Human studies also suggest that a low serotonin turnover rate is associated with anxious behavior and perceptions of insecurity and threat, which may secondarily contribute to impulsive aggression Praag, ; ;. Serotonin function may contribute to negative mood states via modulation of amygdala responses to emotionally negative stimuli. Indeed, in healthy human volunteers amygdala activation elicited by aversive stimuli was modulated by 5-HTT genotype, with carriers of a short allele of the 5-HTT gene displaying more amygdala activation; healthy volunteers carrying this genotype may be able to maintain limited levels of negative mood states because aversive stimuli also elicit increased activation of the medial prefrontal cortex, which can contribute to emotion regulation ; ; Figure 4.
In human studies, 5-HTT s -carriers who had experienced a high number of traumatizing events displayed a higher risk of developing negative mood states such as major depression. Also in primate studies, 5HTT s -carriers were specifically vulnerable to social isolation stress, which induced significant reductions in the central serotonin turnover rate. Gender-specific effects on the interaction between early social isolation stress, serotonin transporter genotype and HPA axis were implicated in studies among non-human primates. Female and male rhesus monkeys were either raised without their mothers by their peers peer-raised, PR , or grew up with their mothers mother-raised, MR.
When subjected to complete social isolation, female primates displayed an increase in cortisol concentrations in s -carriers compared with ll-homozygotes only if they were previously raised without their mothers PR — i. Altogether, it appears that 5-HTT genotype modulates stress exposure and stress hormone effects on 5-HTT expression and function. Specifically, stress-related alterations in serotonin turnover and transporter availability may interfere with the 5-HTT genotype-driven interaction between the medial prefrontal cortex and the amygdala, thus impairing prefrontal emotional control.
Studies in humans and non-human primates suggest that gender effects may influence whether serotonin dysfunction results in negative mood states or impulsive aggression — an observation that is particularly relevant for clinical depression, which is more frequently observed in women than in men ;. Understanding gene—environment interactions is a critical aspect of human health risk assessment. Traditionally, genome-wide association studies GWAS and candidate gene association studies are the main approaches to understand gene—environment interactions.
However, GWAS are expensive, and the candidate gene association studies are often limited by incomplete understanding of mechanisms Shen et al. Here, we review recent advances in functional genomic approaches for toxicological studies, focusing on metal toxicities.
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The transcriptome is defined as the complete set of transcripts in a cell or a population of cells for a specific developmental stage or physiological condition Wang et al. Analyzing the transcriptome not only is critical for measuring gene expression but also helps elucidate the function of the genes Wang et al. Next-generation sequencing RNA-seq harnesses advances in DNA sequencing technologies for the study of transcriptome profiling. Each molecule is then sequenced in a high-throughput manner to obtain short sequences from one end single-end sequencing or both ends pair-end sequencing Wang et al.
RNA-seq has several advantages that make it a powerful tool for transcriptome profiling. RNA-seq produces up to several hundred million reads per sequencing run, has a very dynamic range, and is able to measure very low abundance transcripts. Because it directly sequences the transcripts, RNA-seq can identify with confidence variants or isoforms.
Finally, with barcodes, RNA-seq samples can be multiplexed, thus significantly reducing the cost. Relevant to this review, several studies have used RNA-seq to study gene expression changes in response to metal toxicity Gao et al. RNA-seq is expected to provide a platform to study the complexities of genetic variations and to study concentration-dependent differential responses to a given metal with higher sensitivity than microarrays.
Indeed, a recent study using RNA-seq identified subtle yet significant gene expression changes in an oxidative stress pathway that is induced by physiological levels of lead in neural stem cells Wagner et al. One way to identify these critical genes is through functional genomic screening, which usually involves systematic knockdown or knockout of genes followed by assessment of cell phenotypes such as lethality, viability, or fitness in response to toxicant treatment Shen et al.
RNAi is a cellular process, in which RNA molecules, in particular double-stranded ones, suppress target gene expression Rana, Subsequently, development of vector libraries containing shRNAs with unique barcodes targeting 17, human genes has enabled a pooled high-throughput RNAi screening approach Root et al. Individual shRNAs conferring cells with a specific phenotype under toxicant exposure can be efficiently identified by PCR amplification of barcodes followed by detection by microarray or direct sequencing Shen et al.
RNAi screening has been utilized to identify genes whose knockdown plays a role in breast cancer progression, drug resistance, and pathogen response Berns and Bernards, ; Berns et al. Relevant to metal toxicity, Cui et al. A study from our lab utilized a fluorescent reporter cell line to elucidate the genetic pathways regulating arsenite-induced ER stress Fig. One of the screen's hits, SNAT2, a neutral amino acid transporter, was found to be transcriptionally regulated by arsenite, with consequential increase in glutamine uptake and mTOR activity Oh et al.
RNAi screening is limited by the incomplete target gene suppression and off-target effects Booker et al. The recent development of CRISPR—Cas9 technology has provided an effective way of genome editing including complete knockout in a variety of cell types Barrangou et al. CRISPR—Cas9 functions as an adaptive immune system in many bacteria and most archaea by cleaving foreign nucleic acids Barrangou et al. Recognition of cleavage sites and thus target specificity is determined by sgRNA—DNA homology over 20 base pairs and an adjacent protospacer-adjacent motif Jinek et al.
In addition to gene knockout, the CRISPR system has been engineered to allow for robust activation of endogenous gene expression Gilbert et al.
Gene–environment interaction - Wikipedia
Using pooled mutant libraries, genes responsible for resistance to 6-thioguanine, etoposide, anthrax toxin, diphtheria toxin, Clostridium septicum alpha toxin, and 6-thioguanine have been identified in human and mouse cells Koike-Yusa et al. On the other hand, using a catalytically dead Cas9 protein, Jonathan Weissman's group screened for genes involved in response to a chimeric cholera diphtheria toxin CTx—DTA in K cells and identified genes and pathways involved in pathogen entry, retrotranslocation, and toxicity Gilbert et al.
Similarly, Konermann et al. While there are no published studies using CRISPR screening on metal toxicity, we expect CRISPR systems to allow for more efficient large-scale studies and thus contribute to elucidating the mechanisms of metal-induced cellular stress responses. A gene—environment interaction is a common theme in the etiology of PD. With the discovery of MPTP as a toxin causing parkinsonism due to dopamine neuron degeneration Langston et al.
Once there, it inhibits complex I in the mitochondrial respiratory chain, thereby reducing ATP synthesis and increasing superoxide radicals for review, see Dauer and Przedborski, Whether it is an important environmental factor is questionable, although the association of PD in men with pesticide exposure is positive whereas exposure to other chemicals is not Frigerio et al. Two pesticides in particular, maneb and paraquat, show a high risk for PD, and the risk is even greater if a person is exposed to both Costello et al.
One study showed the world's highest prevalence of PD may be among the Amish in the northeast United States. Since normal subjects were more related to each other than were subjects with clinically definite PD, this suggests that environmental factors, such as high use of pesticides, may contribute to the high prevalence of PD in this community.
To investigate occupation, specific job tasks or exposures, and risk of parkinsonism and clinical subtypes, a multicenter case-control study comparing lifelong occupation and job-task histories was created to determine associations with parkinsonism. Increased risk of parkinsonism was found with pesticide use OR 1. Other than pesticides, there is little consistent evidence for environmental factors. Other putative risk factors are head trauma, certain occupations, and milk consumption Tanner, Against a strong, new environmental factor is the lack of clusters of PD and an unchanging incidence rate over decades Rocca et al.
With the discovery of individual genes causing PD, a major genetic contribution to the etiology of PD has become predominant. It is likely that there are specific genes that can cause familial PD and susceptibility genes that make the individual with those genes sensitive to triggers to pathogenesis from environmental or endogenous factors, such as oxidative stress.
Endogenous factors, such as dopamine in nigral neurons and autonomous cell firing through calcium channels with the aging process, are discussed below under pathogenesis. Infectious agents remain a possible etiologic factor. Lynn Waterhouse, in Rethinking Autism , Complex competing gene—environment interaction theories have been proposed to unify autism. They argued that the interaction of genetic and environmental factors caused the development of abnormalities in cortical organization and neuronal circuitry and neuroinflammatory changes, which together caused autistic symptoms.
They argued that if negative environment effects occur during the early stages of fetal development, and if a fetal brain is specifically vulnerable to those environmental factors, autism will result. In , Herbert and Ziegler argued that autism was unlikely to be a prenatal gene effect, because the genes of strongest effect in autism, fragile X and Rett syndrome, did not consistently result in autism, and because many brain changes in autism occurred postnatally during the course of development.
They proposed a cascading mechanism. In contrast to previous debates, Moffitt and Caspi were now using the statistical analysis to prove that interaction existed and could be used to uncover the mechanisms of a vulnerability trait. Contention came from Zammit, Owen and Lewis who reiterated the concerns of Fisher in that the statistical effect was not related to the developmental process and would not be replicable with a difference of scale. There are two different conceptions of gene—environment interaction today. Tabery  has labeled them biometric and developmental interaction, while Sesardic  uses the terms statistical and commonsense interaction.
The biometric or statistical conception has its origins in research programs that seek to measure the relative proportions of genetic and environmental contributions to phenotypic variation within populations. Biometric gene—environment interaction has particular currency in population genetics and behavioral genetics. Biometric interaction is relevant in the context of research on individual differences rather than in the context of the development of a particular organism.
Developmental gene—environment interaction is a concept more commonly used by developmental geneticists and developmental psychobiologists. Developmental interaction is not seen merely as a statistical phenomenon. Whether statistical interaction is present or not, developmental interaction is in any case manifested in the causal interaction of genes and environments in producing an individual's phenotype. In epidemiology, the following models can be used to group the different interactions between gene and environment.
Model A describes a genotype that increases the level of expression of a risk factor but does not cause the disease itself. For example, the PKU gene results in higher levels of phenylalanine than normal which in turn causes mental retardation. The risk factor in Model B in contrast has a direct effect on disease susceptibility which is amplified by the genetic susceptibility. Model C depicts the inverse, where the genetic susceptibility directly effects disease while the risk factor amplifies this effect. In each independent situation, the factor directly effecting the disease can cause disease by itself.
Model D differs as neither factor in this situation can effect disease risk, however, when both genetic susceptibility and risk factor are present the risk is increased. For example, the G6PD deficiency gene when combined with fava bean consumption results in hemolytic anemia. This disease does not arise in individuals that eat fava beans and lack G6PD deficiency nor in G6PD-deficient people who do not eat fava beans.
Lastly, Model E depicts a scenario where the environmental risk factor and genetic susceptibility can individually both influence disease risk. When combined, however, the effect on disease risk differs. The models are limited by the fact that the variables are binary and so do not consider polygenic or continuous scale variable scenarios.
Adoption studies have been used to investigate how similar individuals that have been adopted are to their biological parents with whom they did not share the same environment with. Additionally, adopted individuals are compared to their adoptive family due to the difference in genes but shared environment. For example, an adoption study showed that Swedish men with disadvantaged adoptive environments and a genetic predisposition were more likely to abuse alcohol.
Using monozygotic twins , the effects of different environments on identical genotypes could be observed. Later studies leverage biometrical modelling techniques to include the comparisons of dizygotic twins to ultimately determine the different levels of gene expression in different environments. Family-based research focuses on the comparison of low-risk controls to high risk children to determine the environmental effect on subjects with different levels of genetic risk.
For example, a Danish study on high-risk children with schizophrenic mothers depicted that children without a stable caregiver were associated with an increased risk of schizophrenia. The often used method to detect gene-environment interactions is by studying the effect a single gene variation candidate gene has with respect to a particular environment.
Candidate studies such as these require strong biological hypotheses which are currently difficult to select given the little understanding of biological mechanisms that lead to higher risk. These studies are also often difficult to replicate commonly due to small sample sizes which typically results in disputed results.
The polygenic nature of complex phenotypes suggests single candidate studies could be ineffective in determining the various smaller scale effects from the large number of influencing gene variants. Since the same environmental factor could interact with multiple genes, a polygenic approach can be taken to analyze GxE interactions. A polygenic score is generated using the alleles associated with a trait and their respective weights based on effect and examined in combination with environmental exposure.
Though this method of research is still early, it is consistent with psychiatric disorders. As a result of the overlap of endophenotypes amongst disorders this suggests that the outcomes of gene-environment interactions are applicable across various diagnoses.
An effective approach to this all-encompassing study occurs in two-steps where the genome is first filtered using gene-level tests and pathway based gene set analyses. The differential susceptibility hypothesis has been reaffirmed through genome wide approaches. A particular concern with gene-environment interaction studies is the lack of reproducibility. Specifically complex traits studies have come under scrutiny for producing results that cannot be replicated.
For example, studies of the 5HTTLPR gene and stress resulting in modified risk of depression have had conflicting results. A possible explanation behind the inconsistent results is the heavy use of multiple testing. Studies are suggested to produce inaccurate results due to the investigation of multiple phenotypes and environmental factors in individual experiments.
There are two different models for the scale of measurement that helps determine if gene-environment interaction exists in a statistical context. There is disagreement on which scale should be used. Under these analyses, if the combined variables fit either model then there is no interaction.
The combined effects must either be greater for synergistic or less than for an antagonistic outcome. The additive model measures risk differences while the multiplicative model uses ratios to measure effects. The additive model has been suggested to be a better fit for predicting disease risk in a population while a multiplicative model is more appropriate for disease etiology. Epigenetics is an example of an underlying mechanism of gene-environment effects, however, it does not conclude whether environment effects are additive, multiplicative or interactive.
New studies have also revealed the interactive effect of multiple environment factors. For example, a child with a poor quality environment would be more sensitive to a poor environment as an adult which ultimately led to higher psychological distress scores. This depicts a three way interaction Gene x Environment x Environment. The same study suggests taking a life course approach to determining genetic sensitivity to environmental influences within the scope of mental illnesses.
Doctors are interested in knowing whether disease can be prevented by reducing exposure to environmental risks. Some people carry genetic factors that confer susceptibility or resistance to a certain disorder in a particular environment. The interaction between the genetic factors and environmental stimulus is what results in the disease phenotype.
This would allow doctors to more precisely select a certain drug and dosage to achieve therapeutic response in a patient while minimizing side effects and adverse drug reactions.