Epigenetic-mediated N -methyl-D-aspartate receptor changes in the brain of isolated reared rats

Aim: We investigated: Grin1, Grin2a, Grin2b DNA methylation; NR1 and NR2 mRNA/protein in the prefrontal cortex (PFC); and hippocampus of male Wistar rats exposed to isolation rearing. Materials & methods: Animals were kept isolated or grouped (n = 10/group) from weaning for 10 weeks. Tissues were dissected for RNA/DNA extraction and N-methyl-D-aspartate receptor subunits were analyzed using quantitative reverse transcription (RT)-PCR, ELISA and pyrosequencing. Results: Isolated-reared animals had: decreased mRNA in PFC for all markers, increased NR1 protein in hippocampus and hypermethylation of Grin1 in PFC and Grin2b in hippocampus, compared with grouped rats. Associations between mRNA/protein and DNA methylation were found for both brain areas. Conclusion: This study indicates that epigenetic DNA methylation may underlie N-methyl-D-aspartate receptor mRNA/protein expression alterations caused by isolation rearing

Prolonged Periods of Social Isolation From Weaning Reduce the Anti-inflammatory Cytokine IL-10 in Blood and Brain

Life stressors during critical periods are reported to trigger an immune dysfunction characterised by abnormal production of inflammatory cytokines. Despite the relationship between early stressors and schizophrenia is described, the evidence on inflammatory biomarkers remains limited. We aimed to investigate whether an imbalance between pro- and anti-inflammatory cytokines in the brain is reflected in the peripheral blood of rats submitted to post-weaning social isolation (pwSI), a model with validity to study schizophrenia. We evaluated pro- and anti-inflammatory cytokines (IL-6, TNF-α, and IL-10) simultaneously at blood, prefrontal cortex and hippocampal tissues (Milliplex MAP), including the respective cytokines gene expression (mRNA) (qRT-PCR TaqMan mastermix). We also performed a correlation matrix to explore significant correlations among cytokines (protein and mRNA) in blood and brain, as well as cytokines and total number of square crossings in the open field for isolated-reared animals. Male Wistar rats (n = 10/group) were kept isolated (n = 1/cage) or grouped (n = 3–4/cage) since weaning for 10 weeks. After this period, rats were assessed for locomotion and sacrificed for blood and brain cytokines measurements. Prolonged pwSI decreased IL-10 protein and mRNA in the blood, and IL-10 protein in the hippocampus, along with decreased IL-6 and its mRNA expression in the prefrontal cortex. Our results also showed that cytokines tend to correlate to one-another among the compartments investigated, although blood and brain correlations are far from perfect. IL-10 hippocampal levels were negatively correlated with hyperlocomotion in the open field. Despite the unexpected decrease in IL-6 and unchanged TNF-α levels contrast to the expected pro-inflammatory phenotype, this may suggest that reduced anti-inflammatory signalling may be critical for eliciting abnormal behaviour in adulthood. Altogether, these results suggest that prolonged early-life adverse events reduce the ability to build proper anti-inflammatory cytokine that is translated from blood-to-brain

Lifetime cannabis use and childhood trauma associated with CNR1 genetic variants increase the risk of psychosis: findings from the STREAM study

Objectives: Gene-environment interactions increase the risk of psychosis. The objective of this study was to investigate gene-gene and gene-environment interactions in psychosis, including single nucleotide variants (SNVs) of dopamine-2 receptor (D2R), N-methyl-d-aspartate receptor (NMDAR), and cannabinoid receptor type 1 (CB1R), lifetime cannabis use, and childhood trauma. Methods: Twenty-three SNVs of genes encoding D2R (DRD2: rs1799978, rs7131056, rs6275), NMDAR (GRIN1: rs4880213, rs11146020; GRIN2A: rs1420040, rs11866328; GRIN2B: rs890, rs2098469, rs7298664), and CB1R (CNR1: rs806380, rs806379, rs1049353, rs6454674, rs1535255, rs2023239, rs12720071, rs6928499, rs806374, rs7766029, rs806378, rs10485170, rs9450898) were genotyped in 143 first-episode psychosis patients (FEPp) and 286 communitybased controls by Illumina HumanCoreExome-24 BeadChip. Gene-gene and gene-environment associations were assessed using nonparametric Multifactor Dimensionality Reduction software. Results: Single-locus analyses among the 23 SNVs for psychosis and gene-gene interactions were not significant (p 4 0.05 for all comparisons); however, both environmental risk factors showed an association with psychosis (p o 0.001). Moreover, gene-environment interactions were significant for an SNV in CNR1 and cannabis use. The best-performing model was the combination of CNR1 rs12720071 and lifetime cannabis use (p o 0.001), suggesting an increased risk of psychosis. Conclusion: Our study supports the hypothesis of gene-environment interactions for psychosis involving T-allele carriers of CNR1 SNVs, childhood trauma, and cannabis use

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI): Incidence and First-Episode Case–Control Programme

Funder: FP7 Ideas: European Research Council; doi: http://dx.doi.org/10.13039/100011199; Grant(s): HEALTH-F2-2010-241909Abstract: Purpose: The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) study contains an unparalleled wealth of comprehensive data that allows for testing hypotheses about (1) variations in incidence within and between countries, including by urbanicity and minority ethnic groups; and (2) the role of multiple environmental and genetic risk factors, and their interactions, in the development of psychotic disorders. Methods: Between 2010 and 2015, we identified 2774 incident cases of psychotic disorders during 12.9 million person-years at risk, across 17 sites in 6 countries (UK, The Netherlands, France, Spain, Italy, and Brazil). Of the 2774 incident cases, 1130 cases were assessed in detail and form the case sample for case–control analyses. Across all sites, 1497 controls were recruited and assessed. We collected data on an extensive range of exposures and outcomes, including demographic, clinical (e.g. premorbid adjustment), social (e.g. childhood and adult adversity, cannabis use, migration, discrimination), cognitive (e.g. IQ, facial affect processing, attributional biases), and biological (DNA via blood sample/cheek swab). We describe the methodology of the study and some descriptive results, including representativeness of the cohort. Conclusions: This resource constitutes the largest and most extensive incidence and case–control study of psychosis ever conducted

The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI): Incidence and First-Episode Case–Control Programme

Funder: FP7 Ideas: European Research Council; doi: http://dx.doi.org/10.13039/100011199; Grant(s): HEALTH-F2-2010-241909Abstract: Purpose: The EUropean Network of National Schizophrenia Networks Studying Gene–Environment Interactions (EU-GEI) study contains an unparalleled wealth of comprehensive data that allows for testing hypotheses about (1) variations in incidence within and between countries, including by urbanicity and minority ethnic groups; and (2) the role of multiple environmental and genetic risk factors, and their interactions, in the development of psychotic disorders. Methods: Between 2010 and 2015, we identified 2774 incident cases of psychotic disorders during 12.9 million person-years at risk, across 17 sites in 6 countries (UK, The Netherlands, France, Spain, Italy, and Brazil). Of the 2774 incident cases, 1130 cases were assessed in detail and form the case sample for case–control analyses. Across all sites, 1497 controls were recruited and assessed. We collected data on an extensive range of exposures and outcomes, including demographic, clinical (e.g. premorbid adjustment), social (e.g. childhood and adult adversity, cannabis use, migration, discrimination), cognitive (e.g. IQ, facial affect processing, attributional biases), and biological (DNA via blood sample/cheek swab). We describe the methodology of the study and some descriptive results, including representativeness of the cohort. Conclusions: This resource constitutes the largest and most extensive incidence and case–control study of psychosis ever conducted

The continuity of effect of schizophrenia polygenic risk score and patterns of cannabis use on transdiagnostic symptom dimensions at first-episode psychosis: findings from the EU-GEI study

Abstract: Diagnostic categories do not completely reflect the heterogeneous expression of psychosis. Using data from the EU-GEI study, we evaluated the impact of schizophrenia polygenic risk score (SZ-PRS) and patterns of cannabis use on the transdiagnostic expression of psychosis. We analysed first-episode psychosis patients (FEP) and controls, generating transdiagnostic dimensions of psychotic symptoms and experiences using item response bi-factor modelling. Linear regression was used to test the associations between these dimensions and SZ-PRS, as well as the combined effect of SZ-PRS and cannabis use on the dimensions of positive psychotic symptoms and experiences. We found associations between SZ-PRS and (1) both negative (B = 0.18; 95%CI 0.03–0.33) and positive (B = 0.19; 95%CI 0.03–0.35) symptom dimensions in 617 FEP patients, regardless of their categorical diagnosis; and (2) all the psychotic experience dimensions in 979 controls. We did not observe associations between SZ-PRS and the general and affective dimensions in FEP. Daily and current cannabis use were associated with the positive dimensions in FEP (B = 0.31; 95%CI 0.11–0.52) and in controls (B = 0.26; 95%CI 0.06–0.46), over and above SZ-PRS. We provide evidence that genetic liability to schizophrenia and cannabis use map onto transdiagnostic symptom dimensions, supporting the validity and utility of the dimensional representation of psychosis. In our sample, genetic liability to schizophrenia correlated with more severe psychosis presentation, and cannabis use conferred risk to positive symptomatology beyond the genetic risk. Our findings support the hypothesis that psychotic experiences in the general population have similar genetic substrates as clinical disorders

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost