Resident Macrophages Are Locally Programmed for Silent Clearance of Apoptotic Cells
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Table 2. Functional Classification of Genes Differentially Regulated After HOXB4 Inactivation in HSC/Hpcs
Table 2. Functional classification of genes differentially regulated after HOXB4 inactivation in HSC/HPCs Symbol Gene description Fold-change (mean ± SD) Signal transduction Adam8 A disintegrin and metalloprotease domain 8 1.91 ± 0.51 Arl4 ADP-ribosylation factor-like 4 - 1.80 ± 0.40 Dusp6 Dual specificity phosphatase 6 (Mkp3) - 2.30 ± 0.46 Ksr1 Kinase suppressor of ras 1 1.92 ± 0.42 Lyst Lysosomal trafficking regulator 1.89 ± 0.34 Mapk1ip1 Mitogen activated protein kinase 1 interacting protein 1 1.84 ± 0.22 Narf* Nuclear prelamin A recognition factor 2.12 ± 0.04 Plekha2 Pleckstrin homology domain-containing. family A. (phosphoinosite 2.15 ± 0.22 binding specific) member 2 Ptp4a2 Protein tyrosine phosphatase 4a2 - 2.04 ± 0.94 Rasa2* RAS p21 activator protein 2 - 2.80 ± 0.13 Rassf4 RAS association (RalGDS/AF-6) domain family 4 3.44 ± 2.56 Rgs18 Regulator of G-protein signaling - 1.93 ± 0.57 Rrad Ras-related associated with diabetes 1.81 ± 0.73 Sh3kbp1 SH3 domain kinase bindings protein 1 - 2.19 ± 0.53 Senp2 SUMO/sentrin specific protease 2 - 1.97 ± 0.49 Socs2 Suppressor of cytokine signaling 2 - 2.82 ± 0.85 Socs5 Suppressor of cytokine signaling 5 2.13 ± 0.08 Socs6 Suppressor of cytokine signaling 6 - 2.18 ± 0.38 Spry1 Sprouty 1 - 2.69 ± 0.19 Sos1 Son of sevenless homolog 1 (Drosophila) 2.16 ± 0.71 Ywhag 3-monooxygenase/tryptophan 5- monooxygenase activation protein. - 2.37 ± 1.42 gamma polypeptide Zfyve21 Zinc finger. FYVE domain containing 21 1.93 ± 0.57 Ligands and receptors Bambi BMP and activin membrane-bound inhibitor - 2.94 ± 0.62 -
Investigation of Functional Genes at Homologous Loci Identified Based
Journal of Atherosclerosis and Thrombosis Vol.22, No.5 455 Original Article Investigation of Functional Genes at Homologous Loci Identified Based on Genome-wide Association Studies of Blood Lipids via High-fat Diet Intervention in Rats using an in vivo Approach Koichi Akiyama, Yi-Qiang Liang, Masato Isono and Norihiro Kato Department of Gene Diagnostics and Therapeutics, Research Institute, National Center for Global Health and Medicine, Tokyo, Japan Aim: It is challenging to identify causal (or target) genes at individual loci detected using genome- wide association studies (GWAS). In order to follow up GWAS loci, we investigated functional genes at homologous loci identified using human lipid GWAS that responded to a high-fat, high-choles- terol diet (HFD) intervention in an animal model. Methods: The HFD intervention was carried out for four weeks in male rats of the spontaneously hypertensive rat strain. The liver and adipose tissues were subsequently excised for analyses of changes in the gene expression as compared to that observed in rats fed normal rat chow (n=8 per group). From 98 lipid-associated loci reported in previous GWAS, 280 genes with rat orthologs were initially selected as targets for the two-staged analysis involving screening with DNA microarray and validation with quantitative PCR (qPCR). Consequently, genes showing a differential expression due to HFD were examined for changes in the expression induced by atorvastatin, which was indepen- dently administered to the rats. Results: Using the HFD intervention in the rats, seven known (Abca1, Abcg5, Abcg8, Lpl, Nr1h3, Pcsk9 and Pltp) and three novel (Madd, Stac3 and Timd4) genes were identified as potential signifi- cant targets, with an additional list of 23 suggestive genes. -
Recombinant T-Cell Immunoglobulin and Mucin Domain Containing Protein 4 (TIMD4)
RPH854Hu01 100µg Recombinant T-Cell Immunoglobulin And Mucin Domain Containing Protein 4 (TIMD4) Organism Species: Homo sapiens (Human) Instruction manual FOR RESEARCH USE ONLY NOT FOR USE IN CLINICAL DIAGNOSTIC PROCEDURES 12th Edition (Revised in Aug, 2016) 1 / 4 [ PROPERTIES ] Source: Prokaryotic expression Host: E.coli Residues: Glu25~Gln314 Tags: N-terminal His Tag Subcellular Location: Secreted Purity: > 95% Traits: Freeze-dried powder Buffer formulation: 100mMNaHCO3, 500mMNaCl, pH8.3, containing 0.01% SKL, 5% Trehalose. Original Concentration: 100µg/mL Applications: Positive Control; Immunogen; SDS-PAGE; WB. (May be suitable for use in other assays to be determined by the end user.) Predicted isoelectric point: Predicted Molecular Mass: 35.0kDa Accurate Molecular Mass: 40kDa as determined by SDS-PAGE reducing conditions. Phenomenon explanation: The possible reasons that the actual band size differs from the predicted are as follows: 1.Splice variants: Alternative splicing may create different sized proteins from the same gene. 2. Relative charge: The composition of amino acids may affects the charge of the protein. 3. Post-translational modification: Phosphorylation, glycosylation, methylation etc. 4. Post-translation cleavage: Many proteins are synthesized as pro-proteins, and then cleaved to give the active form. 5. Polymerization of the target protein: Dimerization, multimerization etc. [ USAGE ] Reconstitute in 100mM NaHCO3, 500mM NaCl (pH8.3) to a concentration of 0.1-1.0 mg/mL. Do not vortex. 2 / 4 [ STORAGE AND STABILITY ] Storage: Avoid repeated freeze/thaw cycles. Store at 2-8ºC for one month. Aliquot and store at -80ºC for 12 months. Stability Test: The thermal stability is described by the loss rate. -
Global H3k4me3 Genome Mapping Reveals Alterations of Innate Immunity Signaling and Overexpression of JMJD3 in Human Myelodysplastic Syndrome CD34 Þ Cells
Leukemia (2013) 27, 2177–2186 & 2013 Macmillan Publishers Limited All rights reserved 0887-6924/13 www.nature.com/leu ORIGINAL ARTICLE Global H3K4me3 genome mapping reveals alterations of innate immunity signaling and overexpression of JMJD3 in human myelodysplastic syndrome CD34 þ cells YWei1, R Chen2, S Dimicoli1, C Bueso-Ramos3, D Neuberg4, S Pierce1, H Wang2, H Yang1, Y Jia1, H Zheng1, Z Fang1, M Nguyen3, I Ganan-Gomez1,5, B Ebert6, R Levine7, H Kantarjian1 and G Garcia-Manero1 The molecular bases of myelodysplastic syndromes (MDS) are not fully understood. Trimethylated histone 3 lysine 4 (H3K4me3) is present in promoters of actively transcribed genes and has been shown to be involved in hematopoietic differentiation. We performed a genome-wide H3K4me3 CHIP-Seq (chromatin immunoprecipitation coupled with whole genome sequencing) analysis of primary MDS bone marrow (BM) CD34 þ cells. This resulted in the identification of 36 genes marked by distinct higher levels of promoter H3K4me3 in MDS. A majority of these genes are involved in nuclear factor (NF)-kB activation and innate immunity signaling. We then analyzed expression of histone demethylases and observed significant overexpression of the JmjC-domain histone demethylase JMJD3 (KDM6b) in MDS CD34 þ cells. Furthermore, we demonstrate that JMJD3 has a positive effect on transcription of multiple CHIP-Seq identified genes involved in NF-kB activation. Inhibition of JMJD3 using shRNA in primary BM MDS CD34 þ cells resulted in an increased number of erythroid colonies in samples isolated from patients with lower-risk MDS. Taken together, these data indicate the deregulation of H3K4me3 and associated abnormal activation of innate immunity signals have a role in the pathogenesis of MDS and that targeting these signals may have potential therapeutic value in MDS. -
Supplementary Materials
Supplementary materials Supplementary Table S1: MGNC compound library Ingredien Molecule Caco- Mol ID MW AlogP OB (%) BBB DL FASA- HL t Name Name 2 shengdi MOL012254 campesterol 400.8 7.63 37.58 1.34 0.98 0.7 0.21 20.2 shengdi MOL000519 coniferin 314.4 3.16 31.11 0.42 -0.2 0.3 0.27 74.6 beta- shengdi MOL000359 414.8 8.08 36.91 1.32 0.99 0.8 0.23 20.2 sitosterol pachymic shengdi MOL000289 528.9 6.54 33.63 0.1 -0.6 0.8 0 9.27 acid Poricoic acid shengdi MOL000291 484.7 5.64 30.52 -0.08 -0.9 0.8 0 8.67 B Chrysanthem shengdi MOL004492 585 8.24 38.72 0.51 -1 0.6 0.3 17.5 axanthin 20- shengdi MOL011455 Hexadecano 418.6 1.91 32.7 -0.24 -0.4 0.7 0.29 104 ylingenol huanglian MOL001454 berberine 336.4 3.45 36.86 1.24 0.57 0.8 0.19 6.57 huanglian MOL013352 Obacunone 454.6 2.68 43.29 0.01 -0.4 0.8 0.31 -13 huanglian MOL002894 berberrubine 322.4 3.2 35.74 1.07 0.17 0.7 0.24 6.46 huanglian MOL002897 epiberberine 336.4 3.45 43.09 1.17 0.4 0.8 0.19 6.1 huanglian MOL002903 (R)-Canadine 339.4 3.4 55.37 1.04 0.57 0.8 0.2 6.41 huanglian MOL002904 Berlambine 351.4 2.49 36.68 0.97 0.17 0.8 0.28 7.33 Corchorosid huanglian MOL002907 404.6 1.34 105 -0.91 -1.3 0.8 0.29 6.68 e A_qt Magnogrand huanglian MOL000622 266.4 1.18 63.71 0.02 -0.2 0.2 0.3 3.17 iolide huanglian MOL000762 Palmidin A 510.5 4.52 35.36 -0.38 -1.5 0.7 0.39 33.2 huanglian MOL000785 palmatine 352.4 3.65 64.6 1.33 0.37 0.7 0.13 2.25 huanglian MOL000098 quercetin 302.3 1.5 46.43 0.05 -0.8 0.3 0.38 14.4 huanglian MOL001458 coptisine 320.3 3.25 30.67 1.21 0.32 0.9 0.26 9.33 huanglian MOL002668 Worenine -
1 Self-Maintaining Gut Macrophages Are Essential for Intestinal
Self-maintaining Gut Macrophages Are Essential for Intestinal Homeostasis De Schepper Sebastiaan1*, Verheijden Simon1*†, Aguilera-Lizarraga Javi1, Viola Maria Francesca1, Boesmans Werend2, Stakenborg Nathalie1, Voytyuk Iryna3, Smidt Inga3, Boeckx Bram4,5, Dierckx de Casterlé Isabelle6, Baekelandt Veerle7, Gonzalez Dominguez Erika1, Mack Matthias8, Depoortere Inge9, De Strooper Bart3, Sprangers Ben4, Himmelreich Uwe10, Soenen Stefaan10, Guilliams Martin11, Vanden Berghe Pieter2, Jones Elizabeth12, Lambrechts Diether5,6, Boeckxstaens Guy1 1Laboratory for Intestinal Neuroimmune Interactions, Translational Research Center for Gastrointestinal Disorders, Department of Chronic Diseases, Metabolism and Ageing, University of Leuven, Leuven, Belgium. 2Laboratory for Enteric Neuroscience, Translational Research Center for Gastrointestinal Disorders, Department of Chronic Diseases, Metabolism and Ageing, University of Leuven, Leuven, Belgium. 3VIB – KU Leuven Center for Brain and Disease Research, Leuven, Belgium. 4VIB Center for Cancer Biology, Leuven, Belgium. 5Laboratory for Translational Genetics, Department of Human Genetics, University of Leuven, Leuven, Belgium. 6Laboratory of Experimental Transplantation, Department of Microbiology and Immunology, University of Leuven, Leuven, Belgium. 7Research Group for Neurobiology and Gene Therapy, Department of Neurosciences, University of Leuven, Leuven, Belgium. 8Department of Internal Medicine – Nephrology, University Hospital Regensburg, Regensburg, Germany. 9Gut Peptide Research Lab, Translational -
Self-Renewing Resident Cardiac Macrophages Limit Adverse Remodeling Following Myocardial Infarction
JOURNAL CLUB 3/19/19 SAHIL MAHAJAN ARTICLES https://doi.org/10.1038/s41590-018-0272-2 Corrected: Publisher Correction Self-renewing resident cardiac macrophages limit adverse remodeling following myocardial infarction Sarah A. Dick1,2,11, Jillian A. Macklin 1,2,3,4,11, Sara Nejat 1,11, Abdul Momen1, Xavier Clemente-Casares1, Marwan G. Althagafi1,4, Jinmiao Chen 5,6, Crystal Kantores 1, Siyavash Hosseinzadeh1,4, Laura Aronoff1,4, Anthony Wong 1,7, Rysa Zaman 1,7, Iulia Barbu1,7, Rickvinder Besla1,4, Kory J. Lavine8, Babak Razani8,9, Florent Ginhoux 5,6, Mansoor Husain1,2,3,4,10, Myron I. Cybulsky1,4,10, Clinton S. Robbins1,4,7,10 and Slava Epelman 1,2,3,4,7,10* Macrophages promote both injury and repair after myocardial infarction, but discriminating functions within mixed populations remains challenging. Here we used fate mapping, parabiosis and single-cell transcriptomics to demonstrate that at steady state, TIMD4+LYVE1+MHC-IIloCCR2− resident cardiac macrophages self-renew with negligible blood monocyte input. Monocytes par- tially replaced resident TIMD4–LYVE1–MHC-IIhiCCR2− macrophages and fully replaced TIMD4−LYVE1−MHC-IIhiCCR2+ macro- phages, revealing a hierarchy of monocyte contribution to functionally distinct macrophage subsets. Ischemic injury reduced TIMD4+ and TIMD4– resident macrophage abundance, whereas CCR2+ monocyte-derived macrophages adopted multiple cell fates within infarcted tissue, including those nearly indistinguishable from resident macrophages. Recruited macrophages did not express TIMD4, highlighting the ability of TIMD4 to track a subset of resident macrophages in the absence of fate map- ping. Despite this similarity, inducible depletion of resident macrophages using a Cx3cr1-based system led to impaired cardiac function and promoted adverse remodeling primarily within the peri-infarct zone, revealing a nonredundant, cardioprotective role of resident cardiac macrophages. -
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. -
Genome Sequencing Unveils a Regulatory Landscape of Platelet Reactivity
ARTICLE https://doi.org/10.1038/s41467-021-23470-9 OPEN Genome sequencing unveils a regulatory landscape of platelet reactivity Ali R. Keramati 1,2,124, Ming-Huei Chen3,4,124, Benjamin A. T. Rodriguez3,4,5,124, Lisa R. Yanek 2,6, Arunoday Bhan7, Brady J. Gaynor 8,9, Kathleen Ryan8,9, Jennifer A. Brody 10, Xue Zhong11, Qiang Wei12, NHLBI Trans-Omics for Precision (TOPMed) Consortium*, Kai Kammers13, Kanika Kanchan14, Kruthika Iyer14, Madeline H. Kowalski15, Achilleas N. Pitsillides4,16, L. Adrienne Cupples 4,16, Bingshan Li 12, Thorsten M. Schlaeger7, Alan R. Shuldiner9, Jeffrey R. O’Connell8,9, Ingo Ruczinski17, Braxton D. Mitchell 8,9, ✉ Nauder Faraday2,18, Margaret A. Taub17, Lewis C. Becker1,2, Joshua P. Lewis 8,9,125 , 2,14,125✉ 3,4,125✉ 1234567890():,; Rasika A. Mathias & Andrew D. Johnson Platelet aggregation at the site of atherosclerotic vascular injury is the underlying patho- physiology of myocardial infarction and stroke. To build upon prior GWAS, here we report on 16 loci identified through a whole genome sequencing (WGS) approach in 3,855 NHLBI Trans-Omics for Precision Medicine (TOPMed) participants deeply phenotyped for platelet aggregation. We identify the RGS18 locus, which encodes a myeloerythroid lineage-specific regulator of G-protein signaling that co-localizes with expression quantitative trait loci (eQTL) signatures for RGS18 expression in platelets. Gene-based approaches implicate the SVEP1 gene, a known contributor of coronary artery disease risk. Sentinel variants at RGS18 and PEAR1 are associated with thrombosis risk and increased gastrointestinal bleeding risk, respectively. Our WGS findings add to previously identified GWAS loci, provide insights regarding the mechanism(s) by which genetics may influence cardiovascular disease risk, and underscore the importance of rare variant and regulatory approaches to identifying loci contributing to complex phenotypes. -
Supplementary Information.Pdf
Supplementary Information Whole transcriptome profiling reveals major cell types in the cellular immune response against acute and chronic active Epstein‐Barr virus infection Huaqing Zhong1, Xinran Hu2, Andrew B. Janowski2, Gregory A. Storch2, Liyun Su1, Lingfeng Cao1, Jinsheng Yu3, and Jin Xu1 Department of Clinical Laboratory1, Children's Hospital of Fudan University, Minhang District, Shanghai 201102, China; Departments of Pediatrics2 and Genetics3, Washington University School of Medicine, Saint Louis, Missouri 63110, United States. Supplementary information includes the following: 1. Supplementary Figure S1: Fold‐change and correlation data for hyperactive and hypoactive genes. 2. Supplementary Table S1: Clinical data and EBV lab results for 110 study subjects. 3. Supplementary Table S2: Differentially expressed genes between AIM vs. Healthy controls. 4. Supplementary Table S3: Differentially expressed genes between CAEBV vs. Healthy controls. 5. Supplementary Table S4: Fold‐change data for 303 immune mediators. 6. Supplementary Table S5: Primers used in qPCR assays. Supplementary Figure S1. Fold‐change (a) and Pearson correlation data (b) for 10 cell markers and 61 hypoactive and hyperactive genes identified in subjects with acute EBV infection (AIM) in the primary cohort. Note: 23 up‐regulated hyperactive genes were highly correlated positively with cytotoxic T cell (Tc) marker CD8A and NK cell marker CD94 (KLRD1), and 38 down‐regulated hypoactive genes were highly correlated positively with B cell, conventional dendritic cell -
The Genetic Basis of Emotional Behaviour in Mice
European Journal of Human Genetics (2006) 14, 721–728 & 2006 Nature Publishing Group All rights reserved 1018-4813/06 $30.00 www.nature.com/ejhg REVIEW The genetic basis of emotional behaviour in mice Saffron AG Willis-Owen*,1 and Jonathan Flint1 1Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Headington, Oxford, UK The last decade has witnessed a steady expansion in the number of quantitative trait loci (QTL) mapped for complex phenotypes. However, despite this proliferation, the number of successfully cloned QTL has remained surprisingly low, and to a great extent limited to large effect loci. In this review, we follow the progress of one complex trait locus; a low magnitude moderator of murine emotionality identified some 10 years ago in a simple two-strain intercross, and successively resolved using a variety of crosses and fear-related phenotypes. These experiments have revealed a complex underlying genetic architecture, whereby genetic effects fractionate into several separable QTL with some evidence of phenotype specificity. Ultimately, we describe a method of assessing gene candidacy, and show that given sufficient access to genetic diversity and recombination, progression from QTL to gene can be achieved even for low magnitude genetic effects. European Journal of Human Genetics (2006) 14, 721–728. doi:10.1038/sj.ejhg.5201569 Keywords: mouse; emotionality; quantitative trait locus; QTL Introduction tude genetic effects and their interactions (both with other Emotionality is a psychological trait of complex aetiology, genetic loci (ie epistasis) and nongenetic (environmental) which moderates an organism’s response to stress. Beha- factors) ultimately producing a quasi-continuously distrib- vioural evidence of emotionality has been documented uted phenotype. -
Methylation Epigenetic Landscape of Cpg DNA Defining CD4 T Cell Memory By
The Journal of Immunology Defining CD4 T Cell Memory by the Epigenetic Landscape of CpG DNA Methylation H. Kiyomi Komori, Traver Hart, Sarah A. LaMere, Pamela V. Chew, and Daniel R. Salomon Memory T cells are primed for rapid responses to Ag; however, the molecular mechanisms responsible for priming remain incom- pletely defined. CpG methylation in promoters is an epigenetic modification, which regulates gene transcription. Using targeted bisulfite sequencing, we examined methylation of 2100 genes (56,000 CpGs) mapped by deep sequencing of T cell activation in human naive and memory CD4 T cells. Four hundred sixty-six CpGs (132 genes) displayed differential methylation between naive and memory cells. Twenty-one genes exhibited both differential methylation and gene expression before activation, linking pro- moter DNA methylation states to gene regulation; 6 of 21 genes encode proteins closely studied in T cells, whereas 15 genes represent novel targets for further study. Eighty-four genes demonstrated differential methylation between memory and naive cells that cor- related to differential gene expression following activation, of which 39 exhibited reduced methylation in memory cells coupled with increased gene expression upon activation compared with naive cells. These reveal a class of primed genes more rapidly expressed in memory compared with naive cells and putatively regulated by DNA methylation. These findings define a DNA methylation sig- nature unique to memory CD4 T cells that correlates with activation-induced gene expression. The Journal of Immunology, 2015, 194: 1565–1579. ifferentiation into fast-acting memory cells following Ag have a demethylated IL-4 gene promoter (10), do not express IFN-g, recognition is a central tenet of T cell immunology.