scholarly journals A correlation-based network for biomarker discovery in obesity with metabolic syndrome

2019 ◽  
Vol 20 (S6) ◽  
Author(s):  
Pin-Yen Chen ◽  
Allan W. Cripps ◽  
Nicholas P. West ◽  
Amanda J. Cox ◽  
Ping Zhang
Keyword(s):  
Metabolic Syndrome ◽  
Immune Cells ◽  
Immune Cell ◽  
Healthy Weight ◽  
Network Approach ◽  
Serum Cytokine ◽  
Cytokine Concentration ◽  
Microbial Composition ◽  
Cell Abundance ◽  
Serum Cytokine Concentration

Abstract Background Obesity is associated with chronic activation of the immune system and an altered gut microbiome, leading to increased risk of chronic disease development. As yet, no biomarker profile has been found to distinguish individuals at greater risk of obesity-related disease. The aim of this study was to explore a correlation-based network approach to identify existing patterns of immune-microbiome interactions in obesity. Results The current study performed correlation-based network analysis on five different datasets obtained from 11 obese with metabolic syndrome (MetS) and 12 healthy weight men. These datasets included: anthropometric measures, metabolic measures, immune cell abundance, serum cytokine concentration, and gut microbial composition. The obese with MetS group had a denser network (total number of edges, n = 369) compared to the healthy network (n = 299). Within the obese with MetS network, biomarkers from the immune cell abundance group was found to be correlated to biomarkers from all four other datasets. Conversely in the healthy network, immune cell abundance was only correlated with serum cytokine concentration and gut microbial composition. These observations suggest high involvement of immune cells in obese with MetS individuals. There were also three key hubs found among immune cells in the obese with MetS networks involving regulatory T cells, neutrophil and cytotoxic cell abundance. No hubs were present in the healthy network. Conclusion These results suggest a more complex interaction of inflammatory markers in obesity, with high connectivity of immune cells in the obese with MetS network compared to the healthy network. Three key hubs were identified in the obese with MetS network, involving Treg, neutrophils and cytotoxic cell abundance. Compared to a t-test, the network approach offered more meaningful results when comparing obese with MetS and healthy weight individuals, demonstrating its superiority in exploratory analysis.

Immunogenomic signatures to predict outcome in ovarian and endometrial cancers: Potential strategies in targeting the tumor immune microenvironment to improve response to immunotherapy.

2020 ◽  
Vol 38 (5_suppl) ◽  
pp. 4-4
Author(s):  
Haider Mahdi ◽  
Ying Ni
Keyword(s):  
Endometrial Cancer ◽  
Immune Cells ◽  
Transforming Growth Factor ◽  
Immune Cell ◽  
Checkpoint Inhibitors ◽  
Individual Gene ◽  
Cell Abundance ◽  
Ifn Γ ◽  
Endometrial Cancers ◽  
The Impact

4 Background: Ovarian and MSS endometrial cancers are characterized by immunosuppressive microenvironment (TME) and low response to immunotherapy with checkpoint inhibitors (CPI). Targeting immunosuppressive factors within TMErepresents an attractive approach to enhance response to CPI. Therefore, we sought to investigate different immunogenomic signatures and immune cells within TME and correlate them with survival. Methods: We used whole transcriptome sequencing of matched tumor-normal samples from 38 uterine serous cancer and TCGA data of ovarian (n = 374) and endometrial cancers (n = 541). Immunogenomic signatures focusing on Transforming Growth Factor (TGFβ), 18-genes IFN-γ and myeloid signatures (CD47 and B7H4 expressions) and immune cell abundance were investigated. Gene expression score was calculated by averaging the normalized and log transformed individual gene read counts. The optimized score cut off was selected to best separate the survival in the pilot cohort. Then the score was tested in TCGA RNAseq datasets. Population abundance of tissue-infiltrating immune cells were estimated using MCPcounter R package from bulk transcriptome data. Results: Higher IFN-γ and lower TGF-β signatures predicted better survival for endometrial and ovarian cancers (p < 0.05). The impact of TGF-β was higher in MSI-H vs. MSS cancers (p = 0.013 vs. 0.09). High CD47 predicted poor survival in endometrial cancer. Combined IFN-γ and TGF-β signatures predicted survival in the ovarian and endometrial cohorts (p < 0.001). Combined IFN-γ and CD47 expression predicted survival in endometrial cancer (p = 0.033). Analysis of immune cell abundance revealed enrichment of monocytic lineage and neutrophils but paucity of cytotoxic T-cells, NK cells, dendritic cells and B-cells. Immune cell abundance is being correlated with survival. Conclusions: Our data support the role of immunogenomic markers in predicting survival. We are evaluating these markers in predicting response to CPI in a pilot cohort. Immunogenomic markers represent the tumor microenvironment, can potentially guide rationale combination immunotherapy.

scholarly journals Circulating immune cell phenotype dynamics reflect the strength of tumor-immune cell interactions in patients during immunotherapy

2020 ◽  
Author(s):  
Jason I Griffiths ◽  
Pierre Wallet ◽  
Lance T. Pflieger ◽  
David Stenehjem ◽  
Xuan Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Cell Interaction ◽  
Tumor Burden ◽  
Predator Prey ◽  
Cell Abundance ◽  
Single Cell Rna Sequencing ◽  
Peripheral Immune Cell ◽  
Cell Phenotypes

AbstractThe extent that immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled on a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients, but not those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached, tumor burden decreases, similar to models of predator-prey interactions; these dynamic patterns were absent in non-responder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients’ tumor-immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T-cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.One sentence summaryPeripheral immune cell differentiation and signaling, upon initiation of immunotherapy, reflects tumor attacking ability and patient response.Significance statementThe evolution of peripheral immune cell abundance and signaling over time, as well as how these immune cells interact with the tumor, may impact a cancer patient’s response to therapy. By developing an ecological population model, we provide evidence of a dynamic predator-prey like relationship between circulating immune cell abundance and tumor size in patients that respond to immunotherapy. This relationship is not found either in patients that are non-responsive to immunotherapy or during chemotherapy. Single cell RNA-sequencing (scRNAseq) of serial peripheral blood samples from patients show that the strength of tumor-immune cell interactions is reflected in T-cells interferon activation and differentiation early in treatment. Thus, circulating immune cell dynamics reflect a tumor’s response to immunotherapy.

scholarly journals Circulating immune cell phenotype dynamics reflect the strength of tumor–immune cell interactions in patients during immunotherapy

2020 ◽  
Vol 117 (27) ◽  
pp. 16072-16082 ◽  
Author(s):  
Jason I. Griffiths ◽  
Pierre Wallet ◽  
Lance T. Pflieger ◽  
David Stenehjem ◽  
Xuan Liu ◽  
...  
Keyword(s):  
Single Cell ◽  
Immune Cells ◽  
Immune Cell ◽  
Joint Modeling ◽  
Cell Interaction ◽  
Tumor Burden ◽  
Cell Phenotype ◽  
Cell Abundance ◽  
Peripheral Immune Cell ◽  
Cell Phenotypes

The extent to which immune cell phenotypes in the peripheral blood reflect within-tumor immune activity prior to and early in cancer therapy is unclear. To address this question, we studied the population dynamics of tumor and immune cells, and immune phenotypic changes, using clinical tumor and immune cell measurements and single-cell genomic analyses. These samples were serially obtained from a cohort of advanced gastrointestinal cancer patients enrolled in a trial with chemotherapy and immunotherapy. Using an ecological population model, fitted to clinical tumor burden and immune cell abundance data from each patient, we find evidence of a strong tumor-circulating immune cell interaction in responder patients but not in those patients that progress on treatment. Upon initiation of therapy, immune cell abundance increased rapidly in responsive patients, and once the peak level is reached tumor burden decreases, similar to models of predator–prey interactions; these dynamic patterns were absent in nonresponder patients. To interrogate phenotype dynamics of circulating immune cells, we performed single-cell RNA sequencing at serial time points during treatment. These data show that peripheral immune cell phenotypes were linked to the increased strength of patients’ tumor–immune cell interaction, including increased cytotoxic differentiation and strong activation of interferon signaling in peripheral T cells in responder patients. Joint modeling of clinical and genomic data highlights the interactions between tumor and immune cell populations and reveals how variation in patient responsiveness can be explained by differences in peripheral immune cell signaling and differentiation soon after the initiation of immunotherapy.

scholarly journals Transition of Serum Cytokine Concentration in Rickettsia japonica Infection

2020 ◽  
Vol 12 (3) ◽  
pp. 127-131
Author(s):  
Makoto Kondo ◽  
Yoshiaki Matsushima ◽  
Kento Mizutani ◽  
Shohei Iida ◽  
Koji Habe ◽  
...  
Keyword(s):  
Acute Phase ◽  
Defense Mechanism ◽  
Acute Stage ◽  
Serum Cytokine ◽  
High Concentration ◽  
Cytokine Concentration ◽  
Th17 Cytokines ◽  
Ifn Γ ◽  
Induced Apoptosis ◽  
Serum Cytokine Concentration

(1) Background. Rickettsia japonica (R. japonica) infection induces severe inflammation, and the disappearance of eosinophil in the acute stage is one of the phenomena. (2) Materials and Methods. In the current study, we measured the serum concentrations of Th1, Th2, and Th17 cytokines in the acute and recovery stages. (3) Results. In the acute phase, IL-6 and IFN-γ levels were elevated and we speculated that they played a role as a defense mechanism against R. japonica. The high concentration of IFN-γ suppressed the differentiation of eosinophil and induced apoptosis of eosinophil, leading to the disappearance of eosinophil. On day 7, IL-6 and IFN-γ concentrations were decreased, and Th2 cytokines such as IL-5 and IL-9 were slightly increased. On day 14, eosinophil count recovered to the normal level. The transition of serum cytokine concentration in R. japonica infection was presented. (4) Conclusions. IL-6 and IFN-γ seem to be critical cytokines as defense mechanism against R. japonica in the acute phase, and this may deeply connect to the decrease of eosinophil.

scholarly journals Profiles of Peripheral Immune Cells of Uncomplicated COVID-19 Cases with Distinct Viral RNA Shedding Periods

Viruses ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 514
Author(s):  
Denise Utami Putri ◽  
Cheng-Hui Wang ◽  
Po-Chun Tseng ◽  
Wen-Sen Lee ◽  
Fu-Lun Chen ◽  
...  
Keyword(s):  
Immune Response ◽  
Immune Cells ◽  
Mononuclear Cells ◽  
Immune Cell ◽  
Severe Disease ◽  
Viral Rna ◽  
Disease Course ◽  
Peripheral Blood Mononuclear ◽  
Peripheral Immune Cells ◽  
Cell Profile

The heterogeneity of immune response to COVID-19 has been reported to correlate with disease severity and prognosis. While so, how the immune response progress along the period of viral RNA-shedding (VRS), which determines the infectiousness of disease, is yet to be elucidated. We aim to exhaustively evaluate the peripheral immune cells to expose the interplay of the immune system in uncomplicated COVID-19 cases with different VRS periods and dynamic changes of the immune cell profile in the prolonged cases. We prospectively recruited four uncomplicated COVID-19 patients and four healthy controls (HCs) and evaluated the immune cell profile throughout the disease course. Peripheral blood mononuclear cells (PBMCs) were collected and submitted to a multi-panel flowcytometric assay. CD19+-B cells were upregulated, while CD4, CD8, and NK cells were downregulated in prolonged VRS patients. Additionally, the pro-inflammatory-Th1 population showed downregulation, followed by improvement along the disease course, while the immunoregulatory cells showed upregulation with subsequent decline. COVID-19 patients with longer VRS expressed an immune profile comparable to those with severe disease, although they remained clinically stable. Further studies of immune signature in a larger cohort are warranted.

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