Table 1 Multiple Omic layers are available to serve both as an exposure and health outcome biomarkers. Other omics methods such as glycomics or ncRNomics have been used to understand human health but they have not been extensively used in toxicology
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] |