From: Commentary: Novel strategies and new tools to curtail the health effects of pesticides
Omics | Identified health hazards | Identified harmful exposure | Predicted health outcomes |
---|---|---|---|
Transcriptomics RNA transcripts | ▪ Genotoxicity: In vitro transcriptional signatures classify agents as genotoxic or non-genotoxic in human lymphoblastoid TK6 cells [70] ▪ Endocrine disruption: A Gene Expression Biomarker Accurately Predicts Estrogen Receptor α Modulation in breast cancer MCF-7 cells [71] ▪ Toxicity point of departure: Rat liver transcriptional signatures in a short-term exposure can estimate a toxicity point of departure for longer-term effects [72] | ▪ Tobacco smoking: Smoking-related gene expression signatures can be detected in whole-blood [73] ▪ Metformin use: Transcriptome signatures identify metformin use and discriminate between metformin responders and non-responders, explaining variance in therapeutic efficacy [74] | ▪ Nonalcoholic fatty liver disease: Hepatic transcriptome signatures predict disease progression in patients with varying degrees of liver diseases [75] ▪ Cancer prognostic. A gene expression signature supports physicians' treatment decisions in a population with early breast cancer [76] ▪ Tumor tissue origin: Transcriptional signatures can help identify the tissue of origin for metastatic cancers [77] |
Metabolomics small molecules | ▪ Gut microbiome alterations: Caecum metabolites levels reflect shikimate pathway inhibition by the herbicide glyphosate in the rat gut [30] ▪ Improved toxicity predictions: Results of 90-day rat toxicity studies can be predicted using metabolome data of 28 day studies [78] ▪ In vivo toxicity from in vitro assays: Liver toxicity mechanisms can be predicted from metabolite profiles of HepG2 cells [79] | ▪ Pesticides: An exposure to complex pesticide mixtures induces modifications of metabolic fingerprints in pregnant women according to the intensity of agricultural cereal activities [80] ▪ Heavy metals: Urinary metabolite profiles could reflect arsenic internal dose-related biochemical alterations [81] ▪ DDE and HCB: Circulating levels of 16 metabolites related to lipid metabolism reflect p,p'-DDE and HCB exposure in humans [82] | ▪ All-cause mortality: A set of 14 metabolites act as predictors of long-term mortality in the circulation of 44,168 individuals [83] ▪ Breast cancer: Metabolites show potential as biomarkers for early diagnosis of breast cancer, predicting tumor size and hormone receptor expression [84] ▪ Neurodegeneration: Blood lipids identify antecedent memory impairment and can act as diagnostic tools for early neurodegeneration of preclinical Alzheimer's disease [85] |
Genomics genome sequence | ▪ Susceptibility to chemical toxicity: Response to chemical exposure in genetically heterogeneous zebrafish can help elucidate gene-environment interactions [86] | Not applicable | ▪ Variations in disease risks: Genome-wide polygenic scores can identify individuals at high risk for five common diseases [87] |
Metagenomics microbial communities | ▪ Microbiome drug metabolism. Interpersonal differences in drug metabolism can be identified by high-throughput genetic analyses of gut microbiomes [88] ▪ Melamine toxicity: Melamine-induced renal toxicity depends on the exact composition and metabolic activities of the gut microbiota [89] | ▪ Personalised responses to diet: The composition of the gut microbiome predicts personalised glycemic responses to food [90] ▪ Heavy metals: Rats exposed daily to arsenic, cadmium, cobalt, chromium, nickel display changes to microbiota composition which can help identifying exposures to specific heavy metals [91] | ▪ Cardiometabolic health: Gut microbiome composition is predictive for cardiometabolic blood markers in 1,098 deeply phenotyped individuals [92] ▪ Cirrhosis: Gut microbiome species can be a non-invasive diagnostic test for cirrhosis [93] |
Epigenomics DNA modifications and chromatin structure | ▪ Transgenerational inheritance: The study of epigenomes and chromatin accessibility informed on transgenerational inheritance after ancestral perinatal obesogen exposure [94] ▪ Tissue susceptibility: Epigenetic marks determine differential tissue susceptibility to tumorigenesis induced by 1,3-butadiene [95] | ▪ Maternal smoking: Epigenetic changes in response to maternal smoking in pregnancy persist into later childhood [96] ▪ Air pollution: DNA methylation reprogramming after prenatal exposure to air pollution was associated with markers of cardiovascular risk in childhood [97] | ▪ Death risk: Site-specific blood DNA methylation sites predict death risk in a longitudinal study of 12,300 individuals [98] ▪ Colorectal cancer: A panel of DNA methylation biomarkers in peripheral blood could predict colorectal cancer susceptibility [99] |
Proteomics Proteins and peptides | ▪ Sex differences: Proteomics was used to detect sex-related differences in effects of toxicants [100] ▪ Drug toxicity prediction: proteomic signatures associate with hepatocellular steatosis in rats [101] | ▪ Radiation injury: comparative proteomic allowed the discovery of new radiation biomarkers [102] ▪ Heavy metals: A panel of six proteins was proposed to serve as marker of occupational exposures to arsenic, cadmium, and lead [103] | ▪ Health and life span: A 76-protein proteomic age signature predicted chronic diseases and all-cause mortality in 997 individuals between 21 and 102 years of age [104] ▪ Cancer: Differentially expressed serum proteins could be used for early diagnosis and pathogenic investigation of Non-Hodgkin lymphoma [105] |