Project description:To investigate the mechanistic role of NanA and Siglec-5 in this excessive inflammation, we systemically analyzed genes and signaling pathways differentially regulated in macrophages infected with wild type and NanA-deficient pneumococcus.
Project description:CD14+ Monocytes from healthy volunteers were purified by MACS (negative selection) and FACSorting and either left untreated or stimulated for 24h and 48h with LPS. THP-1 cells were stimulated for 4h, 24h and 48h with LPS. Glycoproteins were captured with hydrazide chemistry and tryptic and PNGase F-released peptide fractions analyzed by MS/MS. Quantitative assessment revealed differential glycoprotein expression in activated/LPS-tolerized monocytes and naïve monocytes and THP-1 cells.
Project description:Human melanoma tumor cells (HS294T) and monocytes (THP-1) were infected with a double deleted (-VGF, -TK) oncolytic vaccinia virus expressing human DAI (DNA-dependent activator of interferon-regulatory factors). Total RNA was collected and gene expresson profiles were determined with Agilent microarray. An oncolytic vaccinia virus that does not express DAI was used to control the effect of DAI and uninfected cells (PBS treated) were used to control the effect of virus infection. In oncolytic virotherapy the ability of the virus to activate the immune system against tumors is nowadays generally understood to be a key mechanism in full eradication of cancer and for long-term anti-tumor effects. We armed an oncolytic vaccinia virus with DAI to increase the immunogenicity and the vaccine potency of the virus. The aim of this study was to study if the expression of DAI by a replicating vaccinia virus would alter the gene expression profile of infected cells and to study what are the differentially expressed genes. Three-condition experiment: vvdd-tdTomato-hDAI vs. vvdd-tdTomato vs. PBS treated cells. 2 cell lines: HS294T tumor cells and THP-1 monocytes. 3 biological replicates of virus infected cells per cell line and 2 uninfected replicates per cell line. HS294T and THP-1 cells were treated with vvdd-tdTomato-hDAI or vvdd-tdTomato control virus, or with PBS only to have an uninfected control. 16 hours after infection total RNA was extracted and whole genome gene pfofiles were analyzed and differentially expressed genes determined.
Project description:Here we profile nascent transcription, RNA polymerase III occupancy, chromatin accessibility, and H3K27ac levels in THP-1 monocytes and THP-1 derived macrophages after 72 hr exposure to phorbol myristate acetate (PMA).
Project description:To directly compare the SLE monocyte transcriptional program with that of blood mDC precursors, we purified lineage HLA-DRhighCD11chigh mDCs and CD14+ monocytes from the blood of five healthy donors. Their gene expression profiles were then compared to those of blood SLE monocytes. An unsupervised clustering analysis of transcripts present in >20% of the samples classified healthy monocytes, SLE monocytes and healthy mDCs into three well defined groups. A supervised analysis was then performed to find genes: 1) differentially expressed in healthy mDCs compared to monocytes; 2) shared by healthy blood mDCs and SLE blood monocytes. To directly compare the SLE monocyte transcriptional program with that of blood mDC precursors, we purified lineage HLA-DRhighCD11chigh mDCs and CD14+ monocytes from the blood of five healthy donors. Their gene expression profiles were then compared to those of blood SLE monocytes. An unsupervised clustering analysis of transcripts present in >20% of the samples classified healthy monocytes, SLE monocytes and healthy mDCs into three well defined groups. A supervised analysis was then performed to find genes: 1) differentially expressed in healthy mDCs compared to monocytes; 2) shared by healthy blood mDCs and SLE blood monocytes.
Project description:Senescence was induced in THP-1 monocytes in culture and the senescence associated secretory phenotype (SASP) was measured for the complete conditioned medium by applying nanoparticle based Proteograph technology for enrichment followed by DIA mass spectrometry
Project description:To directly compare the SLE monocyte transcriptional program with that of blood mDC precursors, we purified lineage HLA-DRhighCD11chigh mDCs and CD14+ monocytes from the blood of five healthy donors. Their gene expression profiles were then compared to those of blood SLE monocytes. An unsupervised clustering analysis of transcripts present in >20% of the samples classified healthy monocytes, SLE monocytes and healthy mDCs into three well defined groups. A supervised analysis was then performed to find genes: 1) differentially expressed in healthy mDCs compared to monocytes; 2) shared by healthy blood mDCs and SLE blood monocytes.
Project description:On the basis of the cell-surface molecule expression, CD16+ monocytes are likely comprised of distinct subpopulations of monocytes rather than a continuum of CD14+ monocytes with differing levels of cell activation. To better study this, we used gene array analysis that compared overall gene expression profiles of CD16+ subpopulations (CD14+CD16+ and CD16+) with that of CD14+CD16-. Gene expression in three FACS-sorted monocyte subsets was assessed by Affymetrix rhesus macaque oligonucleotide gene arrays that contain 52,024 probe sets covering 47,000 monkey genes. There were 29,361 probe sets that expressed in at least one subpopulation (raw array signal intensity > 32). Raw data were processed using robust multi-array average. To identify the most strongly, differentially expressed genes in each subpopulation, we only selected transcripts with consistently greater than four-fold difference (P < .05). In comparison to CD14+CD16- monocyte subset, a large number of genes (9098/29361, 30.9%) were differentially expressed in both CD14+CD16+ and CD16+ subsets: 1999 genes down-regulated; and 7099 genes up-regulated. Altogether, we observed large-scale gene expression differences between the CD14+CD16- subset and the two CD16+ subsets (CD14+CD16+ and CD16+), demonstrating transcriptional heterogeneity. The differential gene expression between CD16- and CD16+ monocytes underscore the fundamental differences between these cells. Comparisons between CD14+CD16+ and CD16+ were made to identify the genes that distinguish between these two CD16+ subpopulations. A relatively small number of genes were specifically associated with each subpopulation. Thirty-one genes were expressed strongly in CD14+CD16+ subset compared to CD16+ subset, and 94 genes were expressed strongly in CD16+ subset compared to CD14+CD16+ subset. A small set of genes that were expressed differentially between the two CD16+ subpopulations highlights similarity between the two cell types, but differentially expressed genes of function observed in each subset suggest different roles that these two subpopulations may play in vivo. To identify differentially expressed genes in subpopulations of monkey monocytes, three monocyte subsets from two normal uninfected rhesus macaques were FACS sorted based on their CD14 and CD16 expression. RNA purification and labeling, hybridization, array scanning, and image quantification were performed according to the manufacturerâs instructions. Briefly, FACS-isolated monocytes were spun down and lysed in Trizol reagents (Invitrogen), and total RNA was prepared using PureLink Micro-to-Midi Total RNA Purification system (Invitrogen). Quality of RNA was determined by 2100 Bioanalyzer RNA LabChip (Agilent Technologies). One hundred ng of high-quality total RNA was subjected to Affymetrix 1-cycle or 2-cycle synthesis amplification, fluorescent labeling, and hybridization to Affymetrix Rhesus Genome Arrays. Expression data was obtained from two aligned replicates using an Affymetrix GSC3000 scanner and processed by GCOS software (Affymetrix). Partek Genomic Suite System was used for downstream analysis of GCOS processed data. Signals from all probe sets were normalized using Rhesus Array Normalization Controls.
Project description:On the basis of the cell-surface molecule expression, CD16+ monocytes are likely comprised of distinct subpopulations of monocytes rather than a continuum of CD14+ monocytes with differing levels of cell activation. To better study this, we used gene array analysis that compared overall gene expression profiles of CD16+ subpopulations (CD14+CD16+ and CD16+) with that of CD14+CD16-. Gene expression in three FACS-sorted monocyte subsets was assessed by Affymetrix rhesus macaque oligonucleotide gene arrays that contain 52,024 probe sets covering 47,000 monkey genes. There were 29,361 probe sets that expressed in at least one subpopulation (raw array signal intensity > 32). Raw data were processed using robust multi-array average. To identify the most strongly, differentially expressed genes in each subpopulation, we only selected transcripts with consistently greater than four-fold difference (P < .05). In comparison to CD14+CD16- monocyte subset, a large number of genes (9098/29361, 30.9%) were differentially expressed in both CD14+CD16+ and CD16+ subsets: 1999 genes down-regulated; and 7099 genes up-regulated. Altogether, we observed large-scale gene expression differences between the CD14+CD16- subset and the two CD16+ subsets (CD14+CD16+ and CD16+), demonstrating transcriptional heterogeneity. The differential gene expression between CD16- and CD16+ monocytes underscore the fundamental differences between these cells. Comparisons between CD14+CD16+ and CD16+ were made to identify the genes that distinguish between these two CD16+ subpopulations. A relatively small number of genes were specifically associated with each subpopulation. Thirty-one genes were expressed strongly in CD14+CD16+ subset compared to CD16+ subset, and 94 genes were expressed strongly in CD16+ subset compared to CD14+CD16+ subset. A small set of genes that were expressed differentially between the two CD16+ subpopulations highlights similarity between the two cell types, but differentially expressed genes of function observed in each subset suggest different roles that these two subpopulations may play in vivo.
Project description:Macrophages play a key role in both innate and adaptive immunity, but our knowledge on the changes in transcription regulation that occurs during their differentiation from monocytes is still limited. In this study, we used a meta-analysis followed by a systems biology approach for the identification of differentially expressed genes between monocytes and macrophages and possible regulators of these changes in transcription. Based on the pattern of gene expression change, transcription regulator analysis predicted a decrease in Enhancer of Zeste homolog 2 (EZH2), a histone 3 lysine 27 methyl transferase, activity after differentiation of monocytes into macrophages. This inhibition was validated by a significant decrease in trimethylated H3K27 during differentiation of both human primary monocytes into macrophages and the THP-1 cell line into macrophage-like cells. Overexpressing EZH2 during differentiation of monocytes and THP-1 cells obstructs cellular adhesion, thus preventing the first step in differentiation. Another facet of macrophage differentiation is the cessation of proliferation, and inhibition of EZH2 by the small molecule inhibitor GSK126 in THP-1 cells indeed impedes proliferation. This study shows an important part for epigenetic changes during monocyte differentiation. It highlights the role of EZH2 activity behind the changes needed in adhesion and proliferation mechanisms for macrophage formation. THP-1s were differentiated into macrophage like cells by PMA stimulation.