* | 8.30E-02 | Nickel chloride* | 9.45E-02 |
Helgoland | FDR | Tyne | FDR |
Epicatechin gallate | 3.78E-02 | Melatonin | 1.30E-02 |
Gallocatechol | 3.78E-02 | Paraquat | 3.32E-02 |
Permethrin | 3.78E-02 | Epigallocatechin gallate | 4.03E-02 |
Ethinyl Estradiol | 4.08E-02 | Astemizole | 4.82E-02 |
Astemizole | 4.39E-02 | Catechin | 4.97E-02 |
Melatonin | 4.48E-02 | Dieldrin | 5.11E-02 |
Zinc* | 4.57E-02 | Epicatechin gallate | 5.64E-02 |
Nickel sulfate* | 6.60E-02 | Estrone | 5.64E-02 |
Polychlorinated biphenyls* | 7.15E-02 | Sodium arsenite* | 8.18E-02 |
Manganese* | 9.73E-02 | Polychlorinated biphenyls* | 8.82E-02 |
CTD gene-chemical interactions statistically significantly (FDR<0.1) associated with gene expression changes in flounders from each sampling site in comparison with Alde fish. The most significant associations are shown together with those supported by chemistry and other data (starred), full data are shown in
|
|
|
Protein Ubiquitination Pathway | 3.55E-04 | |
Glutathione Metabolism | 5.66E-04 | |
NRF2-mediated Oxidative Stress Response | 6.73E-04 | Phospholipidosis |
Lysine Degradation | 8.01E-04 | Intersex |
Acute Phase Response Signaling | 1.77E-03 | Vacuolar FCA |
Granzyme B Signaling | 1.03E-02 | |
Glycine, Serine and Threonine Metabolism | 2.00E-02 | |
Methane Metabolism | 2.10E-02 | |
Glycolysis/Gluconeogenesis | 2.01E-02 | |
Histidine Metabolism | 2.01E-02 | |
Pyruvate Metabolism | 2.01E-02 | |
Metabolism of Xenobiotics by Cytochrome P450 | 2.36E-02 | Phospholipidosis |
Regulation of eIF4 and p70S6K Signaling | 2.98E-02 | |
Oxidative Phosphorylation | 3.27E-02 | Phospholipidosis |
Aryl Hydrocarbon Receptor Signaling | 3.19E-02 | |
Bile Acid Biosynthesis | ns | |
Mitochondrial Dysfunction | ns | Intersex, Phospholipidosis |
|
|
|
Liver Proliferation | 2.11E-02 | |
Hepatocellular Carcinoma | 2.11E-02 | |
Liver Cholestsasis | 2.11E-02 | Phospholipidosis |
Liver Steatohepatitis | 2.95E-02 | Phospholipidosis |
Liver Cirrhosis | ns |
|
|
|
Oxidative Stress Response Mediated by Nrf2 | 2.95E-03 | Phospholipidosis |
Oxidative Stress | 1.26E-02 | Intersex |
Xenobiotic metabolism | 1.26E-02 | Phospholipidosis, Vacuolar FCA |
Mitochondrial Dysfunction | ns | Intersex, Phospholipidosis |
Negative Acute Phase Response Proteins | ns | Intersex. Vacuolar FCA |
Fatty Acid Metabolism | ns | Phospholipidosis |
Significant Ingenuity pathways and toxicology functions and lists (FDR<0.05) among genes and metabolites significantly differentially expressed between sites (FDR<0.05) and between samples showing presence or absence of liver histologies.
As there were clear relationships between geographical location, chemical exposure and molecular profiles of flounder livers, we proceeded to reconstruct a network model representing the relationships between transcriptomic and metabolomic data, morphological measurements, protein biomarkers and microsatellite markers (
A schematic representation of the analysis workflow and the network inference methodology.
Inspection of the resulting network (
Modules (consisting of transcripts, metabolite bins and morphological measurements) are numbered; sizes are proportional to the number of nodes within each module. Red colouring is proportional to the percentage of each module that consisted of metabolite bins. Modules whose seeds were transcripts are shown as circles; metabolites as triangles; morphological measurements as squares. Annotation terms significantly enriched (FDR<0.05 by DAVID) in areas of the network are shown (* = not statistically significant).
Different areas of the inferred network (
Each individual network module was tested for its ability to predict geographic sampling sites (
Modules in red predict membership of at least one environmental sampling site by GALGO with a sensitivity and specificity of >70%. The number of sites that each module could predict is illustrated by shading. Modules are split into a major group (A) and a minor group (B).
A: Modules coloured red predict parasite infections using GALGO with a sensitivity and specificity of >70%. Clustering of profiles is shown. B: Modules coloured red significantly overlap (Fisher's Exact Test FDR<0.05) with transcripts significantly altering (ANOVA FDR<0.05, 2-fold change at each timepoint) in response to laboratory treatments of flounders with lindane over a 16-day time course.
Modules that were predictive of parasitic copepod infection by
We have previously shown that there is a strong link between laboratory exposure to individual chemicals and flounder hepatic gene expression
The results for all treatments are illustrated graphically in
Having defined network modules predictive of geographical location, Ingenuity Pathway Analysis was used to elucidate the detailed structure of molecular pathways and their potential association with specific signatures of liver pathology. We performed these analyses under the hypothesis that the underlying response to chemical exposure would be consistent with what is known of human liver molecular pathophysiology. It was therefore expected that significant associations between the modules defined by our analysis and networks stored in the Ingenuity database would be informative of the underlying molecular mechanisms. We indeed observed a remarkable overlap between modules predictive of geographical location and modules containing genes whose transcriptional profile has been previously associated with liver fibrosis, cirrhosis and hepatocellular carcinoma in mammals.
Modules whose component genes related to hepatotoxicity are shown in
Modules predictive of sampling site are shown in red. Those associated with hepatocellular carcinoma are indicated by a solid line, those associated with liver cholestasis and liver fibrosis are indicated by dashed lines. Annotation terms were derived from Ingenuity.
|
|
Bile Acid Biosynthesis | 5 |
Molecular Transport | 5 |
Cellular Compromise | 5 |
5 | |
Amino Acid Metabolism | 4 |
Cell Morphology | 4 |
4 | |
Gly,Ser,Thr Metabolism | 3 |
Nitrogen Metabolism | 3 |
Reproductive Disorder | 3 |
Hepatocellular Carcinoma | 3 |
Liver Cirrhosis | 3 |
3 | |
Cd Anova | 3 |
Cd d01 | 3 |
tBHP Anova | 3 |
PFOA d04 | 3 |
E2 Anova | 3 |
Inflammatory Disease | 2 |
Infectious Disease | 2 |
Nucleic Acid Metabolism | 2 |
Citrate Cycle | 2 |
Aroclor Anova | 2 |
Huntingtin Regulation | 2 |
Insulin Regulation | 2 |
Myc Regulation | 2 |
HNF4A Regulation | 2 |
Lindane Anova | 2 |
PFOA Anova | 2 |
3MC Anova | 2 |
Fatty Acid Metabolism | 2 |
The number of algorithms clustering a given term with environmental site prediction is shown, out of a maximum of 5.
The models we have developed are a high level representation of the molecular network's underlying response to environmental exposure. In order to generate specific hypotheses on the molecular pathways modulated during compensatory adaptation and toxicity further in-depth analyses of the specific interactions between genes and metabolites were performed. In this context, we combined the genes and metabolites represented in each group of predictive modules (Groups A and B in
A– Most significant Ingenuity network derived from the union of modules that were highly predictive of sampling sites (5 or more), shown as major area A in
This is the first network level analysis of an environmental study integrating multilevel omic datasets. We discovered that the overall molecular state of the flounder liver (transcriptomics and metabolomics) is representative of the chemical contaminant burden of the sediments. Network reconstruction showed that the interface between transcriptional and metabolic network domains is linked to fish morphometric indices and is predictive of environmental exposure. In-depth analyses of predictive networks have identified putative novel pathways representative of responses to exposure. This approach provides a framework both for prediction of chemical pollutants in complex mixtures and for prediction of the health outcomes for exposed animals.
The chemical exposures predicted from CTD interactions were partly confirmed by chemical data (
These results support the use of a knowledge-based approach to infer chemical exposure profiles from molecular responses and validate the underlying assumptions in the study. Predictions from interrogation of the CTD database (
The development of a modular network, representing the integration between molecular and physiological readouts, provided us with an interpretive framework to analyse the complex molecular signatures linked to exposure. One of the most interesting findings is that the modules that predict environmental exposure with greatest accuracy represent the interface between metabolite and transcriptional networks and link to higher level indicators of fish health, such as condition factor and hepatosomatic index (
Consistent with this observation, network modules at the interface between metabolite and transcriptional networks were also differentially regulated in response to single chemical laboratory exposures. It should be borne in mind that the environmentally sampled fish have been chronically exposed to pollutants, and that chronic exposure can result in different responses than acute exposure
The characterisation of transcripts and metabolites that differed between sites was undertaken to provide insights into the molecular mechanisms that they describe, and to inform on the potential health outcomes for the fish. Canonical pathways that contributed to these differences included those relevant to metabolism of toxicants; AhR signalling, metabolism of xenobiotics by cytochromes P450, the NRF2-mediated oxidative stress response, glutathione metabolism and bile acid bioysnthesis (
This change in metabolic state and gene expression could be viewed as a successful compensatory response to toxicants and thus of little concern for the health of individual fish and these fish populations. Further examination of the annotation of transcripts and metabolites differing between sites implied that this hypothesis was false. As illustrated in
Ingenuity networks, based on mammalian interaction data, permitted more detailed biological characterisation of the site-associated modules. Complete pathways were not recapitulated by these analyses, as only a minority of the transcripts and metabolites from flounder liver were examined. Nevertheless, the analyses highlighted important processes and inferred key regulators. Here the most significant network derived from site-predictive modules is discussed in detail and additional networks are discussed in terms of their key inferred regulators.
The most striking finding from the Ingenuity analyses was the co-ordinated repression of proteasomal subunit genes at the Brunsbuttel site (
Relationship between the AhR pathway and proteasome, generated within Ingenuity, coloured deep red or green for changes exceeding 2-fold up and down respectively between Brunsbuttel and Alde fish, and light red and green for those less than 2-fold.
From the Ingenuity networks a number of key regulatory molecules were inferred. These included insulin (
Insulin, in fish as in mammals, is a key hormonal regulator of energy, glucose and lipid metabolism, all pathways that were identified as affected by sampling site. By the Ingenuity networks it was linked to protein kinases, metabolites (including glucose and lactate) and the glucose transporter SLC2A4. The most obvious explanation for changes in insulin and related parameters would be differences in diet between fish from different sites. Amino-acid levels are more important regulators of insulin in carnivorous fish such as the flounder than sugars
PDGFBB is the dimeric form of platelet derived growth factor beta (PDGF-B). Notably, PDGF-B over-expressing mice spontaneously developed liver fibrosis
Angiotensinogen (AGT) is the precursor of angiotensin and was found to be repressed at all sites in comparison to the Alde reference site (
VEGF, TGF beta, TNF alpha, PDGF and AGT are all intimately related to the progression of fibrosis to cirrhosis and hepatocellular carcinoma in mammals. These molecules were all highlighted as important regulators of the differences between molecular profiles of flounder livers from different sampling sites using an unbiased approach combining network inference and predictive algorithms.
A combination of omics, multiple biomarkers and bioinformatics were used to identify and characterise hepatic molecular changes between fish sampled from several environmental sites. Based on these data, parasite infection, fish morphology and genetics do contribute to the differences between sites, but do not explain the majority of changes seen. For example, within-site tests showed that morphometric parameters and parasite infections could be significantly associated only with a small proportion (<3%) of the gene expression differences between sites (
The different methodologies employed displayed different strengths and weaknesses. Histopathology was a good guide to broad levels of pollution effect, but provided little information upon the nature of the contaminant profile. Protein biomarkers and enzyme activities were useful for categorising sites by major classes of toxicant, but gave little information on the potential health outcomes. 1H NMR metabolomics showed low technical variability, and metabolite profiles alone were more predictive of sampling site than gene expression profiles alone, however the annotation of metabolites is not yet well advanced, limiting the functional information currently available. Transcriptomics exhibited higher variability than metabolomics, but was more informative due to better annotation. Overall the methodologies were highly complementary, allowing analyses that would be impossible if one were limited to a single technique.
The gene expression signatures associated with fish from each sampling site were used to predict the presence of chemical contaminants using the CTD gene expression-chemical interaction database. Mixture effects, other environmental influences and the similarity of certain stressors, such as the metals, might be expected to confound this approach. Additionally the incomplete nature of the flounder microarray and the CTD database and the limited numbers of samples for certain sites, which is a common issue in field studies, reduce the potential of this analysis. Therefore we did not expect to predict all environmental contaminants by this method. While this approach was useful with the current dataset, it may be expected to improve in future as both the CTD database and transcriptomic data become more comprehensive.
Data integration and network analyses were essential; both to predicting health outcomes and to identifying and examining affected biological pathways. They allowed visualisation of the highly complex dataset and facilitated comparison of the effects of different stimuli upon the model system. Modules associated with specific parameters could then be examined in detail, utilising interaction databases (Ingenuity) for further characterisation. Detailed examination of these networks illustrated the changes detected by broader classification of modules by annotation terms. In addition to potential interactions with diet and salinity, the majority of networks contained key regulators of inflammation, hepatic fibrosis and hepatocellular carcinoma. Therefore we propose that network biology approaches can lead to the identification of health impacts of environmental pollutants upon non-model organisms.
The molecular differences between reference and contaminated sampling sites were associated with carcinogenesis, and this outcome is supported by previous histopathology
The sampling sites employed in this study were: In UK waters; on the Irish Sea, the Mersey estuary, at Eastham Sands, Liverpool (lat 53°19N, long 2°55W) and Morecambe Bay (lat 54°10N, long 2°58W); on the North Sea, the Alde estuary, Suffolk (lat 52°95N, long 01°33E) and the Tyne estuary at Howdon, Tyne and Wear (lat 54°57N, long 1°38W): In North Sea waters off Schleswig-Holstein, Germany; the Elbe estuary at Cuxhaven (lat 53°53N, long 08°15–19E) and Brunsbuttel (lat53°52N, long 09°09–10E) and off Helgoland (lat 54°06N, long07°15–08°00E). Adult European flounders (
Chemical determinations were carried out on sediment samples and independent sets of flounder liver samples from the same samplings by Cefas and Deutsches Ozeanographiches Datenzentrum, Germany and submitted to the International Council for the Exploration of the Sea (ICES), Copenhagen, Denmark as part of the national marine monitoring programmes. UK data was analysed from that collected as part of the Clean Safe Seas Environmental Monitoring Programme (CSEMP) and archived in the UK's Marine Environment Monitoring and Assessment National database (MERMAN). For sediment; metal concentrations (Al, As, Cd, Cr, Cu, Fe, Hg, Li, Mn, Ni, Pb and Zn); polycyclic aromatic hydrocarbons (PAHs) (anthracene, benzo[a]anthracene, benzo[a]pyrene, benzo[ghi]perylene, chrysene/triphenylene, fluoroanthrene, indo[123-c]pyrene, naphthalene, phenanthrene and pyrene); total organic carbon and polychlorinated biphenyls (PCBs) (congeners CB28, 52, 101, 118, 138, 153 and 180) were determined and the sum of PAHs and the sum of ICES 7 priority PCBs calculated for all sites. For flounder livers; metals (As, Cd, Cr, Cu, Fe, Hg, Ni, Pb, Se and Zn) and PAHs (acenaphthylene, acenaphthene, benzo[a]anthracene, C1-, C2- and C3- naphthalene, C1-phenanthrene/anthracene, chrysene, fluoroanthrene, fluorene, naphthalene, phenanthrene and sum of PAHs) were determined for Alde, Tyne and Mersey fish, with partial metal concentration data for Morecambe Bay, Helgoland, Cuxhaven and Brunsbuttel samples. Polychlorinated biphenyls (PCBs) (congeners CB28, 52,101, 118, 138, 153, 180 and sum of ICES 7 PCBs) were determined for liver samples from all sites. Data are available from the Merman database (
UK flounders were examined for external lesions, liver gross appearance and parasite infection. Liver pathology was assessed according to the criteria of Feist
Plasma vitellogenin (VTG) concentrations (mg/ml) were determined by the method described by Kirby
Flounder fin-clip samples (n = 50) from all sites were surveyed for six neutral microsatellite markers (all polymorphic) and 13 detoxification gene-associated size variants within introns of flounder cytochrome P450 1A (CYP1A)
The GENIPOL flounder cDNA microarray has been described previously
For metabolomics, liver homogenate aliquots were further extracted individually using methanol/chloroform/water (2∶2∶1.8 final volumes)
Microarray data were filtered to remove spots where 20% or more of the data were undetectable over all samples and background-subtracted intensity values of 0 or below were set to 0.5. Data were log2 transformed, quantile normalised and de-noised by a) removing data where SD/mean was more than 0.9 and b) removing data where maximum–minimum was less than 1.5. Missing data were estimated using MetaGeneAlyse probabilistic principal components analysis (PCA) algorithm
Normalised combined microarray and metabolomic data were input to Genespring GX 7.3.1 (Agilent Technologies, Santa Clara, CA, USA). Statistically significantly changing genes were found by 1-way ANOVA with a multiple testing correction
Chemical-gene expression interactions were downloaded from the Comparative Toxicology Database (CTD)
The overall approach taken for network construction and analysis is shown in
The network was constructed from all measured variables, including transcript, metabolite, morphometric, protein biomarker and genetic data. Within the network each individual variable is described as a node. We selected 99 ‘hub’ nodes representing transcripts with known toxicological and regulatory relevance in order to identify the molecular network representing the interactions between these hubs and all the other nodes in the multi-level dataset. In addition, morphometric indices and metabolite peaks were also included in the list of hubs to represent the complexity of the metabolic networks, which, we reasoned are likely to closely influence liver physiology. The network inference methodology ARACNE
Subsequently the multivariate selection algorithm GALGO
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We thank S. Jondhale and A. Jones for assistance with transcriptomics, J. Byrne for metabolite identification and K. Dempsey (GCAL), V. Sabine (Stirling), K. Broeg, A. Koehler and colleagues (AWI, Bremerhaven) for provision of flounder samples.