Distinct Transcriptomic Profiles in the Dorsal Hippocampus and Prelimbic
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated. -
PSD-95 Binding Dynamically Regulates NLGN1 Trafficking and Function
PSD-95 binding dynamically regulates NLGN1 trafficking and function Jaehoon Jeonga, Saurabh Pandeya, Yan Lia, John D. Badger IIa, Wei Lua, and Katherine W. Rochea,1 aNational Institute of Neurological Disorders and Stroke, National Institutes of Health, Bethesda, MD 20892 Edited by Solomon H. Snyder, Johns Hopkins University School of Medicine, Baltimore, MD, and approved May 3, 2019 (received for review December 20, 2018) PSD-95 is a scaffolding protein that regulates the synaptic localization necessary for maintaining presynaptic release probability (15). of many receptors, channels, and signaling proteins. The NLGN gene Meanwhile, synaptic targeting of NLGN1 is known to be in- family encodes single-pass transmembrane postsynaptic cell adhesion dependent of PSD-95, as PSD-95 is recruited to the plasma molecules that are important for synapse assembly and function. At membrane after NLGN1 (13, 16, 17). In addition, non-PDZ excitatory synapses, NLGN1 mediates transsynaptic binding with ligand-dependent NLGN1 and NLGN3 functions have been in- neurexin, a presynaptic cell adhesion molecule, and also binds to vestigated (18), suggesting a complicated physiological interplay PSD-95, although the relevance of the PSD-95 interaction is not clear. between NLGNs and PSD-95. We now show that disruption of the NLGN1 and PSD-95 interaction Posttranslational modifications have been reported for several decreases surface expression of NLGN1 in cultured neurons. Further- NLGN isoforms and are postulated to confer distinct properties (11, more, PKA phosphorylates NLGN1 on S839, near the PDZ ligand, and 12). For example, phosphorylation of the cytoplasmic region of dynamically regulates PSD-95 binding. A phosphomimetic mutation NLGN1 has recently emerged as a mechanism for isoform-specific of NLGN1 S839 significantly reduced PSD-95 binding. -
Identification of Key Pathways and Genes in Dementia Via Integrated Bioinformatics Analysis
bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Identification of Key Pathways and Genes in Dementia via Integrated Bioinformatics Analysis Basavaraj Vastrad1, Chanabasayya Vastrad*2 1. Department of Biochemistry, Basaveshwar College of Pharmacy, Gadag, Karnataka 582103, India. 2. Biostatistics and Bioinformatics, Chanabasava Nilaya, Bharthinagar, Dharwad 580001, Karnataka, India. * Chanabasayya Vastrad [email protected] Ph: +919480073398 Chanabasava Nilaya, Bharthinagar, Dharwad 580001 , Karanataka, India bioRxiv preprint doi: https://doi.org/10.1101/2021.04.18.440371; this version posted July 19, 2021. The copyright holder for this preprint (which was not certified by peer review) is the author/funder. All rights reserved. No reuse allowed without permission. Abstract To provide a better understanding of dementia at the molecular level, this study aimed to identify the genes and key pathways associated with dementia by using integrated bioinformatics analysis. Based on the expression profiling by high throughput sequencing dataset GSE153960 derived from the Gene Expression Omnibus (GEO), the differentially expressed genes (DEGs) between patients with dementia and healthy controls were identified. With DEGs, we performed a series of functional enrichment analyses. Then, a protein–protein interaction (PPI) network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network was constructed, analyzed and visualized, with which the hub genes miRNAs and TFs nodes were screened out. Finally, validation of hub genes was performed by using receiver operating characteristic curve (ROC) analysis. -
Gabaergic Deficits and Schizophrenia-Like Behaviors in A
bioRxiv preprint doi: https://doi.org/10.1101/225524; this version posted November 27, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. GABAergic deficits and schizophrenia-like behaviors in a mouse model carrying patient-derived neuroligin-2 R215H mutation Dong-Yun Jiang1, Zheng Wu1, Yi Hu1, Siu-Pok Yee2, Gong Chen1, * 1. Department of Biology, Huck Institutes of Life Sciences, Pennsylvania State University, University Park, PA 16802 2. Department of Cell Biology, University of Connecticut Health center, Farmington, CT, 06030 Keywords: Schizophrenia, GABA, neuroligin-2, mouse model, mutation * Correspondence: Dr. Gong Chen Professor and Verne M. Willaman Chair in Life Sciences Department of Biology Pennsylvania State University University Park, PA 16802, USA Email: [email protected] 1 bioRxiv preprint doi: https://doi.org/10.1101/225524; this version posted November 27, 2017. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY 4.0 International license. Abstract Schizophrenia (SCZ) is a severe mental disorder characterized by delusion, hallucination, and cognitive deficits. We have previously identified from schizophrenia patients a loss-of-function mutation Arg215àHis215 (R215H) of neuroligin 2 (NLGN2) gene, which encodes a cell adhesion molecule critical for GABAergic synapse formation and function. Here, we generated a novel transgenic mouse line with neuroligin-2 (NL2) R215H mutation, which showed a significant loss of NL2 protein, reduced GABAergic transmission, and impaired hippocampal activation. -
Neuroligin 2 Deletion Alters Inhibitory Synapse Function and Anxiety-Associated Neuronal Activation in the Amygdala
Neuropharmacology xxx (2015) 1e10 Contents lists available at ScienceDirect Neuropharmacology journal homepage: www.elsevier.com/locate/neuropharm Neuroligin 2 deletion alters inhibitory synapse function and anxiety-associated neuronal activation in the amygdala Olga Babaev a, Paolo Botta b, Elisabeth Meyer b, Christian Müller b, * Hannelore Ehrenreich c, Nils Brose a, Andreas Lüthi b, Dilja Krueger-Burg a, a Department of Molecular Neurobiology, Max Planck Institute of Experimental Medicine, Hermann-Rein-Str. 3, 37075 Gottingen,€ Germany b Friedrich Miescher Institute for Biomedical Research, Maulbeerstr. 66, 4058 Basel, Switzerland c Clinical Neuroscience, Max Planck Institute of Experimental Medicine, Hermann-Rein-Str. 3, 37075 Gottingen,€ Germany article info abstract Article history: Neuroligin 2 (Nlgn2) is a synaptic adhesion protein that plays a central role in the maturation and Received 26 April 2015 function of inhibitory synapses. Nlgn2 mutations have been associated with psychiatric disorders such as Received in revised form schizophrenia, and in mice, deletion of Nlgn2 results in a pronounced anxiety phenotype. To date, 20 June 2015 however, the molecular and cellular mechanisms linking Nlgn2 deletion to psychiatric phenotypes Accepted 25 June 2015 remain completely unknown. The aim of this study was therefore to define the role of Nlgn2 in anxiety- Available online xxx related neural circuits. To this end, we used a combination of behavioral, immunohistochemical, and electrophysiological approaches in Nlgn2 knockout (KO) mice to expand the behavioral characterization Keywords: Neuroligins of these mice and to assess the functional consequences of Nlgn2 deletion in the amygdala. Moreover, we Amygdala investigated the differential activation of anxiety-related circuits in Nlgn2 KO mice using a cFOS acti- Anxiety vation assay following exposure to an anxiogenic stimulus. -
Sequence Variations of the EGR4 Gene in Korean Men With
Sung et al. BMC Medical Genetics (2017) 18:47 DOI 10.1186/s12881-017-0408-5 RESEARCH ARTICLE Open Access Sequence variations of the EGR4 gene in Korean men with spermatogenesis impairment Se Ra Sung1, Seung Hun Song2, Kyung Min Kang1, Ji Eun Park1, Yeo Jung Nam5, Yun-jeong Shin1, Dong Hyun Cha4, Ju Tae Seo3, Tae Ki Yoon4 and Sung Han Shim1,5* Abstract Background: Egr4 is expressed in primary and secondary spermatocytes in adult mouse testes and has a crucial role in regulating germ cell maturation. The functional loss of Egr4 blocks spermatogenesis, significantly reducing the number of spermatozoa that are produced. In this study, we examined whether EGR4 variants are present in Korean men with impaired spermatogenesis. Methods: A total 170 Korean men with impaired spermatogenesis and 272 normal controls were screened. The coding regions including exon-intron boundaries of EGR4 were sequenced by PCR-direct sequencing method. Results: We identified eight sequence variations in the coding region and 3′-UTR regions of the EGR4 gene. Four were nonsynonymous variants (rs771189047, rs561568849, rs763487015, and rs546250227), three were synonymous variants (rs115948271, rs528939702, and rs7558708), and one variant (rs2229294) was localized in the 3′-UTR. Three nonsynonymous variants [c.65_66InsG (p. Cys23Leufs*37), c.236C > T (p. Pro79Leu), c.1294G > T (p. Val432Leu)] and one synonymous variant [c.1230G > A (p. Thr410)] were not detected in controls. To evaluate the pathogenic effects of nonsynonymous variants, we used seven prediction methods. The c.214C > A (p. Arg72Ser) and c.236C > T (p. Pro79Leu) variants were predicted as “damaging” by SIFT and SNAP2.The c.65_66insG (p. -
Table SII. Significantly Differentially Expressed Mrnas of GSE23558 Data Series with the Criteria of Adjusted P<0.05 And
Table SII. Significantly differentially expressed mRNAs of GSE23558 data series with the criteria of adjusted P<0.05 and logFC>1.5. Probe ID Adjusted P-value logFC Gene symbol Gene title A_23_P157793 1.52x10-5 6.91 CA9 carbonic anhydrase 9 A_23_P161698 1.14x10-4 5.86 MMP3 matrix metallopeptidase 3 A_23_P25150 1.49x10-9 5.67 HOXC9 homeobox C9 A_23_P13094 3.26x10-4 5.56 MMP10 matrix metallopeptidase 10 A_23_P48570 2.36x10-5 5.48 DHRS2 dehydrogenase A_23_P125278 3.03x10-3 5.40 CXCL11 C-X-C motif chemokine ligand 11 A_23_P321501 1.63x10-5 5.38 DHRS2 dehydrogenase A_23_P431388 2.27x10-6 5.33 SPOCD1 SPOC domain containing 1 A_24_P20607 5.13x10-4 5.32 CXCL11 C-X-C motif chemokine ligand 11 A_24_P11061 3.70x10-3 5.30 CSAG1 chondrosarcoma associated gene 1 A_23_P87700 1.03x10-4 5.25 MFAP5 microfibrillar associated protein 5 A_23_P150979 1.81x10-2 5.25 MUCL1 mucin like 1 A_23_P1691 2.71x10-8 5.12 MMP1 matrix metallopeptidase 1 A_23_P350005 2.53x10-4 5.12 TRIML2 tripartite motif family like 2 A_24_P303091 1.23x10-3 4.99 CXCL10 C-X-C motif chemokine ligand 10 A_24_P923612 1.60x10-5 4.95 PTHLH parathyroid hormone like hormone A_23_P7313 6.03x10-5 4.94 SPP1 secreted phosphoprotein 1 A_23_P122924 2.45x10-8 4.93 INHBA inhibin A subunit A_32_P155460 6.56x10-3 4.91 PICSAR P38 inhibited cutaneous squamous cell carcinoma associated lincRNA A_24_P686965 8.75x10-7 4.82 SH2D5 SH2 domain containing 5 A_23_P105475 7.74x10-3 4.70 SLCO1B3 solute carrier organic anion transporter family member 1B3 A_24_P85099 4.82x10-5 4.67 HMGA2 high mobility group AT-hook 2 A_24_P101651 -
Supplementary Table 1
Supplementary Table 1. 492 genes are unique to 0 h post-heat timepoint. The name, p-value, fold change, location and family of each gene are indicated. Genes were filtered for an absolute value log2 ration 1.5 and a significance value of p ≤ 0.05. Symbol p-value Log Gene Name Location Family Ratio ABCA13 1.87E-02 3.292 ATP-binding cassette, sub-family unknown transporter A (ABC1), member 13 ABCB1 1.93E-02 −1.819 ATP-binding cassette, sub-family Plasma transporter B (MDR/TAP), member 1 Membrane ABCC3 2.83E-02 2.016 ATP-binding cassette, sub-family Plasma transporter C (CFTR/MRP), member 3 Membrane ABHD6 7.79E-03 −2.717 abhydrolase domain containing 6 Cytoplasm enzyme ACAT1 4.10E-02 3.009 acetyl-CoA acetyltransferase 1 Cytoplasm enzyme ACBD4 2.66E-03 1.722 acyl-CoA binding domain unknown other containing 4 ACSL5 1.86E-02 −2.876 acyl-CoA synthetase long-chain Cytoplasm enzyme family member 5 ADAM23 3.33E-02 −3.008 ADAM metallopeptidase domain Plasma peptidase 23 Membrane ADAM29 5.58E-03 3.463 ADAM metallopeptidase domain Plasma peptidase 29 Membrane ADAMTS17 2.67E-04 3.051 ADAM metallopeptidase with Extracellular other thrombospondin type 1 motif, 17 Space ADCYAP1R1 1.20E-02 1.848 adenylate cyclase activating Plasma G-protein polypeptide 1 (pituitary) receptor Membrane coupled type I receptor ADH6 (includes 4.02E-02 −1.845 alcohol dehydrogenase 6 (class Cytoplasm enzyme EG:130) V) AHSA2 1.54E-04 −1.6 AHA1, activator of heat shock unknown other 90kDa protein ATPase homolog 2 (yeast) AK5 3.32E-02 1.658 adenylate kinase 5 Cytoplasm kinase AK7 -
A Causal Gene Network with Genetic Variations Incorporating Biological Knowledge and Latent Variables
A CAUSAL GENE NETWORK WITH GENETIC VARIATIONS INCORPORATING BIOLOGICAL KNOWLEDGE AND LATENT VARIABLES By Jee Young Moon A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy (Statistics) at the UNIVERSITY OF WISCONSIN–MADISON 2013 Date of final oral examination: 12/21/2012 The dissertation is approved by the following members of the Final Oral Committee: Brian S. Yandell. Professor, Statistics, Horticulture Alan D. Attie. Professor, Biochemistry Karl W. Broman. Professor, Biostatistics and Medical Informatics Christina Kendziorski. Associate Professor, Biostatistics and Medical Informatics Sushmita Roy. Assistant Professor, Biostatistics and Medical Informatics, Computer Science, Systems Biology in Wisconsin Institute of Discovery (WID) i To my parents and brother, ii ACKNOWLEDGMENTS I greatly appreciate my adviser, Prof. Brian S. Yandell, who has always encouraged, inspired and supported me. I am grateful to him for introducing me to the exciting research areas of statis- tical genetics and causal gene network analysis. He also allowed me to explore various statistical and biological problems on my own and guided me to see the problems in a bigger picture. Most importantly, he waited patiently as I progressed at my own pace. I would also like to thank Dr. Elias Chaibub Neto and Prof. Xinwei Deng who my adviser arranged for me to work together. These three improved my rigorous writing and thinking a lot when we prepared the second chapter of this dissertation for publication. It was such a nice opportunity for me to join the group of Prof. Alan D. Attie, Dr. Mark P. Keller, Prof. Karl W. Broman and Prof. -
RNA-Seq Analysis Reveals Hub Genes Involved in Chicken Intramuscular Fat and Abdominal Fat Deposition During Development
fgene-11-01009 August 26, 2020 Time: 18:42 # 1 ORIGINAL RESEARCH published: 28 August 2020 doi: 10.3389/fgene.2020.01009 RNA-Seq Analysis Reveals Hub Genes Involved in Chicken Intramuscular Fat and Abdominal Fat Deposition During Development Siyuan Xing1,2†, Ranran Liu1†, Guiping Zhao1, Lu Liu1, Martien A. M. Groenen2, Ole Madsen2, Maiqing Zheng1, Xinting Yang1, Richard P. M. A. Crooijmans2* and Jie Wen1* Edited by: 1 State Key Laboratory of Animal Nutrition, Key Laboratory of Animal (Poultry) Genetics Breeding and Reproduction, Ministry Huaijun Zhou, of Agriculture and Rural Affairs, Institute of Animal Sciences, Chinese Academy of Agricultural Sciences, Beijing, China, University of California, Davis, 2 Animal Breeding and Genomics, Wageningen University and Research, Wageningen, Netherlands United States Reviewed by: Fat traits are important in the chicken industry where there is a desire for high Yachun Wang, China Agricultural University, China intramuscular fat (IMF) and low abdominal fat. However, there is limited knowledge Romi Pena i Subirà, on the relationship between the dynamic status of gene expression and the body Universitat de Lleida, Spain fat deposition in chicken. Transcriptome data were obtained from breast muscle and *Correspondence: Jie Wen abdominal fat of female chickens from nine developmental stages (from embryonic day [email protected] 12 to hatched day 180). In total, 8,545 genes in breast muscle and 6,824 genes in Richard P. M. A. Crooijmans abdominal fat were identified as developmentally dynamic genes. Weighted correlation [email protected] network analysis was used to identify gene modules and the hub genes. Twenty-one hub †These authors have contributed equally to this work genes were identified, e.g., ENSGALG00000041996, which represents a candidate for high IMF, and CREB3L1, which relates to low abdominal fat weight. -
A Computational Approach for Identifying the Impact Of
ering & B ne io Harishchander, J Bioengineer & Biomedical Sci 2017, 7:3 gi m n e e d io i c DOI: 10.4172/2155-9538.1000235 B a f l S Journal of o l c a i e n n r c u e o J ISSN: 2155-9538 Bioengineering & Biomedical Science Short Communication Open Access A Computational Approach for Identifying the Impact of Pharmacogenomics in Regulatory Networks to Understand Disease Pathology Harishchander A* Department of Bioinformatics, Sathyabama University, Chennai, Tamil Nadu, India Abstract In the current era of post genomics, there exist a varity of computational methodologies to understand the nature of disease pathology like RegNetworks, DisgiNet, pharmGkb, pharmacomiR and etc. In most of these computational methodologies either a seed pairing approach or a base pair complement is being followed. Hence there exists a gap in understanding the nature of disease pathology in a holistic view point. In this manuscript we combine the approach of seed pairing and graph theory to illustrate the impact of Pharmacogenomics in Regulatory Networks to understand disease Pathology. Keywords: Pharmacogenomics; Macromolecules; Phenotypes potentials [16,17]. This information can still be used to predict the new regulatory relationships between TFs and genes by matching Introduction the binding motifs in DNA sequences. Hence, these predictions were Events in gene regulation play vital roles in a various developmental based on TFBS to integrate into PharmacoReg to provide a more and physiological processes in a cell, in which macromolecules such as comprehensive landscape in gene regulation. Moreover, to include the RNAs, genes and proteins to coordinate and organize responses under post- transcriptional regulatory relationship, consideration of miRNAs ∼ various conditions [1]. -
Supplementary Material 1
Supplementary material 1 350 300 265 250 200 158 139 150 100 50 4 0 Biased Forward- Reverse- any orientation reverse (FR) forward (RF) orientation (FR+RF) Supplemental Figure S1. Biased-orientation of DNA motif sequences of transcription factors in T cells. Total 265 of biased orientation of DNA binding motif sequences of transcription factors were found to affect the expression level of putative transcriptional target genes in T cells of four people in common, whereas only four any orientation (i.e. without considering orientation) of DNA binding motif sequences were found to affect the expression level. 1 Forward-reverse orientation in monocytes ZNF93_2 ZNF93_1 ZNF92 ZNF90 ZNF836 ZNF716 ZNF709 ZNF695 ZNF676_2 ZNF676_1 ZNF675 ZNF670 ZNF660 ZNF648 ZNF646 ZNF623 ZNF573 ZNF521 ZNF460 ZNF366 ZNF33B ZNF317 ZNF316 ZNF28 ZNF274 ZNF263_2 ZNF263_1 ZNF219 ZNF214 ZNF148 ZNF143_2 ZNF143_1 ZIC3 ZIC1 ZFP30 ZBTB6 ZBTB33 ZBTB24 YY1 YBX1 XRCC4_2 XRCC4_1 XBP1 WT1 USF TP63 TP53 TFE3 TFAP2A TCF3_2 TCF3_1 TCF12 TBX5 TBP SULT1A2 STAT5B STAT5A_3 STAT5A_2 STAT5A_1 STAT4 STAT3_2 STAT3_1 STAT1_6 STAT1_5 STAT1_4 STAT1_3 STAT1_2 STAT1_1 SRF_2 SRF_1 SPI1_2 SPI1_1 SPEF1 SP1_2 SP1_1 SNTB1 SMC3_2 SMC3_1 SMARCC2_2 SMARCC2_1 SMAD2_SMAD3_SMAD4 SMAD2_2 SMAD2_1 SLC25A20 SIX5 SIRT6 SIN3A SETDB1 RXRA_VDR RUNX2 RREB1_3 RREB1_2 RREB1_1 RFTN1 REST_2 Gene REST_1 RELA RAD21_3 RAD21_2 RAD21_1 PTF1A PROX1 PRDM9 PRDM15 2000 PPARGC1A POU6F1 POU3F2 PLAGL1_2 PLAGL1_1 PITX3 Reverse PITX1 PHOX2B 1000 PAX8 PAX5 PARG_2 PARG_1 NR3C1 NR2F6 NR2F2 NR2C2 0 NR1I2 NKX2−5 NFYB NFKB2 NFKB1 NFIB NFE2