Bortezomib Inhibits mTOR Pathway in Multiple Myeloma Cell Lines Via Induced Expression of REDD1.

Blood ◽  
2007 ◽  
Vol 110 (11) ◽  
pp. 242-242
Author(s):  
Olivier Decaux ◽  
Monique Clement ◽  
Florence Magrangeas ◽  
Laurence Lode ◽  
Catherine Charbonnel ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Size ◽  
Cell Lines ◽  
Stress Responses ◽  
Cellular Proliferation ◽  
Expression Profiles ◽  
Mtor Pathway ◽  
Molecular Signature ◽  
Gene And Protein Expression

Abstract Pharmacogenomic profiles of genes involved in bortezomib - dexamethasone response may help to understand resistance and could provide new therapeutic targets as well as contributing to novel prognostic markers in multiple myeloma. We have used gene expression profiling to analyze the complex signaling pathways regulating the response to bortezomib - dexamethasone. Gene expression profiles were established in 9 cell lines, derived from 9 myeloma patients, incubated or not with a combination of bortezomib 10 nM and dexamethasone 1 μM. These concentrations correspond to the ones used for patients in the IFM 2005-01. Cells were collected after 6 hours of treatment. We focused our interest in early response genes, making the hypothesis that the comprehension of early effects would help to better understand the mechanisms of resistance that take place in at least two third of myeloma patients. Supervised analysis with permutations identified significantly up regulated genes involved in stress responses (heat shocks proteins, RTP801/dig2/REDD1/DDIT4), endoplasmic reticulum stress (HERP/HERPUD1, gadd145/CHOP/DDIT3), ubiquitin/proteasome pathway (proteasome 26S subunits PSMB7, PSMC4, PSMD3 and PSMD13), unfolded protein response (such as SQSTM1, ATF4) or redox equilibrium (PLRX, PRDX1). We assumed that these genes might represent a molecular signature of response to bortezomib and provide important insight into the complex mechanisms of action of these drugs. We focused on REDD1 a gene cloned in 2002 that is known to be rapidly induced by a wide variety of stress conditions (arsenic, hypoxia, dexamethasone, thapsigargin, tunimycin and heat shock) and DNA damages (ionizing radiation, ultraviolet radiation, DNA alkylant). We found that both REDD1 gene and protein expression were early and highly induced after bortezomib exposure alone or in combinaison with dexamethasone. This effect was dependent upon cell line: REDD1 was overexpressed within two hours in resistant cell lines in association with a cell size decrease while in sensitive cell lines, neither REDD1 induction nor morphological changes occured. REDD1 induction was associated with the dephosphorylation of S6K1, a key substrat of mTOR, a protein kinase which controls cell growth and cell size in response to various signals. SiRNA studies confirmed that bortezomib lead to a negative regulation of mRTor activity mediated by REDD1: disruption of REDD1 abrogates both S6K1 phosphorylation and early transitory cell size reduction. Our results are in accordance with data obtained in mouse showing an early regulation of mTOR pathway and cellular proliferation induced by REDD1 expression in response to stress. Our study suggests that mTOR regulation could be a resistance mechanism mediated by REDD1 expression. As we found that REDD1 was differentially induced in primary plasma cells from patients, this gene expression could help to predict response to bortezomib. Our objective is now to clarify the pathway that links bortezomib to REDD1 in multiple myeloma and to investigate REDD1 expression in patients enrolled in IFM 2005-01 clinical trial.

In Silico Prediction of Novel Drug Combinations to Combat Bortezomib-Resistant Multiple Myeloma

Blood ◽  
2012 ◽  
Vol 120 (21) ◽  
pp. 1344-1344
Author(s):  
Holly A. F. Stessman ◽  
Tian Xia ◽  
Aatif Mansoor ◽  
Raamesh Deshpande ◽  
Linda B. Baughn ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Lines ◽  
In Silico ◽  
Research Funding ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Mouse Cell ◽  
Resistant Mouse ◽  
Novel Therapeutic

Abstract Abstract 1344 Bortezomib/VELCADE® (Bz) is a proteasome inhibitor that has been used successfully in the treatment of multiple myeloma (MM) patients. However, acquired resistance to Bz is an emerging problem. Thus, there is a need for novel therapeutic combinations that enhance Bz sensitivity or re-sensitize Bz resistant MM cells to Bz. The Connectivity Map (CMAP; Broad Institute) database contains treatment-induced transcriptional signatures from 1,309 bioactive compounds in 4 human cancer cell lines. An input signature can be used to query the database for correlated drug signatures, a technique that has been used previously to identify drugs that combat chemoresistance in cancer (Wei, et al. Cancer Cell (2006) 10:331). In this study we used in silico bioinformatic screening of gene expression profiles from isogenic pairs of Bz sensitive and resistant mouse cell lines derived from the iMycCα/Bcl-xL mouse model of plasma cell malignancy to identify compounds that combat Bz resistance. We established Bz-induced kinetic gene expression profiles (GEPs) in 3 pairs of Bz sensitive and resistant mouse cell lines over the course of 24 hours. GEPs were collected in the absence of large-scale cell death. The 16 and 24 hour time points were averaged and compared between each Bz sensitive and resistant pair. Genes in the sensitive cell line with a fold change greater than 2, relative to the resistant line, were given the binary distinction of “up” or “down” depending on the direction of change. Genes that met these criteria were assembled into signatures, and then used as inputs for CMAP queries to identify compounds that induce similar transcriptional responses. In all pairs, treatment of the Bz sensitive line correlated with GEPs of drugs that target the proteasome, NF-κB, HSP90 and microtubules, as indicated by positive connectivity scores. However eight compounds, all classified as Topoisomerase (Topo) I and/or II inhibitors, were negatively correlated to our input signature. A negative connectivity score could have two interpretations: (1) this could indicate simply that Topos are upregulated by Bz treatment in Bz sensitive lines, which has been previously reported (Congdan, et al. Biochem. Pharmacol. (2008) 74: 883); or (2) this score could be interpreted as Topos are inhibited in Bz resistant cells upon Bz treatment. This led us to ask whether Topo inhibitors could target Bz resistant MM cells and re-sensitize them to Bz. Indeed, we found that multiple Topo inhibitors were significantly more active against Bz resistant cells as single agents and restored sensitivity to Bz when combined with Bz as a cocktail regimen. This work demonstrates the potential of this in silico bioinformatic approach for identifying novel therapeutic combinations that overcome Bz resistance in MM. Furthermore, it identifies Topo inhibitors – drugs that are already approved for clinical use – as agents that may have utility in combating Bz resistance in refractory MM patients. Disclosures: Stessman: Millennium: The Takeda Oncology Company: Research Funding. Van Ness:Millennium: The Takeda Oncology Company: Research Funding.

scholarly journals NEK2 Inhibition Enhances the Efficacy of PD-1/PD-L1 Blockade in Multiple Myeloma

Blood ◽  
2021 ◽  
Vol 138 (Supplement 1) ◽  
pp. 2671-2671
Author(s):  
Yan Cheng ◽  
Fumou Sun ◽  
Huojun Cao ◽  
Dongzheng Gai ◽  
Bailu Peng ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
B Cells ◽  
Cell Lines ◽  
Immune Checkpoint ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Gene Set Enrichment Analysis ◽  
Myeloma Cells

Abstract Introduction The development of new treatments for high-risk multiple myeloma (HRMM) are needed. The PD-1/PD-L1 axis is one of the chief inhibitory immune checkpoints in antitumor immunity. Despite the success of PD-1 (PDCD1) / PD-L1 (CD274) blockade in some neoplasms, use of it as a monotherapy has failed to improve outcome in RRMM. We have previously demonstrated that the cell-cycle-regulated serine-threonine kinase, NEK2 is elevated in HRMM and that inhibition of NEK2 can overcome drug-resistance and prolong survival of xenografted MM cells. Here, we aimed to investigate the possible role of NEK2 in regulating the immune checkpoint response in MM and development of possible anti-PD1/PDL1 combination therapies. Methods Gene expression profiles and pathway enrichment analyses were conducted on oligonucleotide microarray gene expression profiles from over 1000 primary MM samples to evaluate the correlation of NEK2 and immune checkpoint expression levels. To elucidate the underlying mechanism, we used Nek2 -/- mice crossed with EμMyc mice to generate B cell tumor mouse model with NEK2 deficiency. RNA-sequencing analyses of premalignant B cells was compared between EμMyc/Nek2 WT and EμMyc/Nek2 -/- mice. The hub molecular regulators in the NEK2 correlated pathways were further determined by western blot using NEK2 overexpressing and knockdown cell lines and then verified by co-immunoprecipitation with a NEK2 antibody. Lastly, to establish its clinic significance, the efficacy of INH1 (small compound NEK2 inhibitor), (D)-PPA 1 (peptide-based PD-1/PD-L1 interaction inhibitor) or a PD-L1 (monoclonal antibody) was tested in bone marrow BM mononuclear cells from primary MM patients in-vitro as well as in MM xenografts. Tumor burden and T cell immune responses were monitored by M-spike and mass cytometry. Results Gene expression profiles demonstrated that CD274 expression was significantly higher in the non-proliferative hyperdiploid (HY) subtype of MM, representing between 25-35% of all MM. NEK2 was negatively correlated with CD274 gene expression across all 7 MM subtypes. Gene set enrichment analysis showed that the IFN-γ signaling pathway, which can induce CD274 expression, was significantly enriched in the HY subtype as well as premalignant B cells from EμMyc/Nek2 -/- mice. Elevated expression of EZH2, a histone methyltransferase gene, is also highly correlated wirth NEK2 levels in primary MM. We found that NEK2 inhibition increases CD274 expression as well as reduced EZH2 expression and H3K27me3 levels in MM cell lines. In contrarst, myeloma cells overexpressing NEK2 showed increased expression and activity of EZH2 and H3K27me3 levels. Thus, NEK2 appears to regulate CD274/PD-L1 expression through EZH2-mediated histone methylation. Next we demonstrated that NEK2 and EZH2 directly interact and that overexpression of NEK2 leads to increased methylation of the CD274/PD-L1 gene. We treated BM mononuclear cells from primary MM with PD-1/PD-L1 inhibitor with and without a NEK2 inhibitor. The combination was most effective at eliminating CD138 + myeloma cells while having no effects on T, B and myeloid cell populations. Conclusion Our study showed that expression of CD274/PD-L1 is suppressed in primary HRMM and that CD274/PD-L1 expression is negatively regulated by NEK2 via EZH2-mediated methylation. Inhibition of NEK2 sensitizes myeloma cells to PD-1/PD-L1 blockade, showing either a synergistic or an additive effect in MM cell cytotoxicity. Disclosures No relevant conflicts of interest to declare.

Melphalan Resistance in Multiple Myeloma Can Be Overcome by Proteasome Inhibition or Histone Deacetylation: Lack of Syngergism of the Combination.

Blood ◽  
2005 ◽  
Vol 106 (11) ◽  
pp. 3393-3393
Author(s):  
Pieter Sonneveld ◽  
Eric Kamst ◽  
Yvonne de Knegt ◽  
Naomi Klarenbeek ◽  
Martijn Schoester
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Line ◽  
Cell Lines ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Proteasome Inhibition ◽  
Histone Deacetylation ◽  
Interaction Factor ◽  
Resistant Cells

Abstract Multiple Myeloma (MM) is a disease of monoclonal plasma cells in the bone marrow which has a transient response to classic chemotherapy. At diagnosis, induction chemotherapy followed by high-dose melphalan (HDM) with stem cell support is used in most patients to achieve a clinical response. Because all patients will ultimately relapse, the treatment of melphalan-refractory disease represents a major clinical challenge and new agents are needed to overcome melphalan resistance. We have investigated the anti-myeloma efficacy of two new classes of targeted agents, i.e. proteasome inhibition and histone deacetylation inhibition alone or in combination in the melphalan sensitive MM1S and the Melphalan refractory MM1MEL2000 cell lines. The IC50 values of Bortezomib (B), Melphalan (M) and LAQ824 (L) in MM1S were 2.1 nM, 1.9 uM and 1.7 nM, respectively and in MM1MEL2000 3.9 nM, 50 uM and 4.0 nM. Using isobologram analysis a synergysm between B and L was observed in the sensitive, however not in the melphalan refractory cell line. These data indicate that B proteasome inhibition and histone deacetylation inhibition may be effective ways to overcome melphalan resistance. However, the previously reported synergism between these drugs does not seem to occur in melphalan resistant cells. The gene expression profiles of these cell lines were analysed using the Affymetrix U133plus 2.0 gene chip before and after treatment with melfaphalan or the proteasome inhibitor B or the histone deacetylation inhibitor L or the combination of B and L. Genes that were highly expressed in the melphalan refractory derivate cell line MM1MEL2000 as compared with wild-type MM1S included GP M6B, ADAM23 and HTPAP. Following melphalan exposure, TMF1, a CEBp glucocorticoid interaction factor, WHSC1L1, a MMSET homologue with EGF like domain and several transcription factors had highly increased expression as compared to MM1S. With exposure to B combined with L, increased expression in MM1MEL2000 over MM1S was observed for GTP exchange factor TIAM1 which interacts with RAS and JNK, and the lymphoid enhancer factor, a notch transcription factor. It is concluded that Bortezomib and the histone deacetylase inhibitor LAQ824 are effective agents to overcome melphalan resistance in multiple myeloma. However, the combination fails to show the synergism observed in melphalan sensitive cells. Gene analysis sofar does not provide a clear explanation for this lack of synergism. A comprehensive summary of the observed shifts of gene expression profiles in melphalan resistant cells following exposure to these agents, will be presented.

A Molecular Signature of the Transcription Factor GATA1 in Acute Megakaryoblastic Leukemia of Childhood.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 1117-1117
Author(s):  
Jean-Pierre Bourquin ◽  
Claudia Langebrake ◽  
Aravind Subramanian ◽  
Xiaochun Li ◽  
Dirk Reinhardt ◽  
...  
Keyword(s):  
Gene Expression ◽  
Transcription Factor ◽  
Target Genes ◽  
Cellular Proliferation ◽  
Expression Profiles ◽  
Chromosome 21 ◽  
Molecular Signature ◽  
Consensus Clustering ◽  
Acute Megakaryoblastic Leukemia ◽  
Megakaryoblastic Leukemia

Abstract To investigate the pathogenesis of acute megakaryoblastic leukemia, AML M7 (M7), we analyzed the gene expression profiles of 113 patient samples on Affymetrix U133A GeneChips. Classification by unsupervised clustering discriminates 70 M7 samples from 12 with other AML FAB subtypes, 3 normal controls, 10 normal and remission samples of patients with Down Syndrome (DS) and 18 ALL samples from DS patients. Further, M7 subclasses can be identified. DS samples (21 DS-M7 and 9 transient myeloproliferative disease (TMD) samples) cluster apart from non-DS M7 samples, with a few exceptions, most notably the 4 M7 samples with the translocation t(1;22). Smaller subgroups can be detected by consensus clustering in DS and non-DS M7. The DS-non-DS distinction is not driven by differential expression of chromosome 21 genes, in particular RUNX1 expression levels are not increased in DS. Consistent with differences observed by flow cytometry, DS samples show a higher expression of ANK1, GYPA, GYPB and CD36, while non-DS M7 samples have higher VWF and CD34 expression, reflecting known morphologic differences. The hematopoietic transcription factor GATA1 is overexpressed in DS M7/TMD when compared to non-DS M7s or controls, and is among the most significant markers of the DS class. The mutation of GATA1 that is characteristic for DS is likely to affect its transcriptome specifically. By nearest neighbour analysis, about 300 genes follow the pattern of the GATA1 gene expression within a significant range by permutation testing. This GATA-1 signature is highly enriched in the DS samples and includes several known target genes of GATA1, such as GATA2, GYPA, ANK1 and ALAD. This approach should lead to the identification of genes contributing to cellular proliferation and differentiation in the context of the GATA1 mutation of DS M7.

Gene Expression Profiles in Acute Myeloid Leukemias (AML): A Novel Approach Using SAGE and Custom Microarray.

Blood ◽  
2004 ◽  
Vol 104 (11) ◽  
pp. 197-197
Author(s):  
Sanggyu Lee ◽  
JianJun Chen ◽  
Guolin Zhou ◽  
Edward Touma ◽  
Run Shi ◽  
...  
Keyword(s):  
Gene Expression ◽  
Cell Lines ◽  
Intracellular Signaling ◽  
De Novo ◽  
Expression Profiles ◽  
Cell Surface Receptor ◽  
Molecular Signature ◽  
Human Leukemia ◽  
Novel Transcripts ◽  
Sage Data

Abstract Chromosome translocations are among the most common genetic abnormalities in human leukemia. Each translocation may affect a different pair of genes. The abnormally expressed genes that result from the different translocations provide a rich source for identifying specific markers for clinical diagnosis of each translocation. Microarrays have identified genes differentially expressed in different translocations but the results between laboratories are not always compatible. We used SAGE to quantitate gene expression in bone marrow (BM) samples from 22 patients with four types of AML, namely de novo AML M2 with t(8;21), AML M3 or M3V with t(15;17), AML M4Eo with inv(16), AML M5 with t(9;11) or secondary t(9;11).We generated SAGE libraries from CD15+ leukemic myeloid progenitor cells, collecting over 106 SAGE tags, of which 209,486 were unique tags; 136,010 were known genes and ESTs, and 73,476 were novel transcripts. SAGE tags for further analysis were selected based on a 5-fold difference between patients’ samples and normal CD15+ BM; they were also statistically significantly different at the 5 % level. Using these strict criteria, we identified 1,571 unique tags, of which 1,405 were known genes and ESTs, and 166 were novel transcripts that were either specific for each translocation or were common for all four translocations. Changes in expression of these known genes which fall into different gene ontogeny functional categories varied by translocation. For example, those associated with macromolecular biosynthesis, transport and transcription were most altered in the t(8;21); those related to defense response and apoptosis were altered in the t(15;17); cell proliferation genes were most affected by the t(9;11). Cell surface receptor signaling, intracellular signaling and RNA processing were altered in treatment related but not in de novo t(9;11). From this analysis, we identified the functional molecular signature of each translocation. We designed a custom microarray to validate our SAGE data analysis. Our initial pilot microarray experiment with 96 genes that were specific for each translocation or common for all translocations used mononuclear cells from normal and patient BM and translocation cell lines, ME-1, THP-1, Mono Mac-6, Kasumi 1, NB-4; the array data from BM matched the SAGE data for 48-75 % of genes and the majority of cell lines, except ME-1, matched at least 70 % with the SAGE results for the appropriate translocation. We have now designed a full-scale microarray that contains over 400 probes including 250 known genes, 61 ESTs, 45 novel sequences and 48 genes reported by others. We will test at least 100 patients’ samples with the four translocations to validate which genes provide a robust, reproducible “fingerprint” for each translocation and for all translocations. We will correlate our microarray data with age, sex, race, response to treatment, survival and other mutations (FLT3, MLL ITD, etc) to identify any transcripts that might reliably define these categories. Our results will provide new insights into genes that collaborate with each translocation to lead to a fully leukemic phenotype.

scholarly journals Intelligent Microarray Data Analysis through Non-negative Matrix Factorization to Study Human Multiple Myeloma Cell Lines

2019 ◽  
Vol 9 (24) ◽  
pp. 5552 ◽  
Author(s):  
Gabriella Casalino ◽  
Mauro Coluccia ◽  
Maria L. Pati ◽  
Alessandra Pannunzio ◽  
Angelo Vacca ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Cell Lines ◽  
Microarray Data ◽  
Matrix Factorization ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Myeloma Cell ◽  
Human Multiple Myeloma ◽  
Non Negative Matrix Factorization

Microarray data are a kind of numerical non-negative data used to collect gene expression profiles. Since the number of genes in DNA is huge, they are usually high dimensional, therefore they require dimensionality reduction and clustering techniques to extract useful information. In this paper we use NMF, non-negative matrix factorization, to analyze microarray data, and also develop “intelligent” results visualization with the aim to facilitate the analysis of the domain experts. For this purpose, a case study based on the analysis of the gene expression profiles (GEPs), representative of the human multiple myeloma diseases, was investigated in 40 human myeloma cell lines (HMCLs). The aim of the experiments was to study the genes involved in arachidonic acid metabolism in order to detect gene patterns that possibly could be connected to the different gene expression profiles of multiple myeloma. NMF results have been verified by western blotting analysis in six HMCLs of proteins expressed by some of the most abundantly expressed genes. The experiments showed the effectiveness of NMF in intelligently analyzing microarray data.

scholarly journals Analysis of the transcriptome of bovine endometrial cells isolated by laser micro-dissection (1): specific signatures of stromal, glandular and luminal epithelial cells

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Wiruntita Chankeaw ◽  
Sandra Lignier ◽  
Christophe Richard ◽  
Theodoros Ntallaris ◽  
Mariam Raliou ◽  
...  
Keyword(s):  
Gene Expression ◽  
Protein Localization ◽  
Expression Profiles ◽  
Cell Types ◽  
Molecular Signature ◽  
Receptor Protein ◽  
Specific Gene ◽  
Specific Cell ◽  
Biological Processes ◽  
Endometrial Cells

Abstract Background A number of studies have examined mRNA expression profiles of bovine endometrium at estrus and around the peri-implantation period of pregnancy. However, to date, these studies have been performed on the whole endometrium which is a complex tissue. Consequently, the knowledge of cell-specific gene expression, when analysis performed with whole endometrium, is still weak and obviously limits the relevance of the results of gene expression studies. Thus, the aim of this study was to characterize specific transcriptome of the three main cell-types of the bovine endometrium at day-15 of the estrus cycle. Results In the RNA-Seq analysis, the number of expressed genes detected over 10 transcripts per million was 6622, 7814 and 8242 for LE, GE and ST respectively. ST expressed exclusively 1236 genes while only 551 transcripts were specific to the GE and 330 specific to LE. For ST, over-represented biological processes included many regulation processes and response to stimulus, cell communication and cell adhesion, extracellular matrix organization as well as developmental process. For GE, cilium organization, cilium movement, protein localization to cilium and microtubule-based process were the only four main biological processes enriched. For LE, over-represented biological processes were enzyme linked receptor protein signaling pathway, cell-substrate adhesion and circulatory system process. Conclusion The data show that each endometrial cell-type has a distinct molecular signature and provide a significantly improved overview on the biological process supported by specific cell-types. The most interesting result is that stromal cells express more genes than the two epithelial types and are associated with a greater number of pathways and ontology terms.

scholarly journals Comprehensive analysis of the gene expression profiles in human gastric cancer cell lines

Oncogene ◽  
2002 ◽  
Vol 21 (42) ◽  
pp. 6549-6556 ◽  
Author(s):  
Jiafu Ji ◽  
Xin Chen ◽  
Suet Yi Leung ◽  
Jen-Tsan A Chi ◽  
Kent Man Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Gastric Cancer ◽  
Cell Lines ◽  
Cancer Cell ◽  
Gastric Cancer Cell ◽  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Comprehensive Analysis ◽  
Human Gastric Cancer ◽  
Gastric Cancer Cell Lines

scholarly journals Classify Hyperdiploidy Status of Multiple Myeloma Patients Using Gene Expression Profiles

PLoS ONE ◽  
2013 ◽  
Vol 8 (3) ◽  
pp. e58809 ◽  
Author(s):  
Yingxiang Li ◽  
Xujun Wang ◽  
Haiyang Zheng ◽  
Chengyang Wang ◽  
Stéphane Minvielle ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Expression Profiles ◽  
Gene Expression Profiles

scholarly journals Multiple Myeloma Cells Alter Adipogenesis, Increase Senescence-Related and Inflammatory Gene Transcript Expression, and Alter Metabolism in Preadipocytes

2021 ◽  
Vol 10 ◽  
Author(s):  
Heather Fairfield ◽  
Samantha Costa ◽  
Carolyne Falank ◽  
Mariah Farrell ◽  
Connor S. Murphy ◽  
...  
Keyword(s):  
Gene Expression ◽  
Multiple Myeloma ◽  
Bone Marrow ◽  
Lipid Accumulation ◽  
Expression Profiles ◽  
Adipogenic Differentiation ◽  
Gene Expression Profiles ◽  
Mouse Bone Marrow ◽  
Gene Transcript ◽  
Bone Marrow Mscs

Within the bone marrow microenvironment, mesenchymal stromal cells (MSCs) are an essential precursor to bone marrow adipocytes and osteoblasts. The balance between this progenitor pool and mature cells (adipocytes and osteoblasts) is often skewed by disease and aging. In multiple myeloma (MM), a cancer of the plasma cell that predominantly grows within the bone marrow, as well as other cancers, MSCs, preadipocytes, and adipocytes have been shown to directly support tumor cell survival and proliferation. Increasing evidence supports the idea that MM-associated MSCs are distinct from healthy MSCs, and their gene expression profiles may be predictive of myeloma patient outcomes. Here we directly investigate how MM cells affect the differentiation capacity and gene expression profiles of preadipocytes and bone marrow MSCs. Our studies reveal that MM.1S cells cause a marked decrease in lipid accumulation in differentiating 3T3-L1 cells. Also, MM.1S cells or MM.1S-conditioned media altered gene expression profiles of both 3T3-L1 and mouse bone marrow MSCs. 3T3-L1 cells exposed to MM.1S cells before adipogenic differentiation displayed gene expression changes leading to significantly altered pathways involved in steroid biosynthesis, the cell cycle, and metabolism (oxidative phosphorylation and glycolysis) after adipogenesis. MM.1S cells induced a marked increase in 3T3-L1 expression of MM-supportive genes including Il-6 and Cxcl12 (SDF1), which was confirmed in mouse MSCs by qRT-PCR, suggesting a forward-feedback mechanism. In vitro experiments revealed that indirect MM exposure prior to differentiation drives a senescent-like phenotype in differentiating MSCs, and this trend was confirmed in MM-associated MSCs compared to MSCs from normal donors. In direct co-culture, human mesenchymal stem cells (hMSCs) exposed to MM.1S, RPMI-8226, and OPM-2 prior to and during differentiation, exhibited different levels of lipid accumulation as well as secreted cytokines. Combined, our results suggest that MM cells can inhibit adipogenic differentiation while stimulating expression of the senescence associated secretory phenotype (SASP) and other pro-myeloma molecules. This study provides insight into a novel way in which MM cells manipulate their microenvironment by altering the expression of supportive cytokines and skewing the cellular diversity of the marrow.

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