Interaction of Tau with the RNA-Binding Protein TIA1 Regulates Tau Pathophysiology and Toxicity Tara Vanderweyde Boston University
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Analysis of the Stability of 70 Housekeeping Genes During Ips Reprogramming Yulia Panina1,2*, Arno Germond1 & Tomonobu M
www.nature.com/scientificreports OPEN Analysis of the stability of 70 housekeeping genes during iPS reprogramming Yulia Panina1,2*, Arno Germond1 & Tomonobu M. Watanabe1 Studies on induced pluripotent stem (iPS) cells highly rely on the investigation of their gene expression which requires normalization by housekeeping genes. Whether the housekeeping genes are stable during the iPS reprogramming, a transition of cell state known to be associated with profound changes, has been overlooked. In this study we analyzed the expression patterns of the most comprehensive list to date of housekeeping genes during iPS reprogramming of a mouse neural stem cell line N31. Our results show that housekeeping genes’ expression fuctuates signifcantly during the iPS reprogramming. Clustering analysis shows that ribosomal genes’ expression is rising, while the expression of cell-specifc genes, such as vimentin (Vim) or elastin (Eln), is decreasing. To ensure the robustness of the obtained data, we performed a correlative analysis of the genes. Overall, all 70 genes analyzed changed the expression more than two-fold during the reprogramming. The scale of this analysis, that takes into account 70 previously known and newly suggested genes, allowed us to choose the most stable of all genes. We highlight the fact of fuctuation of housekeeping genes during iPS reprogramming, and propose that, to ensure robustness of qPCR experiments in iPS cells, housekeeping genes should be used together in combination, and with a prior testing in a specifc line used in each study. We suggest that the longest splice variants of Rpl13a, Rplp1 and Rps18 can be used as a starting point for such initial testing as the most stable candidates. -
Effects of Changes in TIA1 on Stress Granule Formation
Effects of changes in TIA1 on stress granule formation Master’s thesis Lydia Sagath University of Helsinki Faculty of Biological and Environmental Sciences Genetics August 2015 Tiedekunta – Fakultet – Faculty Laitos – Institution– Department Bio- och miljövetenskapliga fakulteten Biovetenskapliga institutionen Tekijä – Författare – Author Lydia Johanna Sagath Työn nimi – Arbetets titel – Title Effects of changes in TIA1 on stress granule formation Oppiaine – Läroämne – Subject Humangenetik Työn laji – Arbetets art – Level Aika – Datum – Month and year Sivumäärä – Sidoantal – Number of pages Pro gradu Augusti 2015 63 Tiivistelmä – Referat – Abstract Welanders distala myopati (WDM) orsakas av mutationen p.E384K i genen TIA1. Mutationen antas vara sjukdomsalstrande på grund av en ökad produktion av protein, som relaterats till formationen av stressgranuler (Hackman et al. 2013). Även omgivningsfaktorer har föreslagits verka i sjukdomens utveckling: en ökad mängd stressgranuler har observerats i celler som behandlats med köldshock jämfört med celler som förvarats i 37°C (Hofmann et al. 2012). I patienter med WDM-liknande symptom som undersökts för förändringar i TIA1 har en p.N357S-förändring noterats förrikad. Denna förändring har tidigare anmälts som en polymorfism. Förändringen i fråga ligger i samma prionliknande domän i exon 5 som WDM-orsakande förändringen p.E384K. Därmed kunde p.N357S-förändringen öka predispositionen till aggregering. Pro gradu –arbetet är uppdelat i två delar: • p.N357S-polymorfismens effekt på stressgranulsbildningen i arsenitbehandle celler • Köldshockens effekt på stressgranulsbildningen Resultaten påvisar, att förändringen p.N357S i TIA1 orsakar en förändring i det translaterade proteinets beteende. I likhet med p.E384K-förändringen orsakar även p.N357S en ökad mängd stressgranuler i arsenitbehandlade celler. Däremot tyder resultaten på att stressgranulerna återbildas snabbare i fluorescence recovery after photobleaching-studier (FRAP) I p.N357S-transfekterade celler än i celler som transfekterats med TIA1 p.E384K och vildtyp. -
Clinical Application of Chromosomal Microarray Analysis for Fetuses With
Xu et al. Molecular Cytogenetics (2020) 13:38 https://doi.org/10.1186/s13039-020-00502-5 RESEARCH Open Access Clinical application of chromosomal microarray analysis for fetuses with craniofacial malformations Chenyang Xu1†, Yanbao Xiang1†, Xueqin Xu1, Lili Zhou1, Huanzheng Li1, Xueqin Dong1 and Shaohua Tang1,2* Abstract Background: The potential correlations between chromosomal abnormalities and craniofacial malformations (CFMs) remain a challenge in prenatal diagnosis. This study aimed to evaluate 118 fetuses with CFMs by applying chromosomal microarray analysis (CMA) and G-banded chromosome analysis. Results: Of the 118 cases in this study, 39.8% were isolated CFMs (47/118) whereas 60.2% were non-isolated CFMs (71/118). The detection rate of chromosomal abnormalities in non-isolated CFM fetuses was significantly higher than that in isolated CFM fetuses (26/71 vs. 7/47, p = 0.01). Compared to the 16 fetuses (16/104; 15.4%) with pathogenic chromosomal abnormalities detected by karyotype analysis, CMA identified a total of 33 fetuses (33/118; 28.0%) with clinically significant findings. These 33 fetuses included cases with aneuploidy abnormalities (14/118; 11.9%), microdeletion/microduplication syndromes (9/118; 7.6%), and other pathogenic copy number variations (CNVs) only (10/118; 8.5%).We further explored the CNV/phenotype correlation and found a series of clear or suspected dosage-sensitive CFM genes including TBX1, MAPK1, PCYT1A, DLG1, LHX1, SHH, SF3B4, FOXC1, ZIC2, CREBBP, SNRPB, and CSNK2A1. Conclusion: These findings enrich our understanding of the potential causative CNVs and genes in CFMs. Identification of the genetic basis of CFMs contributes to our understanding of their pathogenesis and allows detailed genetic counselling. -
Dysregulated Expression of Lipid Storage and Membrane Dynamics Factors in Tia1 Knockout Mouse Nervous Tissue
Neurogenetics DOI 10.1007/s10048-014-0397-x ORIGINAL ARTICLE Dysregulated expression of lipid storage and membrane dynamics factors in Tia1 knockout mouse nervous tissue Melanie Vanessa Heck & Mekhman Azizov & Ta n j a S t e h n i n g & Michael Walter & Nancy Kedersha & Georg Auburger Received: 28 January 2014 /Accepted: 3 March 2014 # The Author(s) 2014. This article is published with open access at Springerlink.com Abstract During cell stress, the transcription and translation decreased levels for Bid and Inca1 transcripts. Novel and sur- of immediate early genes are prioritized, while most other mes- prisingly strong expression alterations were detected for fat stor- senger RNAs (mRNAs) are stored away in stress granules or age and membrane trafficking factors, with prominent +3-fold degraded in processing bodies (P-bodies). TIA-1 is an mRNA- upregulations of Plin4, Wdfy1, Tbc1d24,andPnpla2 vs. a −2.4- binding protein that needs to translocate from the nucleus to seed fold downregulation of Cntn4 transcript, encoding an axonal the formation of stress granules in the cytoplasm. Because other membrane adhesion factor with established haploinsufficiency. stress granule components such as TDP-43, FUS, ATXN2, In comparison, subtle effects on the RNA processing machinery SMN, MAPT, HNRNPA2B1, and HNRNPA1 are crucial for included up to 1.2-fold upregulations of Dcp1b and Tial1.The the motor neuron diseases amyotrophic lateral sclerosis (ALS)/ effect on lipid dynamics factors is noteworthy, since also the gene spinal muscular atrophy (SMA) and for the frontotemporal de- deletion of Ta rd b p (encoding TDP-43) and Atxn2 ledtofat mentia (FTD), here we studied mouse nervous tissue to identify metabolism phenotypes in mouse. -
Supplementary Materials
Supplementary Materials COMPARATIVE ANALYSIS OF THE TRANSCRIPTOME, PROTEOME AND miRNA PROFILE OF KUPFFER CELLS AND MONOCYTES Andrey Elchaninov1,3*, Anastasiya Lokhonina1,3, Maria Nikitina2, Polina Vishnyakova1,3, Andrey Makarov1, Irina Arutyunyan1, Anastasiya Poltavets1, Evgeniya Kananykhina2, Sergey Kovalchuk4, Evgeny Karpulevich5,6, Galina Bolshakova2, Gennady Sukhikh1, Timur Fatkhudinov2,3 1 Laboratory of Regenerative Medicine, National Medical Research Center for Obstetrics, Gynecology and Perinatology Named after Academician V.I. Kulakov of Ministry of Healthcare of Russian Federation, Moscow, Russia 2 Laboratory of Growth and Development, Scientific Research Institute of Human Morphology, Moscow, Russia 3 Histology Department, Medical Institute, Peoples' Friendship University of Russia, Moscow, Russia 4 Laboratory of Bioinformatic methods for Combinatorial Chemistry and Biology, Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry of the Russian Academy of Sciences, Moscow, Russia 5 Information Systems Department, Ivannikov Institute for System Programming of the Russian Academy of Sciences, Moscow, Russia 6 Genome Engineering Laboratory, Moscow Institute of Physics and Technology, Dolgoprudny, Moscow Region, Russia Figure S1. Flow cytometry analysis of unsorted blood sample. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S2. Flow cytometry analysis of unsorted liver stromal cells. Representative forward, side scattering and histogram are shown. The proportions of negative cells were determined in relation to the isotype controls. The percentages of positive cells are indicated. The blue curve corresponds to the isotype control. Figure S3. MiRNAs expression analysis in monocytes and Kupffer cells. Full-length of heatmaps are presented. -
Bioinformatics-Based Screening of Key Genes for Transformation of Liver
Jiang et al. J Transl Med (2020) 18:40 https://doi.org/10.1186/s12967-020-02229-8 Journal of Translational Medicine RESEARCH Open Access Bioinformatics-based screening of key genes for transformation of liver cirrhosis to hepatocellular carcinoma Chen Hao Jiang1,2, Xin Yuan1,2, Jiang Fen Li1,2, Yu Fang Xie1,2, An Zhi Zhang1,2, Xue Li Wang1,2, Lan Yang1,2, Chun Xia Liu1,2, Wei Hua Liang1,2, Li Juan Pang1,2, Hong Zou1,2, Xiao Bin Cui1,2, Xi Hua Shen1,2, Yan Qi1,2, Jin Fang Jiang1,2, Wen Yi Gu4, Feng Li1,2,3 and Jian Ming Hu1,2* Abstract Background: Hepatocellular carcinoma (HCC) is the most common type of liver tumour, and is closely related to liver cirrhosis. Previous studies have focussed on the pathogenesis of liver cirrhosis developing into HCC, but the molecular mechanism remains unclear. The aims of the present study were to identify key genes related to the transformation of cirrhosis into HCC, and explore the associated molecular mechanisms. Methods: GSE89377, GSE17548, GSE63898 and GSE54236 mRNA microarray datasets from Gene Expression Omni- bus (GEO) were analysed to obtain diferentially expressed genes (DEGs) between HCC and liver cirrhosis tissues, and network analysis of protein–protein interactions (PPIs) was carried out. String and Cytoscape were used to analyse modules and identify hub genes, Kaplan–Meier Plotter and Oncomine databases were used to explore relationships between hub genes and disease occurrence, development and prognosis of HCC, and the molecular mechanism of the main hub gene was probed using Kyoto Encyclopedia of Genes and Genomes(KEGG) pathway analysis. -
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 -
Supplementary Table 9. Functional Annotation Clustering Results for the Union (GS3) of the Top Genes from the SNP-Level and Gene-Based Analyses (See ST4)
Supplementary Table 9. Functional Annotation Clustering Results for the union (GS3) of the top genes from the SNP-level and Gene-based analyses (see ST4) Column Header Key Annotation Cluster Name of cluster, sorted by descending Enrichment score Enrichment Score EASE enrichment score for functional annotation cluster Category Pathway Database Term Pathway name/Identifier Count Number of genes in the submitted list in the specified term % Percentage of identified genes in the submitted list associated with the specified term PValue Significance level associated with the EASE enrichment score for the term Genes List of genes present in the term List Total Number of genes from the submitted list present in the category Pop Hits Number of genes involved in the specified term (category-specific) Pop Total Number of genes in the human genome background (category-specific) Fold Enrichment Ratio of the proportion of count to list total and population hits to population total Bonferroni Bonferroni adjustment of p-value Benjamini Benjamini adjustment of p-value FDR False Discovery Rate of p-value (percent form) Annotation Cluster 1 Enrichment Score: 3.8978262119731335 Category Term Count % PValue Genes List Total Pop Hits Pop Total Fold Enrichment Bonferroni Benjamini FDR GOTERM_CC_DIRECT GO:0005886~plasma membrane 383 24.33290978 5.74E-05 SLC9A9, XRCC5, HRAS, CHMP3, ATP1B2, EFNA1, OSMR, SLC9A3, EFNA3, UTRN, SYT6, ZNRF2, APP, AT1425 4121 18224 1.18857065 0.038655922 0.038655922 0.086284383 UP_KEYWORDS Membrane 626 39.77128335 1.53E-04 SLC9A9, HRAS, -
U1 Snrnp Regulates Chromatin Retention of Noncoding Rnas
bioRxiv preprint doi: https://doi.org/10.1101/310433; this version posted April 29, 2018. 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. U1 snRNP regulates chromatin retention of noncoding RNAs Yafei Yin,1,* J. Yuyang Lu,1 Xuechun Zhang,1 Wen Shao,1 Yanhui Xu,1 Pan Li,1,2 Yantao Hong,1 Qiangfeng Cliff Zhang,2 and Xiaohua Shen 1,3,* 1 Tsinghua-Peking Center for Life Sciences, School of Medicine and School of Life Sciences, Tsinghua University, Beijing 100084, China 2 MOE Key Laboratory of Bioinformatics, Beijing Advanced Innovation Center for Structural Biology, Center for Synthetic and Systems Biology, School of Life Sciences, Tsinghua University, Beijing 100084, China 3 Lead Contact *Correspondence: [email protected] (Y.Y.), [email protected] (X.S.) Running title: U1 snRNP regulates ncRNA-chromatin retention Keywords: RNA-chromatin localization, U1 snRNA, lncRNA, PROMPTs and eRNA, splicing 1 bioRxiv preprint doi: https://doi.org/10.1101/310433; this version posted April 29, 2018. 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 Thousands of noncoding transcripts exist in mammalian genomes, and they preferentially localize to chromatin. Here, to identify cis-regulatory elements that control RNA-chromatin association, we developed a high-throughput method named RNA element for subcellular localization by sequencing (REL-seq). Coupling REL-seq with random mutagenesis (mutREL-seq), we discovered a key 7-nt U1 recognition motif in chromatin-enriched RNA elements. -
Anti-TIA1 Antibody (ARG58772)
Product datasheet [email protected] ARG58772 Package: 100 μl anti-TIA1 antibody Store at: -20°C Summary Product Description Rabbit Polyclonal antibody recognizes TIA1 Tested Reactivity Hu, Ms Tested Application IHC-P, WB Host Rabbit Clonality Polyclonal Isotype IgG Target Name TIA1 Antigen Species Human Immunogen Synthetic peptide from Human TIA1. Conjugation Un-conjugated Alternate Names T-cell-restricted intracellular antigen-1; WDM; TIA-1; RNA-binding protein TIA-1; p40-TIA-1; Nucleolysin TIA-1 isoform p40 Application Instructions Application table Application Dilution IHC-P 1:50 - 1:200 WB 1:500 - 1:2000 Application Note IHC-P: Antigen Retrieval: Heat mediated. * The dilutions indicate recommended starting dilutions and the optimal dilutions or concentrations should be determined by the scientist. Calculated Mw 43 kDa Observed Size ~ 45 kDa Properties Form Liquid Purification Affinity purified. Buffer PBS (pH 7.4), 150mM NaCl, 0.02% Sodium azide and 50% Glycerol. Preservative 0.02% Sodium azide Stabilizer 50% Glycerol Storage instruction For continuous use, store undiluted antibody at 2-8°C for up to a week. For long-term storage, aliquot and store at -20°C. Storage in frost free freezers is not recommended. Avoid repeated freeze/thaw cycles. Suggest spin the vial prior to opening. The antibody solution should be gently mixed before use. www.arigobio.com 1/2 Note For laboratory research only, not for drug, diagnostic or other use. Bioinformation Gene Symbol TIA1 Gene Full Name TIA1 cytotoxic granule-associated RNA binding protein Background The product encoded by this gene is a member of a RNA-binding protein family and possesses nucleolytic activity against cytotoxic lymphocyte (CTL) target cells. -
MOCHI Enables Discovery of Heterogeneous Interactome Modules in 3D Nucleome
Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press MOCHI enables discovery of heterogeneous interactome modules in 3D nucleome Dechao Tian1,# , Ruochi Zhang1,# , Yang Zhang1, Xiaopeng Zhu1, and Jian Ma1,* 1Computational Biology Department, School of Computer Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA #These two authors contributed equally *Correspondence: [email protected] Contact To whom correspondence should be addressed: Jian Ma School of Computer Science Carnegie Mellon University 7705 Gates-Hillman Complex 5000 Forbes Avenue Pittsburgh, PA 15213 Phone: +1 (412) 268-2776 Email: [email protected] 1 Downloaded from genome.cshlp.org on October 4, 2021 - Published by Cold Spring Harbor Laboratory Press Abstract The composition of the cell nucleus is highly heterogeneous, with different constituents forming complex interactomes. However, the global patterns of these interwoven heterogeneous interactomes remain poorly understood. Here we focus on two different interactomes, chromatin interaction network and gene regulatory network, as a proof-of-principle, to identify heterogeneous interactome modules (HIMs), each of which represents a cluster of gene loci that are in spatial contact more frequently than expected and that are regulated by the same group of transcription factors. HIM integrates transcription factor binding and 3D genome structure to reflect “transcriptional niche” in the nucleus. We develop a new algorithm MOCHI to facilitate the discovery of HIMs based on network motif clustering in heterogeneous interactomes. By applying MOCHI to five different cell types, we found that HIMs have strong spatial preference within the nucleus and exhibit distinct functional properties. Through integrative analysis, this work demonstrates the utility of MOCHI to identify HIMs, which may provide new perspectives on the interplay between transcriptional regulation and 3D genome organization. -
U1 Snrnp Regulates Chromatin Retention of Noncoding Rnas
Article U1 snRNP regulates chromatin retention of noncoding RNAs https://doi.org/10.1038/s41586-020-2105-3 Yafei Yin1 ✉, J. Yuyang Lu1, Xuechun Zhang1, Wen Shao1, Yanhui Xu1, Pan Li1,2, Yantao Hong1, Li Cui1, Ge Shan3, Bin Tian4, Qiangfeng Cliff Zhang1,2 & Xiaohua Shen1 ✉ Received: 26 November 2018 Accepted: 9 January 2020 Long noncoding RNAs (lncRNAs) and promoter- or enhancer-associated unstable Published online: xx xx xxxx transcripts locate preferentially to chromatin, where some regulate chromatin structure, Check for updates transcription and RNA processing1–13. Although several RNA sequences responsible for nuclear localization have been identifed—such as repeats in the lncRNA Xist and Alu-like elements in long RNAs14–16—how lncRNAs as a class are enriched at chromatin remains unknown. Here we describe a random, mutagenesis-coupled, high-throughput method that we name ‘RNA elements for subcellular localization by sequencing’ (mutREL-seq). Using this method, we discovered an RNA motif that recognizes the U1 small nuclear ribonucleoprotein (snRNP) and is essential for the localization of reporter RNAs to chromatin. Across the genome, chromatin-bound lncRNAs are enriched with 5′ splice sites and depleted of 3′ splice sites, and exhibit high levels of U1 snRNA binding compared with cytoplasm-localized messenger RNAs. Acute depletion of U1 snRNA or of the U1 snRNP protein component SNRNP70 markedly reduces the chromatin association of hundreds of lncRNAs and unstable transcripts, without altering the overall transcription rate in cells. In addition, rapid degradation of SNRNP70 reduces the localization of both nascent and polyadenylated lncRNA transcripts to chromatin, and disrupts the nuclear and genome-wide localization of the lncRNA Malat1.