The inter-individual variability outperforms the intra-individual variability of differentially expressed proteins prior and post irradiation in lymphoblastoid cell lines

2014 ◽  
Vol 120 (5) ◽  
pp. 198-207 ◽  
Author(s):  
A. Gürtler ◽  
M. Hauptmann ◽  
S. Pautz ◽  
U. Kulka ◽  
A. A. Friedl ◽  
...  
Blood ◽  
2009 ◽  
Vol 114 (22) ◽  
pp. 4779-4779
Author(s):  
Nader El-Mallawany ◽  
Janet Ayello ◽  
Nancy Day ◽  
Carmella van de Ven ◽  
Kevin P Conlon ◽  
...  

Abstract Abstract 4779 Background EBV infection of normal B-cells is commonly associated with the pathogenesis of BL (Brady et al, Clin Path, 2007). Endemic BL (eBL) is characteristically positive (100%) for EBV, contrasting with sporadic BL (sBL), where approximately 30% of cases are positive for EBV. eBL vs. sBL have significantly different breakpoint regions within c-myc (Shiramizu/Magrath et al, Blood, 1991). Overexpression of c-myc is the sine quae non of BL. C-myc interactions with other genes/proteins is multilayered and complex (Basso/Della-Favera, Nat Gen, 2005). Apoptotic pathway disruption is propelled by EBV and is critically important in c-myc deregulation and subsequent lymphomagenesis that occurs in EBV+ eBL vs. sBL (Ruf et al, J Vir, 2000). Global analysis of proteins expressed in EBV+ eBL vs. sBL may provide insights into biologic, pathogenetic, and molecular differences between the two subtypes of lymphoma, and potentially identify targets for the development of therapeutic agents. Objectives To compare the proteomic expression profile and signal transduction pathways of EBV+ eBL vs. sBL. Methods Whole cell lysates obtained from the EBV+ eBL cell line Raji and the EBV+ sBL cell line NC37 were digested and labeled with iTRAQ” labeling reagents, following manufacturer's protocol. The peptides were resolved by 2D-LC technique (off-line Strong cation exchange followed by on-line reverse-phase liquid chromatography). Data-dependant High energy C-trap Dissociation MS/MS spectra were acquired using an Orbitrap XL Tandem Mass Spectrometer (ThermoFisher). The MS/MS data was searched using X!Tandem/TPP software suite against human IPI database (v3.50) appended with decoy (reverse) sequences. iTRAQ” ratios of proteins (ProteinProphet probability of >0.9) were normalized and differentially expressed proteins were determined through Mixture Modeling. Protein interactions were further analyzed using the GoMiner and Ingenuity pathway analysis tools. Results Over 400 proteins were identified as being differentially expressed by a ≥ 1.25 fold change between the two cell lines. We identified differentially expressed proteins in both cell lines that are involved in a wide array of cellular processes as exhibited in Figure 1. Cellular processes uniquely involved by proteins over-expressed in eBL included immune response, hematopoiesis, cell proliferation, heat shock, and B-cell activation, while those uniquely identified in sBL included cell division, response to virus, and NF-kB cascade proteins. Specific cell-regulatory pathways implicated by the differential protein profile expressions (with associated proteins in parentheses) included the p53 apoptosis pathway (PCNA, MSH6, C1QBP, MAP4, and BAX), the caspase network of apoptosis (HCLS1, ACIN1, and AIFM1), the tumor suppressor protein RB network (MCM7, PA2G4, and API5), general apoptotic pathways (HSP90 and PDCD4), B-cell differentiation and proliferation pathways (TPD52 and IKBKG), and the ubiquitin-proteasome pathway (UBE2J1, UBE2C, and UBE2S). Seven of these proteins are c-myc target genes. Ingenuity protein network analysis revealed nine proteins identified in the experiment with interactions connected through the p53, caspase, and tumor necrosis factor apoptosis pathways. Conclusion Proteomic profile analysis of EBV+ eBL and sBL revealed over and under-expression of multiple proteins that may be implicated in the multi-factorial nature of disease pathogenesis. This is the first MS-based direct proteomic comparison of eBL and sBL. Our results suggest that there are potentially different mechanisms driving cell proliferation and resistance to apoptosis in eBL versus sBL and that EBV infection may be involved in the processes that drive lymphomagenesis. Ultimately, identification of proteins unique to the distinct disease subtypes will serve to establish tumor markers that may enable development of new diagnostic, prognostic, and therapeutic strategies. Disclosures: No relevant conflicts of interest to declare.


PROTEOMICS ◽  
2012 ◽  
Vol 12 (13) ◽  
pp. 2115-2126 ◽  
Author(s):  
Kathleen O'Connell ◽  
Maria Prencipe ◽  
Amanda O'Neill ◽  
Claire Corcoran ◽  
Sweta Rani ◽  
...  

2021 ◽  
Author(s):  
Yue He ◽  
Su-Bin Han ◽  
Yang Liu ◽  
Jing-Jing Zhang ◽  
Yu-Mei Wu

Abstract Introduction: To investigate the mechanism by which apolipoprotein A1 (APOA1) enhances the resistance of cervical squamous carcinoma to platinum-based chemotherapy.Methods: Two cervical squamous carcinoma cell lines (SiHa and Caski) overexpressing APOA1 (OE) were constructed, treated with carboplatin (CBP), and compared to normal control (NC) cells.Results: In both cell lines, the clone-forming ability of CBP-treated cells was lower than that of untreated cells, and the clone formation rate of OE cells was lower than that of NC cells (p < 0.05), indicating that APOA1 overexpression enhanced chemoresistance. A screen for APOA1 downstream proteins affecting platinum-based chemoresistance using TMT revealed 64 differentially expressed proteins, which were subjected to GO annotation, KEGG enrichment, subcellular localization, structural domain annotation and enrichment, clustering, and interaction network analyses. Twenty-nine differentially expressed proteins matching cancer-relavent association terms were screened and parallel response monitoring identified 21 as possibly involved in the mechanism of platinum-based chemoresistance.Conclusions: Our analysis suggested that the mechanism may involve numerous regulatory pathways, including promoting tumor growth via the P38 MAPK signaling pathway through STAT1, promoting tumor progression via the PI3K signaling pathway through CD81 and C3, and promoting resistance to platinum-based chemotherapy resistance through TOP2A.


Blood ◽  
2013 ◽  
Vol 122 (21) ◽  
pp. 1670-1670
Author(s):  
Surendra Dasari ◽  
Kenneth L Johnson ◽  
Carrie J. H. Hepplemann ◽  
Ariel J Caride ◽  
Jason D Theis ◽  
...  

Abstract Background The lymphoma proteome is the phenotypic representation of the underlying genetic and epigenetic makeup of each individual patient's tumor. The proteome is rich in druggable targets and offers a unique opportunity for the hematologist to personalize therapy. We developed a deep proteomic profiling method using ion exchange fractionation and tandem mass spectrometry. In this pilot study, we applied this method to detect differentially expressed proteins in anaplastic large cell lymphoma (ALCL) cell lines with previously known (ALK-positive) and unknown (ALK-negative) therapeutic targets. We then asked whether integrative informatic analysis of these data could be used to predict drug sensitivity in each of the cell lines. Methods To examine reproducibility of our method, proteins were extracted independently from 4 pellets each of FE-PD (ALK-negative) and Karpas 299 (ALK-positive) ALCL cells, reduced with dithiothreitol, alkylated with iodoacetamide, and digested with trypsin. Resulting peptides were separated into 6 fractions using strong anion exchange (SAX) chromatography. Peptides in each fraction were analyzed via shotgun proteomics on a QExactive mass spectrometer. Peptide mass spectra (MS/MS) were matched against a RefSeq human protein sequence database using MyriMatch software. Reversed sequences were added to the database to measure identification false discovery rates (FDRs). IDPicker filtered the peptide identifications at 2% FDR. Proteins with at least two unique peptide identifications and five MS/MS matches were considered to be present in the sample. Filtered protein identifications and corresponding spectral counts were used as input to QuasiTel software, which was configured to use proteins with at least one spectrum per biological replicate. Proteins that were significantly differentially expressed (quasi p-value < 0.05) with an absolute log2 fold-change of at least 0.5 fold were loaded into the Ingenuity Pathway Analysis (IPA) software, and a master list of drugs and corresponding gene targets was assembled using PharmGKB database and the Drug-Gene Interaction Database (DGID). The resulting drug-gene target list was merged with the differentially expressed protein identifications. Candidate targets were validated by Western blot and candidate drugs were assessed in viability assays. Results The SAX-LC-MS/MS method identified 10,111 proteins from all replicate analyses of FE-PD and Karpas 299 samples, and 93% of the identified proteome was detected in all 4 replicate analyses. The detected proteome was well represented by key transcription factors, phophatases, kinases, translation regulators and transmembrane regulators. There were 1369 proteins differentially expressed between the 2 cell lines, 709 up regulated in Karpas 299 cell line and 673 up regulated in FE-PD. Differentially expressed proteins also showed consistent expression across the biological replicates (Figure 1). Our integrated approach to identify candidate targets and drugs “rediscovered” ALK in Karpas299 and unexpectedly identified relative overexpression of the IL2-IL2RA-STAT5A-STAT5B network in ALK-negative FE-PD cells. Western blot confirmed these findings. As expected, 50-100 nM crizotinib (ALK inhibitor) decreased Karpas 299 viability (p =0.016) but had no effect on FE-PD (Figure 2). In contrast, 50-100 nM of the experimental STAT5 inhibitor 573108 (EMD Millipore) decreased FE-PD viability (p=0.002) but had no effect on Karpas 299 (Figure 2). Conclusion The lymphoma proteome is complex with 10,000 proteins and contains druggable targets that can be reproducibly identified using SAX-LC-MS/MS. These targets vary across samples and integrated informatic analysis can predict target-drug combinations that have efficacy in an experimental model. These data suggest that SAX-LC-MS/MS could be used to personalize treatment regimens in lymphoma patients. Figure 1: Spectral counts of 1369 differentially expressed proteins between Karpas299 and FE-PD cell lines were normalized and plotted in a heat map. Red color indicates down regulation and yellow color indicates up regulation. Disclosures: No relevant conflicts of interest to declare.


Glia ◽  
2003 ◽  
Vol 42 (2) ◽  
pp. 194-208 ◽  
Author(s):  
Rulin Zhang ◽  
Tammy-Lynn Tremblay ◽  
Angela Mcdermid ◽  
Pierre Thibault ◽  
Danica Stanimirovic

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e8779
Author(s):  
Jingyu Wu ◽  
Zhifang Hao ◽  
Chen Ma ◽  
Pengfei Li ◽  
Liuyi Dang ◽  
...  

Background Evidences indicated that non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) might originate from the same cell type, which however ended up to be two different subtypes of lung carcinoma, requiring different therapeutic regimens. We aimed to identify the differences between these two subtypes of lung cancer by using integrated proteome and genome approaches. Methods and Materials Two representative cell lines for each lung cancer subtype were comparatively analysed by quantitative proteomics, and their corresponding transcriptomics data were obtained from the Gene Expression Omnibus database. The integrated analyses of proteogenomic data were performed to determine key differentially expressed proteins that were positively correlated between proteomic and transcriptomic data. Result The proteomics analysis revealed 147 differentially expressed proteins between SCLC and NSCLC from a total of 3,970 identified proteins. Combined with available transcriptomics data, we further confirmed 14 differentially expressed proteins including six known and eight new lung cancer related proteins that were positively correlated with their transcriptomics data. These proteins are mainly involved in cell migration, proliferation, and invasion. Conclusion The proteogenomic data on both NSCLC and SCLC cell lines presented in this manuscript is complementary to existing genomic and proteomic data related to lung cancers and will be crucial for a systems biology-level understanding of the molecular mechanism of lung cancers. The raw mass spectrometry data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD015270.


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