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: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: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: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: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: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:Differentially regulated miRNA candidates in H37Rv infected THP-1 cells were analysed with respect to uninfected THP-1 reference samples. THP-1 cells are monocytes differentiated to macrophages after treatment with PMA for 48 hrs. Total RNA was isolated from infected THP-1 cells after 24 hrs of infection, cDNA was synthesized for TLDA real time PCR reaction using TaqMan MicroRNA Reverse Transcription kit and Megaplex Human Pool A and Pool B stem loop RT primers (version 3.0) as per manufacturer’s protocol. Further real time reaction was performed on QuantStudio 12K Flex Real-Time PCR System (Applied Biosystems) by using cDNA (without pre-amplification) on TLDA card A and card B (version 3.0).