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An integrative omics approach to unravel toxicity mechanisms of environmental chemicals: effects of a formulated herbicide.

ABSTRACT: The use of integrative molecular approaches can aid in a comprehensive understanding of the effects of toxicants at different levels of biological organization, also supporting risk assessment. The present study aims to unravel the toxicity mechanisms of a widely used herbicide to the arthropod Folsomia candida exposed in a natural soil, by linking effects on reproduction, proteomics and genome-wide gene expression. The EC50 effects on reproduction over 4 weeks was 4.63?mg glyphosate/kg of soil. The formulation included a polyethoxylated tallowamine as an adjuvant, which at 50% effect on reproduction had an estimated concentration of 0.87-1.49?mg/kg of soil. No effects were observed on survival and reproduction when using the isolated active substance, pointing the toxicity of the formulated product to the co-formulant instead of the active ingredient, glyphosate. RNA sequencing and shotgun proteomics were applied to assess differential transcript and protein expressions between exposed and control organisms in time, respectively. Specific functional categories at protein and transcriptome levels were concordant with each other, despite overall limited correlations between datasets. The exposure to this formulation affected normal cellular respiration and lipid metabolism, inducing oxidative stress and leading to impairment in biological life cycle mechanisms such as molting and reproduction.

SUBMITTER: Simoes T

PROVIDER: S-EPMC6063884 | biostudies-literature | 2018 Jul

REPOSITORIES: biostudies-literature

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An integrative omics approach to unravel toxicity mechanisms of environmental chemicals: effects of a formulated herbicide.

Simões Tiago T Novais Sara C SC Natal-da-Luz Tiago T Devreese Bart B de Boer Tjalf T Roelofs Dick D Sousa José P JP van Straalen Nico M NM Lemos Marco F L MFL

Scientific reports 20180727 1

The use of integrative molecular approaches can aid in a comprehensive understanding of the effects of toxicants at different levels of biological organization, also supporting risk assessment. The present study aims to unravel the toxicity mechanisms of a widely used herbicide to the arthropod Folsomia candida exposed in a natural soil, by linking effects on reproduction, proteomics and genome-wide gene expression. The EC50 effects on reproduction over 4 weeks was 4.63 mg glyphosate/kg of soil. ...[more]

PMID: 30054531

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Project description:BackgroundGenotype-phenotype analyses of rare diseases often suffer from a lack of power, due to small sample size, which makes identifying significant associations difficult. Sinusoidal obstruction syndrome (SOS) of the liver is a rare but life-threatening complication of hematopoietic stem cell transplantation (HSCT). The alkylating agent busulfan is commonly used in HSCT and known to trigger SOS. We developed a novel pipeline to identify genetic determinants in rare diseases by combining in vitro information with clinical whole-exome sequencing (WES) data and applied it in SOS patients and controls.MethodsFirst, we analysed differential gene expression in six lymphoblastoid cell lines (LCLs) before and after incubation with busulfan. Second, we used WES data from 87 HSCT patients and estimated the association with SOS at the SNP and the gene levels. We then combined the results of the expression and the association analyses into an association statistic at the gene level. We used an over-representation analysis to functionally characterize the genes that were associated with a significant combined test statistic.ResultsAfter treatment of LCLs with busulfan, 1708 genes were significantly up-, and 1385 down-regulated. The combination of the expression experiment and the association analysis of WES data into a single test statistic revealed 35 genes associated with the outcome. These genes are involved in various biological functions and processes, such as "Cell growth and death", "Signalling molecules and interaction", "Cancer", and "Infectious disease".ConclusionsThis novel data analysis pipeline integrates two independent omics datasets and increases statistical power for identifying genotype-phenotype associations. The analysis of the transcriptomics profile of cell lines treated with busulfan and WES data from HSCT patients allowed us to identify potential genetic contributors to SOS. Our pipeline could be useful for identifying genetic contributors to other rare diseases where limited power renders genome-wide analyses unpromising.Trial registrationFor the clinical dataset: Clinicaltrials.gov: NCT01257854. https://clinicaltrials.gov/ct2/history/NCT01257854.

Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

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An integrative omics approach to unravel toxicity mechanisms of environmental chemicals: effects of a formulated herbicide.

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An integrative omics approach to unravel toxicity mechanisms of environmental chemicals: effects of a formulated herbicide.

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