RNA-Seq of transgenic plasma cells from the described subgroup-specific MM mouse models
Ontology highlight
ABSTRACT:
ORGANISM(S): Mus musculus
PROVIDER: GSE183177 | GEO | 2022/08/30
REPOSITORIES: GEO
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Project description:RNA-Seq of transgenic plasma cells from the described subgroup-specific MM mouse models
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Project description:Mouse models of cancer recapitulate many of the molecular and biological features of the human disease. We sought to exploit these experimental merits in a systematic comparative proteomics search for circulating proteins associated with lung tumor development. In-depth quantitative proteomics was applied to plasmas from three mouse models of lung adenocarcinoma driven by mutant EGFR or Kras or induced by urethane exposure and a mouse model of small cell lung cancer driven by loss of Trp53 and Rb. To further refine our lung cancer-specific and broad carcinoma signatures, we intersected these lung cancer proteome profiles with those from other well-established mouse models of pancreatic, ovarian, colon, prostate and breast cancer, as well as two mouse models of inflammation. A set of proteins regulated by Titf1/Nkx2-1, a master transcription factor in cells from the peripheral airways and a known lineage-survival oncogene in lung cancer was identified in plasmas of mouse models of lung adenocarcinoma. An EGFR network of proteins was discerned in the plasma of mice with lung tumors driven by a mutant human EGFR. Levels of these proteins returned toward baseline upon treatment with a tyrosine kinase inhibitor. Moreover, a distinct plasma signature was uncovered in the Trp53/Rb mutant small cell lung cancer model that included a set of proteins associated with neuroendocrine development. Our studies have identified novel plasma protein signatures among molecularly or histopathologically defined lung cancer subtypes.
2012-12-01 | GSE28480 | GEO
Project description:Transcriptomic profiling of normal plasma cells and MM models using bulk RNA-Seq
| PRJNA643511 | ENA
Project description:Mouse models of cancer recapitulate many of the molecular and biological features of the human disease. We sought to exploit these experimental merits in a systematic comparative proteomics search for circulating proteins associated with lung tumor development. In-depth quantitative proteomics was applied to plasmas from three mouse models of lung adenocarcinoma driven by mutant EGFR or Kras or induced by urethane exposure and a mouse model of small cell lung cancer driven by loss of Trp53 and Rb. To further refine our lung cancer-specific and broad carcinoma signatures, we intersected these lung cancer proteome profiles with those from other well-established mouse models of pancreatic, ovarian, colon, prostate and breast cancer, as well as two mouse models of inflammation. A set of proteins regulated by Titf1/Nkx2-1, a master transcription factor in cells from the peripheral airways and a known lineage-survival oncogene in lung cancer was identified in plasmas of mouse models of lung adenocarcinoma. An EGFR network of proteins was discerned in the plasma of mice with lung tumors driven by a mutant human EGFR. Levels of these proteins returned toward baseline upon treatment with a tyrosine kinase inhibitor. Moreover, a distinct plasma signature was uncovered in the Trp53/Rb mutant small cell lung cancer model that included a set of proteins associated with neuroendocrine development. Our studies have identified novel plasma protein signatures among molecularly or histopathologically defined lung cancer subtypes. siRNA transfection experiments were performed in NCI-H3255 and HCC4019 lung adenocarcinoma cell lines using ON-TARGETplus SMARTpool small interfering RNAs (siRNAs) targeting TITF1 (L-019105-01-0005) along with a negative control (ON-TARGETplus siCONTROL nontargeting siRNA pool; D-001810-10-05) obtained from Dharmacon. 400000 cells were seeded in antibiotic-free RPMI-1640 media supplemented with 10% FBS, in 6-well culture plates. The next day, cells were transfected at a final concentration of 100nM siRNA using 6ul DharmaFECT 1 (Dharmacon) according to the manufacturer's instructions. 72-hours post-transfection, RNA was harvested using Trizol (Invitrogen) and protein using RIPA buffer for microarray expression and western blotting, respectively. RNA from TITF1 knockdown and control experiments was profiled by the MSKCC Genomics Core using the Illumina Human HT-12 v3.0 array platform according to manufacturer's instructions. Two biological replicates were profiled for each condition. Resulting data files were exported using GenomeStudio software, log2 transformed, quantile-normalized and analyzed using Partek Genomics Suite (v6.5). Average values of replicates for each gene were then compared between the TITF1 knockdown and non-targeting treatments for each cell line to identify candidate TITF1 regulated genes.
2012-12-01 | E-GEOD-28480 | biostudies-arrayexpress
Project description:We report the application of paired Assay for Transposase-Accessible Chromatin (ATAC-seq) and RNA-seq data to profile the regulatory landscape and expression of 5 primary Plasma Cell (PC), 28 Multiple Myeloma (MM) PC as well as 5 cell lines samples. Findings discriminated MM and MM subgroup-specific changes such as the exclusive overexpression of CCND1 and CCND2 oncogenes. DNA accessibility changes were correlated to gene expression in a topologically constrained manner to elucidate novel enhancer - promoter interactions, genes and pathways critical for myeloma biology and developmentally activated or de novo formed enhancers. Additionally, the MM subgroup-specific TF network dependencies were studied to help identify prognostic markers. Finally, we discover and functionally validate the critical enhancer that regulates ectopic expression of CCND2 in MM.
2021-06-29 | GSE153380 | GEO
Project description:We report the application of paired Assay for Transposase-Accessible Chromatin (ATAC-seq) and RNA-seq data to profile the regulatory landscape and expression of 5 primary Plasma Cell (PC), 28 Multiple Myeloma (MM) PC as well as 5 cell lines samples. Findings discriminated MM and MM subgroup-specific changes such as the exclusive overexpression of CCND1 and CCND2 oncogenes. DNA accessibility changes were correlated to gene expression in a topologically constrained manner to elucidate novel enhancer - promoter interactions, genes and pathways critical for myeloma biology and developmentally activated or de novo formed enhancers. Additionally, the MM subgroup-specific TF network dependencies were studied to help identify prognostic markers. Finally, we discover and functionally validate the critical enhancer that regulates ectopic expression of CCND2 in MM.
2021-06-29 | GSE153379 | GEO
Project description:Plasma cells play an important role in the adaptive immune system through a continuous production of antibodies. Several hematological disorders result from the malignant transformation of antibody-secreting plasma cells. A clear understanding of the molecular processes underlying human plasma cell differentiation (PCD) will provide greater insight into such pathologies. Here we present a comprehensive, temporal program of gene expression data encompassing human PCD from memory B cells, using RNA sequencing. Our results reveal 6,374 differentially expressed genes classified into four temporal gene expression patterns. A stringent pathway enrichment analysis of these gene clusters highlights known pathways but also pathways largely unknown in PCD, including the heme biosynthesis and the glutathione conjugation pathways. Additionally, our analysis revealed numerous novel transcriptional regulators and helicases with consistent stage-specific overexpression and potential importance in PCD, including BATF2, BHLHA15/MIST1, EZH2, WHSC1/MMSET and BLM. We have experimentally validated, for the first time, a role for BLM in regulating cell survival and proliferation during human PCD. Taken together, this RNA-seq analysis of PCD temporal stages helped identify co-expressed gene modules with associated up- /down- regulated transcription regulator genes that could represent major regulatory nodes for human plasma cell differentiation. These data constitute a unique resource of human PCD gene expression programs in support of future studies for understanding the underlying mechanisms that control PCD.
2021-04-30 | GSE148924 | GEO
Project description:NRAS Q61 mutations are prevalent in advanced/relapsed multiple myeloma (MM) and correlate with poor patient outcomes. Thus, we generated a novel MM model by conditionally activating expression of endogenous NrasQ61R and a MYC transgene in germinal center B cells (VQ mice). VQ mice developed a highly malignant MM characterized by high proliferation index, hyperactivation of ERK and AKT signaling, impaired hematopoiesis, widespread extramedullary disease, bone lesions, kidney abnormalities, preserved PD1 and TIGIT immune checkpoint pathways, and expression of human high-risk MM gene signatures. VQ MM mice recapitulate most of the biological and clinical features of human advanced/high-risk MM. These MM phenotypes are serially transplantable in syngeneic recipients. Two MM cell lines were also derived to facilitate future genetic manipulations. Combination therapies based on MEK inhibition significantly prolonged the survival of VQ mice with advanced stage MM. Our study provides a strong rationale to develop MEK inhibition-based therapies for treating advanced/relapsed MM.
2020-07-02 | GSE153626 | GEO
Project description:Variance components estimation and mixed model analysis are central themes in statistics with applications in numerous scientific disciplines. Despite the best efforts of generations of statisticians and numerical analysts, maximum likelihood estimation and restricted maximum likelihood estimation of variance component models remain numerically challenging. Building on the minorization-maximization (MM) principle, this paper presents a novel iterative algorithm for variance components estimation. Our MM algorithm is trivial to implement and competitive on large data problems. The algorithm readily extends to more complicated problems such as linear mixed models, multivariate response models possibly with missing data, maximum a posteriori estimation, and penalized estimation. We establish the global convergence of the MM algorithm to a Karush-Kuhn-Tucker (KKT) point and demonstrate, both numerically and theoretically, that it converges faster than the classical EM algorithm when the number of variance components is greater than two and all covariance matrices are positive definite.
| S-EPMC6779174 | biostudies-literature