OP0083 INFECTIOUS AND AUTOINFLAMMATORY MODIC TYPE 1 CHANGES HAVE DIFFERENT PATHOMECHANISMS

Background:Modic type 1 changes (MC1) are vertebral bone marrow (BM) edema that associate with non-specific low back pain (LBP). Two etiologies have been described. In the infectious etiology the anaerobic aerotolerant Cutibacterium acnes (C. acnes) invades damaged intervertebral discs (IVDs) resulting in disc infection and endplate damage, which leads to the evocation of an immune response. In the autoinflammatory etiology disc and endplate damage lead to the exposure of immune privileged disc cells and matrix to leukocytes, thereby evoking an immune response in the BM. Different etiologies require different treatment strategies. However, it is unknown if etiology-specific pathological mechanisms exist.Objectives:The aim of this study was to identify etiology-specific dysregulated pathways of MC1 and to perform in-depth analysis of immune cell populations of the autoinflammatory etiology.Methods:BM aspirates and biopsies were obtained from LBP patients with MC1 undergoing spinal fusion. Aspirates/biopsies were taken prior screw insertion through the pedicle screw trajectory. From each patient, a MC1 and an intra-patient control aspiration/biopsy from the adjacent vertebral level was taken. If C. acnes in IVDs adjacent to MC1 were detected by anaerobic bacterial culture, patients were assigned to the infectious, otherwise to the autoinflammatory etiology.Total RNA was isolated from aspirates and sequenced (Novaseq) (infectious n=3 + 3, autoinflammatory n=5 + 5). Genes were considered as differentially expressed (DEG) if p-value < 0.01 and log2fc > ± 0.5. Gene ontology (GO) enrichment was performed in R (GOseq), gene set enrichment analysis (GSEA) with GSEA software.Changes in cell populations of the autoinflammatory etiology were analyzed with single cell RNA sequencing (scRNAseq): Control and MC1 biopsies (n=1 + 1) were digested, CD45+CD66b- mononuclear cells isolated with fluorescence activated cell sorting (FACS), and 10000 cells were sequenced (10x Genomics). Seurat R toolkit was used for quality-control, clustering, and differential expression analysis.Transcriptomic changes (n=5 + 5) of CD45+CD66b+ neutrophils isolated with flow cytometry from aspirates were analyzed as for total bulk RNAseq. Neutrophil activation (n=3 + 3) was measured as CD66b+ expression with flow cytometry. CD66bhigh and CD66blow fractions in MC1 and control neutrophils were compared with paired t-test.Results:Comparing MC1 to control in total bulk RNAseq, 204 DEG in the autoinflammatory and 444 DEG in the infectious etiology were identified with only 67 shared genes (Fig. 1a). GO enrichment revealed “T-cell activation” (p = 2.50E-03) in the autoinflammatory and “complement activation, classical pathway” (p=1.1E-25) in the infectious etiology as top enriched upregulated biological processes (BP) (Fig 1b). ScRNAseq of autoinflammatory MC1 showed an overrepresentation of T-cells (p= 1.00E-34, OR=1.54) and myelocytes (neutrophil progenitor cells) (p=4.00E-05, OR=2.27) indicating an increased demand of these cells (Fig. 1c). Bulk RNAseq analysis of neutrophils from the autoinflammatory etiology revealed an activated, pro-inflammatory phenotype (Fig 1d), which was confirmed with more CD66bhigh neutrophils in MC1 (+11.13 ± 2.71%, p=0.02) (Fig. 1e).Figure 1.(a) Venn diagram of DEG from total bulk RNAseq (b) Top enriched upregulated BP of autoinflammatory (left) and infectious (right) etiology (c) Cell clustering of autoinflammatory MC1 BM (d) Enrichment of “inflammatory response” gene set in autoinflammatory MC1 neutrophils (e) Representative histogram of CD66b+ expression in MC1 and control neutrophils.Conclusion:Autoinflammatory and infectious etiologies of MC1 have different pathological mechanisms. T-cell and neutrophil activation seem to be important in the autoinflammatory etiology. This has clinical implication as it could be explored for diagnostic approaches to distinguish the two MC1 etiologies and supports developing targeted treatments for both etiologies.Disclosure of Interests:None declared

Download Full-text

611 RNA-sequencing reveals a unique immune transcriptional landscape in the vaccine sites of patients with circulating T-cell responses to cancer immunization

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2020-sitc2020.0611 ◽

2020 ◽

Vol 8 (Suppl 3) ◽

pp. A646-A647

Author(s):

Max Meneveau ◽

Pankaj Kumar ◽

Kevin Lynch ◽

Karlyn Pollack ◽

Craig Slingluff

Keyword(s):

T Cell ◽

Differentially Expressed Genes ◽

T Cell Responses ◽

Differentially Expressed ◽

Gene Set Enrichment ◽

Gene Set ◽

Cell Responses ◽

Vaccine Site ◽

And Control ◽

Transcriptional Landscape

BackgroundVaccines are a promising therapeutic for patients with advanced cancer, but achieving robust T-cell responses remains a challenge. Melanoma-associated antigen-A3 (MAGE-A3) in combination with adjuvant AS15 (a formulation of Toll-Like-Receptor (TLR)-4 and 9 agonists and a saponin), induced systemic CD4+ T-cell responses in 50% of patients when given subcutaneously/intradermally. Little is known about the transcriptional landscape of the vaccine-site microenvironment (VSME) of patients with systemic T-cell responses versus those without. We hypothesized that patients with systemic T-cell responses to vaccination would exhibit increased immune activation in the VSME, higher dendritic cell (DC) activation/maturation, TLR-pathway activation, and enhanced Th1 signatures.MethodsBiopsies of the VSME were obtained from participants on the Mel55 clinical trial (NCT01425749) who were immunized with MAGE-A3/AS15. Biopsies were taken 8 days after immunization. T-cell response to MAGE-A3 was assessed in PBMC after in-vitro stimulation with recMAGE-A3, by IFNγ ELISPOT assay. Gene expression was assessed by RNAseq using DESeq2. Comparisons were made between immune-responders (IR), non-responders (NR), and normal skin controls. FDR p<0.01 was considered significant.ResultsFour IR, four NR, and three controls were evaluated. The 500 most variable genes were used for principal component analysis (PCA). Two IR samples were identified as outliers on PCA and excluded from further analysis. There were 882 differentially expressed genes (DEGs) in the IR group vs the NR group (figure 1A). Unsupervised clustering of the top 500 DEGs revealed clustering according to the experimental groups (figure 1B). Of the 10 most highly upregulated DEGs, 9 were immune-related (figure 1C). Gene-set enrichment analysis revealed that immune-related pathways were highly enriched in IRs vs NRs (figure 1D). CD4 and CD8 expression did not differ between IR and NR (figure 2A), though both were higher in IR compared to control. Markers of DC activation/maturation were higher in IR vs NR (figure 2B), as were several Th1 associated genes (figure 2C). Interestingly, markers of exhaustion were higher in IR v NR (figure 2D). Expression of numerous TLR-pathway genes was higher in IR vs NR, including MYD88, but not TICAM1 (figure 2E).Abstract 611 Figure 1Gene expression profiling of vaccine site samples from patients immunized with MAGE-A3/AS15. (A) Volcano plots showing the distribution of differentially expressed genes (DEGs) between immune responders (IR) and non-responders (NR), IR and control, and NR and control. (B) Heatmap of the top 500 most differentially expressed genes demonstrating hierarchical clustering of sequenced samples according to IR, NR, and control. (C) Table showing the 10 most highly up and down-regulated genes in IR compared to NR. 9 of the top 10 most highly up-regulated genes are related to the immune response. (D) Enrichment plots from a gene set enrichment analysis highlighting the upregulation of immune related pathways in IR compared to NR. Gene set enrichment data was generated from the Reactome gene set database and included all expressed genes. Significance was set at FDR p <0.01Abstract 611 Figure 2Expression of T-cell markers in IR vs NR vs Control samples in the vaccine site microenvironment (VSME). (A) T-cell markers showing similar expression in IR vs NR but higher expression in IR vs control. (B) Markers of dendritic cell activation and maturation in the VSME which are higher in IR vs control but not IR vs NR. (B) Transcription factors and genes associated with Th1/Th2 responses within the VSME. (D) Genes associated with T-cell exhaustion at the VSME. (E) Expression of TLR pathway genes in the VSME. Expression data is provided in terms of normalized counts. Bars demonstrate median and interquartile range. N=9. IR = immune responder, NR = non-responder, TLR = Toll-like Receptor. * = <0.01, ** < 0.001, *** <0.0001, **** < 0.00001ConclusionsThese findings suggest a unique immune-transcriptional landscape in the VSME is associated with circulating T-cell responses to immunization, with differences in DC activation/maturation, Th1 response, and TLR signaling. Thus, immunologic changes in the VSME are useful predictors of systemic immune response, and host factors that modulate immune-related signaling at the vaccine site may have concordant systemic effects on promoting or limiting immune responses to vaccines.Trial RegistrationSamples for this work were collected from patients enrolled on the Mel55 clinical trial NCT01425749.Ethics ApprovalThis work was completed after approval from the UVA institutional review board IRB-HSR# 15398.

Download Full-text

Comparison of Lethal and Nonlethal Mouse Models of Orientia tsutsugamushi Infection Reveals T-Cell Population-Associated Cytokine Signatures Correlated with Lethality and Protection

Tropical Medicine and Infectious Disease ◽

10.3390/tropicalmed6030121 ◽

2021 ◽

Vol 6 (3) ◽

pp. 121

Author(s):

Alison Luce-Fedrow ◽

Suchismita Chattopadhyay ◽

Teik-Chye Chan ◽

Gregory Pearson ◽

John B. Patton ◽

...

Keyword(s):

Flow Cytometry ◽

T Cell ◽

Host Response ◽

Cd8 T Cell ◽

Cd4 T Cell ◽

Cell Populations ◽

Murine Models ◽

Orientia Tsutsugamushi ◽

Multicolor Flow Cytometry ◽

Interstrain Difference

The antigenic diversity of Orientia tsutsugamushi as well as the interstrain difference(s) associated with virulence in mice impose the necessity to dissect the host immune response. In this study we compared the host response in lethal and non-lethal murine models of O. tsutsugamushi infection using the two strains, Karp (New Guinea) and Woods (Australia). The models included the lethal model: Karp intraperitoneal (IP) challenge; and the nonlethal models: Karp intradermal (ID), Woods IP, and Woods ID challenges. We monitored bacterial trafficking to the liver, lung, spleen, kidney, heart, and blood, and seroconversion during the 21-day challenge. Bacterial trafficking to all organs was observed in both the lethal and nonlethal models of infection, with significant increases in average bacterial loads observed in the livers and hearts of the lethal model. Multicolor flow cytometry was utilized to analyze the CD4+ and CD8+ T cell populations and their intracellular production of the cytokines IFNγ, TNF, and IL2 (single, double, and triple combinations) associated with both the lethal and nonlethal murine models of infection. The lethal model was defined by a cytokine signature of double- (IFNγ-IL2) and triple-producing (IL2-TNF-IFNγ) CD4+ T-cell populations; no multifunctional signature was identified in the CD8+ T-cell populations associated with the lethal model. In the nonlethal model, the cytokine signature was predominated by CD4+ and CD8+ T-cell populations associated with single (IL2) and/or double (IL2-TNF) populations of producers. The cytokine signatures associated with our lethal model will become depletion targets in future experiments; those signatures associated with our nonlethal model are hypothesized to be related to the protective nature of the nonlethal challenges.

Download Full-text

614 Investigating immune mediated mechanisms of PARPi resistance in BRCA1-associated triple negative breast cancer (TNBC)

Journal for ImmunoTherapy of Cancer ◽

10.1136/jitc-2021-sitc2021.614 ◽

2021 ◽

Vol 9 (Suppl 3) ◽

pp. A644-A644

Author(s):

Anita Mehta ◽

Madeline Townsend ◽

Madisson Oliwa ◽

Patrice Lee ◽

Nicholas Saccomano ◽

...

Keyword(s):

Breast Cancer ◽

Flow Cytometry ◽

Immune Response ◽

T Cell ◽

Triple Negative Breast Cancer ◽

Triple Negative ◽

Parp Inhibitor ◽

Triple Combination ◽

Tumor Clearance ◽

Sting Pathway

BackgroundPoly(ADP-ribose) polymerase inhibitors (PARPi) have improved the outcomes of BRCA-associated breast cancer; however, treatment responses are often not durable. Our preclinical studies demonstrated that PARPi activates the cGAS/STING pathway and recruitment of anti-tumor CD8+ T-cells that are required for tumor clearance [1]. These studies contributed to development of clinical trials testing PARPi plus immune checkpoint blockade (ICB). Unfortunately, early phase trials of PARPi + ICB have not yet suggested efficacy will be superior to PARPi monotherapy. Lack of demonstrated clinical synergy between PARPi + ICB underscores the need to study the tumor microenvironment (TME) during PARPi therapy to identify optimal strategies to enhance T-cell activation. We recently showed that PARPi induces CSF-1R+ suppressive tumor associated macrophages (TAMs) that restrict antitumor immune responses, contributing to PARPi resistance [2]. Removing TAMs with anti-CSF-1R therapy in combination with PARPi significantly enhanced overall survival (OS) compared to PARPi monotherapy in preclinical models [2]. Here, we investigate how modulating TAMs can enhance PARPi + ICB.MethodsMice bearing BRCA1-deficient TNBC (K14-Cre;Brca1f/f;p53f/f) tumors were treated for 98 days with PARPi (Talazoparib) ± small molecule inhibitor of CSF-1R (ARRAY-382; CSF-1Ri) ± anti-PD-1 and then followed for survival. Flow cytometry was employed to elucidate changes in the TME after treatment.ResultsPARPi conferred a significant survival advantage over vehicle treated mice (median OS 33 v. 14 days; p=0.0034) and 2/8 PARPi-treated mice experienced complete tumor clearance at day 98. PARPi + CSF-1Ri treated mice (median OS 140 days) remarkably cleared 7/10 tumors by day 98. The addition of anti-PD-1 to PARPi did not enhance OS compared to PARPi monotherapy. The triple combination of anti-PD-1 + PARPi + CSF-1Ri has not yet significantly enhanced the median OS compared to PARPi + CSF-1Ri (ongoing; 168 v. 140 days); nor did it increase clearance of tumor by day 98 (7/10). However, the triple combination led to superior long term tumor clearance. At day 161 the triple combination exhibited 5/10 tumor free mice compared to 2/10 treated with PARPi + CSF-1Ri. To elucidate how CSR-1Ri enhanced PARPi + ICB responses, flow cytometry was performed and revealed increased expression of the co-stimulatory molecule CD80, reduced tissue resident macrophages (CX3CR1+) and lower CSF-1R expression compared to PARPi + ICB.ConclusionsThese data suggest that targeting immunosuppressive macrophages may induce a favorable anti-tumor immune response and enhance responses to PARPi plus ICB. We are currently evaluating the adaptive immune response in this context.ReferencesPantelidou, C., et al., PARP inhibitor efficacy depends on CD8+ T cell recruitment via intratumoral STING pathway activation in BRCA-deficient models of triple-negative breast cancer. Cancer Discovery, 2019: p. CD-18-1218.Mehta, A.K., et al., Targeting immunosuppressive macrophages overcomes PARP inhibitor resistance in BRCA1-associated triple-negative breast cancer. Nat Cancer, 2021. 2(1): p. 66–82.

Download Full-text

Downregulation of T helper type 1 immune response and altered pro-inflammatory and anti-inflammatory T cell cytokine balance following conventional but not laparoscopic surgery11This work was supported by DFG grants Si 208/5-1 and Si 208/5-4 to the clinical research group, “Immunosuppression und Postoperative Sepsis.”

The American Journal of Surgery ◽

10.1016/s0002-9610(98)00299-2 ◽

1999 ◽

Vol 177 (1) ◽

pp. 55-60 ◽

Cited By ~ 121

Author(s):

Iris B. Brune ◽

Wido Wilke ◽

Thorsten Hensler ◽

Bernhard Holzmann ◽

Jörg-Rüdiger Siewert

Keyword(s):

Immune Response ◽

T Cell ◽

Clinical Research ◽

Research Group ◽

Postoperative Sepsis ◽

T Helper ◽

Cytokine Balance ◽

Anti Inflammatory ◽

Helper Type

Download Full-text

Introduction to Clinical Immunology: Overview of the Immune Response, Autoimmune Conditions, and Immunosuppressive Therapeutics for Rheumatic Diseases

10.2310/im.1328 ◽

2016 ◽

Author(s):

Steven K. Lundy ◽

Alison Gizinski ◽

David A. Fox

Keyword(s):

Immune Response ◽

Immune System ◽

T Cell ◽

Immune Responses ◽

Autoimmune Diseases ◽

Adaptive Immunity ◽

Cell Populations ◽

Hematopoietic Stem ◽

Innate And Adaptive Immunity ◽

Adaptive Immune

The immune system is a complex network of cells and mediators that must balance the task of protecting the host from invasive threats. From a clinical perspective, many diseases and conditions have an obvious link to improper functioning of the immune system, and insufficient immune responses can lead to uncontrolled acute and chronic infections. The immune system may also be important in tumor surveillance and control, cardiovascular disease, health complications related to obesity, neuromuscular diseases, depression, and dementia. Thus, a working knowledge of the role of immunity in disease processes is becoming increasingly important in almost all aspects of clinical practice. This review provides an overview of the immune response and discusses immune cell populations and major branches of immunity, compartmentalization and specialized immune niches, antigen recognition in innate and adaptive immunity, immune tolerance toward self antigens, inflammation and innate immune responses, adaptive immune responses and helper T (Th) cell subsets, components of the immune response that are important targets of treatment in autoimmune diseases, mechanisms of action of biologics used to treat autoimmune diseases and their approved uses, and mechanisms of other drugs commonly used in the treatment of autoimmune diseases. Figures show the development of erythrocytes, platelets, lymphocytes, and other immune system cells originating from hematopoietic stem cells that first reside in the fetal liver and later migrate to the bone marrow, antigen–major histocompatibility complex recognition by T cell receptor control of T cell survival and activation, and Th cells as central determinants of the adaptive immune response toward different stimuli. Tables list cell populations involved in innate and adaptive immunity, pattern recognition receptors with known ligands, autoantibody-mediated human diseases: examples of pathogenic mechanisms, selected Food and Drug Administration–approved autoimmune disease indications for biologics, and mechanism of action of biologics used to treat autoimmune diseases. This review contains 3 highly rendered figures, 5 tables, and 64 references.

Download Full-text

IM-03 IMMUNOLOGICAL SUBTYPES OF GLIOBLASTOMA BASED ON TUMOR INFILTRATING CELLS

Neuro-Oncology Advances ◽

10.1093/noajnl/vdz039.052 ◽

2019 ◽

Vol 1 (Supplement_2) ◽

pp. ii12-ii12

Author(s):

Kushihara Yoshihiro ◽

Syota Tanaka ◽

Erika Yamasawa ◽

Tsukasa Koike ◽

Taijun Hana ◽

...

Keyword(s):

Principal Component Analysis ◽

T Cell ◽

Tumor Microenvironment ◽

Nk Cells ◽

Principal Component ◽

Component Analysis ◽

Gene Set ◽

Significant Difference

Abstract To discover novel biological targets in glioblastoma, genomic and immunological analysis were performed using The Cancer Genome Atlas (TCGA) data set. The RNA-seq data of 156 primary glioblastoma cases were subjected to CIBERSORT to detect tumor infiltrating cell fractions. Principal component analysis was performed on this data to detect factors that strongly contribute to the first principal component, and hierarchical clustering was performed. Survival curves were compared for each of the derived clusters. Finally, Gene Set Enrichment Analysis (GSEA) using HALLMARK Gene Set was performed. In the principal component analysis, we detected seven factors (NK cells resting, T cell regulatory, NK cells activated, Macrophage type 0, T cell gamma delta, Macrophage type 2, Macrophage type 1) which strongly contribute to the first principal component. Based on these seven factors, hierarchical cluster analysis resulted in T cell regulatory (Treg), Macrophage type 0 (M0), Macrophage type 2 (M2) and Macrophage type 1 (M1) clusters. There was no significant difference between these groups in CD8 T cell. M2 and M1 clusters displayed better OS with a significant difference. TNFA signaling via NFκB in Treg group, IFNα response, IFNγ response and ALLOGRAFT response in M2 group, G2M CHECKPOINT, GLYCOLYSIS, WNTβ catenin signaling, MITOTIC SPINDLE and TGFβ signaling in M1 group were upregulated. In conclusion, tumor microenvironment of glioblastoma can be divided into 4 immunological subtypes, Treg, M0, M1, and M2. Because of the contribution of innate immunity for shaping the tumor microenvironment of glioblastoma, immunotherapies targeting these innate immune cells are anticipated.

Download Full-text

Flow cytometry T cell profiling in a recent-onset narcoleptic type 1 child: a case report

Sleep Medicine ◽

10.1016/j.sleep.2019.08.017 ◽

2020 ◽

Vol 68 ◽

pp. 21-23

Author(s):

Monica Moresco ◽

Mariangela Lecciso ◽

Darina Ocadlikova ◽

Fabio Pizza ◽

Antonio Curti ◽

...

Keyword(s):

Flow Cytometry ◽

Case Report ◽

T Cell ◽

Recent Onset

Download Full-text

Antibiotic Therapy Does Not Alter the Humoral Response to Vaccination for Porcine Circovirus 2 in Weaned Pigs

Veterinary Sciences ◽

10.3390/vetsci6020051 ◽

2019 ◽

Vol 6 (2) ◽

pp. 51

Author(s):

Jonathan E. Fogle ◽

Jenna A. Scott ◽

Glen W. Almond

Keyword(s):

Flow Cytometry ◽

Immune Response ◽

Antibiotic Therapy ◽

Humoral Immunity ◽

Immune Cell ◽

Humoral Response ◽

Porcine Circovirus ◽

Cell Populations ◽

Antibiotic Administration ◽

Weaned Pigs

Recent reports suggest that antibiotic therapy may either reduce or enhance the immune response to various porcine vaccines. Based upon these findings, we asked if antibiotic therapy alters immune cell populations, as measured by flow cytometry and/or vaccine-specific humoral immunity, as measured by sample to positive (S/P) antibody ratios. Here, we investigated the immuno-modulatory effects of enrofloxacin, ceftiofur, and tulathromycin on the immune response to a Mycoplasma hyopneumoniae (M. hyopneumoniae) and porcine circovirus type 2 (PCV-2) combination vaccine in weaned pigs. Maternal antibody likely interfered with the induction of immunity to M. hyopneumoniae. Antibiotic administration did not affect immune cell populations, as assessed by flow cytometry and did not affect the induction of humoral immunity to PCV-2.

Download Full-text