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Microcystins are ubiquitous toxins produced by photoautotrophic cyanobacteria. Human exposures to microcystins occur through the consumption of contaminated drinking water, fish and shellfish, vegetables, and algal dietary supplements and through recreational activities. Microcystin-leucine-arginine (MCLR) is the prototypical microcystin because it is reported to be the most common and toxic variant and is the only microcystin with an established tolerable daily intake of 0.04 µg/kg. Microcystin toxicokinetics is characterized by low intestinal absorption, rapid and specific distribution to the liver, moderate metabolism to glutathione and cysteinyl conjugates, and low urinary and fecal excretion. Molecular toxicology involves covalent binding to and inhibition of protein phosphatases, oxidative stress, cell death (autophagy, apoptosis, necrosis), and cytoskeleton disruption. These molecular and cellular effects are interconnected and are commonly observed together. The main target organs for microcystin toxicity are the intestine, liver, and kidney. Preclinical data indicate microcystins may also have nervous, pulmonary, cardiac, and reproductive system toxicities. Recent evidence suggests that exposure to other hepatotoxic insults could potentiate microcystin toxicity and increase the risk for chronic diseases. This review summarizes the current knowledge for microcystin toxicokinetics, molecular toxicology, and pathophysiology in preclinical rodent models and humans. More research is needed to better understand human toxicokinetics and how multifactorial exposures contribute to disease pathogenesis and progression.

Both environmental exposures and genetics are known to play important roles in shaping human aging. Here we aimed to quantify the relative contributions of environment (referred to as the exposome) and genetics to aging and premature mortality. To systematically identify environmental exposures associated with aging in the UK Biobank, we first conducted an exposome-wide analysis of all-cause mortality ( n  = 492,567) and then assessed the associations of these exposures with a proteomic age clock ( n  = 45,441), identifying 25 independent exposures associated with mortality and proteomic aging. These exposures were also associated with incident age-related multimorbidity, aging biomarkers and major disease risk factors. Compared with information on age and sex, polygenic risk scores for 22 major diseases explained less than 2 percentage points of additional mortality variation, whereas the exposome explained an additional 17 percentage points. Polygenic risk explained a greater proportion of variation (10.3–26.2%) compared with the exposome for incidence of dementias and breast, prostate and colorectal cancers, whereas the exposome explained a greater proportion of variation (5.5–49.4%) compared with polygenic risk for incidence of diseases of the lung, heart and liver. Our findings provide a comprehensive map of the contributions of environment and genetics to mortality and incidence of common age-related diseases, suggesting that the exposome shapes distinct patterns of disease and mortality risk, irrespective of polygenic disease risk. Based on a systematic analysis of environmental exposures associated with aging and mortality in the UK Biobank, the relative contributions of such exposures and genetic risk for mortality and a range of age-related diseases were compared, highlighting the potential beneficial effects of environment-focused interventions.

Purpose of Review We review recent developments on risk factors in schizophrenia. Recent Findings The way we think about schizophrenia today is profoundly different from the way this illness was seen in the twentieth century. We now know that the etiology of schizophrenia is multifactorial and reflects an interaction between genetic vulnerability and environmental contributors. Environmental risk factors such as pregnancy and birth complications, childhood trauma, migration, social isolation, urbanicity, and substance abuse, alone and in combination, acting at a number of levels over time, influence the individual’s likelihood to develop the disorder. Summary Environmental risk factors together with the identification of a polygenic risk score for schizophrenia, research on gene–environment interaction and environment–environment interaction have hugely increased our knowledge of the disorder.

Background Air pollution is widely associated with allergic diseases, including asthma. Although previous studies have suggested an epidemiological link between air pollution and asthma, the combined effects of air pollutants and polygenic risk scores (PRSs) on asthma risk remain incompletely understood. This study aimed to examine the impact of air pollutants and PRS on asthma risk among patients in a Taiwan medical institution. Methods This retrospective matched case-control study utilized data from the Taiwan Precision Medicine Initiative (TPMI) project to compare asthma patients with a non-asthmatic control group. Participants were stratified into quartiles based on their asthma PRS, while air pollutant exposure was assessed by both duration and concentration. Conducted at Taichung Veterans General Hospital, the study followed participants from January 1, 2000, to December 31, 2021. Logistic regression was used to analyze the relationships between air pollution exposure, genetic risk, and asthma incidence. Results A total of 9,756 participants were included (3,252 asthma patients and 6,504 controls). Individuals in the highest PRS quartile demonstrated a significantly increased asthma risk (odds ratio = 1.532, 95% CI = 1.333–1.762, p  < 0.0001). Long-term exposure to low levels of PM 2.5 , PM 10 , NO 2 , Mn, and O 3 further elevated asthma risk, with the association becoming more pronounced under conditions of high air pollution. Conclusion Long-term exposure to low concentrations of air pollutants significantly increases asthma risk, especially among individuals with high genetic susceptibility. These findings emphasize the importance of personalized health management for individuals with elevated PRS. Trial registration Not applicable.

Although gene–environment correlation is recognized and investigated by family studies and recently by SNP-heritability studies, the possibility that genetic effects on traits capture environmental risk factors or protective factors has been neglected by polygenic prediction models. We investigated covariation between traitassociated polygenic variation identified by genome-wide association studies (GWASs) and specific environmental exposures, controlling for overall genetic relatedness using a genomic relatedness matrix restricted maximum-likelihood model. In a UK-representative sample (n = 6,710), we find widespread covariation between offspring trait-associated polygenic variation and parental behavior and characteristics relevant to children’s developmental outcomes—independently of population stratification. For instance, offspring genetic risk for schizophrenia was associated with paternal age (R² = 0.002; P = 1e-04), and offspring education-associated variation was associated with variance in breastfeeding (R² = 0.021; P = 7e-30), maternal smoking during pregnancy (R² = 0.008; P = 5e-13), parental smacking (R² = 0.01; P = 4e-15), household income (R² = 0.032; P = 1e-22), watching television (R² = 0.034; P = 5e-47), and maternal education (R² = 0.065; P = 3e-96). Education-associated polygenic variation also captured covariation between environmental exposures and children’s inattention/hyperactivity, conduct problems, and educational achievement. The finding that genetic variation identified by trait GWASs partially captures environmental risk factors or protective factors has direct implications for risk prediction models and the interpretation of GWAS findings.

Background/Objectives The effects of early life exposures on offspring life-course health are well established. This study assessed whether adding early socio-demographic and perinatal variables to a model based on polygenic risk score (PRS) improves prediction of obesity risk. Methods We used the Jerusalem Perinatal study (JPS) with data at birth and body mass index (BMI) and waist circumference (WC) measured at age 32. The PRS was constructed using over 2.1M common SNPs identified in genome-wide association study (GWAS) for BMI. Linear and logistic models were applied in a stepwise approach. We first examined the associations between genetic variables and obesity-related phenotypes (e.g., BMI and WC). Secondly, socio-demographic variables were added and finally perinatal exposures, such as maternal pre-pregnancy BMI (mppBMI) and gestational weight gain (GWG) were added to the model. Improvement in prediction of each step was assessed using measures of model discrimination (area under the curve, AUC), net reclassification improvement (NRI) and integrated discrimination improvement (IDI). Results One standard deviation (SD) change in PRS was associated with a significant increase in BMI ( β  = 1.40) and WC ( β  = 2.45). These associations were slightly attenuated (13.7–14.2%) with the addition of early life exposures to the model. Also, higher mppBMI was associated with increased offspring BMI ( β  = 0.39) and WC ( β  = 0.79) ( p  < 0.001). For obesity (BMI ≥ 30) prediction, the addition of early socio-demographic and perinatal exposures to the PRS model significantly increased AUC from 0.69 to 0.73. At an obesity risk threshold of 15%, the addition of early socio-demographic and perinatal exposures to the PRS model provided a significant improvement in reclassification of obesity (NRI, 0.147; 95% CI 0.068–0.225). Conclusions Inclusion of early life exposures, such as mppBMI and maternal smoking, to a model based on PRS improves obesity risk prediction in an Israeli population-sample.

Complementary to the genome, the concept of exposome has been proposed to capture the totality of human environmental exposures. While there has been some recent progress on the construction of the exposome, few tools exist that can integrate the genome and exposome for complex trait analyses. Here we propose a linear mixed model approach to bridge this gap, which jointly models the random effects of the two omics layers on phenotypes of complex traits. We illustrate our approach using traits from the UK Biobank (e.g., BMI and height for N ~ 35,000) with a small fraction of the exposome that comprises 28 lifestyle factors. The joint model of the genome and exposome explains substantially more phenotypic variance and significantly improves phenotypic prediction accuracy, compared to the model based on the genome alone. The additional phenotypic variance captured by the exposome includes its additive effects as well as non-additive effects such as genome–exposome (gxe) and exposome–exposome (exe) interactions. For example, 19% of variation in BMI is explained by additive effects of the genome, while additional 7.2% by additive effects of the exposome, 1.9% by exe interactions and 4.5% by gxe interactions. Correspondingly, the prediction accuracy for BMI, computed using Pearson’s correlation between the observed and predicted phenotypes, improves from 0.15 (based on the genome alone) to 0.35 (based on the genome and exposome). We also show, using established theories, that integrating genomic and exposomic data can be an effective way of attaining a clinically meaningful level of prediction accuracy for disease traits. In conclusion, the genomic and exposomic effects can contribute to phenotypic variation via their latent relationships, i.e. genome-exposome correlation, and gxe and exe interactions, and modelling these effects has a potential to improve phenotypic prediction accuracy and thus holds a great promise for future clinical practice.

A history of childhood adversity is associated with psychotic disorder, with an increase in risk according to number or severity of exposures. However, it is not known why only some exposed individuals go on to develop psychosis. One possibility is pre-existing genetic vulnerability. Research on gene-environment interaction in psychosis has primarily focused on candidate genes, although the genetic effects are now known to be polygenic. This pilot study investigated whether the effect of childhood adversity on psychosis is moderated by the polygenic risk score for schizophrenia (PRS). Data were utilised from the Genes and Psychosis (GAP) study set in South London, UK. The GAP sample comprises 285 first-presentation psychosis cases and 256 unaffected controls with information on childhood adversity. We studied only white subjects (80 cases and 110 controls) with PRS data, as the PRS has limited predictive ability in patients of African ancestry. The occurrence of childhood adversity was assessed with the Childhood Experience of Care and Abuse Questionnaire (CECA.Q) and the PRS was based on genome-wide meta-analysis results for schizophrenia from the Psychiatric Genomics Consortium. Higher schizophrenia PRS and childhood adversities each predicted psychosis status. Nevertheless, no evidence was found for interaction as departure from additivity, indicating that the effect of polygenic risk scores on psychosis was not increased in the presence of a history of childhood adversity. These findings are compatible with a multifactorial threshold model in which both genetic liability and exposure to environmental risk contribute independently to the etiology of psychosis.

Aggression and callous, uncaring, and unemotional (CU) traits are clinically related behavioral constructs caused by genetic and environmental factors. We performed polygenic risk score (PRS) analyses to investigate shared genetic etiology between aggression and these three CU-traits. Furthermore, we studied interactions of PRS with smoking during pregnancy and childhood life events in relation to CU-traits. Summary statistics for the base phenotype were derived from the EAGLE-consortium genome-wide association study of children’s aggressive behavior and were used to calculate individual-level genome-wide and gene-set PRS in the NeuroIMAGE target-sample. Target phenotypes were ‘callousness’, ‘uncaring’, and ‘unemotional’ sumscores of the Inventory of Callous-Unemotional traits. A total of 779 subjects and 1,192,414 single-nucleotide polymorphisms were available for PRS-analyses. Gene-sets comprised serotonergic, dopaminergic, glutamatergic, and neuroendocrine signaling pathways. Genome-wide PRS showed evidence of association with uncaring scores (explaining up to 1.59% of variance; self-contained Q = 0.0306, competitive-P = 0.0015). Dopaminergic, glutamatergic, and neuroendocrine PRS showed evidence of association with unemotional scores (explaining up to 1.33, 2.00, and 1.20% of variance respectively; self-contained Q-values 0.037, 0.0115, and 0.0473 respectively, competitive-P-values 0.0029, 0.0002, and 0.0045 respectively). Smoking during pregnancy related to callousness scores while childhood life events related to both callousness and unemotionality. Moreover, dopaminergic PRS appeared to interact with childhood life events in relation to unemotional scores. Our study provides evidence suggesting shared genetic etiology between aggressive behavior and uncaring, and unemotional CU-traits in children. Gene-set PRS confirmed involvement of shared glutamatergic, dopaminergic, and neuroendocrine genetic variation in aggression and CU-traits. Replication of current findings is needed.

Background Air pollutants can activate low-grade subclinical inflammation which further impairs respiratory health. We aimed to investigate the role of polygenic susceptibility to chronic air pollution-induced subclinical airway inflammation. Methods We used data from 296 women (69–79 years) enrolled in the population-based SALIA cohort (Study on the influence of Air pollution on Lung function, Inflammation and Aging). Biomarkers of airway inflammation were measured in induced-sputum samples at follow-up investigation in 2007–2010. Chronic air pollution exposures at residential addresses within 15 years prior to the biomarker assessments were used to estimate main environmental effects on subclinical airway inflammation. Furthermore, we calculated internally weighted polygenic risk scores based on genome-wide derived single nucleotide polymorphisms. Polygenic main and gene-environment interaction (GxE) effects were investigated by adjusted linear regression models. Results Higher exposures to nitrogen dioxide (NO 2 ), nitrogen oxides (NO x ), particulate matter with aerodynamic diameters of ≤ 2.5 μm, ≤ 10 μm, and 2.5–10 µm significantly increased the levels of leukotriene (LT)B 4 by 19.7% (p-value = 0.005), 20.9% (p = 0.002), 22.1% (p = 0.004), 17.4% (p = 0.004), and 23.4% (p = 0.001), respectively. We found significant effects of NO 2 (25.9%, p = 0.008) and NO x (25.9%, p-value = 0.004) on the total number of cells. No significant GxE effects were observed. The trends were mostly robust in sensitivity analyses. Conclusions While this study confirms that higher chronic exposures to air pollution increase the risk of subclinical airway inflammation in elderly women, we could not demonstrate a significant role of polygenic susceptibility on this pathway. Further studies are required to investigate the role of polygenic susceptibility. Graphical Abstract