scholarly journals DLPFC Transcriptome Defines Two Molecular Subtypes of Schizophrenia

2017 ◽  
Author(s):  
C. Harker Rhodes ◽  
Elijah F. W. Bowen ◽  
Jack L. Burgess ◽  
Richard Granger

AbstractLittle is known about the molecular pathogenesis of schizophrenia, possibly because of unrecognized heterogeneity in diagnosed patient populations. We analyzed gene expression data collected from the dorsolateral prefrontal cortex (DLPFC) of post-mortem frozen brains of 189 adult diagnosed schizophrenics and 206 matched controls. Transcripts from 633 genes are differentially expressed in the DLPFC of schizophrenics as compared to controls at Bonferroni-corrected significance levels. Seventeen of those genes are differentially expressed at very high significance levels (< 10−8 after Bonferroni correction).Weighted Gene Co-expression Network Analysis (WGCNA) of the schizophrenic subjects, based on the transcripts differentially expressed in the schizophrenics as compared to controls, divides them into two groups: "Type 1" schizophrenics, have a DLPFC transcriptome similar to that of controls with no expressed genes identified in this subcohort while the "type 2" schizophrenics have a DLPFC transcriptome dramatically different from that of controls, with 3,652 expression array probes to 3,200 genes detecting transcripts that are differentially expressed at very high significance levels. These findings were re-tested and replicated in a separate independent cohort, using the RNAseq data from the DLPFC of an independent set of schizophrenics and control subjects.We suggest the hypothesis that these striking differences in DLPFC transcriptomes, identified and replicated in two populations, imply a fundamental biologic difference between these two groups of patients who have been diagnosed as schizophrenic.

1984 ◽  
Vol 62 (12) ◽  
pp. 1453-1459 ◽  
Author(s):  
M. Zamir ◽  
S. Phipps ◽  
B. L. Langille ◽  
T. H. Wonnacott

The purpose of this study is to examine quantitatively the branching characteristics of the coronary arteries. Branching angles and vessel diameters were measured in a total of 175 arterial bifurcations in the coronary beds of rats, and the results are compared with those of 350 bifurcations in other parts of the cardiovascular system of the same species. Significant differences are found in the values of branch diameters and branching angles, both being found generally lower in the coronary bed than in other parts of the system. On statistical grounds these differences are found to have very high significance levels, with P values less than 0.02 in the case of branching angles and much less than 0.001 in the ease of branch diameters. On physiological grounds, the differences are such as to place the coronary arteries further away from the "theoretical optimum" than are vessels in other parts of the cardiovascular system. The theoretical optimum represents branching angles and branch diameters which make arterial bifurcations more efficient physiologically.


2020 ◽  
Author(s):  
Shahan Mamoor

We mined two microarray datasets (published or public) (1, 2) to identify the most significant changes in gene expression in the brains of patients with psychotic disorders. We found that isoform 2 of phospholipase C beta (PLCB2) was among the genes most differentially expressed in the dorsolateral prefrontal cortex (DLPFC) of patients with schizophrenia and psychotic bipolar disorder (1). In a separate dataset (2), PLCB2 was again among the genes whose expression changed most significantly when comparing parvalbumin-positive layer 3 neurons from the DLPFC between patients with schizophrenia and schizoaffective disorder and control subjects. Multiple studies have documented dysregulated PLCB1 expression in the brains of patients with schizophrenia, including in the dorsolateral prefrontal cortex (3-6). These data suggest that PLCB2 expression may also be perturbed in the brains of patients with psychotic disorders.


2022 ◽  
Vol 12 ◽  
Author(s):  
Jianyi Lv ◽  
Yihan Liu ◽  
Jia Cui ◽  
Hongjuan Fang ◽  
Ying Wu ◽  
...  

Long noncoding RNAs (lncRNAs) have been reported to have multiple functions and can be used as markers of various diseases, including diabetes. This study was conducted to determine the lncRNA profile in leukocytes from patients with type 2 diabetes (T2D). Differential expression of lncRNAs in T2D and type 1 diabetes (T1D) was also examined. RNA sequencing was performed in a critically grouped sample of leukocytes from T2D patients and healthy persons. A total of 845 significantly differentially expressed lncRNAs were identified, with 260 downregulated and 585 upregulated lncRNAs in T2D. The analysis of functions of DE-lncRNA and constructed co-expression networks (CNC) showed that 21 lncRNAs and 117 mRNAs harbored more than 10 related genes in CNC. Fourteen of 21 lncRNAs were confirmed to be significantly differentially expressed was detected by qPCR between the T2D and control validation cohorts. We also identified a panel of 4 lncRNAs showing significant differences in expression between T1D and T2D. Collectively, hundreds of novel DE-lncRNAs we identified in leukocytes from T2D patients will aid in epigenetic mechanism studies. Fourteen confirmed DE-lncRNAs can be regarded as diagnostic markers or regulators of T2D, including 4 lncRNAs that chould distinguish T1D and T2D in clinical practice to avoid misdiagnosis.


2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Melek Pehlivan ◽  
Tülay K. Ayna ◽  
Maşallah Baran ◽  
Mustafa Soyöz ◽  
Aslı Ö. Koçyiğit ◽  
...  

Abstract Objectives There are several hypotheses on the effects of the rs1738074 T/C single nucleotide polymorphism in the TAGAP gene; however, there has been no study on Turkish pediatric patients. We aimed to investigate the association of celiac disease (CD) and type 1 diabetes mellitus (T1DM) comorbidity with the polymorphism in the TAGAP gene of Turkish pediatric patients. Methods Totally, 127 pediatric CD patients and 100 healthy children were included. We determined the polymorphism by the allele-specific polymerase chain reaction method. We used IBM SPSS Statistics version 25.0 and Arlequin 3.5.2 for the statistical analyses. The authors have no conflict of interest. Results It was determined that 72% (n=154) of only CD patients had C allele, whereas 28% (n=60) had T allele. Of the patients with celiac and T1DM, 42.5% (n=17) and 57.5% (n=23) had T and C alleles, respectively. Of the individuals in control group, 67% (n=134) had C allele, whereas 33% (n=66) had T allele. Conclusions There was no significant difference in the genotype and allele frequencies between the patient and control groups (p>0.05). There was no significant association between the disease risk and the polymorphism in our study group.


2020 ◽  
Vol 8 (Suppl 3) ◽  
pp. A646-A647
Author(s):  
Max Meneveau ◽  
Pankaj Kumar ◽  
Kevin Lynch ◽  
Karlyn Pollack ◽  
Craig Slingluff

BackgroundVaccines are a promising therapeutic for patients with advanced cancer, but achieving robust T-cell responses remains a challenge. Melanoma-associated antigen-A3 (MAGE-A3) in combination with adjuvant AS15 (a formulation of Toll-Like-Receptor (TLR)-4 and 9 agonists and a saponin), induced systemic CD4+ T-cell responses in 50% of patients when given subcutaneously/intradermally. Little is known about the transcriptional landscape of the vaccine-site microenvironment (VSME) of patients with systemic T-cell responses versus those without. We hypothesized that patients with systemic T-cell responses to vaccination would exhibit increased immune activation in the VSME, higher dendritic cell (DC) activation/maturation, TLR-pathway activation, and enhanced Th1 signatures.MethodsBiopsies of the VSME were obtained from participants on the Mel55 clinical trial (NCT01425749) who were immunized with MAGE-A3/AS15. Biopsies were taken 8 days after immunization. T-cell response to MAGE-A3 was assessed in PBMC after in-vitro stimulation with recMAGE-A3, by IFNγ ELISPOT assay. Gene expression was assessed by RNAseq using DESeq2. Comparisons were made between immune-responders (IR), non-responders (NR), and normal skin controls. FDR p<0.01 was considered significant.ResultsFour IR, four NR, and three controls were evaluated. The 500 most variable genes were used for principal component analysis (PCA). Two IR samples were identified as outliers on PCA and excluded from further analysis. There were 882 differentially expressed genes (DEGs) in the IR group vs the NR group (figure 1A). Unsupervised clustering of the top 500 DEGs revealed clustering according to the experimental groups (figure 1B). Of the 10 most highly upregulated DEGs, 9 were immune-related (figure 1C). Gene-set enrichment analysis revealed that immune-related pathways were highly enriched in IRs vs NRs (figure 1D). CD4 and CD8 expression did not differ between IR and NR (figure 2A), though both were higher in IR compared to control. Markers of DC activation/maturation were higher in IR vs NR (figure 2B), as were several Th1 associated genes (figure 2C). Interestingly, markers of exhaustion were higher in IR v NR (figure 2D). Expression of numerous TLR-pathway genes was higher in IR vs NR, including MYD88, but not TICAM1 (figure 2E).Abstract 611 Figure 1Gene expression profiling of vaccine site samples from patients immunized with MAGE-A3/AS15. (A) Volcano plots showing the distribution of differentially expressed genes (DEGs) between immune responders (IR) and non-responders (NR), IR and control, and NR and control. (B) Heatmap of the top 500 most differentially expressed genes demonstrating hierarchical clustering of sequenced samples according to IR, NR, and control. (C) Table showing the 10 most highly up and down-regulated genes in IR compared to NR. 9 of the top 10 most highly up-regulated genes are related to the immune response. (D) Enrichment plots from a gene set enrichment analysis highlighting the upregulation of immune related pathways in IR compared to NR. Gene set enrichment data was generated from the Reactome gene set database and included all expressed genes. Significance was set at FDR p <0.01Abstract 611 Figure 2Expression of T-cell markers in IR vs NR vs Control samples in the vaccine site microenvironment (VSME). (A) T-cell markers showing similar expression in IR vs NR but higher expression in IR vs control. (B) Markers of dendritic cell activation and maturation in the VSME which are higher in IR vs control but not IR vs NR. (B) Transcription factors and genes associated with Th1/Th2 responses within the VSME. (D) Genes associated with T-cell exhaustion at the VSME. (E) Expression of TLR pathway genes in the VSME. Expression data is provided in terms of normalized counts. Bars demonstrate median and interquartile range. N=9. IR = immune responder, NR = non-responder, TLR = Toll-like Receptor. * = <0.01, ** < 0.001, *** <0.0001, **** < 0.00001ConclusionsThese findings suggest a unique immune-transcriptional landscape in the VSME is associated with circulating T-cell responses to immunization, with differences in DC activation/maturation, Th1 response, and TLR signaling. Thus, immunologic changes in the VSME are useful predictors of systemic immune response, and host factors that modulate immune-related signaling at the vaccine site may have concordant systemic effects on promoting or limiting immune responses to vaccines.Trial RegistrationSamples for this work were collected from patients enrolled on the Mel55 clinical trial NCT01425749.Ethics ApprovalThis work was completed after approval from the UVA institutional review board IRB-HSR# 15398.


2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Alexander Lind ◽  
Ilaria Marzinotto ◽  
Cristina Brigatti ◽  
Anita Ramelius ◽  
Lorenzo Piemonti ◽  
...  

AbstractAn increased incidence of narcolepsy type 1 (NT1) was observed in Scandinavia following the 2009–2010 influenza Pandemrix vaccination. The association between NT1 and HLA-DQB1*06:02:01 supported the view of the vaccine as an etiological agent. A/H1N1 hemagglutinin (HA) is the main antigenic determinant of the host neutralization antibody response. Using two different immunoassays, the Luciferase Immunoprecipitation System (LIPS) and Radiobinding Assay (RBA), we investigated HA antibody levels and affinity in an exploratory and in a confirmatory cohort of Swedish NT1 patients and healthy controls vaccinated with Pandemrix. HA antibodies were increased in NT1 patients compared to controls in the exploratory (LIPS p = 0.0295, RBA p = 0.0369) but not in the confirmatory cohort (LIPS p = 0.55, RBA p = 0.625). HA antibody affinity, assessed by competition with Pandemrix vaccine, was comparable between patients and controls (LIPS: 48 vs. 39 ng/ml, p = 0.81; RBA: 472 vs. 491 ng/ml, p = 0.65). The LIPS assay also detected higher HA antibody titres as associated with HLA-DQB1*06:02:01 (p = 0.02). Our study shows that following Pandemrix vaccination, HA antibodies levels and affinity were comparable NT1 patients and controls and suggests that HA antibodies are unlikely to play a role in NT1 pathogenesis.


2021 ◽  
Vol 80 (Suppl 1) ◽  
pp. 45.2-45
Author(s):  
I. Heggli ◽  
R. Schüpbach ◽  
N. Herger ◽  
T. A. Schweizer ◽  
A. Juengel ◽  
...  

Background:Modic type 1 changes (MC1) are vertebral bone marrow (BM) edema that associate with non-specific low back pain (LBP). Two etiologies have been described. In the infectious etiology the anaerobic aerotolerant Cutibacterium acnes (C. acnes) invades damaged intervertebral discs (IVDs) resulting in disc infection and endplate damage, which leads to the evocation of an immune response. In the autoinflammatory etiology disc and endplate damage lead to the exposure of immune privileged disc cells and matrix to leukocytes, thereby evoking an immune response in the BM. Different etiologies require different treatment strategies. However, it is unknown if etiology-specific pathological mechanisms exist.Objectives:The aim of this study was to identify etiology-specific dysregulated pathways of MC1 and to perform in-depth analysis of immune cell populations of the autoinflammatory etiology.Methods:BM aspirates and biopsies were obtained from LBP patients with MC1 undergoing spinal fusion. Aspirates/biopsies were taken prior screw insertion through the pedicle screw trajectory. From each patient, a MC1 and an intra-patient control aspiration/biopsy from the adjacent vertebral level was taken. If C. acnes in IVDs adjacent to MC1 were detected by anaerobic bacterial culture, patients were assigned to the infectious, otherwise to the autoinflammatory etiology.Total RNA was isolated from aspirates and sequenced (Novaseq) (infectious n=3 + 3, autoinflammatory n=5 + 5). Genes were considered as differentially expressed (DEG) if p-value < 0.01 and log2fc > ± 0.5. Gene ontology (GO) enrichment was performed in R (GOseq), gene set enrichment analysis (GSEA) with GSEA software.Changes in cell populations of the autoinflammatory etiology were analyzed with single cell RNA sequencing (scRNAseq): Control and MC1 biopsies (n=1 + 1) were digested, CD45+CD66b- mononuclear cells isolated with fluorescence activated cell sorting (FACS), and 10000 cells were sequenced (10x Genomics). Seurat R toolkit was used for quality-control, clustering, and differential expression analysis.Transcriptomic changes (n=5 + 5) of CD45+CD66b+ neutrophils isolated with flow cytometry from aspirates were analyzed as for total bulk RNAseq. Neutrophil activation (n=3 + 3) was measured as CD66b+ expression with flow cytometry. CD66bhigh and CD66blow fractions in MC1 and control neutrophils were compared with paired t-test.Results:Comparing MC1 to control in total bulk RNAseq, 204 DEG in the autoinflammatory and 444 DEG in the infectious etiology were identified with only 67 shared genes (Fig. 1a). GO enrichment revealed “T-cell activation” (p = 2.50E-03) in the autoinflammatory and “complement activation, classical pathway” (p=1.1E-25) in the infectious etiology as top enriched upregulated biological processes (BP) (Fig 1b). ScRNAseq of autoinflammatory MC1 showed an overrepresentation of T-cells (p= 1.00E-34, OR=1.54) and myelocytes (neutrophil progenitor cells) (p=4.00E-05, OR=2.27) indicating an increased demand of these cells (Fig. 1c). Bulk RNAseq analysis of neutrophils from the autoinflammatory etiology revealed an activated, pro-inflammatory phenotype (Fig 1d), which was confirmed with more CD66bhigh neutrophils in MC1 (+11.13 ± 2.71%, p=0.02) (Fig. 1e).Figure 1.(a) Venn diagram of DEG from total bulk RNAseq (b) Top enriched upregulated BP of autoinflammatory (left) and infectious (right) etiology (c) Cell clustering of autoinflammatory MC1 BM (d) Enrichment of “inflammatory response” gene set in autoinflammatory MC1 neutrophils (e) Representative histogram of CD66b+ expression in MC1 and control neutrophils.Conclusion:Autoinflammatory and infectious etiologies of MC1 have different pathological mechanisms. T-cell and neutrophil activation seem to be important in the autoinflammatory etiology. This has clinical implication as it could be explored for diagnostic approaches to distinguish the two MC1 etiologies and supports developing targeted treatments for both etiologies.Disclosure of Interests:None declared


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lior Rennert ◽  
Moonseong Heo ◽  
Alain H. Litwin ◽  
Victor De Gruttola

Abstract Background Beginning in 2019, stepped-wedge designs (SWDs) were being used in the investigation of interventions to reduce opioid-related deaths in communities across the United States. However, these interventions are competing with external factors such as newly initiated public policies limiting opioid prescriptions, media awareness campaigns, and the COVID-19 pandemic. Furthermore, control communities may prematurely adopt components of the intervention as they become available. The presence of time-varying external factors that impact study outcomes is a well-known limitation of SWDs; common approaches to adjusting for them make use of a mixed effects modeling framework. However, these models have several shortcomings when external factors differentially impact intervention and control clusters. Methods We discuss limitations of commonly used mixed effects models in the context of proposed SWDs to investigate interventions intended to reduce opioid-related mortality, and propose extensions of these models to address these limitations. We conduct an extensive simulation study of anticipated data from SWD trials targeting the current opioid epidemic in order to examine the performance of these models in the presence of external factors. We consider confounding by time, premature adoption of intervention components, and time-varying effect modification— in which external factors differentially impact intervention and control clusters. Results In the presence of confounding by time, commonly used mixed effects models yield unbiased intervention effect estimates, but can have inflated Type 1 error and result in under coverage of confidence intervals. These models yield biased intervention effect estimates when premature intervention adoption or effect modification are present. In such scenarios, models incorporating fixed intervention-by-time interactions with an unstructured covariance for intervention-by-cluster-by-time random effects result in unbiased intervention effect estimates, reach nominal confidence interval coverage, and preserve Type 1 error. Conclusions Mixed effects models can adjust for different combinations of external factors through correct specification of fixed and random time effects. Since model choice has considerable impact on validity of results and study power, careful consideration must be given to how these external factors impact study endpoints and what estimands are most appropriate in the presence of such factors.


2019 ◽  
Vol 26 (11) ◽  
pp. 1485-1492
Author(s):  
Xiaochun Yi ◽  
Jie Zhang ◽  
Huixiang Liu ◽  
Tianxia Yi ◽  
Yuhua Ou ◽  
...  

The adverse clinical result and poor treatment outcome in recurrent spontaneous abortion (RSA) make it necessary to understand the pathogenic mechanism. The mating combination CBA/J × DBA/2 has been widely used as an abortion-prone model compared to DBA/2-mated CBA/J mice. Here, we used RNA-seq to get a comprehensive catalogue of genes differentially expressed between survival placenta in abortion-prone model and control. Five hundred twenty-four differentially expressed genes were obtained followed by clustering analysis, Gene Ontology analysis, and pathway analysis. We paid more attention to immune-related genes namely “immune response” and “immune system process” including 33 downregulated genes and 28 upregulated genes. Twenty-one genes contribute to suppressing immune system and 7 are against it. Six genes were validated by reverse transcription-polymerase chain reaction, namely Ccr1l1, Tlr4, Tgf-β1, Tyro3, Gzmb, and Il-1β. Furthermore, Tlr4, Tgf-β1, and Il-1β were analyzed by Western blot. Such immune profile gives us a better understanding of the complicated immune processing in RSA and immunosuppression can rescue pregnancy loss.


2007 ◽  
Vol 3 ◽  
pp. 117693510700300
Author(s):  
Yingye Zheng ◽  
Margaret Pepe

Consider a gene expression array study comparing two groups of subjects where the goal is to explore a large number of genes in order to select for further investigation a subset that appear to be differently expressed. There has been much statistical research into the development of formal methods for designating genes as differentially expressed. These procedures control error rates such as the false detection rate or family wise error rate. We contend however that other statistical considerations are also relevant to the task of gene selection. These include the extent of differential expression and the strength of evidence for differential expression at a gene. Using real and simulated data we first demonstrate that a proper exploratory analysis should evaluate these aspects as well as decision rules that control error rates. We propose a new measure called the mp-value that quantifies strength of evidence for differential expression. The mp-values are calculated with a resampling based algorithm taking into account the multiplicity and dependence encountered in microarray data. In contrast to traditional p-values our mp-values do not depend on specification of a decision rule for their definition. They are simply descriptive in nature. We contrast the mp-values with multiple testing p-values in the context of data from a breast cancer prognosis study and from a simulation model.


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