scholarly journals COVID-19 Severity Potentially Modulated by Cardiovascular-Disease-Associated Immune Dysregulation

Viruses ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 1018
Author(s):  
Abby C. Lee ◽  
Grant Castaneda ◽  
Wei Tse Li ◽  
Chengyu Chen ◽  
Neil Shende ◽  
...  
Keyword(s):  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Expression Profiles ◽  
Immune Dysregulation ◽  
Left Ventricular ◽  
Recurrent Event ◽  
Healthy Controls ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear

Patients with underlying cardiovascular conditions are particularly vulnerable to severe COVID-19. In this project, we aimed to characterize similarities in dysregulated immune pathways between COVID-19 patients and patients with cardiomyopathy, venous thromboembolism (VTE), or coronary artery disease (CAD). We hypothesized that these similarly dysregulated pathways may be critical to how cardiovascular diseases (CVDs) exacerbate COVID-19. To evaluate immune dysregulation in different diseases, we used four separate datasets, including RNA-sequencing data from human left ventricular cardiac muscle samples of patients with dilated or ischemic cardiomyopathy and healthy controls; RNA-sequencing data of whole blood samples from patients with single or recurrent event VTE and healthy controls; RNA-sequencing data of human peripheral blood mononuclear cells (PBMCs) from patients with and without obstructive CAD; and RNA-sequencing data of platelets from COVID-19 subjects and healthy controls. We found similar immune dysregulation profiles between patients with CVDs and COVID-19 patients. Interestingly, cardiomyopathy patients display the most similar immune landscape to COVID-19 patients. Additionally, COVID-19 patients experience greater upregulation of cytokine- and inflammasome-related genes than patients with CVDs. In all, patients with CVDs have a significant overlap of cytokine- and inflammasome-related gene expression profiles with that of COVID-19 patients, possibly explaining their greater vulnerability to severe COVID-19.

scholarly journals Using protein microarray reveals clinical correlation between self-perception of patient and the apoptosis-related proteins in rheumatoid arthritis

2020 ◽  
Author(s):  
Jian-ting Wen ◽  
Jian Liu ◽  
Hui Jiang ◽  
Lei Wan ◽  
Ling Xin ◽  
...  
Keyword(s):  
Rheumatoid Arthritis ◽  
Mononuclear Cells ◽  
Expression Profiles ◽  
Enzyme Linked Immunosorbent Assay ◽  
Healthy Controls ◽  
Self Perception ◽  
Roc Curve Analysis ◽  
Peripheral Blood Mononuclear ◽  
Related Proteins ◽  
Potential Biomarkers

Abstract Background: The most severe effects of rheumatoid arthritis (RA) are loss of physical function, which may have a significant impact on self-perception of patient (SPP). However, the inherent relationship between SPP and the key proteins is not clear. The aim of this study was to get an insight into SPP of RA in connection with the the apoptosis-related proteins. Methods: We set out to investigate changes of the apoptosis-related proteins expression in the peripheral blood mononuclear cells (PBMCs) of RA. Additionally, we aimed to correlate the apoptosis-related proteins expression profiles with SPP and clinical indexes. To this end, we employed antibody microarrays of the the apoptosis-related proteins in PBMCs from four RA patients and seven healthy controls. We used bioinformatics to screen several the apoptosis-related proteins. To validate key protein candidates, we performed Enzyme linked immunosorbent assay (ELISA) on 30 RA patients and 30 healthy controls. Results: We found the expression of ten the apoptosis-related proteins (caspase3, CD40, SMAC, HSP27, HTRA, IGFBP-1, IGFBP-6, sTNF-R1, sTNF-R2, TRAILR-3) were significantly altered in PBMCs of RA patients. Receiver operating characteristic (ROC) curve analysis suggested that these ten the apoptosis-related proteins are potential biomarkers of RA. Spearman Correlation analysis and Logistic-regression analysis revealed that the 10 selected the apoptosis-related proteins correlated with SPP and clinical indexes. Conclusion: Therefore, we highlight some the apoptosis-related proteins may serve as potential biomarkers in prediction of SPP for RA patients, although the underlying mechanisms need to be further explored.

Apremilast Inhibits Inflammatory Osteoclastogenesis

Rheumatology ◽  
2021 ◽  
Author(s):  
Yannick Degboé ◽  
Flavia Sunzini ◽  
Shatakshi Sood ◽  
Aline Bozec ◽  
Maria V Sokolova ◽  
...  
Keyword(s):  
Mononuclear Cells ◽  
Cytokine Production ◽  
Bone Erosion ◽  
Ex Vivo ◽  
Human Peripheral Blood ◽  
Osteoclast Differentiation ◽  
Healthy Controls ◽  
Blood Monocytes ◽  
Peripheral Blood Mononuclear ◽  
Phosphodiesterase 4

Abstract Background Psoriatic arthritis (PsA) is associated with bone erosion and inflammation-induced bone loss, which are mediated by osteoclasts and modulated by inflammatory cytokines. Apremilast (a selective phosphodiesterase 4 inhibitor) is efficacious in PsA and acts by inhibiting cytokine production. However, there are no direct data informing whether and how apremilast affects osteoclast formation in humans. Methods Osteoclastogenic cytokine production by activated human peripheral blood mononuclear cells (PBMCs) was measured in the presence and absence of apremilast. Effects of apremilast on osteoclast differentiation were tested (i) in co-cultures of activated PBMCs and human CD14+ blood monocytes as well as (ii) in CD14+ blood monocytes stimulated with activated-PBMCs supernatant, TNF or IL-17A. Bone resorption was measured on OsteoAssay plates. Effects of apremilast on ex vivo osteoclast differentiation were compared in PsA, pre-PsA and psoriasis patients as well as in healthy controls. Results Apremilast significantly impaired the expression of key osteoclastogenic cytokines in activated PBMCs. Furthermore, apremilast dose-dependently and significantly inhibited activated PBMC-driven osteoclast differentiation, and ex-vivo osteoclast differentiation of PBMCs derived from PsA and pre-PsA patients, but not from psoriasis patients or healthy controls. TNF and IL-17A-enhanced osteoclastogenesis and osteolytic activity of CD14+ blood monocytes from PsA patients was also significantly inhibited by apremilast. Finally, apremilast inhibited expression of the key osteoclast fusion protein DC-STAMP. Conclusion Phosphodiesterase-4 targeting by apremilast not only inhibits osteoclastogenic cytokine production, but also directly suppresses inflammation-driven osteoclastogenesis. These data provide initial evidence that apremilast has the potential to provide a direct bone protective effect in PsA.

scholarly journals FEM: mining biological meaning from cell level in single-cell RNA sequencing data

PeerJ ◽  
2021 ◽  
Vol 9 ◽  
pp. e12570
Author(s):  
Yunqing Liu ◽  
Na Lu ◽  
Changwei Bi ◽  
Tingyu Han ◽  
Guo Zhuojun ◽  
...  
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Mononuclear Cells ◽  
Differentially Expressed Gene ◽  
Biological Significance ◽  
Human Pancreas ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Expression Matrix

Background One goal of expression data analysis is to discover the biological significance or function of genes that are differentially expressed. Gene Set Enrichment (GSE) analysis is one of the main tools for function mining that has been widely used. However, every gene expressed in a cell is valuable information for GSE for single-cell RNA sequencing (scRNA-SEQ) data and not should be discarded. Methods We developed the functional expression matrix (FEM) algorithm to utilize the information from all expressed genes. The algorithm converts the gene expression matrix (GEM) into a FEM. The FEM algorithm can provide insight on the biological significance of a single cell. It can also integrate with GEM for downstream analysis. Results We found that FEM performed well with cell clustering and cell-type specific function annotation in three datasets (peripheral blood mononuclear cells, human liver, and human pancreas).

scholarly journals Scalable full-transcript coverage single cell RNA sequencing with Smart-seq3xpress

2021 ◽  
Author(s):  
Michael Hagemann-Jensen ◽  
Christoph Ziegenhain ◽  
Rickard Sandberg
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Peripheral Blood Mononuclear Cells ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Cell Types ◽  
Peripheral Blood Mononuclear ◽  
Single Cell Rna Sequencing ◽  
Blood Mononuclear Cells ◽  
Additional Protein

Plate-based single-cell RNA-sequencing methods with full-transcript coverage typically excel at sensitivity but are more resource and time-consuming. Here, we miniaturized and streamlined the Smart-seq3 protocol for drastically reduced cost and increased throughput. Applying Smart-seq3xpress to 16,349 human peripheral blood mononuclear cells revealed a highly granular atlas complete with both common and rare cell types whose identification previously relied on additional protein measurements or the integration with a reference atlas.

scholarly journals Latent variable modelling and variational inference for scRNA-seq differential expression analysis

2019 ◽  
Author(s):  
Joana Godinho ◽  
Alexandra M. Carvalho ◽  
Susana Vinga
Keyword(s):  
Single Cell ◽  
Rna Sequencing ◽  
Latent Variable ◽  
Negative Binomial ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Differential Expression Analysis ◽  
Differential Analysis ◽  
Peripheral Blood Mononuclear ◽  
Link Type

AbstractDisease profiling, treatment development, and the identification of new cell populations are some of the most relevant applications relying on differentially expressed genes (DEG) analysis. In this context, three leading technologies emerged; namely, DNA microarrays, bulk RNA sequencing (RNA-seq), and single-cell RNA sequencing (scRNA-seq), the main focus of this work. Although scRNA-seq tends to offer more accurate data, it is still limited by many confounding factors. We introduce two novel approaches to assess DEG: extended Bayesian zero-inflated negative binomial factorization (ext-ZINBayes) and single-cell differential analysis (SIENA). In addition, we benchmark the proposed methods with known DEG analysis tools for single-cell and bulk RNA data, using two real public datasets. One contains house mouse cells of two different types, while the other gathers human peripheral blood mononuclear cells divided into four types. The results show that the two procedures can be very competitive with existing methods (scVI, SCDE, MAST, and DEseq) in identifying relevant putative biomarkers. In terms of scalability and correctness, SIENA stands out from ext-ZINBayes and some of the existing methods. As single-cell datasets become increasingly larger, SIENA may emerge as a powerful tool to discover functional differences between two conditions. Both methods are publicly available (https://github.com/JoanaGodinho/SIENA, https://github.com/JoanaGodinho/ext-ZINBayes).

scholarly journals RNA-Seq profiling revealed PBMC RNA as potential biomarker for hepatocellular carcinoma

2020 ◽  
Author(s):  
Zhiyi Han ◽  
Wenxing Feng ◽  
Rui Hu ◽  
Qinyu Ge ◽  
Wenfeng Ma ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Mononuclear Cells ◽  
Healthy Controls ◽  
Rna Seq ◽  
Sequencing Data ◽  
Peripheral Blood Mononuclear ◽  
Qrt Pcr ◽  
Potential Biomarker ◽  
Incidence And Mortality ◽  
Differential Expressed Gene

Abstract Background Hepatocellular carcinoma is one of the most common malignancies with extremely high incidence and mortality rates. Although there have been many studies focus on biomarkers study, few have been reported on PBMC RNA profiles of hepatocellular carcinoma. Methods In this study, we attempted to profile the expression of Peripheral Blood Mononuclear Cells (PBMCs) RNA by using RNA-seq technology and compared the transcriptome between hepatocellular carcinoma patients and the healthy controls. 17 patients and 17 healthy controls involved in this study, PBMCs RNA were sequenced. The sequencing data were analyzed with bioinformatics tools and qRT-PCR was used for selected differential expressed gene validation. Results It is showed that 1578 dysregulated genes found including 1334 upregulated genes and 244 downregulated genes. GO enrichment and KEGG analysis denoted most of the differential expressed genes (DEGs) involved in immune response are closely related to hepatocellular carcinoma. Expression of the 6 selected genes (DEGs, SELENBP1, SLC4A1, SLC26A8, HSPA8P4, CALM1, and RPL7p24) were confirmed by qRT-PCR, and higher sensitivity and specificity obtained by ROC analysis of the 6 genes. CALM1 was found gradually decreasing along with the tumor enlarged. Conclusions It is suggested potential biomarker for diagnosis, classification and therapeutic target of hepatocellular carcinomas. This study provided new visions into development of liver cancer and potential efficient clinical diagnosis in the future.

scholarly journals Enhancement of Chemokine mRNA Expression by Toll-Like Receptor 2 Stimulation in Human Peripheral Blood Mononuclear Cells of Patients with Atopic Dermatitis

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Yangyang Yu ◽  
Dongxu Lin ◽  
Xiaoqiong Cai ◽  
Danni Cui ◽  
Ran Fang ◽  
...  
Keyword(s):  
Atopic Dermatitis ◽  
Mrna Expression ◽  
Peripheral Blood Mononuclear Cells ◽  
Mononuclear Cells ◽  
Human Peripheral Blood ◽  
Healthy Controls ◽  
Toll Like Receptor ◽  
Peripheral Blood Mononuclear ◽  
Blood Mononuclear Cells ◽  
Toll Like Receptor 2

Atopic dermatitis (AD) is a chronic inflammatory skin disease which is often associated with Staphylococcus aureus (S. aureus) colonization. S. aureus ingredients are potential ligands to activate the Toll-like receptor 2 (TLR2) and drive inflammatory cytokine or chemokine production. However, the role of TLR2-mediated chemokine expression in AD development has not been systematically investigated. In this study, we sought to determine the mode of TLR2-mediated chemokine expression in AD patients. Human peripheral blood mononuclear cells (PBMCs) were isolated from AD patients and healthy controls. Upon incubation with TLR2 ligands Pam3CSK4 and PGN, mRNA expression of chemokines, including CCL1, CCL5, CCL8, CCL13, CCL17, CCL18, CCL22, and CCL27, were determined by quantitative real-time polymerase chain reaction (qRT-PCR) analysis. The results showed that basal mRNA expression of CCL17 in PBMCs from AD patients was upregulated compared with healthy controls, while those of CCL8 and CCL13 were downregulated. When stimulated with TLR2 ligands, the mRNA expression of CCL5, CCL8, CCL13, CCL18, and CCL22 in PBMCs from AD patients was significantly higher than those from healthy controls. The different basal chemokine mRNA expression profiles indicate the different immune status in patients with AD compared with healthy controls. Excessive chemokine mRNA expression induced by TLR2 activation is associated with the development of AD.

scholarly journals SARS-CoV-2 early infection signature identified potential key infection mechanisms and drug targets

BMC Genomics ◽  
2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Yue Li ◽  
Ashley Duche ◽  
Michael R. Sayer ◽  
Don Roosan ◽  
Farid G. Khalafalla ◽  
...  
Keyword(s):  
Gene Expression ◽  
Rna Sequencing ◽  
Drug Targets ◽  
Mononuclear Cells ◽  
Cultured Cells ◽  
Expression Profiles ◽  
Treatment Options ◽  
Infection Level ◽  
Peripheral Blood Mononuclear ◽  
Signature Genes

Abstract Background The ongoing COVID-19 outbreak has caused devastating mortality and posed a significant threat to public health worldwide. Despite the severity of this illness and 2.3 million worldwide deaths, the disease mechanism is mostly unknown. Previous studies that characterized differential gene expression due to SARS-CoV-2 infection lacked robust validation. Although vaccines are  now available, effective treatment options are still out of reach. Results To characterize the transcriptional activity of SARS-CoV-2 infection, a gene signature consisting of 25 genes was generated using a publicly available RNA-Sequencing (RNA-Seq) dataset of cultured cells infected with SARS-CoV-2. The signature estimated infection level accurately in bronchoalveolar lavage fluid (BALF) cells and peripheral blood mononuclear cells (PBMCs) from healthy and infected patients (mean 0.001 vs. 0.958; P < 0.0001). These signature genes were investigated in their ability to distinguish the severity of SARS-CoV-2 infection in a single-cell RNA-Sequencing dataset. TNFAIP3, PPP1R15A, NFKBIA, and IFIT2 had shown bimodal gene expression in various immune cells from severely infected patients compared to healthy or moderate infection cases. Finally, this signature was assessed using the publicly available ConnectivityMap database to identify potential disease mechanisms and drug repurposing candidates. Pharmacological classes of tricyclic antidepressants, SRC-inhibitors, HDAC inhibitors, MEK inhibitors, and drugs such as atorvastatin, ibuprofen, and ketoconazole showed strong negative associations (connectivity score < − 90), highlighting the need for further evaluation of these candidates for their efficacy in treating SARS-CoV-2 infection. Conclusions Thus, using the 25-gene SARS-CoV-2 infection signature, the SARS-CoV-2 infection status was captured in BALF cells, PBMCs and postmortem lung biopsies. In addition, candidate SARS-CoV-2 therapies with known safety profiles were identified. The signature genes could potentially also be used to characterize the COVID-19 disease severity in patients’ expression profiles of BALF cells.

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