Persistent Inflammatory Pathways Associated with Early Onset Myocardial Infarction in a Medicated Multiethnic Hawaiian Cohort

2011 ◽  
Vol 4 ◽  
pp. BCI.S6976 ◽  
Author(s):  
Kornelia M. Szauter ◽  
Matthias K. Jansen ◽  
Gordon Okimoto ◽  
Michael Loomis ◽  
James H. Kimura ◽  
...  

In spite of current standard therapies to target the major pathomechanisms in myocardial infarction (MI), inflammatory gene expression patterns have been consistently revealed in MI patients. In a multiethnic cohort, we aimed to identify MI-associated pathomechanisms that may be unresponsive to medical treatment to improve diagnosis and therapy. Gene expression profiles in whole blood were analyzed in medicated Asian, African American and Caucasian patients living in Hawaii with a history of early MI and age, ethnicity, risk factor and medication-matched controls. PANTHER ontological and Ingenuity Pathway analysis and functional evaluation of the consistently differentially expressed genes identified coordinated up-regulation of genes for inflammation (LGALS3, PTX3, ZBTB32, BCL2L1), T-cell activation (IL12RB1, VAV3, JAG1, CAMP), immune imbalance (IL-8, IL2RA, CCR7, AHNAK), and active atherosclerosis (NR1H4, BIN1, GSTT1, MARCO) that persist in MI patients in spite of concerted treatment efforts to control vascular pathology. Furthermore, significant ethnic differences appear to exist within the active disease mechanisms that need to be further investigated to identify key targets for effective medical intervention.

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zhi-jie Zhao ◽  
Dong-po Wei ◽  
Rui-zhe Zheng ◽  
Tinghua Peng ◽  
Xiang Xiao ◽  
...  

Traumatic brain injury (TBI) is a major cause of morbidity and mortality, both in adult and pediatric populations. However, the dynamic changes of gene expression profiles following TBI have not been fully understood. In this study, we identified the differentially expressed genes (DEGs) following TBI. Remarkably, Serpina3n, Asf1b, Folr1, LOC100366216, Clec12a, Olr1, Timp1, Hspb1, Lcn2, and Spp1 were identified as the top 10 with the highest statistical significance. The weighted gene coexpression analysis (WGCNA) identified 12 functional modules from the DEGs, which showed specific expression patterns over time and were characterized by enrichment analysis. Specifically, the black and turquoise modules were mainly involved in energy metabolism and protein translation. The green yellow and yellow modules including Hmox1, Mif, Anxa2, Timp1, Gfap, Cd9, Gja1, Pdpn, and Gpx1 were related to response to wounding, indicating that expression of these genes such as Hmox1, Anxa2, and Timp1 could protect the brains from brain injury. The green yellow module highlighted genes involved in microglial cell activation such as Tyrobp, Cx3cr1, Grn, Trem2, C1qa, and Aif1, suggesting that these genes were responsible for the inflammatory response caused by TBI. The upregulation of these genes has been validated in an independent dataset. These results indicated that the key genes in microglia cell activation may serve as a promising therapeutic target for TBI. In summary, the present study provided a full view of the dynamic gene expression changes following TBI.


2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Zhao ◽  
Jiaoyun Lv ◽  
Hongwei Zhang ◽  
Jiawei Xie ◽  
Hui Dai ◽  
...  

BackgroundPigmented villonodular synovitis (PVNS) is a rare condition that involves benign proliferation of the synovial tissue and is characterized by severe joint destruction and high recurrence even after surgical resection. However, poor understanding of the pathogenesis limits its effective therapy.MethodIn this study, gene expression profiles of six patients with PVNS, 11 patients with osteoarthritis (OA), nine patients with rheumatoid arthritis (RA) (E-MTAB-6141), and three healthy subjects (GSE143514) were analyzed using integrating RNA sequencing (RNA-seq) and microarray to investigate the PVNS transcriptome. Gene ontology, string, and cytoscape were used to determine the gene functional enrichment. Cell functional molecules were detected using flow cytometry or immunohistochemical test to identify the cell subset and function. CD14+ cells were isolated and induced to osteoclast to evaluate the monocyte/macrophage function.ResultsThe most obvious local manifestations of PVNS were inflammation, including increased immune cells infiltration and cytokine secretion, and tumor phenotypes. High proportion of inflammatory cells, including T cells, natural killer (NK) cells, NKT cells, and B cells were recruited from the blood. Th17 and monocytes, especially classical monocytes but not nonclassical monocytes, increased in PVNS synovium. An obvious increase in osteoclastogenesis and macrophage activation was observed locally. Elevated expression of MMP9, SIGLEC 15, and RANK were observed in myeloid cell of PVNS than OA. When compared with RA, osteoclast differentiation and myeloid cell activation are PVNS-specific characters, whereas T cell activation is shared by PVNS and RA.ConclusionThe transcriptional expression characteristics of PVNS showed increased immune response, cell migration, and osteoclastogenesis. Osteoclast differentiation is only observed in PVNS but not RA, whereas T-cell activation is common in inflammatory arthritis.


mSphere ◽  
2018 ◽  
Vol 3 (1) ◽  
Author(s):  
Christel Rothe Brinkmann ◽  
Jesper Falkesgaard Højen ◽  
Thomas Aagaard Rasmussen ◽  
Anne Sofie Kjær ◽  
Rikke Olesen ◽  
...  

The effect of treatment with histone deacetylase inhibitors on the immune system in HIV-infected individuals is not clear. Analysis of results from a clinical trial in which 15 HIV-infected individuals received 12 doses of panobinostat identified a significant impact on both T cell activation status and regulatory T cell suppressive marker expression and a reduced level of monocytic responsiveness to inflammatory stimuli. These changes were substantiated by global gene expression analysis. Collectively, the results suggest that panobinostat has multiple effects on innate and adaptive immune responses. Importantly, all the effects were transient, and further panobinostat treatment did not cause persistent long-term changes in gene expression patterns in HIV-infected individuals.


Author(s):  
Daniel He ◽  
Chen Xi Yang ◽  
Basak Sahin ◽  
Amrit Singh ◽  
Casey P. Shannon ◽  
...  

Abstract Background Blood has proven to be a useful resource for molecular analysis in numerous biomedical studies, with peripheral blood mononuclear cells (PBMCs) and whole blood being the major specimen types. However, comparative analyses between these two major compartments (PBMCs and whole blood) are few and far between. In this study, we compared gene expression profiles of PBMCs and whole blood samples obtained from research subjects with or without mild allergic asthma. Methods Whole blood (PAXgene) and PBMC samples were obtained from 5 mild allergic asthmatics and 5 healthy controls. RNA from both sample types was measured for expression of 730 immune-related genes using the NanoString nCounter platform. Results We identified 64 uniquely expressed transcripts in whole blood that reflected a variety of innate, humoral, and adaptive immune processes, and 13 uniquely expressed transcripts in PBMCs which were representative of T-cell and monocyte-mediated processes. Furthermore, analysis of mild allergic asthmatics versus non-asthmatics revealed 47 differentially expressed transcripts in whole blood compared to 1 differentially expressed transcript in PBMCs (FDR < 0.25). Finally, through simultaneous measurement of PBMC proteins on the nCounter assay, we identified CD28 and OX40 (TNFRSF4), both of which are critical co-stimulatory molecules during T-cell activation, as significantly upregulated in asthmatics. Conclusions Whole blood RNA preserved in PAXgene tubes is excellent for producing gene expression data with minimal variability and good sensitivity, suggesting its utility in multi-centre studies requiring measurement of blood gene expression.


2021 ◽  
Vol 17 ◽  
pp. 174480692110072
Author(s):  
Debra Morrison ◽  
Anthony A Arcese ◽  
Janay Parrish ◽  
Katie Gibbs ◽  
Andrew Beaufort ◽  
...  

Pain affects most individuals with traumatic spinal cord injury (SCI). Major pain types after SCI are neuropathic or nociceptive, often experienced concurrently. Pain after SCI may be refractory to treatments and negatively affects quality of life. Previously, we analyzed whole blood gene expression in individuals with chronic SCI compared to able-bodied (AB) individuals. Most participants with SCI reported pain (N = 19/28). Here, we examined gene expression of participants with SCI by pain status. Compared to AB, participants with SCI with pain had 468 differentially expressed (DE) genes; participants without pain had 564 DE genes (FDR < 0.05). Among DE genes distinct to participants with SCI with pain, Gene Ontology Biological Process (GOBP) analysis showed upregulated genes were enriched in categories related to T cell activation or inflammation; downregulated genes were enriched in categories related to protein proteolysis and catabolism. Although most participants with pain reported multiple pain types concurrently, we performed a preliminary comparison of gene expression by worst pain problem type. Compared to AB, participants with SCI who ranked neuropathic (N = 9) as worst had one distinct DE gene (TMEM156); participants who ranked nociceptive (N = 10) as worst had 61 distinct DE genes (FDR < 0.05). In the nociceptive group, the GOBP category with the lowest P-value identified among upregulated genes was “positive regulation of T cell activation”; among downregulated genes it was “receptor tyrosine kinase binding”. An exploratory comparison of pain groups by principal components analysis also showed that the nociceptive group was enriched in T-cell related genes. A correlation analysis identified genes significantly correlated with pain intensity in the neuropathic or nociceptive groups (N = 145, 65, respectively, Pearson’s correlation r > 0.8). While this pilot study highlights challenges of identifying gene expression profiles that correlate with specific types of pain in individuals with SCI, it suggests that T-cell signaling should be further investigated in this context.


MicroRNA ◽  
2015 ◽  
Vol 4 (2) ◽  
pp. 117-122 ◽  
Author(s):  
Nato Teteloshvili ◽  
Katarzyna Smigielska-Czepiel ◽  
Bart-Jan Kroesen ◽  
Elisabeth Brouwer ◽  
Joost Kluiver ◽  
...  

2021 ◽  
Vol 22 (4) ◽  
pp. 1901
Author(s):  
Brielle Jones ◽  
Chaoyang Li ◽  
Min Sung Park ◽  
Anne Lerch ◽  
Vimal Jacob ◽  
...  

Mesenchymal stromal cells derived from the fetal placenta, composed of an amnion membrane, chorion membrane, and umbilical cord, have emerged as promising sources for regenerative medicine. Here, we used next-generation sequencing technology to comprehensively compare amniotic stromal cells (ASCs) with chorionic stromal cells (CSCs) at the molecular and signaling levels. Principal component analysis showed a clear dichotomy of gene expression profiles between ASCs and CSCs. Unsupervised hierarchical clustering confirmed that the biological repeats of ASCs and CSCs were able to respectively group together. Supervised analysis identified differentially expressed genes, such as LMO3, HOXA11, and HOXA13, and differentially expressed isoforms, such as CXCL6 and HGF. Gene Ontology (GO) analysis showed that the GO terms of the extracellular matrix, angiogenesis, and cell adhesion were significantly enriched in CSCs. We further explored the factors associated with inflammation and angiogenesis using a multiplex assay. In comparison with ASCs, CSCs secreted higher levels of angiogenic factors, including angiogenin, VEGFA, HGF, and bFGF. The results of a tube formation assay proved that CSCs exhibited a strong angiogenic function. However, ASCs secreted two-fold more of an anti-inflammatory factor, TSG-6, than CSCs. In conclusion, our study demonstrated the differential gene expression patterns between ASCs and CSCs. CSCs have superior angiogenic potential, whereas ASCs exhibit increased anti-inflammatory properties.


2008 ◽  
Vol 5 (2) ◽  
Author(s):  
Li Teng ◽  
Laiwan Chan

SummaryTraditional analysis of gene expression profiles use clustering to find groups of coexpressed genes which have similar expression patterns. However clustering is time consuming and could be diffcult for very large scale dataset. We proposed the idea of Discovering Distinct Patterns (DDP) in gene expression profiles. Since patterns showing by the gene expressions reveal their regulate mechanisms. It is significant to find all different patterns existing in the dataset when there is little prior knowledge. It is also a helpful start before taking on further analysis. We propose an algorithm for DDP by iteratively picking out pairs of gene expression patterns which have the largest dissimilarities. This method can also be used as preprocessing to initialize centers for clustering methods, like K-means. Experiments on both synthetic dataset and real gene expression datasets show our method is very effective in finding distinct patterns which have gene functional significance and is also effcient.


Blood ◽  
2020 ◽  
Vol 136 (Supplement 1) ◽  
pp. 30-31
Author(s):  
Hanyin Wang ◽  
Shulan Tian ◽  
Qing Zhao ◽  
Wendy Blumenschein ◽  
Jennifer H. Yearley ◽  
...  

Introduction: Richter's syndrome (RS) represents transformation of chronic lymphocytic leukemia (CLL) into a highly aggressive lymphoma with dismal prognosis. Transcriptomic alterations have been described in CLL but most studies focused on peripheral blood samples with minimal data on RS-involved tissue. Moreover, transcriptomic features of RS have not been well defined in the era of CLL novel therapies. In this study we investigated transcriptomic profiles of CLL/RS-involved nodal tissue using samples from a clinical trial cohort of refractory CLL and RS patients treated with Pembrolizumab (NCT02332980). Methods: Nodal samples from 9 RS and 4 CLL patients in MC1485 trial cohort were reviewed and classified as previously published (Ding et al, Blood 2017). All samples were collected prior to Pembrolizumab treatment. Targeted gene expression profiling of 789 immune-related genes were performed on FFPE nodal samples using Nanostring nCounter® Analysis System (NanoString Technologies, Seattle, WA). Differential expression analysis was performed using NanoStringDiff. Genes with 2 fold-change in expression with a false-discovery rate less than 5% were considered differentially expressed. Results: The details for the therapy history of this cohort were illustrated in Figure 1a. All patients exposed to prior ibrutinib before the tissue biopsy had developed clinical progression while receiving ibrutinib. Unsupervised hierarchical clustering using the 300 most variable genes in expression revealed two clusters: C1 and C2 (Figure 1b). C1 included 4 RS and 3 CLL treated with prior chemotherapy without prior ibrutinib, and 1 RS treated with prior ibrutinib. C2 included 1 CLL and 3 RS received prior ibrutinib, and 1 RS treated with chemotherapy. The segregation of gene expression profiles in samples was largely driven by recent exposure to ibrutinib. In C1 cluster (majority had no prior ibrutinb), RS and CLL samples were clearly separated into two subgroups (Figure 1b). In C2 cluster, CLL 8 treated with ibrutinib showed more similarity in gene expression to RS, than to other CLL samples treated with chemotherapy. In comparison of C2 to C1, we identified 71 differentially expressed genes, of which 34 genes were downregulated and 37 were upregulated in C2. Among the upregulated genes in C2 (majority had prior ibrutinib) are known immune modulating genes including LILRA6, FCGR3A, IL-10, CD163, CD14, IL-2RB (figure 1c). Downregulated genes in C2 are involved in B cell activation including CD40LG, CD22, CD79A, MS4A1 (CD20), and LTB, reflecting the expected biological effect of ibrutinib in reducing B cell activation. Among the 9 RS samples, we compared gene profiles between the two groups of RS with or without prior ibrutinib therapy. 38 downregulated genes and 10 upregulated genes were found in the 4 RS treated with ibrutinib in comparison with 5 RS treated with chemotherapy. The top upregulated genes in the ibrutinib-exposed group included PTHLH, S100A8, IGSF3, TERT, and PRKCB, while the downregulated genes in these samples included MS4A1, LTB and CD38 (figure 1d). In order to delineate the differences of RS vs CLL, we compared gene expression profiles between 5 RS samples and 3 CLL samples that were treated with only chemotherapy. RS samples showed significant upregulation of 129 genes and downregulation of 7 genes. Among the most significantly upregulated genes are multiple genes involved in monocyte and myeloid lineage regulation including TNFSF13, S100A9, FCN1, LGALS2, CD14, FCGR2A, SERPINA1, and LILRB3. Conclusion: Our study indicates that ibrutinib-resistant, RS-involved tissues are characterized by downregulation of genes in B cell activation, but with PRKCB and TERT upregulation. Furthermore, RS-involved nodal tissues display the increased expression of genes involved in myeloid/monocytic regulation in comparison with CLL-involved nodal tissues. These findings implicate that differential therapies for RS and CLL patients need to be adopted based on their prior therapy and gene expression signatures. Studies using large sample size will be needed to verify this hypothesis. Figure Disclosures Zhao: Merck: Current Employment. Blumenschein:Merck: Current Employment. Yearley:Merck: Current Employment. Wang:Novartis: Research Funding; Incyte: Research Funding; Innocare: Research Funding. Parikh:Verastem Oncology: Honoraria; GlaxoSmithKline: Honoraria; Pharmacyclics: Honoraria, Research Funding; MorphoSys: Research Funding; Ascentage Pharma: Research Funding; Genentech: Honoraria; AbbVie: Honoraria, Research Funding; Merck: Research Funding; TG Therapeutics: Research Funding; AstraZeneca: Honoraria, Research Funding; Janssen: Honoraria, Research Funding. Kenderian:Sunesis: Research Funding; MorphoSys: Research Funding; Humanigen: Consultancy, Patents & Royalties, Research Funding; Gilead: Research Funding; BMS: Research Funding; Tolero: Research Funding; Lentigen: Research Funding; Juno: Research Funding; Mettaforge: Patents & Royalties; Torque: Consultancy; Kite: Research Funding; Novartis: Patents & Royalties, Research Funding. Kay:Astra Zeneca: Membership on an entity's Board of Directors or advisory committees; Acerta Pharma: Research Funding; Juno Theraputics: Membership on an entity's Board of Directors or advisory committees; Dava Oncology: Membership on an entity's Board of Directors or advisory committees; Oncotracker: Membership on an entity's Board of Directors or advisory committees; Sunesis: Research Funding; MEI Pharma: Research Funding; Agios Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol Meyer Squib: Membership on an entity's Board of Directors or advisory committees, Research Funding; Tolero Pharmaceuticals: Membership on an entity's Board of Directors or advisory committees, Research Funding; Abbvie: Research Funding; Pharmacyclics: Membership on an entity's Board of Directors or advisory committees, Research Funding; Rigel: Membership on an entity's Board of Directors or advisory committees; Morpho-sys: Membership on an entity's Board of Directors or advisory committees; Cytomx: Membership on an entity's Board of Directors or advisory committees. Braggio:DASA: Consultancy; Bayer: Other: Stock Owner; Acerta Pharma: Research Funding. Ding:DTRM: Research Funding; Astra Zeneca: Research Funding; Abbvie: Research Funding; Merck: Membership on an entity's Board of Directors or advisory committees, Research Funding; Octapharma: Membership on an entity's Board of Directors or advisory committees; MEI Pharma: Membership on an entity's Board of Directors or advisory committees; alexion: Membership on an entity's Board of Directors or advisory committees; Beigene: Membership on an entity's Board of Directors or advisory committees.


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