Dataset Information

Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry.

ABSTRACT: Data from PASSEL: [[https://db.systemsbiology.net/sbeams/cgi/PeptideAtlas/PASS_View?datasetPassword=QU5589mw&identifier=PASS00016 PASS00016]]. Experiment: 0hr-chrom, file: 0hr-chrom_1d.mzXML. Data published as part of Mol Syst Biol. 2012 8:573 . From the Abstract: {{i}}Regulating the transition of cells such as T lymphocytes from quiescence (G(0)) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G(0). We also provide proof of principle that blastogenesis and proliferation programs are separable in primary human T cells. We employed a proteomic profiling method to identify large-scale changes in chromatin/nuclear matrix-bound and unbound proteins in human T lymphocytes during the transition from G(0) into the first cell cycle and mapped them to form functionally annotated, dynamic protein interaction networks. Inhibiting the induction of two proteins involved in two of the most significantly upregulated cellular processes, ribosome biogenesis (eIF6) and hnRNA splicing (SF3B2/SF3B4), showed, respectively, that human T cells can enter the cell cycle without growing in size, or increase in size without entering the cell cycle.{{/i}}

INSTRUMENT(S): Instrument

ORGANISM(S): Human, Human_adenovirus_54, Human_adenovirus_a, Human_adenovirus_b, Human_adenovirus_c, Human_adenovirus_d, Human_adenovirus_e, Human_adenovirus_f, Human_adenovirus_g, Human_astrovirus, Human_bocavirus, Human_bocavirus_2, Human_bocavirus_3, Human_bocavirus_4, Human_coronavirus_229e, Human_coronavirus_hku1, Human_coronavirus_nl63, Human_coronavirus_oc43, Human_cosavirus_a, Human_cosavirus_b, Human_cosavirus_d, Human_cosavirus_e, Human_enteric_coronavirus_4408, Human_enterovirus_100, Human_enterovirus_a, Human_enterovirus_b, Human_enterovirus_c, Human_enterovirus_d, Human_erythrovirus_v9, Human_herpesvirus_1, Human_herpesvirus_2, Human_herpesvirus_3, Human_herpesvirus_4_type_1, Human_herpesvirus_4_type_2, Human_herpesvirus_5, Human_herpesvirus_6a, Human_herpesvirus_6b, Human_herpesvirus_7, Human_herpesvirus_8_type_p, Human_immunodeficiency_virus_1, Human_immunodeficiency_virus_2, Human_metapneumovirus, Human_papillomavirus_fa75_ki88_03, Human_papillomavirus_rtrx7, Human_papillomavirus_type_10, Human_papillomavirus_type_100, Human_papillomavirus_type_101, Human_papillomavirus_type_103, Human_papillomavirus_type_104, Human_papillomavirus_type_105, Human_papillomavirus_type_108, Human_papillomavirus_type_109, Human_papillomavirus_type_112, Human_papillomavirus_type_113, Human_papillomavirus_type_16, Human_papillomavirus_type_24, Human_papillomavirus_type_26, Human_papillomavirus_type_32, Human_papillomavirus_type_34, Human_papillomavirus_type_4, Human_papillomavirus_type_41, Human_papillomavirus_type_48, Human_papillomavirus_type_49, Human_papillomavirus_type_5, Human_papillomavirus_type_50, Human_papillomavirus_type_53, Human_papillomavirus_type_60, Human_papillomavirus_type_63, Human_papillomavirus_type_6b, Human_papillomavirus_type_7, Human_papillomavirus_type_71, Human_papillomavirus_type_88, Human_papillomavirus_type_9, Human_papillomavirus_type_92, Human_papillomavirus_type_96, Human_papillomavirus_type_98, Human_papillomavirus_type_99, Human_papillomavirus___1, Human_papillomavirus___18, Human_papillomavirus___2, Human_papillomavirus___54, Human_papillomavirus___61, Human_papillomavirus___cand90, Human_parainfluenza_virus_1, Human_parainfluenza_virus_2, Human_parainfluenza_virus_3, Human_parechovirus, Human_parvovirus_4, Human_parvovirus_b19, Human_picobirnavirus, Human_respiratory_syncytial_virus, Human_rhinovirus_a, Human_rhinovirus_b, Human_rhinovirus_c, Human_tmev_like_cardiovirus, Human_t_lymphotropic_virus_1, Human_t_lymphotropic_virus_2, Human_t_lymphotropic_virus_4

DISEASE(S): Not Available

SUBMITTER: Orr SJ, et al.

PROVIDER: GPM11210014830 | GPMDB |

REPOSITORIES: GPMDB

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Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry.

Orr Stephen J SJ Boutz Daniel R DR Wang Rong R Chronis Constantinos C Lea Nicholas C NC Thayaparan Thivyan T Hamilton Emma E Milewicz Hanna H Blanc Eric E Mufti Ghulam J GJ Marcotte Edward M EM Thomas N Shaun B NS

Molecular systems biology 20120313

Regulating the transition of cells such as T lymphocytes from quiescence (G(0)) into an activated, proliferating state involves initiation of cellular programs resulting in entry into the cell cycle (proliferation), the growth cycle (blastogenesis, cell size) and effector (functional) activation. We show the first proteomic analysis of protein interaction networks activated during entry into the first cell cycle from G(0). We also provide proof of principle that blastogenesis and proliferation p ...[more]

PMID: 22415777

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Project description:Background: FGF signaling controls numerous processes during cell lineage specification, organogenesis and terminal differentiation. In lens, FGF signaling was implicated as the key pathway that controls lens fiber cell differentiation, but little is known about its full range and spectrum of regulated genes. Results: Herein, we employed rat lens epithelial explant system and performed RNA and microRNA expression profiling in cells induced to differentiate by FGF2. The primary data were collected at explants grown overnight in the presence of 5 ng/ml of FGF2, followed by a treatment with 100 ng/ml of FGF2 and collection of samples at 2, 4, 12 and 24 hours. Global analysis identified extensive FGF2-regulated cellular responses that were both independent and dependent on microRNAs (miRNAs). We identified a total number of 131 FGF2-regulated miRNAs. Forty-four of these microRNAs had at least two predicted and inversely regulated target RNA molecules. The genes regulated by the highest number of miRs include Nfib, Nfat5, c-Maf, Ets1 and N-Myc, all encoding DNA-binding transcription factors. Analysis of RNA data revealed that activated FGF signaling influenced other major signaling pathways known to regulate lens differentiation including BMP/TGF-M-NM-2, Notch, and Wnt signaling. In the M-bM-^@M-^\earlyM-bM-^@M-^] response phase (2-4 hours), miRNAs targeted expression of batteries of genes that control transcription, cell death, cell proliferation, cell junction, and protein serine/threonine kinase activity. In M-bM-^@M-^\lateM-bM-^@M-^] stages (12-24 hours), the main miRNA targets included regulators of cell cycle arrest and cellular differentiation. Specific miRNA:mRNA interaction networks were identified for c-Maf, N-Myc, and Nfib (DNA-binding transcription factors); Cnot6, Dicer1, Fbx33 and Wdr47 (RNA processing); Ash1l, Med1/PBP and Kdm5b (chromatin remodeling); and c-Maf, Ets1 and Stc1 (FGF signaling). MicroRNAs including miR-9, -143, -155, -455 and -543 downregulated expression of c-Maf in the 3M-bM-^@M-^Y-UTR luciferase reporter asssays. The functional requirement for miRNAs in lens was further demonstrated via disrupted lens fiber cell differentiation in lenses with inactivated Dicer1. Conclusions: These studies demonstrate for the first time global impact of activated FGF signaling in lens cell culture system and identified novel gene regulatory networks (GRNs) connected by multiple miRNAs. Differential gene expression of FGF2-treated lens cell cultures was analyzed at 0, 2, 4, 12 and 24 hours following the treatment. Two biological replicate experiments were performed.

Dataset Information

Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry.

Publications

Proteomic and protein interaction network analysis of human T lymphocytes during cell-cycle entry.

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