Chromatin Modifier MTA1 Regulates Mitotic Transition and Tumorigenesis
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Single Nucleotide Variants in Metastasis-Related Genes Are
View metadata, citation and similar papers at core.ac.uk brought to you by CORE HHS Public Access provided by CDC Stacks Author manuscript Author ManuscriptAuthor Manuscript Author Mol Carcinog Manuscript Author . Author manuscript; Manuscript Author available in PMC 2018 March 01. Published in final edited form as: Mol Carcinog. 2017 March ; 56(3): 1000–1009. doi:10.1002/mc.22565. Single nucleotide variants in metastasis-related genes are associated with breast cancer risk, by lymph node involvement and estrogen receptor status, in women with European and African ancestry Michelle R. Roberts1,2,3, Lara E. Sucheston-Campbell4, Gary R. Zirpoli5, Michael Higgins6, Jo L. Freudenheim3, Elisa V. Bandera7, Christine B. Ambrosone2, and Song Yao2 1Channing Division of Network Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 2Department of Cancer Prevention and Control, Roswell Park Cancer Institute, Buffalo, NY 3Department of Epidemiology and Environmental Health, University at Buffalo, Buffalo, NY 4Division of Pharmacy Practice and Science, The Ohio State University, Columbus, OH 5Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 6Department of Molecular and Cellular Biology, Roswell Park Cancer Institute, Buffalo, NY 7Rutgers Cancer Institute of New Jersey, New Brunswick, NJ Abstract Background—Single nucleotide polymorphisms (SNPs) in pathways influencing lymph node (LN) metastasis and estrogen receptor (ER) status in breast cancer may partially explain inter- patient variability in prognosis. We examined 154 SNPs in 12 metastasis-related genes for associations with breast cancer risk, stratified by LN and ER status, in European-American (EA) and African-American (AA) women. Methods—2,671 women enrolled in the Women’s Circle of Health Study were genotyped. -
A Gene Regulatory Network Armature for T Lymphocyte Specification
Supporting Information Georgescu et al. 10.1073/pnas.0806501105 SI Text orientation in the multidimensional space, while the relative magnitude of gene expression variation is still visible on the 2D Computational Methods pls projection. All of the 2D visualizations included use the Partial Least Squares (PLS) Analysis of Normal Developmental Gene above transformations. Expression Change. PLS regression analysis was performed to find The lengths of the DN-stage vectors in the pls principal axis a hyper plane of reduced dimensionality relating the expression space indicate the extent to which these axes are explanatory of variation of the studied genes to the developmental stages DN1, the corresponding DN state. Because in the full multidimen- DN2, DN3A, DN3B, and DN4. PLS begins by characterizing the sional space this length is the same for all five DN stages, the five developmental stages DN1–DN4 as orthogonal axes in length of these vectors in the 2D projection shows the relative five-dimensional space. Each of the assayed genes is represented extent to which the DN stages are represented in the projection. as a point in this space, such that the gene’s coordinate along Long vectors indicate DN stages well represented in the projec- each axis corresponds to its expression level at the corresponding tion. In the multidimensional space, the cosine of the angle developmental stage. Genes with similar expression changes between the vectors corresponding to two variables (genes during development will tend to form clusters in this space, and/or DN stages) gives the correlation between the two vari- whereas genes with distinctly different expression profiles will ables. -
Seq2pathway Vignette
seq2pathway Vignette Bin Wang, Xinan Holly Yang, Arjun Kinstlick May 19, 2021 Contents 1 Abstract 1 2 Package Installation 2 3 runseq2pathway 2 4 Two main functions 3 4.1 seq2gene . .3 4.1.1 seq2gene flowchart . .3 4.1.2 runseq2gene inputs/parameters . .5 4.1.3 runseq2gene outputs . .8 4.2 gene2pathway . 10 4.2.1 gene2pathway flowchart . 11 4.2.2 gene2pathway test inputs/parameters . 11 4.2.3 gene2pathway test outputs . 12 5 Examples 13 5.1 ChIP-seq data analysis . 13 5.1.1 Map ChIP-seq enriched peaks to genes using runseq2gene .................... 13 5.1.2 Discover enriched GO terms using gene2pathway_test with gene scores . 15 5.1.3 Discover enriched GO terms using Fisher's Exact test without gene scores . 17 5.1.4 Add description for genes . 20 5.2 RNA-seq data analysis . 20 6 R environment session 23 1 Abstract Seq2pathway is a novel computational tool to analyze functional gene-sets (including signaling pathways) using variable next-generation sequencing data[1]. Integral to this tool are the \seq2gene" and \gene2pathway" components in series that infer a quantitative pathway-level profile for each sample. The seq2gene function assigns phenotype-associated significance of genomic regions to gene-level scores, where the significance could be p-values of SNPs or point mutations, protein-binding affinity, or transcriptional expression level. The seq2gene function has the feasibility to assign non-exon regions to a range of neighboring genes besides the nearest one, thus facilitating the study of functional non-coding elements[2]. Then the gene2pathway summarizes gene-level measurements to pathway-level scores, comparing the quantity of significance for gene members within a pathway with those outside a pathway. -
Gene Knockdown of CENPA Reduces Sphere Forming Ability and Stemness of Glioblastoma Initiating Cells
Neuroepigenetics 7 (2016) 6–18 Contents lists available at ScienceDirect Neuroepigenetics journal homepage: www.elsevier.com/locate/nepig Gene knockdown of CENPA reduces sphere forming ability and stemness of glioblastoma initiating cells Jinan Behnan a,1, Zanina Grieg b,c,1, Mrinal Joel b,c, Ingunn Ramsness c, Biljana Stangeland a,b,⁎ a Department of Molecular Medicine, Institute of Basic Medical Sciences, The Medical Faculty, University of Oslo, Oslo, Norway b Norwegian Center for Stem Cell Research, Department of Immunology and Transfusion Medicine, Oslo University Hospital, Oslo, Norway c Vilhelm Magnus Laboratory for Neurosurgical Research, Institute for Surgical Research and Department of Neurosurgery, Oslo University Hospital, Oslo, Norway article info abstract Article history: CENPA is a centromere-associated variant of histone H3 implicated in numerous malignancies. However, the Received 20 May 2016 role of this protein in glioblastoma (GBM) has not been demonstrated. GBM is one of the most aggressive Received in revised form 23 July 2016 human cancers. GBM initiating cells (GICs), contained within these tumors are deemed to convey Accepted 2 August 2016 characteristics such as invasiveness and resistance to therapy. Therefore, there is a strong rationale for targeting these cells. We investigated the expression of CENPA and other centromeric proteins (CENPs) in Keywords: fi CENPA GICs, GBM and variety of other cell types and tissues. Bioinformatics analysis identi ed the gene signature: fi Centromeric proteins high_CENP(AEFNM)/low_CENP(BCTQ) whose expression correlated with signi cantly worse GBM patient Glioblastoma survival. GBM Knockdown of CENPA reduced sphere forming ability, proliferation and cell viability of GICs. We also Brain tumor detected significant reduction in the expression of stemness marker SOX2 and the proliferation marker Glioblastoma initiating cells and therapeutic Ki67. -
Supplemental Materials ZNF281 Enhances Cardiac Reprogramming
Supplemental Materials ZNF281 enhances cardiac reprogramming by modulating cardiac and inflammatory gene expression Huanyu Zhou, Maria Gabriela Morales, Hisayuki Hashimoto, Matthew E. Dickson, Kunhua Song, Wenduo Ye, Min S. Kim, Hanspeter Niederstrasser, Zhaoning Wang, Beibei Chen, Bruce A. Posner, Rhonda Bassel-Duby and Eric N. Olson Supplemental Table 1; related to Figure 1. Supplemental Table 2; related to Figure 1. Supplemental Table 3; related to the “quantitative mRNA measurement” in Materials and Methods section. Supplemental Table 4; related to the “ChIP-seq, gene ontology and pathway analysis” and “RNA-seq” and gene ontology analysis” in Materials and Methods section. Supplemental Figure S1; related to Figure 1. Supplemental Figure S2; related to Figure 2. Supplemental Figure S3; related to Figure 3. Supplemental Figure S4; related to Figure 4. Supplemental Figure S5; related to Figure 6. Supplemental Table S1. Genes included in human retroviral ORF cDNA library. Gene Gene Gene Gene Gene Gene Gene Gene Symbol Symbol Symbol Symbol Symbol Symbol Symbol Symbol AATF BMP8A CEBPE CTNNB1 ESR2 GDF3 HOXA5 IL17D ADIPOQ BRPF1 CEBPG CUX1 ESRRA GDF6 HOXA6 IL17F ADNP BRPF3 CERS1 CX3CL1 ETS1 GIN1 HOXA7 IL18 AEBP1 BUD31 CERS2 CXCL10 ETS2 GLIS3 HOXB1 IL19 AFF4 C17ORF77 CERS4 CXCL11 ETV3 GMEB1 HOXB13 IL1A AHR C1QTNF4 CFL2 CXCL12 ETV7 GPBP1 HOXB5 IL1B AIMP1 C21ORF66 CHIA CXCL13 FAM3B GPER HOXB6 IL1F3 ALS2CR8 CBFA2T2 CIR1 CXCL14 FAM3D GPI HOXB7 IL1F5 ALX1 CBFA2T3 CITED1 CXCL16 FASLG GREM1 HOXB9 IL1F6 ARGFX CBFB CITED2 CXCL3 FBLN1 GREM2 HOXC4 IL1F7 -
Supplementary Table S5. Differentially Expressed Gene Lists of PD-1High CD39+ CD8 Tils According to 4-1BB Expression Compared to PD-1+ CD39- CD8 Tils
BMJ Publishing Group Limited (BMJ) disclaims all liability and responsibility arising from any reliance Supplemental material placed on this supplemental material which has been supplied by the author(s) J Immunother Cancer Supplementary Table S5. Differentially expressed gene lists of PD-1high CD39+ CD8 TILs according to 4-1BB expression compared to PD-1+ CD39- CD8 TILs Up- or down- regulated genes in Up- or down- regulated genes Up- or down- regulated genes only PD-1high CD39+ CD8 TILs only in 4-1BBneg PD-1high CD39+ in 4-1BBpos PD-1high CD39+ CD8 compared to PD-1+ CD39- CD8 CD8 TILs compared to PD-1+ TILs compared to PD-1+ CD39- TILs CD39- CD8 TILs CD8 TILs IL7R KLRG1 TNFSF4 ENTPD1 DHRS3 LEF1 ITGA5 MKI67 PZP KLF3 RYR2 SIK1B ANK3 LYST PPP1R3B ETV1 ADAM28 H2AC13 CCR7 GFOD1 RASGRP2 ITGAX MAST4 RAD51AP1 MYO1E CLCF1 NEBL S1PR5 VCL MPP7 MS4A6A PHLDB1 GFPT2 TNF RPL3 SPRY4 VCAM1 B4GALT5 TIPARP TNS3 PDCD1 POLQ AKAP5 IL6ST LY9 PLXND1 PLEKHA1 NEU1 DGKH SPRY2 PLEKHG3 IKZF4 MTX3 PARK7 ATP8B4 SYT11 PTGER4 SORL1 RAB11FIP5 BRCA1 MAP4K3 NCR1 CCR4 S1PR1 PDE8A IFIT2 EPHA4 ARHGEF12 PAICS PELI2 LAT2 GPRASP1 TTN RPLP0 IL4I1 AUTS2 RPS3 CDCA3 NHS LONRF2 CDC42EP3 SLCO3A1 RRM2 ADAMTSL4 INPP5F ARHGAP31 ESCO2 ADRB2 CSF1 WDHD1 GOLIM4 CDK5RAP1 CD69 GLUL HJURP SHC4 GNLY TTC9 HELLS DPP4 IL23A PITPNC1 TOX ARHGEF9 EXO1 SLC4A4 CKAP4 CARMIL3 NHSL2 DZIP3 GINS1 FUT8 UBASH3B CDCA5 PDE7B SOGA1 CDC45 NR3C2 TRIB1 KIF14 TRAF5 LIMS1 PPP1R2C TNFRSF9 KLRC2 POLA1 CD80 ATP10D CDCA8 SETD7 IER2 PATL2 CCDC141 CD84 HSPA6 CYB561 MPHOSPH9 CLSPN KLRC1 PTMS SCML4 ZBTB10 CCL3 CA5B PIP5K1B WNT9A CCNH GEM IL18RAP GGH SARDH B3GNT7 C13orf46 SBF2 IKZF3 ZMAT1 TCF7 NECTIN1 H3C7 FOS PAG1 HECA SLC4A10 SLC35G2 PER1 P2RY1 NFKBIA WDR76 PLAUR KDM1A H1-5 TSHZ2 FAM102B HMMR GPR132 CCRL2 PARP8 A2M ST8SIA1 NUF2 IL5RA RBPMS UBE2T USP53 EEF1A1 PLAC8 LGR6 TMEM123 NEK2 SNAP47 PTGIS SH2B3 P2RY8 S100PBP PLEKHA7 CLNK CRIM1 MGAT5 YBX3 TP53INP1 DTL CFH FEZ1 MYB FRMD4B TSPAN5 STIL ITGA2 GOLGA6L10 MYBL2 AHI1 CAND2 GZMB RBPJ PELI1 HSPA1B KCNK5 GOLGA6L9 TICRR TPRG1 UBE2C AURKA Leem G, et al. -
1 AGING Supplementary Table 2
SUPPLEMENTARY TABLES Supplementary Table 1. Details of the eight domain chains of KIAA0101. Serial IDENTITY MAX IN COMP- INTERFACE ID POSITION RESOLUTION EXPERIMENT TYPE number START STOP SCORE IDENTITY LEX WITH CAVITY A 4D2G_D 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ B 4D2G_E 52 - 69 52 69 100 100 2.65 Å PCNA X-RAY DIFFRACTION √ C 6EHT_D 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ D 6EHT_E 52 - 71 52 71 100 100 3.2Å PCNA X-RAY DIFFRACTION √ E 6GWS_D 41-72 41 72 100 100 3.2Å PCNA X-RAY DIFFRACTION √ F 6GWS_E 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ G 6GWS_F 41-72 41 72 100 100 2.9Å PCNA X-RAY DIFFRACTION √ H 6IIW_B 2-11 2 11 100 100 1.699Å UHRF1 X-RAY DIFFRACTION √ www.aging-us.com 1 AGING Supplementary Table 2. Significantly enriched gene ontology (GO) annotations (cellular components) of KIAA0101 in lung adenocarcinoma (LinkedOmics). Leading Description FDR Leading Edge Gene EdgeNum RAD51, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, CENPW, HJURP, NDC80, CDCA5, NCAPH, BUB1, ZWILCH, CENPK, KIF2C, AURKA, CENPN, TOP2A, CENPM, PLK1, ERCC6L, CDT1, CHEK1, SPAG5, CENPH, condensed 66 0 SPC24, NUP37, BLM, CENPE, BUB3, CDK2, FANCD2, CENPO, CENPF, BRCA1, DSN1, chromosome MKI67, NCAPG2, H2AFX, HMGB2, SUV39H1, CBX3, TUBG1, KNTC1, PPP1CC, SMC2, BANF1, NCAPD2, SKA2, NUP107, BRCA2, NUP85, ITGB3BP, SYCE2, TOPBP1, DMC1, SMC4, INCENP. RAD51, OIP5, CDK1, SPC25, CCNB1, BIRC5, NCAPG, ZWINT, MAD2L1, SKA3, NUF2, BUB1B, CENPA, SKA1, AURKB, NEK2, ESCO2, CENPW, HJURP, TTK, NDC80, CDCA5, BUB1, ZWILCH, CENPK, KIF2C, AURKA, DSCC1, CENPN, CDCA8, CENPM, PLK1, MCM6, ERCC6L, CDT1, HELLS, CHEK1, SPAG5, CENPH, PCNA, SPC24, CENPI, NUP37, FEN1, chromosomal 94 0 CENPL, BLM, KIF18A, CENPE, MCM4, BUB3, SUV39H2, MCM2, CDK2, PIF1, DNA2, region CENPO, CENPF, CHEK2, DSN1, H2AFX, MCM7, SUV39H1, MTBP, CBX3, RECQL4, KNTC1, PPP1CC, CENPP, CENPQ, PTGES3, NCAPD2, DYNLL1, SKA2, HAT1, NUP107, MCM5, MCM3, MSH2, BRCA2, NUP85, SSB, ITGB3BP, DMC1, INCENP, THOC3, XPO1, APEX1, XRCC5, KIF22, DCLRE1A, SEH1L, XRCC3, NSMCE2, RAD21. -
Autosomal Recessive Primary Microcephaly
Mahmood et al. Orphanet Journal of Rare Diseases 2011, 6:39 http://www.ojrd.com/content/6/1/39 REVIEW Open Access Autosomal recessive primary microcephaly (MCPH): clinical manifestations, genetic heterogeneity and mutation continuum Saqib Mahmood1, Wasim Ahmad2 and Muhammad J Hassan3* Abstract Autosomal Recessive Primary Microcephaly (MCPH) is a rare disorder of neurogenic mitosis characterized by reduced head circumference at birth with variable degree of mental retardation. In MCPH patients, brain size reduced to almost one-third of its original volume due to reduced number of generated cerebral cortical neurons during embryonic neurogensis. So far, seven genetic loci (MCPH1-7) for this condition have been mapped with seven corresponding genes (MCPH1, WDR62, CDK5RAP2, CEP152, ASPM, CENPJ, and STIL) identified from different world populations. Contribution of ASPM and WDR62 gene mutations in MCPH World wide is more than 50%. By and large, primary microcephaly patients are phenotypically indistinguishable, however, recent studies in patients with mutations in MCPH1, WDR62 and ASPM genes showed a broader clinical and/or cellular phenotype. It has been proposed that mutations in MCPH genes can cause the disease phenotype by disturbing: 1) orientation of mitotic spindles, 2) chromosome condensation mechanism during embryonic neurogenesis, 3) DNA damage- response signaling, 4) transcriptional regulations and microtubule dynamics, 5) certain unknown centrosomal mechanisms that control the number of neurons generated by neural precursor cells. Recent discoveries of mammalian models for MCPH have open up horizons for researchers to add more knowledge regarding the etiology and pathophysiology of MCPH. High incidence of MCPH in Pakistani population reflects the most probable involvement of consanguinity. -
Supplementary Table S1. Correlation Between the Mutant P53-Interacting Partners and PTTG3P, PTTG1 and PTTG2, Based on Data from Starbase V3.0 Database
Supplementary Table S1. Correlation between the mutant p53-interacting partners and PTTG3P, PTTG1 and PTTG2, based on data from StarBase v3.0 database. PTTG3P PTTG1 PTTG2 Gene ID Coefficient-R p-value Coefficient-R p-value Coefficient-R p-value NF-YA ENSG00000001167 −0.077 8.59e-2 −0.210 2.09e-6 −0.122 6.23e-3 NF-YB ENSG00000120837 0.176 7.12e-5 0.227 2.82e-7 0.094 3.59e-2 NF-YC ENSG00000066136 0.124 5.45e-3 0.124 5.40e-3 0.051 2.51e-1 Sp1 ENSG00000185591 −0.014 7.50e-1 −0.201 5.82e-6 −0.072 1.07e-1 Ets-1 ENSG00000134954 −0.096 3.14e-2 −0.257 4.83e-9 0.034 4.46e-1 VDR ENSG00000111424 −0.091 4.10e-2 −0.216 1.03e-6 0.014 7.48e-1 SREBP-2 ENSG00000198911 −0.064 1.53e-1 −0.147 9.27e-4 −0.073 1.01e-1 TopBP1 ENSG00000163781 0.067 1.36e-1 0.051 2.57e-1 −0.020 6.57e-1 Pin1 ENSG00000127445 0.250 1.40e-8 0.571 9.56e-45 0.187 2.52e-5 MRE11 ENSG00000020922 0.063 1.56e-1 −0.007 8.81e-1 −0.024 5.93e-1 PML ENSG00000140464 0.072 1.05e-1 0.217 9.36e-7 0.166 1.85e-4 p63 ENSG00000073282 −0.120 7.04e-3 −0.283 1.08e-10 −0.198 7.71e-6 p73 ENSG00000078900 0.104 2.03e-2 0.258 4.67e-9 0.097 3.02e-2 Supplementary Table S2. -
Novel Insights Into the Thaumarchaeota in the Deepest Oceans: Their Metabolism and Potential Adaptation Mechanisms
Zhong et al. Microbiome (2020) 8:78 https://doi.org/10.1186/s40168-020-00849-2 RESEARCH Open Access Novel insights into the Thaumarchaeota in the deepest oceans: their metabolism and potential adaptation mechanisms Haohui Zhong1,2, Laura Lehtovirta-Morley3, Jiwen Liu1,2, Yanfen Zheng1, Heyu Lin1, Delei Song1, Jonathan D. Todd3, Jiwei Tian4 and Xiao-Hua Zhang1,2,5* Abstract Background: Marine Group I (MGI) Thaumarchaeota, which play key roles in the global biogeochemical cycling of nitrogen and carbon (ammonia oxidizers), thrive in the aphotic deep sea with massive populations. Recent studies have revealed that MGI Thaumarchaeota were present in the deepest part of oceans—the hadal zone (depth > 6000 m, consisting almost entirely of trenches), with the predominant phylotype being distinct from that in the “shallower” deep sea. However, little is known about the metabolism and distribution of these ammonia oxidizers in the hadal water. Results: In this study, metagenomic data were obtained from 0–10,500 m deep seawater samples from the Mariana Trench. The distribution patterns of Thaumarchaeota derived from metagenomics and 16S rRNA gene sequencing were in line with that reported in previous studies: abundance of Thaumarchaeota peaked in bathypelagic zone (depth 1000–4000 m) and the predominant clade shifted in the hadal zone. Several metagenome-assembled thaumarchaeotal genomes were recovered, including a near-complete one representing the dominant hadal phylotype of MGI. Using comparative genomics, we predict that unexpected genes involved in bioenergetics, including two distinct ATP synthase genes (predicted to be coupled with H+ and Na+ respectively), and genes horizontally transferred from other extremophiles, such as those encoding putative di-myo-inositol-phosphate (DIP) synthases, might significantly contribute to the success of this hadal clade under the extreme condition. -
Molecular Genetics of Microcephaly Primary Hereditary: an Overview
brain sciences Review Molecular Genetics of Microcephaly Primary Hereditary: An Overview Nikistratos Siskos † , Electra Stylianopoulou †, Georgios Skavdis and Maria E. Grigoriou * Department of Molecular Biology & Genetics, Democritus University of Thrace, 68100 Alexandroupolis, Greece; [email protected] (N.S.); [email protected] (E.S.); [email protected] (G.S.) * Correspondence: [email protected] † Equal contribution. Abstract: MicroCephaly Primary Hereditary (MCPH) is a rare congenital neurodevelopmental disorder characterized by a significant reduction of the occipitofrontal head circumference and mild to moderate mental disability. Patients have small brains, though with overall normal architecture; therefore, studying MCPH can reveal not only the pathological mechanisms leading to this condition, but also the mechanisms operating during normal development. MCPH is genetically heterogeneous, with 27 genes listed so far in the Online Mendelian Inheritance in Man (OMIM) database. In this review, we discuss the role of MCPH proteins and delineate the molecular mechanisms and common pathways in which they participate. Keywords: microcephaly; MCPH; MCPH1–MCPH27; molecular genetics; cell cycle 1. Introduction Citation: Siskos, N.; Stylianopoulou, Microcephaly, from the Greek word µικρoκεϕαλi´α (mikrokephalia), meaning small E.; Skavdis, G.; Grigoriou, M.E. head, is a term used to describe a cranium with reduction of the occipitofrontal head circum- Molecular Genetics of Microcephaly ference equal, or more that teo standard deviations -
XBP1 Negatively Regulates CENPF Expression Via Recruiting Atf6α to the Promoter During ER Stress Tao Shen1* , Yan Li2,3, Shuang Liang4 and Zhiguang Chen1
Shen et al. Cancer Cell Int (2020) 20:459 https://doi.org/10.1186/s12935-020-01553-9 Cancer Cell International PRIMARY RESEARCH Open Access XBP1 negatively regulates CENPF expression via recruiting ATF6α to the promoter during ER stress Tao Shen1* , Yan Li2,3, Shuang Liang4 and Zhiguang Chen1 Abstract Background: Centromere protein F (CENPF) is a key component of the kinetochore complex involved in mitosis, cell diferentiation and cellular response to stresses. However, the alteration of CENPF in response to endoplasmic reticulum (ER) stress has not been well described. In the present study, we investigate CENPF regulation in response to ER stress. Methods: Quantitative real-time polymerase chain reaction and western blotting were used to determine CENPF expression under ER stress. Luciferase activity analysis was performed to investigate the promoter regions contribut- ing to CENPF transcription in response to TG. Chromatin immunoprecipitation (ChIP) and ChIP Re-IP assays were used to determine if X-box binding protein 1 (XBP1) and/or activating transcription factor 6α (ATF6α) bind in the CENPF promoter region. Cell apoptosis and proliferation were analyzed using TUNEL, cell growth and clonogenic assays. Results: CENPF expression is dramatically reduced under ER stress induced by thapsigargin (TG), brefeldin A (BFA), or tunicamycin (TM) and this downregulation of CENPF expression was dependent on XBP1 and ATF6α. Luciferase activity analysis of the truncated CENPF promoter indicates that regions from bases 679 to 488 and from 241 to 78 in the CENPF promoter were sensitive to TG treatment. Additionally, ChIP and− ChIP Re-IP− assays reveal −that XBP1− and ATF6α were assembled on the same regions of CENPF promoter.