Gene Expression Profiling in Fish Toxicology: A Review

Assessing developmental and transcriptional effects of PM2.5 on zebrafish embryos

Investigating fine particulate matter (PM2.5) toxicity is crucial for health risk assessment and pollution control. This study explores the developmental toxicity of two PM2.5 sources: standard reference material 2786 (NIST, USA) and PM2.5 from Chakri Naruebodindra Medical Institute (CNMI, Thailand) located in the Bangkok Metropolitan area. Zebrafish embryos exposed to these samples exhibited embryonic mortality, with 50% lethal concentration (LC50) values of 1476 µg/mL for standard PM2.5 and 512 µg/mL for CNMI PM2.5. Morphological analysis revealed malformations, including pericardial and yolk sac edema, and blood clotting in both groups. Gene expression analysis highlighted source-specific effects. Standard PM2.5 downregulated sod1 and cat while upregulating gstp2. Inflammatory genes tnf-α and il-1b were upregulated, and nfkbi-αa was downregulated. Apoptosis-related genes bax, bcl-2, and casp3a were downregulated. CNMI PM2.5 consistently downregulated all examined genes. These findings underscore PM2.5 source variability's significance in biological system impact assessment, providing insights into pollutant-gene expression interactions. The study emphasizes the need for source-specific risk assessment and interventions to address PM2.5 exposure's health impacts effectively.

Toxicogenomic Analysis

The biological functions of a cell may change in response to exposure to toxic agents. Toxicogenomics employs the recent developments in genomics, transcriptomics, and proteomics to study how a chemical impacts gene/protein expression and cell functions. We describe a method for transcriptomic analysis by RNA sequencing based on Illumina HiSeq, NextSeq, or NovaSeq Systems followed by real-time qPCR validation. We also depict a method for proteomic analysis by "one-dimensional sodium dodecyl sulfate-polyacrylamide gel electrophoresis" (1D SDS-PAGE) and a sample preparation procedure for "liquid chromatography in tandem with mass spectrometry" (LC-MS/MS), and we present some generic points to consider during LC-MS/MS.

Environmental Enrichment Improved Learning and Memory, Increased Telencephalic Cell Proliferation, and Induced Differential Gene Expression in Colossoma macropomum

Fish use spatial cognition based on allocentric cues to navigate, but little is known about how environmental enrichment (EE) affects learning and memory in correlation with hematological changes or gene expression in the fish brain. Here we investigated these questions in Colossoma macropomum (Teleostei). Fish were housed for 192 days in either EE or in an impoverished environment (IE) aquarium. EE contained toys, natural plants, and a 12-h/day water stream for voluntary exercise, whereas IE had no toys, plants, or water stream. A third plus maze aquarium was used for spatial and object recognition tests. Compared with IE, the EE fish showed greater learning rates, body length, and body weight. After behavioral tests, whole brain tissue was taken, stored in RNA-later, and then homogenized for DNA sequencing after conversion of isolated RNA. To compare read mapping and gene expression profiles across libraries for neurotranscriptome differential expression, we mapped back RNA-seq reads to the C. macropomum de novo assembled transcriptome. The results showed significant differential behavior, cell counts and gene expression in EE and IE individuals. As compared with IE, we found a greater number of cells in the telencephalon of individuals maintained in EE but no significant difference in the tectum opticum, suggesting differential plasticity in these areas. A total of 107,669 transcripts were found that ultimately yielded 64 differentially expressed transcripts between IE and EE brains. Another group of adult fish growing in aquaculture conditions were either subjected to exercise using running water flow or maintained sedentary. Flow cytometry analysis of peripheral blood showed a significantly higher density of lymphocytes, and platelets but no significant differences in erythrocytes and granulocytes. Thus, under the influence of contrasting environments, our findings showed differential changes at the behavioral, cellular, and molecular levels. We propose that the differential expression of selected transcripts, number of telencephalic cell counts, learning and memory performance, and selective hematological cell changes may be part of Teleostei adaptive physiological responses triggered by EE visuospatial and somatomotor stimulation. Our findings suggest abundant differential gene expression changes depending on environment and provide a basis for exploring gene regulation mechanisms under EE in C. macropomum.

A statistical framework for applying RNA profiling to chemical hazard detection

Use of 'omics technologies in environmental science is expanding. However, application is mostly restricted to characterizing molecular steps leading from toxicant interaction with molecular receptors to apical endpoints in laboratory species. Use in environmental decision-making is limited, due to difficulty in elucidating mechanisms in sufficient detail to make quantitative outcome predictions in any single species or in extending predictions to aquatic communities. Here we introduce a mechanism-agnostic statistical approach, supplementing mechanistic investigation by allowing probabilistic outcome prediction even when understanding of molecular pathways is limited, and facilitating extrapolation from results in laboratory test species to predictions about aquatic communities. We use concepts familiar to environmental managers, supplemented with techniques employed for clinical interpretation of 'omics-based biomedical tests. We describe the framework in step-wise fashion, beginning with single test replicates of a single RNA variant, then extending to multi-gene RNA profiling, collections of test replicates, and integration of complementary data. In order to simplify the presentation, we focus on using RNA profiling for distinguishing presence versus absence of chemical hazards, but the principles discussed can be extended to other types of 'omics measurements, multi-class problems, and regression. We include a supplemental file demonstrating many of the concepts using the open source R statistical package.