Somatic deletions implicated in functional diversity of brain cells of individuals with schizophrenia and unaffected controls.
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ABSTRACT:
SUBMITTER: Kim J
PROVIDER: S-EPMC3897957 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:The role of the human microbiome in schizophrenia remains largely unexplored. The microbiome has been shown to alter brain development and modulate behavior and cognition in animals through gut-brain connections, and research in humans suggests that it may be a modulating factor in many disorders. This study reports findings from a shotgun metagenomic analysis of the oropharyngeal microbiome in 16 individuals with schizophrenia and 16 controls. High-level differences were evident at both the phylum and genus levels, with Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria dominating both schizophrenia patients and controls, and Ascomycota being more abundant in schizophrenia patients than controls. Controls were richer in species but less even in their distributions, i.e., dominated by fewer species, as opposed to schizophrenia patients. Lactic acid bacteria were relatively more abundant in schizophrenia, including species of Lactobacilli and Bifidobacterium, which have been shown to modulate chronic inflammation. We also found Eubacterium halii, a lactate-utilizing species. Functionally, the microbiome of schizophrenia patients was characterized by an increased number of metabolic pathways related to metabolite transport systems including siderophores, glutamate, and vitamin B12. In contrast, carbohydrate and lipid pathways and energy metabolism were abundant in controls. These findings suggest that the oropharyngeal microbiome in individuals with schizophrenia is significantly different compared to controls, and that particular microbial species and metabolic pathways differentiate both groups. Confirmation of these findings in larger and more diverse samples, e.g., gut microbiome, will contribute to elucidating potential links between schizophrenia and the human microbiota.
| S-EPMC4556144 | biostudies-literature
Project description:BackgroundProtein encoding genes have long been the major targets for research in schizophrenia genetics. However, with the identification of regulatory microRNAs (miRNAs) as important in brain development and function, miRNAs genes have emerged as candidates for schizophrenia-associated genetic factors. Indeed, the growing understanding of the regulatory properties and pleiotropic effects that miRNA have on molecular and cellular mechanisms, suggests that alterations in the interactions between miRNAs and their mRNA targets may contribute to phenotypic variation.Methodology/principal findingsWe have studied the association between schizophrenia and genetic variants of miRNA genes associated with brain-expression using a case-control study design on three Scandinavian samples. Eighteen known SNPs within or near brain-expressed miRNAs in three samples (Danish, Swedish and Norwegian: 420/163/257 schizophrenia patients and 1006/177/293 control subjects), were analyzed. Subsequently, joint analysis of the three samples was performed on SNPs showing marginal association. Two SNPs rs17578796 and rs1700 in hsa-mir-206 (mir-206) and hsa-mit-198 (mir-198) showed nominal significant allelic association to schizophrenia in the Danish and Norwegian sample respectively (P = 0.0021 & p = 0.038), of which only rs17578796 was significant in the joint sample. In-silico analysis revealed that 8 of the 15 genes predicted to be regulated by both mir-206 and mir-198, are transcriptional targets or interaction partners of the JUN, ATF2 and TAF1 connected in a tight network. JUN and two of the miRNA targets (CCND2 and PTPN1) in the network have previously been associated with schizophrenia.Conclusions/significanceWe found nominal association between brain-expressed miRNAs and schizophrenia for rs17578796 and rs1700 located in mir-206 and mir-198 respectively. These two miRNAs have a surprising large number (15) of targets in common, eight of which are also connected by the same transcription factors.
| S-EPMC1964806 | biostudies-literature
Project description:Dysfunctional processing of auditory sensory gating has generally been found in schizophrenic patients and ultra-high-risk (UHR) individuals. The aim of the study was to investigate the differences of functional interaction between brain regions and performance during the P50 sensory gating in UHR group compared with those in first-episode schizophrenia patients (FESZ) and healthy controls (HC) groups. The study included 128-channel scalp Electroencephalogram (EEG) recordings during the P50 auditory paradigm for 35 unmedicated FESZ, 30 drug-free UHR, and 40 HC. Cortical sources of scalp electrical activity were recomputed using exact low-resolution electromagnetic tomography (eLORETA), and functional brain networks were built at the source level and compared between the groups (FESZ, UHR, HC). A classifier using decision tree was designed for differentiating the three groups, which uses demographic characteristics, MATRICS Consensus Cognitive Battery parameters, behavioral features in P50 paradigm, and the measures of functional brain networks based on graph theory during P50 sensory gating. The results showed that very few brain connectivities were significantly different between FESZ and UHR groups during P50 sensory gating, and that a large number of brain connectivities were significantly different between FESZ and HC groups and between UHR and HC groups. Furthermore, the FESZ group had a stronger connection in the right superior frontal gyrus and right insula than the HC group. And the UHR group had an enhanced connection in the paracentral lobule and the middle temporal gyrus compared with the HC group. Moreover, comparison of classification analysis results showed that brain network metrics during P50 sensory gating can improve the accuracy of the classification for FESZ, UHR and HC groups. Our findings provide insight into the mechanisms of P50 suppression in schizophrenia and could potentially improve the performance of early identification and diagnosis of schizophrenia for the earliest intervention.
| S-EPMC6870009 | biostudies-literature
Project description:Schizophrenia is a severe psychiatric illness that affects ~1% of the population and has a strong genetic underpinning. Recently, genome wide analysis of copy number variation (CNV) has implicated rare and de novo events as important in schizophrenia. Here we report a genome-wide analysis of 245 schizophrenia cases and 490 controls, all of Ashkenazi Jewish descent. Since many studies have found an excess burden of large, rare deletions in cases, we limited our analysis to deletions over 500 kb in size. We observed seven large, rare deletions in cases with 57% of these being de novo. We focused on one 836 kb de novo deletion at chromosome 3q29 that falls within a 1.3–1.6 Mb deletion previously identified in children with intellectual disability (ID) and autism, as increasing evidence suggests an overlap of specific rare CNVs between autism and schizophrenia. By combining our data with prior CNV studies of schizophrenia and analysis of the data of the Genetic Association Information Network (GAIN), we identified six 3q29 deletions among 7,545 schizophrenic subjects and one among 39,748 controls, resulting in a statistically significant association with schizophrenia (p = 0.02) and an odds ratio estimate of 17 (95% CI: 1.36–1198.4). Moreover, this 3q29 deletion region contains two linkage peaks from prior schizophrenia family studies, and the minimal deletion interval implicates 20 annotated genes, including PAK2 and DLG1, both paralogous to X-linked ID genes and now strong candidates for schizophrenia susceptibility.
2010-08-06 | GSE23201 | GEO
Project description:BACKGROUND:Altered structural network-connectivity has been reported in psychotic disorder but whether these alterations are associated with genetic vulnerability, and/or with phenotypic variation, has been less well examined. This study examined i) whether differences in network-connectivity exist between patients with psychotic disorder, siblings of patients with psychotic disorder and controls, and ii) whether network-connectivity alterations vary with (subclinical) symptomatology. METHODS:Network-connectivity measures (global efficiency (GE), density, local efficiency (LE), clustering coefficient (CC)) were derived from diffusion weighted imaging (DWI) and were compared between 85 patients with psychotic disorder, 93 siblings without psychotic disorder and 80 healthy comparison subjects using multilevel regression models. In patients, associations between Positive and Negative Syndrome Scale (PANSS) symptoms and topological measures were examined. In addition, interactions between subclinical psychopathology and sibling/healthy comparison subject status were examined in models of topological measures. RESULTS:While there was no main effect of group with respect to GE, density, LE and CC, siblings had a significantly higher CC compared to patients (B = 0.0039, p = .002). In patients, none of the PANSS symptom domains were significantly associated with any of the four network-connectivity measures. The two-way interaction between group and SIR-r positive score in the model of LE was significant (χ2 = 6.24, p = .01, df = 1). In the model of CC, the interactions between group and respectively SIS-r positive (χ2 = 5.59, p = .02, df = 1) and negative symptom scores (χ2 = 4.71, p = .03, df = 1) were significant. Stratified analysis showed that, in siblings, decreased LE and CC was significantly associated with increased SIS-r positive scores (LE: B = -0.0049, p = .003, CC: B = -0.0066, p = .01) and that decreased CC was significantly associated with increased SIS-r negative scores (B = -0.012, p = .003). There were no significant interactions between group and SIS-r scores in the models of GE and density. CONCLUSION:The findings indicate absence of structural network-connectivity alterations in individuals with psychotic disorder and in individuals at higher than average genetic risk for psychotic disorder, in comparison with healthy subjects. The differential subclinical symptom-network connectivity associations in siblings with respect to controls may be a sign of psychosis vulnerability in the siblings.
| S-EPMC6658824 | biostudies-literature
Project description:Schizophrenia is a severe psychiatric illness that affects ~1% of the population and has a strong genetic underpinning. Recently, genome wide analysis of copy number variation (CNV) has implicated rare and de novo events as important in schizophrenia. Here we report a genome-wide analysis of 245 schizophrenia cases and 490 controls, all of Ashkenazi Jewish descent. Since many studies have found an excess burden of large, rare deletions in cases, we limited our analysis to deletions over 500 kb in size. We observed seven large, rare deletions in cases with 57% of these being de novo. We focused on one 836 kb de novo deletion at chromosome 3q29 that falls within a 1.3–1.6 Mb deletion previously identified in children with intellectual disability (ID) and autism, as increasing evidence suggests an overlap of specific rare CNVs between autism and schizophrenia. By combining our data with prior CNV studies of schizophrenia and analysis of the data of the Genetic Association Information Network (GAIN), we identified six 3q29 deletions among 7,545 schizophrenic subjects and one among 39,748 controls, resulting in a statistically significant association with schizophrenia (p = 0.02) and an odds ratio estimate of 17 (95% CI: 1.36–1198.4). Moreover, this 3q29 deletion region contains two linkage peaks from prior schizophrenia family studies, and the minimal deletion interval implicates 20 annotated genes, including PAK2 and DLG1, both paralogous to X-linked ID genes and now strong candidates for schizophrenia susceptibility. Copy Number alanysis was performed on 245 cases and 490 controls of Ashkenazi Jewish descent. Samples were analyzed for deletions greater than 500 kb, with 20 or more snps in the interval. Three algorithms were used for analysis, GADA, GLAD and BEAST. The reference was created by using all samples processed here as the reference.
2010-08-06 | E-GEOD-23201 | biostudies-arrayexpress
Project description:The ability to rapidly stop or change a planned action is a critical cognitive process that is impaired in schizophrenia. The current study aimed to examine whether this impairment reflects familial vulnerability to schizophrenia across two experiments comparing unaffected first-degree relatives to healthy controls. First, we examined performance on a saccadic stop-signal task that required rapid inhibition of an eye movement. Then, in a different sample, we investigated behavioral and neural responses (using fMRI) during a stop-signal task variant that required rapid modification of a prepared eye movement. Here, we examined differences between relatives and healthy controls in terms of activation and effective connectivity within an oculomotor control network during task performance. Like individuals with schizophrenia, the unaffected relatives showed behavioral evidence for more inefficient inhibitory processes. Unlike previous findings in individuals with schizophrenia, however, the relatives showed evidence for a compensatory waiting strategy. Behavioral differences were accompanied by more activation among the relatives in task-relevant regions across conditions and group differences in effective connectivity across the task that were modulated differently by the instruction to exert control over a planned saccade. Effective connectivity parameters were related to behavioral measures of inhibition efficiency. The results suggest that individuals at familial risk for schizophrenia were engaging an oculomotor control network differently than controls and in a way that compromises inhibition efficiency.
| S-EPMC8467791 | biostudies-literature
Project description:The profile of brain structural abnormalities in schizophrenia is still not fully understood, despite decades of research using brain scans. To validate a prospective meta-analysis approach to analyzing multicenter neuroimaging data, we analyzed brain MRI scans from 2028 schizophrenia patients and 2540 healthy controls, assessed with standardized methods at 15 centers worldwide. We identified subcortical brain volumes that differentiated patients from controls, and ranked them according to their effect sizes. Compared with healthy controls, patients with schizophrenia had smaller hippocampus (Cohen's d=-0.46), amygdala (d=-0.31), thalamus (d=-0.31), accumbens (d=-0.25) and intracranial volumes (d=-0.12), as well as larger pallidum (d=0.21) and lateral ventricle volumes (d=0.37). Putamen and pallidum volume augmentations were positively associated with duration of illness and hippocampal deficits scaled with the proportion of unmedicated patients. Worldwide cooperative analyses of brain imaging data support a profile of subcortical abnormalities in schizophrenia, which is consistent with that based on traditional meta-analytic approaches. This first ENIGMA Schizophrenia Working Group study validates that collaborative data analyses can readily be used across brain phenotypes and disorders and encourages analysis and data sharing efforts to further our understanding of severe mental illness.
| S-EPMC4668237 | biostudies-literature
Project description:Schizophrenia shows abnormal dynamic functional network connectivity (dFNC), but it is unclear whether these abnormalities are present early in the illness course or precede illness onset in individuals at clinical high risk (CHR) for psychosis. We examined dFNC from resting-state functional magnetic resonance imaging data in CHR (n?=?53), early illness schizophrenia (ESZ; n?=?58), and healthy control (HC; n?=?70) individuals. We applied a sliding temporal window approach capturing five distinct dFNC states. In ESZ patients, the likelihood of transitioning from state 4, a state that exhibited greater cortical-subcortical hyperconnectivity and also lacked typically observed anticorrelation between the default mode network and other functional networks, to a hypoconnected state was increased compared with HC and CHR groups. Furthermore, we investigated the interaction of group and state on dFNC. Overall, HC individuals showed significant changes of connectivity between states that were absent or altered in ESZ patients and CHR individuals. Connectivity differences between groups were identified primarily in two out of the five states, in particular, between HC and ESZ groups. In summary, it appears that the interaction effect was mostly driven by (1) dynamic connectivity changes in HC that were abnormal in CHR and ESZ individuals and (2) the fact that dysconnectivity between groups was only present in some states. These findings underscore the likelihood that abnormalities are present not only in static FNC but also in dFNC, in individuals at CHR for schizophrenia.
| S-EPMC6390658 | biostudies-literature
Project description:ObjectiveIncreasing pieces of evidence suggest that abnormal brain connectivity plays an important role in the pathophysiology of schizophrenia. As an essential strategy in psychiatric neuroscience, the research of brain connectivity-based neuroimaging biomarkers has gained increasing attention. Most of previous studies focused on a single modality of the brain connectomics. Multimodal evidence will not only depict the full profile of the brain abnormalities of patients but also contribute to our understanding of the neurobiological mechanisms of this disease.MethodsIn the current study, 99 schizophrenia patients, 69 sex- and education-matched healthy controls, and 42 unaffected first-degree relatives of patients were recruited and scanned. The brain was parcellated into 246 regions and multimodal network analyses were used to construct brain connectivity networks for each participant.ResultsUsing the brain connectomics from three modalities as the features, the multi-kernel support vector machine method yielded high discrimination accuracies for schizophrenia patients (94.86%) and for the first-degree relatives (95.33%) from healthy controls. Using an independent sample (49 patients and 122 healthy controls), we tested the model and achieved a classification accuracy of 64.57%. The convergent pattern within the basal ganglia and thalamus-cortex circuit exhibited high discriminative power during classification. Furthermore, substantial overlaps of the brain connectivity abnormality between patients and the unaffected first-degree relatives were observed compared to healthy controls.ConclusionThe current findings demonstrate that decreased functional communications between the basal ganglia, thalamus, and the prefrontal cortex could serve as biomarkers and endophenotypes for schizophrenia.
| S-EPMC7947240 | biostudies-literature