Real-time quantitative PCR analysis of miRNAs in HIV patients who did (cases) or did not (controls) experience Type 1 MI
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE135758 | GEO | 2022/08/12
REPOSITORIES: GEO
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Project description:A cross linking mass spectrometry search engine was developed and implemented into Thermo Proteome Discoverer. The search engine is capable to handle several linker types as well as data input formats. To demonstrate its ability processing Bruker -ion mobility data, synthetic peptides (Beveridge, et. al., Nat. Commun., 2020, doi: 10.1038/s41467-020-14608-2) were analyzed and the respective files are availible here.
2021-05-27 | PXD022772 | Pride
Project description:RNAseq of 55 melanoma tumors that were used as a validation dataset in Garg et al Nat Commun,  2021 Feb 18;12(1):1137. doi: 10.1038/s41467-021-21207-2.
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Project description:MAX (MYC Associated Factor X) is generally known as a mandatory partner for MYC transcription factor, which activates various genes involved in cell growth and metabolism. On the other hand, MAX, when interacting with MGA, forms the polycomb repressive complex (PRC) 1.6, one of the subtypes of PRC1, which directs the transcriptionally repressed chromatin state. Although physiological significance is not known at present, we have previously demonstrated that mouse embryonic stem cells (ESCs) bear a potential to onset meiosis, albeit not germ cells, and PRC1.6 prevent ESCs from their ectopic onset of meiosis (Suzuki et al., 2016, Nat. Commun. 7:11056 doi: 10.1038/ncomms11056). In this study, we aimed to investigate the role of Max in germ cells in vivo by performing the primordial germ cell-specific knockout of the Max gene.
2024-03-13 | GSE233556 | GEO
Project description:A highly recurrent, somatic L265P mutation in the TIR domain of the signaling adapter MYD88 constitutively activates NF-kB. It occurs in nearly all human patients with Waldenström’s macroglobulinemia (WM), a B cell malignancy caused by IgM-expressing cells. Here we introduced an inducible leucine to proline point mutation into the mouse Myd88 locus, at the orthologous position L252P. When the mutation was introduced early during B cell development, B cells developed normally. However, IgM-expressing plasma cells accumulated with age in spleen and bone, leading to more than 20-fold elevated serum IgM titers. When introduced into germinal center B cells in the context of an immunization, the Myd88L252P mutation caused prolonged persistence of antigen-specific serum IgM and elevated numbers of antigen-specific IgM plasma cells. Myd88L252P-expressing B cells switched normally, but plasma cells expressing other immunoglobulin isotypes did not increase in numbers, implying that IgM expression may be required for the observed cellular expansion. In order to test whether the Myd88L252P mutation can cause clonal expansions, we introduced it into a small fraction of CD19-positive B cells. In this scenario, five out of five mice developed monoclonal IgM serum paraproteins accompanied by an expansion of clonally related plasma cells that expressed mostly hypermutated VDJ regions. Taken together, our data suggest that the Myd88L252P mutation is sufficient to promote aberrant survival and expansion of IgM-expressing plasma cells which in turn can cause IgM monoclonal gammopathy of undetermined significance (MGUS), the premalignant condition that precedes WM. References: 1. Shevchenko A, Tomas H, Havli J, Olsen JV, Mann M. In-gel digestion for mass spectrometric characterization of proteins and proteomes. Nat Protoc (2006) 1:2856–2860. doi:10.1038/nprot.2006.468, 2. Rappsilber J, Mann M, Ishihama Y. Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat Protoc (2007) 2:1896–1906. doi:10.1038/nprot.2007.261, 3. Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: A Peptide Search Engine Integrated into the MaxQuant Environment. J Proteome Res (2011) 10:1794–1805. doi:10.1021/pr101065j, 4. Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol (2008) 26:1367–1372. doi:10.1038/nbt.1511.
2020-11-06 | PXD017292 | Pride
Project description:We quantified the protein abundances of Mycoplasma pneumoniae (Mpn), a genome-reduced organism, by combining selected reaction monitoring of a reduced set of labeled peptides and label-free shotgun mass spectrometry. A set of 77 labeled peptides were synthesized and used as internal standard peptides to precisely and quantitatively measure the absolute concentrations of proteins in a tandem mass spectrometer [1]. The heavy peptides were chosen based on tryptic peptides of endogenous proteins previously observed in mass spectrometry experiments and that covered more than a 1000-fold range of protein concentrations. Three time points of Mpn growth curve (48, 72 and 96 hours) and the heavy peptides were injected per triplicates in order to calculate the endogenous amount of each peptide (single reference point quantitation) [2], resulting in the absolute quantification of 73 reference proteins. A calibration curve between the absolute amount of reference proteins and the proteome-wide protein intensities allowed the estimation of absolute protein abundances for all detected proteins [3]. In order to further improve the quantification, four additional label-free mass spectrometry datasets of Mpn [4] using different protein extraction methods, from a previous project, were combined with the original dataset, which allowed to quantify the abundances of 528 proteins (75% of the proteome of Mpn). 1. Gerber SA, Rush J, Stemman O, Kirschner MW, Gygi SP. Absolute quantification of proteins and phosphoproteins from cell lysates by tandem MS. Proc Natl Acad Sci U S A. 2003;100: 6940–6945. doi:10.1073/pnas.0832254100 2. Campbell J, Rezai T, Prakash A, Krastins B, Dayon L, Ward M, et al. Evaluation of absolute peptide quantitation strategies using selected reaction monitoring. Proteomics. 2011;11: 1148–1152. doi:10.1002/pmic.201000511 3. Schmidt A, Kochanowski K, Vedelaar S, Ahrné E, Volkmer B, Callipo L, et al. The quantitative and condition-dependent Escherichia coli proteome. Nat Biotechnol. 2016;34: 104–110. doi:10.1038/nbt.3418 4. Miravet-Verde S, Ferrar T, Espadas-García G, Mazzolini R, Gharrab A, Sabido E, et al. Unraveling the hidden universe of small proteins in bacterial genomes. Mol Syst Biol. 2019;15: e8290. doi:10.15252/msb.20188290 5. Yus E, Maier T, Michalodimitrakis K, van Noort V, Yamada T, Chen W-H, et al. Impact of genome reduction on bacterial metabolism and its regulation. Science. 2009;326: 1263–1268. doi:10.1126/science.1177263 6. Wiśniewski JR, Zougman A, Nagaraj N, Mann M. Universal sample preparation method for proteome analysis. Nat Methods. 2009;6: 359–362. doi:10.1038/nmeth.1322 7. Picotti P, Bodenmiller B, Mueller LN, Domon B, Aebersold R. Full dynamic range proteome analysis of S. cerevisiae by targeted proteomics. Cell. 2009;138: 795–806. doi:10.1016/j.cell.2009.05.051 8. Martens L, Van Damme P, Van Damme J, Staes A, Timmerman E, Ghesquière B, et al. The human platelet proteome mapped by peptide-centric proteomics: a functional protein profile. Proteomics. 2005;5: 3193–3204. doi:10.1002/pmic.200401142 9. Desiere F, Deutsch EW, King NL, Nesvizhskii AI, Mallick P, Eng J, et al. The peptideatlas project. Nucleic Acids Res. 2006;34: D655–D658. Available: https://academic.oup.com/nar/article-abstract/34/suppl_1/D655/1132687 10. Lange V, Picotti P, Domon B, Aebersold R. Selected reaction monitoring for quantitative proteomics: a tutorial. Mol Syst Biol. 2008;4: 222. doi:10.1038/msb.2008.61 11. MacLean B, Tomazela DM, Shulman N, Chambers M, Finney GL, Frewen B, et al. Skyline: an open source document editor for creating and analyzing targeted proteomics experiments. Bioinformatics. 2010;26: 966–968. doi:10.1093/bioinformatics/btq054 12. Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999;20: 3551–3567. doi:10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2 13. Elias JE, Gygi SP. Target-decoy search strategy for increased confidence in large-scale protein identifications by mass spectrometry. Nat Methods. 2007;4: 207–214. doi:10.1038/nmeth1019
2023-08-31 | PXD035159 | Pride
Project description:RNA sequencing of A431 cell line samples before and after gefitinib treatment, at 0, 2, 6 and 24 hours, was performed in order to characterize the cell line's early and late response to this drug, and to compare against proteomics (mass spectrometry) characterization of the cell line using the same setup. These data were used in Branca et al., HiRIEF LC-MS enables deep proteome coverage and unbiased proteogenomics., Nat Methods. 2014 Jan;11(1):59-62 (doi: 10.1038/nmeth.2732).
2016-12-01 | E-MTAB-5285 | biostudies-arrayexpress
Project description:Paper title: The antimicrobial potential of Streptomyces from insect microbiomes.
Author list: Chevrette MG, Carlson CM, Ortega HE, Thomas C, Ananiev GE, Barns KJ, Book AJ, Cagnazzo J, Carlos C, Flanigan W, Grubbs KJ, Horn HA, Hoffmann FM, Klassen JL, Knack JJ, Lewin GR, McDonald BR, Muller L, Melo WGP, Pinto-Tomas AA, Schmitz A, Wendt-Pienkowski E, Wildman S, Zhao M, Zhang F, Bugni TS, Andes DR, Pupo MT, Currie CR.
Citation: Nat Commun. 2019 Jan 31;10(1):516. doi: 10.1038/s41467-019-08438-0.
PMID: 30705269
mzXMLs from Chevrette et al 2019
2019-12-18 | MSV000084723 | MassIVE
Project description:The Mothes2020_NFkB_A20_signaling model is based on our previous model published in Murakawa et al., Nat Commun, 2015 (doi: 10.1038/ncomms8367). In the Mothes2020_NFkB_A20_signaling model, we removed the modulating impact of the component RC3H1 (RNA-binding protein ROQUIN) which can alter the degradation rates of A20_mRNA and IkBa_mRNA. The Mothes2020_NFkB_A20_signaling model therefore represents the wild type condition of the Murakawa model and shows identical dynamics of the included components A20, A20_mRNA, IkBa, IkBa_mRNA, NFkB, NFkB_IkBa and IKK_active.
2023-07-11 | MODEL2307110001 | BioModels
Project description:Dataset of adenoma and colon cancer multi-region sequencing. Publication: Nat Ecol Evol. 2018 Oct;2(10):1661-1672. doi: 10.1038/s41559-018-0642-z. Epub 2018 Aug 31.
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Project description:EGFP (control) and TEADi expressing podocytes were generated by lentivirus transduction to analyze transcriptional signaling by YAP/TAZ-TEAD. Tetracycline-inducible TEADi is a GFP-tagged inhibitor of the interaction of YAP1 and TAZ with TEAD transcription factors. pInducer20 EGFP-TEADi was a gift from Ramiro Iglesias-Bartolome (Addgene plasmid # 140145 ; http://n2t.net/addgene:140145 ; RRID:Addgene_140145). TEADi was previously described: YAP1/TAZ-TEAD transcriptional networks maintain skin homeostasis by regulating cell proliferation and limiting KLF4 activity. Yuan Y., Park J., Feng A., Awasthi P., Wang Z., Chen Q., Iglesias-Bartolome R.. Nat Commun 11, 1472 (2020). 10.1038/s41467-020-15301-0. Transcriptome profiling (RNA-Sequencing) and differential gene expression analysis of 3 independent replicates per genotype was performed. TEADi podocytes exhibit transcriptional changes compared to WT cells including downregulation of prominent YAP target genes like CTGF, CYR61 and ANKRD1.
2023-07-03 | GSE220221 | GEO