DOCSLIB.ORG

A Statistical Test on Single-Cell Data Reveals Widespread Recurrent Mutations in Tumor Evolution”

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
A Statistical Test on Single-Cell Data Reveals Widespread Recurrent Mutations in Tumor Evolution” Supplementary material for \A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution" Jack Kuipers1;2;?, Katharina Jahn1;2;∗, Benjamin J. Raphael3, and Niko Beerenwinkel1;2 1 Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland 2 SIB Swiss Institute of Bioinformatics, Basel, Switzerland 3 Department of Computer Science, Princeton University, Princeton, NJ, USA Supplementary Figures x x x x x x x x x loss of x heterozygosity sampled cells non-sampled cells observed back mutation, samples with and without loss sampled unobserved incidence of a parallel mutation observed unique mutation unobserved back mutation of observed mutation observed parallel mutation, both incidences sampled unobserved mutations due to subsampling observed incidence of a parallel mutation x unobserved mutations, extinct at sampling time observed mutation with unobserved back mutation x end of extinct lineage Supp. Fig. 1: Left: A cell lineage tree illustrating somatic cell evolution. Due to subsampling and extinct lineages only part of the mutation history is reconstructable (black edges). Therefore some recurrent mutations may be mistaken as being unique (pink pentagon), or their back mutation may be overlooked (yellow diamond). Right: Reconstructable part of the mutation history showing that loss of heterozygosity is the cause of the back mutation indicated by the red star: the chromosome segment containing the SNV is lost leaving only the normal allele on the matching chromosome behind. ? Equal contributors 120 120 100 100 80 80 60 60 40 40 Number of samples Number of samples 20 20 −4 −3 −2 −1 0 1 2 −4 −3 −2 −1 0 1 2 (a) Log10 Bayes factor (b) Log10 Bayes factor Supp. Fig. 2: The range of Bayes factors estimated from simulated data with no recurrent mutations for trees with 20 mutations and up to 120 sampled cells. The false negative rate is 10% in (a) and 20% in (b). 120 120 100 100 80 80 60 60 40 40 Number of samples Number of samples 20 20 −0.2 0.0 0.2 0.4 0.6 −0.2 0.0 0.2 0.4 0.6 (a) Log10 Bayes factor per sample (b) Log10 Bayes factor per sample Supp. Fig. 3: The range of Bayes factors estimated from simulated data with a single recurrent mutations for trees with 20 mutations and up to 120 sampled cells. To compare the Bayes factors for different numbers of samples, their log values are divided by the number of samples. The false negative rate is 10% in (a) and 20% in (b). 120 120 80 80 Number of samples Number of samples 40 40 −4 −3 −2 −1 0 1 2 −4 −3 −2 −1 0 1 2 (a) Log10 Bayes factor (b) Log10 Bayes factor Supp. Fig. 4: The range of Bayes factors estimated from simulated data with no recurrent mutations for trees with 40 mutations and up to 120 sampled cells. The false negative rate is 10% in (a) and 20% in (b). 120 120 80 80 Number of samples Number of samples 40 40 −0.2 0.0 0.2 0.4 0.6 −0.2 0.0 0.2 0.4 0.6 (a) Log10 Bayes factor per sample (b) Log10 Bayes factor per sample Supp. Fig. 5: The range of Bayes factors estimated from simulated data with a single recurrent mutation for trees with 40 mutations and up to 120 sampled cells. To compare the Bayes factors for different numbers of samples, their log values are divided by the number of samples. The false negative rate is 10% in (a) and 20% in (b). 5 5 4 4 3 3 2 2 1 1 Number of doublets Number of doublets 0 0 −4 −2 0 2 4 6 −4 −2 0 2 4 6 (a) Log10 Bayes factor (b) Log10 Bayes factor Supp. Fig. 6: The range of Bayes factors estimated from simulated data with 20 mutations and no recurrent mutation for an increasing number of doublets out of 50 sampled cells. Doublet modeling is not employed in (a) but its use in (b) removes the spurious signals from sequencing two cells at once. 50 50 40 40 30 30 20 20 10 10 Number of doublets Number of doublets 5 5 0 0 0 5 10 15 −4 −3 −2 −1 0 1 2 (a) Log10 Bayes factor (b) Log10 Bayes factor Supp. Fig. 7: The range of Bayes factors estimated from simulated data with 20 mutations and no recurrent mutation for an increasing number of doublets out of 50 sampled cells. The BFs in (a) are calculated without modeling the presence of doublets, while they are accounted for in (b). 50 5 40 4 3 30 2 20 1 10 Number of doublets Number of doublets 5 0 0 0 5 10 0 5 10 15 (a) Log10 Bayes factor (b) Log10 Bayes factor Supp. Fig. 8: The range of Bayes factors estimated from simulated data with 20 mutations and a single recurrent mutation for an increasing number of doublets out of 50 sampled cells. The doublet rate increases to 10% in (a) and 100% in (b). 1.0 1.0 ρ = 0.95 ρ = 0.95 0.8 0.8 0.6 0.6 0.4 0.4 Inferred doublet rate doublet Inferred rate doublet Inferred 0.2 0.2 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 (a) Real doublet rate (b) Real doublet rate Supp. Fig. 9: The doublet rates estimated from simulated data with 20 mutations and 50 sampled cells as the fraction of doublets increases. The simulations do not include any recurrent mutations in (a) but permit a single recurrence in (b). 1.0 1.0 ρ = 0.99 ρ = 0.98 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 Recurrent mutation doublet rate doublet Recurrent mutation rate doublet Recurrent mutation 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 0.0 0.2 0.4 0.6 0.8 1.0 (a) Infinite sites doublet rate (b) Infinite sites doublet rate Supp. Fig. 10: The doublet rates estimated from simulated data with 20 and 50 sampled cells for a range of doublet rates. For each simulated tree, the doublet rate is learnt under both the infinite sites hypothesis and allowing for a recurrent mutation. The simulations do not include any recurrent mutations in (a) and possess a single recurrence in (b). ABCB5 ABCB5 DNAJC17 DNAJC17 SESN2 PABPC1 DMXL1 PABPC1 PDE4DIP NTRK1 DLEC1 SESN2 NTRK1 DLEC1 DMXL1 TOP1MT TOP1MT ST13 ST13 PDE4DIP ANAPC1 ANAPC1 USP32 USP32 ARHGAP5 ARHGAP5 FAM115C FAM115C FRG1 ASNS FRG1 ASNS RETSAT MLL3 MLL3 RETSAT RETSAT (a) (b) Supp. Fig. 11: The best scoring trees learnt for the data on 18 selected mutations from the whole exome sequencing of 58 cells from a JAK2 -negative myeloproliferative neoplasm [Hou et al., 2012]. The tree in (a) respects the infinite sites hypothesis while (b) allows for the highest scoring recurrent mutation which occurs in the gene RETSAT . The presence or absence of the point mutation in PDE4DIP is unknown for over 60% of the single cells, resulting in high uncertainty and variability in its placement. PIP4K2A PIP4K2A COL5A3 SLC11A2 SLC11A2 THRAP1 ZNF462 ZNF462 NOS1 XPNPEP1 CCKBR NOS1 C8orf45 C17orf27 KIAA0226 C8orf45 FLJ38288 FGFR4 FGFR4 THRAP1 COL5A3 CCKBR C17orf27 KIAA0226 XPNPEP1 FLJ38288 DEPDC2 F2RL2 SCYL2 ADAMTS10 PTPRF SCYL2 ADAMTS10 GPR158 ZBTB2 DEPDC2 KIF6 RPL8 SH3GL1 KIF6 C1orf107 KIAA1718 KIAA1718 SLC36A2 ZBTB2 RPL8 C1orf107 F2RL2 PTPRF MCCC2 SH3GL1 FCRLM1 SLC36A2 PTPRT ZNF71 GPR158 LRRK2 ZNF71 FCRLM1 LRRK2 MCCC2 FLJ10379 PTPRT FLJ10379 PTPRT CDON CDON CUZD1 CUZD1 EPHB4 EPHB4 (a) (b) Supp. Fig. 12: The best scoring trees learnt for the whole exome sequencing of 17 cells from a clear cell renal cell carcinoma [Xu et al., 2012]. The tree in (a) respects the infinite sites hypothesis while (b) allows for the highest scoring recurrent mutation which occurs in the gene PTPRT . BTLA PANK3 DCAF8L1 DCAF8L1 PIK3CA ITGAD ITGAD LSG1 DNM3 DNM3 LSG1 FCHSD2 FCHSD2 PIK3CA BTLA PANK3 CASP3 TRIM58 FUBP3 CALD1 H1ENT FUBP3 CALD1 TRIM58 CASP3 MARCH11 PANK3 TCP11 DUSP12 DUSP12 TCP11 PITRM1 MARCH11 FBN2 FBN2 PITRM1 PPP2RE PPP2RE ROPN1B GPR64 ROPN1B GPR64 PRDM9 ZEHX4 ZNE318 MUTYH PLXNA2 PRDM9 ZEHX4 ZNE318 MUTYH PLXNA2 CABP2 DKEZ WDR16 C1orf223 SEC11A CABP2 H1ENT DKEZ WDR16 C1orf223 SEC11A TRIB2 C15orf23 GLCE KIAA1539 RABGAP1L TRIB2 C15orf23 GLCE KIAA1539 RABGAP1L CNDP1 FGFR2 CNDP1 FGFR2 CXXC1 TECTA TECTA CXXC1 (a) (b) Supp. Fig. 13: The best scoring trees learnt for the whole exome sequencing of 47 cells from a estrogen- receptor positive (ER+) breast cancer tumor [Wang et al., 2014]. The tree in (a) respects the infinite sites hypothesis while (b) allows for the highest scoring recurrent mutation which occurs in the gene PANK3 . MAL2_chr8_120255800 MAL2_chr8_120255800 RYR3_chr15_33952506 RYR3_chr15_33952506 TUFT1_chr1_151512803 PTPRQ_chr12_81013241 TUFT1_chr1_151512803 PTPRQ_chr12_81013241 BBS4_chr15_73021943 OAZ3_chr1_151735456 BBS4_chr15_73021943 OAZ3_chr1_151735456 HIST1H2AG_chr6_27101163 HIPK4_chr19_40895668 HIST1H2AG_chr6_27101163 HIPK4_chr19_40895668 EPHA10_chr1_38226084 SERPINF2_chr17_1657484 EPHA10_chr1_38226084 SERPINF2_chr17_1657484 DOCK3_chr3_51263127 TTN_chr2_179615995 DOCK3_chr3_51263127 TTN_chr2_179615995 RGS11_chr16_320709 INTS8_chr8_95877709 RGS11_chr16_320709 INTS8_chr8_95877709 CAMSAP1_chr9_138702709 PPIG_chr2_170493981 CAMSAP1_chr9_138702709 PPIG_chr2_170493981 SMOC1_chr14_70418996 MYOM3_chr1_24390601 SMOC1_chr14_70418996 MYOM3_chr1_24390601 EYA4_chr6_133850054 ZNF540_chr19_38103617 EYA4_chr6_133850054 ZNF540_chr19_38103617 MAL2_chr8_120255800 (a) (b) Supp. Fig. 14: The best scoring trees learnt for the mutation data for patient 1 from the leukemia dataset of Gawad et al. [2014]. The tree in (a) respects the infinite sites hypothesis while (b) allows for the highest scoring recurrent mutation on chromosome 8 at position 120255800 (hg19) in the gene MAL2 . RIMS2_chr8_105025789 RIMS2_chr8_105025789 SIGLEC10_chr19_51917709 SIGLEC10_chr19_51917709 PLEC_chr8_144995042 PLEC_chr8_144995042 FGD4_chr12_32793457 LINC00052_chr15_88122589 ZC3H3_chr8_144550709 FGD4_chr12_32793457 LINC00052_chr15_88122589 ZC3H3_chr8_144550709 BDNF-AS_chr11_27698664 XPO7_chr8_21827088 BDNF-AS_chr11_27698664 XPO7_chr8_21827088 RRP8_chr11_6622747 BRD7P3_chr6_118822648 RRP8_chr11_6622747 BRD7P3_chr6_118822648 PCDH7_chr4_31148165 PCDH7_chr4_31148165 RIMS2_chr8_105025789 FAM105A_chr5_14612901 FAM105A_chr5_14612901 ATRNL1_chr10_117154148 INHA_chr2_220440226 ATRNL1_chr10_117154148 INHA_chr2_220440226 TRRAP_chr7_98575882 CMTM8_chr3_32280281 TRRAP_chr7_98575882 CMTM8_chr3_32280281 (a) (b) Supp.
Load more

Recommended publications
  • Circular RNA Hsa Circ 0005114‑Mir‑142‑3P/Mir‑590‑5P‑ Adenomatous
    ONCOLOGY LETTERS 21: 58, 2021 Circular RNA hsa_circ_0005114‑miR‑142‑3p/miR‑590‑5p‑ adenomatous polyposis coli protein axis as a potential target for treatment of glioma BO WEI1*, LE WANG2* and JINGWEI ZHAO1 1Department of Neurosurgery, China‑Japan Union Hospital of Jilin University, Changchun, Jilin 130033; 2Department of Ophthalmology, The First Hospital of Jilin University, Jilin University, Changchun, Jilin 130021, P.R. China Received September 12, 2019; Accepted October 22, 2020 DOI: 10.3892/ol.2020.12320 Abstract. Glioma is the most common type of brain tumor APC expression with a good overall survival rate. UALCAN and is associated with a high mortality rate. Despite recent analysis using TCGA data of glioblastoma multiforme and the advances in treatment options, the overall prognosis in patients GSE25632 and GSE103229 microarray datasets showed that with glioma remains poor. Studies have suggested that circular hsa‑miR‑142‑3p/hsa‑miR‑590‑5p was upregulated and APC (circ)RNAs serve important roles in the development and was downregulated. Thus, hsa‑miR‑142‑3p/hsa‑miR‑590‑5p‑ progression of glioma and may have potential as therapeutic APC‑related circ/ceRNA axes may be important in glioma, targets. However, the expression profiles of circRNAs and their and hsa_circ_0005114 interacted with both of these miRNAs. functions in glioma have rarely been studied. The present study Functional analysis showed that hsa_circ_0005114 was aimed to screen differentially expressed circRNAs (DECs) involved in insulin secretion, while APC was associated with between glioma and normal brain tissues using sequencing the Wnt signaling pathway. In conclusion, hsa_circ_0005114‑ data collected from the Gene Expression Omnibus database miR‑142‑3p/miR‑590‑5p‑APC ceRNA axes may be potential (GSE86202 and GSE92322 datasets) and explain their mecha‑ targets for the treatment of glioma.
    [Show full text]
  • Genetic and Genomic Analysis of Hyperlipidemia, Obesity and Diabetes Using (C57BL/6J × TALLYHO/Jngj) F2 Mice
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Nutrition Publications and Other Works Nutrition 12-19-2010 Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P. Stewart Marshall University Hyoung Y. Kim University of Tennessee - Knoxville, [email protected] Arnold M. Saxton University of Tennessee - Knoxville, [email protected] Jung H. Kim Marshall University Follow this and additional works at: https://trace.tennessee.edu/utk_nutrpubs Part of the Animal Sciences Commons, and the Nutrition Commons Recommended Citation BMC Genomics 2010, 11:713 doi:10.1186/1471-2164-11-713 This Article is brought to you for free and open access by the Nutrition at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Nutrition Publications and Other Works by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. Stewart et al. BMC Genomics 2010, 11:713 http://www.biomedcentral.com/1471-2164/11/713 RESEARCH ARTICLE Open Access Genetic and genomic analysis of hyperlipidemia, obesity and diabetes using (C57BL/6J × TALLYHO/JngJ) F2 mice Taryn P Stewart1, Hyoung Yon Kim2, Arnold M Saxton3, Jung Han Kim1* Abstract Background: Type 2 diabetes (T2D) is the most common form of diabetes in humans and is closely associated with dyslipidemia and obesity that magnifies the mortality and morbidity related to T2D. The genetic contribution to human T2D and related metabolic disorders is evident, and mostly follows polygenic inheritance. The TALLYHO/ JngJ (TH) mice are a polygenic model for T2D characterized by obesity, hyperinsulinemia, impaired glucose uptake and tolerance, hyperlipidemia, and hyperglycemia.
    [Show full text]
  • Gwas Meta-Analysis of Intelligence – Supplementary Note 1
    GWAS META-ANALYSIS OF INTELLIGENCE – SUPPLEMENTARY NOTE 1 Supplementary Note for GWAS meta-analysis (N=279,930) identifies new genes and functional links to intelligence Jeanne E Savage1#, Philip R Jansen1,2#, Sven Stringer1, Kyoko Watanabe1, Julien Bryois3, Christiaan A de Leeuw1, Mats Nagel1, Swapnil Awasthi4, Peter B Barr5, Jonathan R I Coleman6,7, Katrina L Grasby8, Anke R Hammerschlag1, Jakob Kaminski4,9, Robert Karlsson3, Eva Krapohl6, Max Lam10, Marianne Nygaard11,12, Chandra A. Reynolds13, Joey W. Trampush14, Hannah Young15, Delilah Zabaneh6, Sara Hägg3, Narelle K Hansell16, Ida K Karlsson3, Sten Linnarsson17, Grant W Montgomery8,18, Ana B. Muñoz-Manchado17, Erin B Quinlan6, Gunter Schumann6, Nathan Skene17, Bradley T Webb19,20,21, Tonya White2, Dan E Arking22, Deborah K Attix23,24, Dimitrios Avramopoulos22,25, Robert M Bilder26, Panos Bitsios27, Katherine E Burdick28,29,30, Tyrone D Cannon31, Ornit Chiba-Falek32, Andrea Christoforou23, Elizabeth T Cirulli33, Eliza Congdon26, Aiden Corvin34, Gail Davies35, 36, Ian J Deary35,36, Pamela DeRosse37, Dwight Dickinson38, Srdjan Djurovic39,40, Gary Donohoe41, Emily Drabant Conley42, Johan G Eriksson43,44, Thomas Espeseth45,46, Nelson A Freimer26,Stella Giakoumaki47, Ina Giegling48, Michael Gill34, David C Glahn49, Ahmad R Hariri50, Alex Hatzimanolis51,52,53, Matthew C Keller54, Emma Knowles49, Bettina Konte48, Jari Lahti55,56, Stephanie Le Hellard23,40, Todd Lencz37,57,58, David C Liewald36, Edythe London26, Astri J Lundervold59, Anil K Malhotra37,57,58, Ingrid Melle40,46, , Derek Morris41,
    [Show full text]
  • Whole Exome Sequencing in Families at High Risk for Hodgkin Lymphoma: Identification of a Predisposing Mutation in the KDR Gene
    Hodgkin Lymphoma SUPPLEMENTARY APPENDIX Whole exome sequencing in families at high risk for Hodgkin lymphoma: identification of a predisposing mutation in the KDR gene Melissa Rotunno, 1 Mary L. McMaster, 1 Joseph Boland, 2 Sara Bass, 2 Xijun Zhang, 2 Laurie Burdett, 2 Belynda Hicks, 2 Sarangan Ravichandran, 3 Brian T. Luke, 3 Meredith Yeager, 2 Laura Fontaine, 4 Paula L. Hyland, 1 Alisa M. Goldstein, 1 NCI DCEG Cancer Sequencing Working Group, NCI DCEG Cancer Genomics Research Laboratory, Stephen J. Chanock, 5 Neil E. Caporaso, 1 Margaret A. Tucker, 6 and Lynn R. Goldin 1 1Genetic Epidemiology Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 2Cancer Genomics Research Laboratory, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; 3Ad - vanced Biomedical Computing Center, Leidos Biomedical Research Inc.; Frederick National Laboratory for Cancer Research, Frederick, MD; 4Westat, Inc., Rockville MD; 5Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD; and 6Human Genetics Program, Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA ©2016 Ferrata Storti Foundation. This is an open-access paper. doi:10.3324/haematol.2015.135475 Received: August 19, 2015. Accepted: January 7, 2016. Pre-published: June 13, 2016. Correspondence: [email protected] Supplemental Author Information: NCI DCEG Cancer Sequencing Working Group: Mark H. Greene, Allan Hildesheim, Nan Hu, Maria Theresa Landi, Jennifer Loud, Phuong Mai, Lisa Mirabello, Lindsay Morton, Dilys Parry, Anand Pathak, Douglas R. Stewart, Philip R. Taylor, Geoffrey S. Tobias, Xiaohong R. Yang, Guoqin Yu NCI DCEG Cancer Genomics Research Laboratory: Salma Chowdhury, Michael Cullen, Casey Dagnall, Herbert Higson, Amy A.
    [Show full text]
  • A Meta-Analysis of Gene Expression Data Highlights Synaptic Dysfunction
    www.nature.com/scientificreports OPEN A meta-analysis of gene expression data highlights synaptic dysfunction in the hippocampus of brains with Alzheimer’s disease Saeedeh Hosseinian1, Ehsan Arefan2,3 ✉ , Hassan Rakhsh-Khorshid4, Mehdi Eivani 5, Ameneh Rezayof6, Hamid Pezeshk 7,8 & Sayed-Amir Marashi 1 Since the world population is ageing, dementia is going to be a growing concern. Alzheimer’s disease is the most common form of dementia. The pathogenesis of Alzheimer’s disease is extensively studied, yet unknown remains. Therefore, we aimed to extract new knowledge from existing data. We analysed about 2700 upregulated genes and 2200 downregulated genes from three studies on the CA1 of the hippocampus of brains with Alzheimer’s disease. We found that only the calcium signalling pathway enriched by 48 downregulated genes was consistent between all three studies. We predicted miR-129 to target nine out of 48 genes. Then, we validated miR-129 to regulate six out of nine genes in HEK cells. We noticed that four out of six genes play a role in synaptic plasticity. Finally, we confrmed the upregulation of miR-129 in the hippocampus of brains of rats with scopolamine-induced amnesia as a model of Alzheimer’s disease. We suggest that future research should investigate the possible role of miR-129 in synaptic plasticity and Alzheimer’s disease. This paper presents a novel framework to gain insight into potential biomarkers and targets for diagnosis and treatment of diseases. Alzheimer’s disease (AD) is the most common form of dementia. It mostly afects people aged 65 and older, pro- gresses slowly and leads to death in an average of nine years afer diagnosis.
    [Show full text]
  • Data Sheet 140
    Rudolf-Wissell-Str. 28a Background 37079 Göttingen, Germany Phone: +49 551-50556-0 RIMs are presynaptic active zone proteins that regulate Ca2+ triggered release of neurotransmitters. Fax: +49 551-50556-384 RIM 1α and RIM 2α are composed of an N-terminal zinc-finger domain, a central PDZ domain and two E-mail: [email protected] C-terminal C2 domains that are seperated by long alternatively spliced sequences. Web: www.sysy.com RIM 2β consists of a specific N-terminus, the central PDZ domain and the C-terminal C2 domains. The RIM 2 mRNA for RIM 2β is transcribed from an internal promoter of the RIM 2α gene. Shorter variants of RIM 2 which comprise only the C-terminal C2B domain and some flanking regions Cat.No. 140 303; Polyclonal rabbit antibody, 50 µg specific antibody (lyophilized) are referred to as NIM 2 / RIM 2γ and NIM 3 / RIM 3γ. Data Sheet Selected References for 140 303 RIM1/2-Mediated Facilitation of Cav1.4 Channel Opening Is Required for Ca2+-Stimulated Release in Mouse Rod Reconstitution/ 50 µg specific antibody, lyophilized. Affinity purified with the immunogen. Photoreceptors. Grabner CP, Gandini MA, Rehak R, Le Y, Zamponi GW, Schmitz F Storage Albumin was added for stabilization. For reconstitution add 50 µl H2O to get a 1mg/ml solution in PBS. Then aliquot and store at -20°C to -80°C until use. The Journal of neuroscience : the official journal of the Society for Neuroscience (2015) 3538: 13133-47. IHC, WB For detailed information, see back of the data sheet. Analysis of RIM Expression and Function at Mouse Photoreceptor Ribbon Synapses.
    [Show full text]
  • Loss of Twist1 in the Mesenchymal Compartment Promotes Increased
    Loss of Twist1 in the Mesenchymal Compartment Promotes Increased Fibrosis in Experimental Lung Injury by Enhanced Expression of CXCL12 This information is current as of October 2, 2021. Jiangning Tan, John R. Tedrow, Mehdi Nouraie, Justin A. Dutta, David T. Miller, Xiaoyun Li, Shibing Yu, Yanxia Chu, Brenda Juan-Guardela, Naftali Kaminski, Kritika Ramani, Partha S. Biswas, Yingze Zhang and Daniel J. Kass J Immunol 2017; 198:2269-2285; Prepublished online 8 Downloaded from February 2017; doi: 10.4049/jimmunol.1600610 http://www.jimmunol.org/content/198/6/2269 http://www.jimmunol.org/ Supplementary http://www.jimmunol.org/content/suppl/2017/02/07/jimmunol.160061 Material 0.DCSupplemental References This article cites 65 articles, 15 of which you can access for free at: http://www.jimmunol.org/content/198/6/2269.full#ref-list-1 Why The JI? Submit online. by guest on October 2, 2021 • Rapid Reviews! 30 days* from submission to initial decision • No Triage! Every submission reviewed by practicing scientists • Fast Publication! 4 weeks from acceptance to publication *average Subscription Information about subscribing to The Journal of Immunology is online at: http://jimmunol.org/subscription Permissions Submit copyright permission requests at: http://www.aai.org/About/Publications/JI/copyright.html Author Choice Freely available online through The Journal of Immunology Author Choice option Email Alerts Receive free email-alerts when new articles cite this article. Sign up at: http://jimmunol.org/alerts The Journal of Immunology is published twice each month by The American Association of Immunologists, Inc., 1451 Rockville Pike, Suite 650, Rockville, MD 20852 Copyright © 2017 by The American Association of Immunologists, Inc.
    [Show full text]
  • Role of PDZ-Binding Motif from West Nile Virus NS5 Protein on Viral
    www.nature.com/scientificreports OPEN Role of PDZ‑binding motif from West Nile virus NS5 protein on viral replication Emilie Giraud1*, Chloé Otero del Val2, Célia Caillet‑Saguy2, Nada Zehrouni2, Cécile Khou5, Joël Caillet4, Yves Jacob3, Nathalie Pardigon5 & Nicolas Wolf2 West Nile virus (WNV) is a Flavivirus, which can cause febrile illness in humans that may progress to encephalitis. Like any other obligate intracellular pathogens, Flaviviruses hijack cellular protein functions as a strategy for sustaining their life cycle. Many cellular proteins display globular domain known as PDZ domain that interacts with PDZ‑Binding Motifs (PBM) identifed in many viral proteins. Thus, cellular PDZ‑containing proteins are common targets during viral infection. The non‑structural protein 5 (NS5) from WNV provides both RNA cap methyltransferase and RNA polymerase activities and is involved in viral replication but its interactions with host proteins remain poorly known. In this study, we demonstrate that the C‑terminal PBM of WNV NS5 recognizes several human PDZ‑ containing proteins using both in vitro and in cellulo high‑throughput methods. Furthermore, we constructed and assayed in cell culture WNV replicons where the PBM within NS5 was mutated. Our results demonstrate that the PBM of WNV NS5 is important in WNV replication. Moreover, we show that knockdown of the PDZ‑containing proteins TJP1, PARD3, ARHGAP21 or SHANK2 results in the decrease of WNV replication in cells. Altogether, our data reveal that interactions between the PBM of NS5 and PDZ‑containing proteins afect West Nile virus replication. Arboviruses include numerous human and animal pathogens that are important global health threats responsible for arboviroses.
    [Show full text]
  • The Molecular Machinery of Neurotransmitter Release Nobel Lecture, 7 December 2013
    The Molecular Machinery of Neurotransmitter Release Nobel Lecture, 7 December 2013 by Thomas C. Südhof Dept. of Molecular and Cellular Physiology, and Howard Hughes Medical Institute, Stanford University, USA. 1. THE NEUROTRANSMITTER RELEASE ENIGMA Synapses have a long history in science. Synapses were frst functionally demon- strated by Emil duBois-Reymond (1818–1896), were morphologically identifed by classical neuroanatomists such as Rudolf von Kölliker (1817–1905) and San- tiago Ramon y Cajal (1852–1934), and named in 1897 by Michael Foster (1836– 1907). Although the chemical nature of synaptic transmission was already sug- gested by duBois-Reymond, it was long disputed because of its incredible speed. Over time, however, overwhelming evidence established that most synapses use chemical messengers called neurotransmitters, most notably with the pioneer- ing contributions by Otto Loewi (1873–1961), Henry Dale (1875–1968), Ulf von Euler (1905–1983), and Julius Axelrod (1912–2004). In parallel, arguably the most important advance to understanding how synapses work was provided by Bernard Katz (1911–2003), who elucidated the principal mechanism of syn- aptic transmission (Katz, 1969). Most initial studies on synapses were carried out on the neuromuscular junction, and central synapses have only come to the fore in recent decades. Here, major contributions by many scientists, including George Palade, Rodolfo Llinas, Chuck Stevens, Bert Sakmann, Eric Kandel, and Victor Whittaker, to name just a few, not only confrmed the principal results obtained in the neuromuscular junction by Katz, but also revealed that synapses 259 6490_Book.indb 259 11/4/14 2:29 PM 260 The Nobel Prizes exhibit an enormous diversity of properties as well as an unexpected capacity for plasticity.
    [Show full text]
  • Rabbit Anti-Epac2/FITC Conjugated Antibody
    SunLong Biotech Co.,LTD Tel: 0086-571- 56623320 Fax:0086-571- 56623318 E-mail:[email protected] www.sunlongbiotech.com Rabbit Anti-Epac2/FITC Conjugated antibody SL10955R-FITC Product Name: Anti-Epac2/FITC Chinese Name: FITC标记的鸟嘌呤核苷酸交换因子4 CAMP GEFII; cAMP regulated guanine nucleotide exchange factor II; cAMP-GEFII; cAMP-regulated guanine nucleotide exchange factor II; CAMPGEFII; CGEF 2; CGEF2; Epac 2; EPAC2; Exchange factor directly activated by cAMP 2; Exchange Alias: protein directly activated by cAMP 2; Nbla00496; Putative protein product of Nbla00496; Rap guanine nucleotide exchange factor (GEF) 4; RAP guanine nucleotide exchange factor 4; RAPGEF 4; RAPGEF4; RPGF4_HUMAN. Organism Species: Rabbit Clonality: Polyclonal React Species: ICC=1:50-200IF=1:50-200 Applications: not yet tested in other applications. optimal dilutions/concentrations should be determined by the end user. Molecular weight: 116kDa Form: Lyophilized or Liquid Concentration: 1mg/ml immunogen: KLHwww.sunlongbiotech.com conjugated synthetic peptide derived from human Epac2 Lsotype: IgG Purification: affinity purified by Protein A Storage Buffer: 0.01M TBS(pH7.4) with 1% BSA, 0.03% Proclin300 and 50% Glycerol. Store at -20 °C for one year. Avoid repeated freeze/thaw cycles. The lyophilized antibody is stable at room temperature for at least one month and for greater than a year Storage: when kept at -20°C. When reconstituted in sterile pH 7.4 0.01M PBS or diluent of antibody the antibody is stable for at least two weeks at 2-4 °C. background: 3',5' cyclic adenosine monophosphate (cAMP)-regulated guanine nucleotide exchange Product Detail: factors Epac1 (cAMP-GEFI) and Epac2 (cAMP-GEFII) activate the ras family GTPases Rap1 and Rap2 by promoting GTP binding in a cAMP-dependent manner (1,2).
    [Show full text]
  • GSE50161, (C) GSE66354, (D) GSE74195 and (E) GSE86574
    Figure S1. Boxplots of normalized samples in five datasets. (A) GSE25604, (B) GSE50161, (C) GSE66354, (D) GSE74195 and (E) GSE86574. The x‑axes indicate samples, and the y‑axes represent the expression of genes. Figure S2. Volanco plots of DEGs in five datasets. (A) GSE25604, (B) GSE50161, (C) GSE66354, (D) GSE74195 and (E) GSE86574. Red nodes represent upregulated DEGs and green nodes indicate downregulated DEGs. Cut‑off criteria were P<0.05 and |log2 FC|>1. DEGs, differentially expressed genes; FC, fold change; adj.P.Val, adjusted P‑value. Figure S3. Transcription factor‑gene regulatory network constructed using the Cytoscape iRegulion plug‑in. Table SI. Primer sequences for reverse transcription‑quantitative polymerase chain reaction. Genes Sequences hsa‑miR‑124 F: 5'‑ACACTCCAGCTGGGCAGCAGCAATTCATGTTT‑3' R: 5'‑CTCAACTGGTGTCGTGGA‑3' hsa‑miR‑330‑3p F: 5'‑CATGAATTCACTCTCCCCGTTTCTCCCTCTGC‑3' R: 5'‑CCTGCGGCCGCGAGCCGCCCTGTTTGTCTGAG‑3' hsa‑miR‑34a‑5p F: 5'‑TGGCAGTGTCTTAGCTGGTTGT‑3' R: 5'‑GCGAGCACAGAATTAATACGAC‑3' hsa‑miR‑449a F: 5'‑TGCGGTGGCAGTGTATTGTTAGC‑3' R: 5'‑CCAGTGCAGGGTCCGAGGT‑3' CD44 F: 5'‑CGGACACCATGGACAAGTTT‑3' R: 5'‑TGTCAATCCAGTTTCAGCATCA‑3' PCNA F: 5'‑GAACTGGTTCATTCATCTCTATGG‑3' F: 5'‑TGTCACAGACAAGTAATGTCGATAAA‑3' SYT1 F: 5'‑CAATAGCCATAGTCGCAGTCCT‑3' R: 5'‑TGTCAATCCAGTTTCAGCATCA‑3' U6 F: 5'‑GCTTCGGCAGCACATATACTAAAAT‑3' R: 5'‑CGCTTCACGAATTTGCGTGTCAT‑3' GAPDH F: 5'‑GGAAAGCTGTGGCGTGAT‑3' R: 5'‑AAGGTGGAAGAATGGGAGTT‑3' hsa, homo sapiens; miR, microRNA; CD44, CD44 molecule (Indian blood group); PCNA, proliferating cell nuclear antigen;
    [Show full text]
  • Analysis and Functional Evaluation of the Hair-Cell Transcriptome
    Analysis and functional evaluation of the hair-cell transcriptome Brian M. McDermott, Jr.*, Jessica M. Baucom, and A. J. Hudspeth† Howard Hughes Medical Institute and Laboratory of Sensory Neuroscience, The Rockefeller University, 1230 York Avenue, New York, NY 10021-6399 Contributed by A. J. Hudspeth, May 17, 2007 (sent for review March 31, 2007) An understanding of the molecular bases of the morphogenesis, ciated from the lagena, a receptor organ of the zebrafish’s ear. organization, and functioning of hair cells requires that the genes Linear amplification of the RNA from 200 hair cells yielded Ϸ40 expressed in these cells be identified and their functions ascer- ␮g of aRNA, an enhancement of Ϸ1 millionfold. The resultant tained. After purifying zebrafish hair cells and detecting mRNAs labeled aRNA was hybridized to an Affymetrix microarray with oligonucleotide microarrays, we developed a subtractive (Affymetrix, Santa Clara, CA) containing Ϸ15,000 oligonucle- strategy that identified 1,037 hair cell-expressed genes whose otide probe sets. Averaging the outcomes of three experiments cognate proteins subserve functions including membrane trans- (SI Data Set 1) resulted in the identification of 6,472 transcripts port, synaptic transmission, transcriptional control, cellular adhe- scored as ‘‘present’’ (SI Data Set 2). sion and signal transduction, and cytoskeletal organization. To In the second step, we defined the transcriptome from cells of assess the validity of the subtracted hair-cell data set, we verified a nonsensory organ, the liver (SI Data Set 1). Hepatocytes were the presence of 11 transcripts in inner-ear tissue. Functional eval- selected for the subtraction process for three reasons: they are uation of two genes from the subtracted data set revealed their nonneuronal and thus unlikely to express synaptic factors; they importance in hair bundles: zebrafish larvae bearing the seahorse lack cilia (http://members.global2000.net/bowser/cilialist.html) and ift 172 mutations display specific kinociliary defects.
    [Show full text]