scholarly journals Zebrafish Chromosome 14 Gene Differential Expression in the fmr1hu2787 Model of Fragile X Syndrome

2021 ◽  
Vol 12 ◽  
Author(s):  
Karissa Barthelson ◽  
Lachlan Baer ◽  
Yang Dong ◽  
Melanie Hand ◽  
Zac Pujic ◽  
...  
Keyword(s):  
Differential Expression ◽  
Fragile X Syndrome ◽  
Differentially Expressed Genes ◽  
Fragile X ◽  
Allelic Diversity ◽  
Leading Edge ◽  
Orthologous Gene ◽  
Selective Advantage ◽  
Differentially Expressed ◽  
Chromosome 14

Zebrafish represent a valuable model for investigating the molecular and cellular basis of Fragile X syndrome (FXS). Reduced expression of the zebrafish FMR1 orthologous gene, fmr1, causes developmental and behavioural phenotypes related to FXS. Zebrafish homozygous for the hu2787 non-sense mutation allele of fmr1 are widely used to model FXS, although FXS-relevant phenotypes seen from morpholino antisense oligonucleotide (morpholino) suppression of fmr1 transcript translation were not observed when hu2787 was first described. The subsequent discovery of transcriptional adaptation (a form of genetic compensation), whereby mutations causing non-sense-mediated decay of transcripts can drive compensatory upregulation of homologous transcripts independent of protein feedback loops, suggested an explanation for the differences reported. We examined the whole-embryo transcriptome effects of homozygosity for fmr1hu2787 at 2 days post fertilisation. We observed statistically significant changes in expression of a number of gene transcripts, but none from genes showing sequence homology to fmr1. Enrichment testing of differentially expressed genes implied effects on lysosome function and glycosphingolipid biosynthesis. The majority of the differentially expressed genes are located, like fmr1, on Chromosome 14. Quantitative PCR tests did not support that this was artefactual due to changes in relative chromosome abundance. Enrichment testing of the “leading edge” differentially expressed genes from Chromosome 14 revealed that their co-location on this chromosome may be associated with roles in brain development and function. The differential expression of functionally related genes due to mutation of fmr1, and located on the same chromosome as fmr1, is consistent with R.A. Fisher’s assertion that the selective advantage of co-segregation of particular combinations of alleles of genes will favour, during evolution, chromosomal rearrangements that place them in linkage disequilibrium on the same chromosome. However, we cannot exclude that the apparent differential expression of genes on Chromosome 14 genes was, (if only in part), caused by differences between the expression of alleles of genes unrelated to the effects of the fmr1hu2787 mutation and made manifest due to the limited, but non-zero, allelic diversity between the genotypes compared.

scholarly journals Differential Gene Expression between Fungal Mating Types Is Associated with Sequence Degeneration

2020 ◽  
Vol 12 (4) ◽  
pp. 243-258 ◽  
Author(s):  
Wen-Juan Ma ◽  
Fantin Carpentier ◽  
Tatiana Giraud ◽  
Michael E Hood
Keyword(s):  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Mating Type ◽  
Sex Chromosomes ◽  
Sex Chromosome ◽  
Gc Content ◽  
Differentially Expressed ◽  
Mating Types ◽  
Sexual Antagonism ◽  
Recombination Suppression

Abstract Degenerative mutations in non-recombining regions, such as in sex chromosomes, may lead to differential expression between alleles if mutations occur stochastically in one or the other allele. Reduced allelic expression due to degeneration has indeed been suggested to occur in various sex-chromosome systems. However, whether an association occurs between specific signatures of degeneration and differential expression between alleles has not been extensively tested, and sexual antagonism can also cause differential expression on sex chromosomes. The anther-smut fungus Microbotryum lychnidis-dioicae is ideal for testing associations between specific degenerative signatures and differential expression because 1) there are multiple evolutionary strata on the mating-type chromosomes, reflecting successive recombination suppression linked to mating-type loci; 2) separate haploid cultures of opposite mating types help identify differential expression between alleles; and 3) there is no sexual antagonism as a confounding factor accounting for differential expression. We found that differentially expressed genes were enriched in the four oldest evolutionary strata compared with other genomic compartments, and that, within compartments, several signatures of sequence degeneration were greater for differentially expressed than non-differentially expressed genes. Two particular degenerative signatures were significantly associated with lower expression levels within differentially expressed allele pairs: upstream insertion of transposable elements and mutations truncating the protein length. Other degenerative mutations associated with differential expression included nonsynonymous substitutions and altered intron or GC content. The association between differential expression and allele degeneration is relevant for a broad range of taxa where mating compatibility or sex is determined by genes located in large regions where recombination is suppressed.

scholarly journals Palmitate and Fructose Interact to Induce Human Hepatocytes to Produce Pro-Fibrotic Transcriptional Responses in Hepatic Stellate Cells Exposed to Conditioned Media

2020 ◽  
Vol 54 (5) ◽  
pp. 1068-1082
Keyword(s):  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Hepatic Stellate Cells ◽  
Stellate Cell ◽  
Stellate Cells ◽  
Human Hepatocytes ◽  
Conditioned Media ◽  
Differentially Expressed ◽  
Transcriptional Responses ◽  
Significant Differential Expression

BACKGROUND/AIMS: Excessive consumption of dietary fat and sugar is associated with an elevated risk of nonalcoholic fatty liver disease (NAFLD). Hepatocytes exposed to saturated fat or sugar exert effects on nearby hepatic stellate cells (HSCs); however, the mechanisms by which this occurs are poorly understood. We sought to determine whether paracrine effects of hepatocytes exposed to palmitate and fructose produced profibrotic transcriptional responses in HSCs. METHODS: We performed expression profiling of mRNA and lncRNA from HSCs treated with conditioned media (CM) from human hepatocytes treated with palmitate (P), fructose (F), or both (PF). RESULTS: In HSCs exposed to CM from palmitate-treated hepatocytes, we identified 374 mRNAs and 607 lncRNAs showing significant differential expression (log2 foldchange ≥ |1|; FDR ≤0.05) compared to control cells. In HSCs exposed to CM from PF-treated hepatocytes, the number of differentially expressed genes was much higher (1198 mRNAs and 3348 lncRNAs); however, CM from fructose-treated hepatocytes elicited no significant changes in gene expression. Pathway analysis of differentially expressed genes showed enrichment for hepatic fibrosis and hepatic stellate cell activation in P- (FDR =1.30E-04) and PF-(FDR =9.24E-06)
groups. We observed 71 lncRNA/nearby mRNA pairs showing differential expression under PF conditions. There were 90 mRNAs and 264 lncRNAs strongly correlated between the PF group and differentially expressed transcripts from a comparison of activated and quiescent HSCs, suggesting that some of the transcriptomic changes occurring in response to PF overlap with HSC activation. CONCLUSION: The results reported here have implications for dietary modifications in the prevention and treatment of NAFLD.

scholarly journals Gene Expression Profiling of Sorted Peripheral Blood Cells Using Microarray and Next Generation Sequencing Reveals Distinct Molecular Signatures in the Polymorphonuclear and Mononuclear Cells of Patients with Polycythemia Vera and Primary Myelofibrosis

Blood ◽  
2015 ◽  
Vol 126 (23) ◽  
pp. 5201-5201
Author(s):  
Chieh Lee Wong ◽  
Baoshan Ma ◽  
Gareth Gerrard ◽  
Martyna Adamowicz-Brice ◽  
Zainul Abidin Norziha ◽  
...  
Keyword(s):  
Gene Expression ◽  
T Cells ◽  
Gene Expression Profiling ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Expression Profiling ◽  
Differentially Expressed ◽  
Cell Type ◽  
Cell Type Specific ◽  
Generation Sequencing

Abstract Background The past decade has witnessed a significant progress in the understanding of the molecular pathogenesis of myeloproliferative neoplasms (MPN). A large number of genes have now been implicated in the pathogenesis of MPN but their relative importance, the mechanisms by which they cause different cell types to predominate and their implications for prognosis remain unknown. We hypothesized that there are other genes which may contribute to the pathogenesis of the different disease subtypes detectable only by cell-type specific analysis. Aim The aim of this study was to perform gene expression profiling on different cell types from patients with MPN in order to identify novel variants and driver mutations, to elucidate the pathogenesis and to identify predictors of survival in patients with MPN in a multiracial country. Methods We performed gene expression profiling on normal controls (NC) and patients with MPN from 3 different races (Malay, Chinese and Indian) in Malaysia who were diagnosed with essential thrombocythemia (ET), polycythemia vera (PV) and primary myelofibrosis (PMF) according to the 2008 WHO diagnostic criteria for MPN. Two cohorts of patients, the patient and validation cohorts, from 3 tertiary-level hospitals were recruited prospectively over 3 years and informed consents were obtained. Peripheral blood samples were taken and sorted into polymorphonuclear cells (PMNs), mononuclear cells (MNCs) and T cells. RNA was extracted from each cell population. Gene expression profiling was performed using the Illumina HumanHT-12 Expression Beadchip for microarray and the Illumina Nextera XT DNA Sample Preparation Kit for next generation sequencing on the patient and validation cohorts respectively. Results Twenty-eight patients (10 ET, 11 PV and 7 PMF) and 11 NC were recruited into the patient cohort. Twelve patients (4 ET, 4 PV and 4 PMF) and 4 NC were recruited into the validation cohort. Gene expression levels for each cell type in each disease were compared with NC. In the patient cohort, the number of differentially expressed genes in ET, PV and PMF was 0, 141 and 15 respectively for PMNs (p < 0.05 after multiple testing correction) and 5, 170 and 562 respectively for MNCs (p < 0.05). No differentially expressed genes were identified for T cells in any of the three disease groups. RNA-seq analysis of samples from the validation cohort was used to corroborate these findings. After combination, we were able to confirm differential expression of 0, 14 and 7 genes in ET, PV and PMF respectively for PMNs (p < 0.05) and 51 genes in only PMF for MNCs (p < 0.05). The validated differentially expressed genes for PMNs and MNCs were mutually exclusive except for one gene. The differentially expressed genes in PV and PMF for PMNs were involved in cellular processes and metabolic pathways whereas the differentially expressed genes for PMF in MNCs were involved in regulation of cytoskeleton, focal adhesion and cell signaling pathways. Conclusion This is the first study to use microarray and next generation sequencing techniques to compare cell type-specific expression of genes between different subtypes of MPN. The lack of differential expression in T cells validates the techniques used and indicates that they are not part of the neoplastic clone. Differential expression of genes for MNCs was seen only in PMF which may be related to their more severe phenotype. Interestingly, there were fewer differentially expressed genes in PMF compared to PV for PMNs. The lack of differential expression in ET may either reflect the relatively milder phenotype of the disease or that differential expression is limited to megakaryocytes-platelets which were not studied. The lists of mutually exclusive cell type-specific differentially expressed genes for PMNs and MNCs provide further insight into the pathogenesis of MPN and into the differences between its different forms. The identified genes also indicate further routes for investigation of pathogenesis and possible disease-specific targets for therapy. Disclosures Aitman: Illumina: Honoraria.

The NOD/SCID Xenograft Model Provides Clinically-Relevant Insights Into Glucocorticoid-Induced Gene Expression In Childhood B-Cell Precursor Acute Lymphoblastic Leukemia (ALL)

Blood ◽  
2010 ◽  
Vol 116 (21) ◽  
pp. 2493-2493
Author(s):  
Vivek A Bhadri ◽  
Mark J Cowley ◽  
Warren Kaplan ◽  
Richard B Lock
Keyword(s):  
Gene Expression ◽  
Acute Lymphoblastic Leukemia ◽  
Mouse Model ◽  
B Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Lymphoblastic Leukemia ◽  
Xenograft Model ◽  
Differentially Expressed ◽  
Cell Precursor

Abstract Abstract 2493 Introduction. Glucocorticoids (GC) such as prednisolone (Pred) and dexamethasone (Dex) are critical drugs in multi-agent chemotherapy protocols used to treat acute lymphoblastic leukemia (ALL). The NOD/SCID ALL xenograft mouse model is a clinically relevant model in which the mice develop a systemic leukemia which retains the fundamental biological characteristics of the original disease. Here we report the results of a study evaluating the NOD/SCID xenograft model to investigate GC-induced gene expression. Methods. Cells from a GC-sensitive xenograft derived from a child with B-cell precursor ALL were inoculated into NOD/SCID mice. Engraftment, defined as the proportion of human vs mouse CD45+ cells in the peripheral blood, was monitored by serial weekly tail-vein sampling. When engraftment levels reached >50%, the mice were randomised and treated with either dexamethasone 15 mg/kg or vehicle control by intraperitoneal injection, and harvested at 0, 8, 24 or 48 h thereafter. The 48 hour groups received a second dose of vehicle or Dex at 24 hours. At the defined timepoints, the mice were euthanized and lymphoblasts harvested from the spleen. RNA was extracted, amplified and hybridised onto Illumina WG-6 V3 chips. The data was pre-processed using variance-stabilisation transformation, and quantile normalisation. Differential expression was determined using limma by comparing all treated groups to time 0, with the positive False Discovery Rate correction for multiple testing. Hierarchical clustering was used to compare groups to each other. The stability of results when reducing the number of replicates was assessed using the Recovery Score method. Functional analysis was performed using gene set enrichment analysis (GSEA) and comparison to publicly available microarray data using parametric GSEA. Results. The 8 hour Dex-treated timepoint had the most number of significantly differentially expressed genes (see Table), with fewer observed at the 24 and 48 hour Dex-treated timepoints. There was minimal significant differential gene expression across the time-matched controls. At the 8 hour timepoint, ZBTB16, a known GC-induced gene, was the most significantly upregulated gene. Other significantly differentially expressed genes included TSC22D3 and SOCS1, both downstream targets of the glucocorticoid receptor (upregulated), and BCL-2 and C-MYC (downregulated). GSEA at 8 hours revealed a significant upregulation of catabolic pathways and downregulation of pathways associated with cell proliferation, particularly C-MYC. GSEA at 24 hours revealed enrichment of pathways associated with NF-kB. Replicate analysis revealed that at the 8 hour Dex treated timepoint, a dataset with high signal and differential expression, using data from 3 replicates instead of 4 resulted in excellent recovery scores of >0.9. However at other timepoints with less signal very poor recovery scores were obtained using 3 replicates. We compared our data to publicly available datasets of GC-induced genes in ALL (Schmidt et al, Blood 2006; Rainer et al, Leukemia 2009) using parametric GSEA, which revealed that the 8 hour gene expression data obtained from the NOD/SCID xenograft model clustered with data from primary patient samples (Schmidt) rather than the cell line data (Rainer). The 24 and 48 hour datasets clustered separately from all other datasets by this method, reflecting fewer and predominantly downregulated gene expression at these timepoints. Conclusions: The NOD/SCID xenograft mouse model provides a reproducible experimental model system in which to investigate clinically-relevant mechanisms of GC-induced gene regulation in ALL; the 8 hour timepoint provides the highest number of significantly differentially expressed genes; time-matched controls are redundant and excellent recovery scores can be obtained with 3 replicates. Disclosures: No relevant conflicts of interest to declare.

scholarly journals NDUFA13/YJEFN3 is differentially expressed in the brains of patients with schizophrenia.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Frontal Cortex ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Schizoaffective Disorder ◽  
Psychotic Disorders ◽  
Auditory Hallucinations ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Microarray Datasets ◽  
Cardinal Feature

Visual and auditory hallucinations are a cardinal feature of psychotic disorders (1). We mined published and public microarray datasets (2, 3) to discover differentially expressed genes in schizophrenia and schizoaffective disorder. We found significant differential expression of transcripts overlapping NDUFA13 and YJEFN3 genes in neurons of the dorsolateral pre-frontal cortex from patients with schizophrenia and schizoaffective disorder.

scholarly journals BrainEXP-NPD: a database of transcriptomic profiles of human brains of six neuropsychiatric disorders

2021 ◽  
Author(s):  
Cuihua Xia ◽  
Teng Ma ◽  
Chuan Jiao ◽  
Chao Chen ◽  
Chunyu Liu
Keyword(s):  
Bipolar Disorder ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Neuropsychiatric Disorders ◽  
Brain Regions ◽  
Autism Spectrum ◽  
Differentially Expressed ◽  
Sequencing Data ◽  
Network Analyses ◽  
Gene Differential Expression

Background: Spatio-temporal gene expression has been widely used to study gene functions and biological mechanisms in diseases. Numerous microarray and RNA sequencing data focusing on brain transcriptomes in neuropsychiatric disorders have accumulated. However, their consistency, reproducibility has not been properly evaluated. Except for a few psychiatric disorders, like schizophrenia, bipolar disorder and autism, most have not been compared to each other for cross-disorder comparisons. Methods: We organized 48 human brain transcriptome datasets from six sources. The original brain donors include patients with schizophrenia (SCZ, N=427), bipolar disorder (BD, N=312), major depressive disorder (MDD, N=219), autism spectrum disorder (ASD, N=53), Alzheimer's disease (AD, N=765), Parkinson's disease (PD, N=163) as well as controls as unaffected by such disorders (CTRL, N=6,378), making it a total of 8,317 samples. Raw data included multiple brain regions of both sexes, with ages ranging from embryonic to seniors. After standardization, quality control, filtering and removal of known and unknown covariates, we performed comprehensive meta- and mega- analyses, including gene differential expression and gene co-expression network. Results: A total of 6922, 3011, 2703, 4389, 3507, 4279 significantly differentially expressed genes (FDR q < 0.05) were detected in the comparisons of 6 brain regions of SCZ-CTRL, 5 brain regions of BD-CTRL, 6 brain regions of MDD-CTRL, 4 brain regions of ASD-CTRL, 7 brain regions of AD-CTRL, and 6 brain regions of PD-CTRL, respectively. Most differentially expressed genes were brain region-specific and disease-specific. SCZ and BD have a maximal transcriptome similarity in striatum (ρ=0.42) among the four brain regions, as measured by Spearman's correlation of differential expression log2 FC values. SCZ and MDD have a maximal transcriptome similarity in hippocampus (ρ=0.30) among the five brain regions. BD and MDD have a maximal transcriptome similarity in frontal cortex (ρ=0.45) among the five brain regions. Other disease pairs have a less transcriptome similarity (ρ<0.1) in all brain regions. PD is negatively correlated with SCZ, BD, and MDD in cerebellum and striatum. We also performed coexpression network analyses for different disorders and controls separately. We developed a database named BrainEXP-NPD (http://brainexpnpd.org:8088/BrainEXPNPD/), to provide a user-friendly web interface for accessing the data, and analytical results of meta- and mega-analyses, including gene differential expression and gene co-expression networks between cases and controls on different brain regions, sexes and age groups. Discussion: BrainEXP-NPD compiled the largest collection of brain transcriptomic data of major neuropsychiatric disorders and presented lists of differentially expressed genes and coexpression modules in multiple brain regions of six major disorders.

scholarly journals A pseudogene of Brd7, Brd7p3 is differentially expressed in the brains of patients with schizophrenia.

2020 ◽  
Author(s):  
Shahan Mamoor
Keyword(s):  
Frontal Cortex ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Schizoaffective Disorder ◽  
Psychotic Disorders ◽  
Auditory Hallucinations ◽  
Differentially Expressed ◽  
Significant Differential Expression ◽  
Microarray Datasets ◽  
Cardinal Feature

Visual and auditory hallucinations are a cardinal feature of psychotic disorders (1). We mined published and public microarray datasets (2, 3) to discover differentially expressed genes in schizophrenia and schizoaffective disorder. We found significant differential expression of pseudogene 3 of the bromodomain containing 7 molecule, Brd7p3, in neurons of the dorsolateral pre-frontal cortex from patients with schizophrenia and schizoaffective disorder.

scholarly journals Transcriptome Analysis of Orbital Adipose Tissue in Active Thyroid Eye Disease Using Next Generation RNA Sequencing Technology

2018 ◽  
Vol 12 (1) ◽  
pp. 41-52 ◽  
Author(s):  
Bradford W. Lee ◽  
Virender B. Kumar ◽  
Pooja Biswas ◽  
Audrey C. Ko ◽  
Ramzi M. Alameddine ◽  
...  
Keyword(s):  
Adipose Tissue ◽  
Differential Expression ◽  
Rna Sequencing ◽  
Differentially Expressed Genes ◽  
Thyroid Eye Disease ◽  
Differentially Expressed ◽  
Eye Disease ◽  
Healthy Controls ◽  
Rna Seq ◽  
Next Generation

Objective: This study utilized Next Generation Sequencing (NGS) to identify differentially expressed transcripts in orbital adipose tissue from patients with active Thyroid Eye Disease (TED) versus healthy controls. Method: This prospective, case-control study enrolled three patients with severe, active thyroid eye disease undergoing orbital decompression, and three healthy controls undergoing routine eyelid surgery with removal of orbital fat. RNA Sequencing (RNA-Seq) was performed on freshly obtained orbital adipose tissue from study patients to analyze the transcriptome. Bioinformatics analysis was performed to determine pathways and processes enriched for the differential expression profile. Quantitative Reverse Transcriptase-Polymerase Chain Reaction (qRT-PCR) was performed to validate the differential expression of selected genes identified by RNA-Seq. Results: RNA-Seq identified 328 differentially expressed genes associated with active thyroid eye disease, many of which were responsible for mediating inflammation, cytokine signaling, adipogenesis, IGF-1 signaling, and glycosaminoglycan binding. The IL-5 and chemokine signaling pathways were highly enriched, and very-low-density-lipoprotein receptor activity and statin medications were implicated as having a potential role in TED. Conclusion: This study is the first to use RNA-Seq technology to elucidate differential gene expression associated with active, severe TED. This study suggests a transcriptional basis for the role of statins in modulating differentially expressed genes that mediate the pathogenesis of thyroid eye disease. Furthermore, the identification of genes with altered levels of expression in active, severe TED may inform the molecular pathways central to this clinical phenotype and guide the development of novel therapeutic agents.

scholarly journals Confronting false discoveries in single-cell differential expression

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Jordan W. Squair ◽  
Matthieu Gautier ◽  
Claudia Kathe ◽  
Mark A. Anderson ◽  
Nicholas D. James ◽  
...  
Keyword(s):  
Single Cell ◽  
Differential Expression ◽  
Differentially Expressed Genes ◽  
Differential Expression Analysis ◽  
Differentially Expressed ◽  
Cell Type ◽  
Mouse Spinal Cord ◽  
Cell Type Specific ◽  
False Discoveries ◽  
Biological Differences

AbstractDifferential expression analysis in single-cell transcriptomics enables the dissection of cell-type-specific responses to perturbations such as disease, trauma, or experimental manipulations. While many statistical methods are available to identify differentially expressed genes, the principles that distinguish these methods and their performance remain unclear. Here, we show that the relative performance of these methods is contingent on their ability to account for variation between biological replicates. Methods that ignore this inevitable variation are biased and prone to false discoveries. Indeed, the most widely used methods can discover hundreds of differentially expressed genes in the absence of biological differences. To exemplify these principles, we exposed true and false discoveries of differentially expressed genes in the injured mouse spinal cord.

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