scholarly journals Blood transcriptomes of anti-SARS-CoV2 antibody positive healthy individuals with prior asymptomatic versus clinical infection

2021 ◽  
Author(s):  
Petros P. Sfikakis ◽  
Kleio-Maria Verrou ◽  
Ourania Tsitsilonis ◽  
Dimitris Paraskevis ◽  
Efstathios Kastritis ◽  
...  
Keyword(s):  
Immune Response ◽  
Mrna Translation ◽  
Gene Signature ◽  
Asymptomatic Infection ◽  
Healthy Individuals ◽  
Clinical Infection ◽  
Protein Coding ◽  
Symptomatic Disease ◽  
Genome Wide ◽  
Individual Variations

Despite tremendous efforts by the international research community to understand the pathophysiology of SARS-CoV-2 infection, the reasons behind the clinical variability, ranging from asymptomatic infection to lethal disease, are still unclear. Existing inter-individual variations of the immune responses, due to environmental exposures and genetic factors, may be critical to the development or not of symptomatic disease after infection with SARS-CoV-2, and transcriptomic differences marking such responses may be observed even later, after convalescence. Herein, we performed genome-wide transcriptional whole-blood profiling to test the hypothesis that immune response-related gene signatures may differ between healthy individuals with prior entirely asymptomatic versus clinical SARS-CoV-2 infection, all of which developed an equally robust antibody response. Among 12.789 protein-coding genes analyzed, there were only six and nine genes with significantly decreased or increased expression, respectively, in those with prior asymptomatic infection (n=17, mean age 34 years) relatively to those with clinical infection (n=15, mean age 37 years). All six genes with decreased expression (IFIT3, IFI44L, RSAD2, FOLR3, PI3, ALOX15), are involved in innate immune response while the first two are interferon-induced proteins. Among genes with increased expression six are involved in immune response (GZMH, CLEC1B, CLEC12A), viral mRNA translation (GCAT), energy metabolism (CACNA2D2) and oxidative stress response (ENC1). Notably, 8/15 differentially expressed genes are regulated by interferons. Our results suggest that an intrinsically weaker expression of some innate immunity- related genes may be associated with an asymptomatic disease course in SARS-CoV-2 infection. Whether a certain gene signature predicts, or not, those who will develop a more efficient immune response upon exposure to SARS-CoV-2, with implications for prioritization for vaccination, warrant further study.

scholarly journals Blood Transcriptomes of Anti-SARS-CoV-2 Antibody-Positive Healthy Individuals Who Experienced Asymptomatic Versus Clinical Infection

2021 ◽  
Vol 12 ◽  
Author(s):  
Petros P. Sfikakis ◽  
Kleio-Maria Verrou ◽  
Giannis Ampatziadis-Michailidis ◽  
Ourania Tsitsilonis ◽  
Dimitrios Paraskevis ◽  
...  
Keyword(s):  
Immune Response ◽  
Mrna Translation ◽  
Gene Signature ◽  
Asymptomatic Infection ◽  
Healthy Individuals ◽  
Clinical Infection ◽  
Protein Coding ◽  
Genome Wide ◽  
Asymptomatic Disease ◽  
And Oxidative Stress

The reasons behind the clinical variability of SARS-CoV-2 infection, ranging from asymptomatic infection to lethal disease, are still unclear. We performed genome-wide transcriptional whole-blood RNA sequencing, bioinformatics analysis and PCR validation to test the hypothesis that immune response-related gene signatures reflecting baseline may differ between healthy individuals, with an equally robust antibody response, who experienced an entirely asymptomatic (n=17) versus clinical SARS-CoV-2 infection (n=15) in the past months (mean of 14 weeks). Among 12.789 protein-coding genes analysed, we identified six and nine genes with significantly decreased or increased expression, respectively, in those with prior asymptomatic infection relatively to those with clinical infection. All six genes with decreased expression (IFIT3, IFI44L, RSAD2, FOLR3, PI3, ALOX15), are involved in innate immune response while the first two are interferon-induced proteins. Among genes with increased expression six are involved in immune response (GZMH, CLEC1B, CLEC12A), viral mRNA translation (GCAT), energy metabolism (CACNA2D2) and oxidative stress response (ENC1). Notably, 8/15 differentially expressed genes are regulated by interferons. Our results suggest that subtle differences at baseline expression of innate immunity-related genes may be associated with an asymptomatic disease course in SARS-CoV-2 infection. Whether a certain gene signature predicts, or not, those who will develop a more efficient immune response upon exposure to SARS-CoV-2, with implications for prioritization for vaccination, warrant further study.

scholarly journals Genome-Wide Analysis Reveals Hypoxic Microenvironment Is Associated With Immunosuppression in Poor Survival of Stage II/III Colorectal Cancer Patients

2021 ◽  
Vol 8 ◽  
Author(s):  
Yu-feng Chen ◽  
Zhao-liang Yu ◽  
Min-yi Lv ◽  
Bin Zheng ◽  
Ying-xin Tan ◽  
...  
Keyword(s):  
Colorectal Cancer ◽  
Immune Response ◽  
Disease Free Survival ◽  
Enrichment Analysis ◽  
Gene Signature ◽  
Stage Ii ◽  
Free Survival ◽  
Genome Wide ◽  
Colorectal Cancer Patients ◽  
Worse Prognosis

Background: Hypoxia is associated with a poorer clinical outcome and resistance to chemotherapy in solid tumors; identifying hypoxic-related colorectal cancer (CRC) and revealing its mechanism are important. The aim of this study was to assess hypoxia signature for predicting prognosis and analyze relevant mechanism.Methods: Patients without chemotherapy were selected for the identification of hypoxia-related genes (HRGs). A total of six independent datasets that included 1,877 CRC patients were divided into a training cohort and two validation cohorts. Functional annotation and analysis were performed to reveal relevant mechanism.Results: A 12-gene signature was derived, which was prognostic for stage II/III CRC patients in two validation cohorts [TCGA, n = 509, hazard ratio (HR) = 2.14, 95% confidence interval (CI) = 1.18 – 3.89, P = 0.01; metavalidation, n = 590, HR = 2.46, 95% CI = 1.59 – 3.81, P < 0.001]. High hypoxic risk was correlated with worse prognosis in CRC patients without adjuvant chemotherapy (HR = 5.1, 95% CI = 2.51 – 10.35, P < 0.001). After integration with clinical characteristics, hypoxia-related gene signature (HRGS) remained as an independent prognostic factor in multivariate analysis. Furthermore, enrichment analysis found that antitumor immune response was suppressed in the high hypoxic group.Conclusions: HRGS is a promising system for estimating disease-free survival of stage II/III CRC patients. Hypoxia tumor microenvironment may be via inhibiting immune response to promote chemoresistance in stage II/III CRC patients.

The Basics of Infectious Diseases

2021 ◽  
Author(s):  
Heinz-Josef Schmitt
Keyword(s):  
Immune Response ◽  
Infectious Diseases ◽  
Asymptomatic Infection ◽  
Host Tissue ◽  
The Body ◽  
Symptomatic Disease ◽  
Immune Mediated ◽  
Long Time ◽  
Micro Organisms ◽  
Underlying Diseases

Infectious Diseases result from exposure and contact between a host (human being) and an (uninvited) guest (micro-organism). Given the fact that billions of micro-organisms are in and around us at any time, overall, infectious diseases are comparatively rare; of the millions of different microbial species, only about 300 are known to cause human diseases. Besides exposure and contact, factors on the side of the host (genetic background, environment, underlying diseases and their therapy) and on the side of the micro-organisms (pathogenicity / virulence factors) are necessary to result in an infectious disease. “Colonization” means that a micro-organism can attach on skin or mucous membrane for some time or even indefinitely but does not invade host tissue and does not cause any symptoms. Colonizers may even induce an immune response. “Infection” is defined as a micro-organism invading through skin or mucous membranes the tissue of a host, leading to no disease (“asymptomatic infection”); or symptomatic disease. It is followed by health, disability, or death. Following the infection, microorganisms may persist in the body for a long time or even for life without causing any symptoms, which is called “latent infection”. Infectious diseases may not only be due to pathogenicity factors of a micro-organism, but may also result from (i.) direct destruction of host tissues (e.g., from viral replication); (ii.) the acute host (immune-) response; and from late immune responses resulting in immune-mediated “post-infectious diseases”. Some infections may cause an immune response that is directed against host-tissue, resulting in an “autoimmune-disease”. Given the increasing number of microbes, the increasing number of exposures, and the increasing number and fraction of susceptible/predisposed humans, it is obvious that infectious diseases will increase in the future. Vaccines and vaccination may help solve this problem.

scholarly journals Inflammation Drives Alternative First Exon usage to Regulate Immune Genes including a Novel Iron Regulated Isoform of Aim2

2020 ◽  
Author(s):  
Elektra K. Robinson ◽  
Pratibha Jagannatha ◽  
Sergio Covarrubias ◽  
Matthew Cattle ◽  
Rojin Safavi ◽  
...  
Keyword(s):  
Immune Response ◽  
Gene Regulation ◽  
Innate Immune Response ◽  
Translational Regulation ◽  
Mrna Translation ◽  
Innate Immune ◽  
Protein Coding ◽  
Immune Genes ◽  
Transcription Start Sites ◽  
Exon Usage

AbstractDetermining the layers of gene regulation within the innate immune response is critical to our understanding of the cellular responses to infection and dysregulation in disease. We identified a conserved mechanism of gene regulation in human and mouse via changes in alternative first exon (AFE) usage following inflammation, resulting in changes to isoform usage. Of these AFE events, we identified 50 unannotated transcription start sites (TSS) in mice using Oxford Nanopore native RNA sequencing, one of which is the cytosolic receptor for dsDNA and known inflammatory inducible gene, Aim2. We show that this unannotated AFE isoform of Aim2 is the predominant isoform transcribed during inflammation and contains an iron-responsive element in its 5′UTR enabling mRNA translation to be regulated by iron levels. This work highlights the importance of examining alternative isoform changes and translational regulation in the innate immune response and uncovers novel regulatory mechanisms of Aim2.Summary SentenceAlternative first exon usage was the major splicing event observed in macrophages during inflammation, which resulted in the elucidation of a novel isoform and iron mediated regulatory mechanism of the protein coding gene, Aim2.

scholarly journals The open targets post-GWAS analysis pipeline

2020 ◽  
Vol 36 (9) ◽  
pp. 2936-2937 ◽  
Author(s):  
Gareth Peat ◽  
William Jones ◽  
Michael Nuhn ◽  
José Carlos Marugán ◽  
William Newell ◽  
...  
Keyword(s):  
Drug Targets ◽  
Gene Expression Regulation ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Protein Coding ◽  
Data Resource ◽  
Coding Regions ◽  
Genome Wide ◽  
Causal Genes ◽  
Interactive Data

Abstract Motivation Genome-wide association studies (GWAS) are a powerful method to detect even weak associations between variants and phenotypes; however, many of the identified associated variants are in non-coding regions, and presumably influence gene expression regulation. Identifying potential drug targets, i.e. causal protein-coding genes, therefore, requires crossing the genetics results with functional data. Results We present a novel data integration pipeline that analyses GWAS results in the light of experimental epigenetic and cis-regulatory datasets, such as ChIP-Seq, Promoter-Capture Hi-C or eQTL, and presents them in a single report, which can be used for inferring likely causal genes. This pipeline was then fed into an interactive data resource. Availability and implementation The analysis code is available at www.github.com/Ensembl/postgap and the interactive data browser at postgwas.opentargets.io.

scholarly journals Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Sarabjot Pabla ◽  
R. J. Seager ◽  
Erik Van Roey ◽  
Shuang Gao ◽  
Carrie Hoefer ◽  
...  
Keyword(s):  
Immune Response ◽  
Cell Proliferation ◽  
Cell Activation ◽  
Response Rate ◽  
Checkpoint Inhibitors ◽  
Host Immune Response ◽  
Gene Signature ◽  
B Cell Activation ◽  
Rna Seq ◽  
Multiple Tumor

Abstract Background Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). Methods A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Results Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. Conclusions TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice.

scholarly journals A novel gene signature based on five immune checkpoint genes predicts the survival of glioma

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Wei Zhang ◽  
You Zhai ◽  
Guanzhang Li ◽  
Tao Jiang
Keyword(s):  
Gene Expression ◽  
Central Nervous System ◽  
Immune Response ◽  
Nervous System ◽  
Cox Regression ◽  
Gene Signature ◽  
Immune Checkpoints ◽  
The Novel ◽  
The Central Nervous System ◽  
Checkpoint Genes

Abstract Background Glioma is the most common and fatal type of nerve neoplasm in the central nervous system. Several biomarkers have been considered for prognosis prediction, which is not accurate enough. We aimed to carry out a gene signature related to the expression of immune checkpoints which was enough for its performance in prediction. Methods Gene expression of immune checkpoints in TGGA database was filtrated. The 5 selected genes underwent verification by COX and Lasso-COX regression. Next, the selected genes were included to build a novel signature for further analysis. Results Patients were sub-grouped into high and low risk according to the novel signature. Immune response, clinicopathologic characters, and survival showed significant differences between those 2 groups. Terms including “naive,” “effector,” and “IL-4” were screened out by GSEA. The results showed strong relevance between the signature and immune response. Conclusions We constructed a gene signature with 5 immune checkpoints. The signature predicted survival effectively. The novel signature performed more functional than previous biomarkers.

scholarly journals Promoter Methylation of PRKCB, ADAMTS12, and NAALAD2 Is Specific to Prostate Cancer and Predicts Biochemical Disease Recurrence

2021 ◽  
Vol 22 (11) ◽  
pp. 6091
Author(s):  
Kristina Daniunaite ◽  
Arnas Bakavicius ◽  
Kristina Zukauskaite ◽  
Ieva Rauluseviciute ◽  
Juozas Rimantas Lazutka ◽  
...  
Keyword(s):  
Prostate Cancer ◽  
Clinical Practice ◽  
Promoter Methylation ◽  
Disease Recurrence ◽  
The Cancer Genome Atlas ◽  
Cancer Dataset ◽  
Protein Coding ◽  
Diagnosis And Prognosis ◽  
Genome Wide ◽  
Cancer Genome Atlas

The molecular diversity of prostate cancer (PCa) has been demonstrated by recent genome-wide studies, proposing a significant number of different molecular markers. However, only a few of them have been transferred into clinical practice so far. The present study aimed to identify and validate novel DNA methylation biomarkers for PCa diagnosis and prognosis. Microarray-based methylome data of well-characterized cancerous and noncancerous prostate tissue (NPT) pairs was used for the initial screening. Ten protein-coding genes were selected for validation in a set of 151 PCa, 51 NPT, as well as 17 benign prostatic hyperplasia samples. The Prostate Cancer Dataset (PRAD) of The Cancer Genome Atlas (TCGA) was utilized for independent validation of our findings. Methylation frequencies of ADAMTS12, CCDC181, FILIP1L, NAALAD2, PRKCB, and ZMIZ1 were up to 91% in our study. PCa specific methylation of ADAMTS12, CCDC181, NAALAD2, and PRKCB was demonstrated by qualitative and quantitative means (all p < 0.05). In agreement with PRAD, promoter methylation of these four genes was associated with the transcript down-regulation in the Lithuanian cohort (all p < 0.05). Methylation of ADAMTS12, NAALAD2, and PRKCB was independently predictive for biochemical disease recurrence, while NAALAD2 and PRKCB increased the prognostic power of multivariate models (all p < 0.01). The present study identified methylation of ADAMTS12, NAALAD2, and PRKCB as novel diagnostic and prognostic PCa biomarkers that might guide treatment decisions in clinical practice.

scholarly journals 521. Similarities and Differences in Transcriptomic Host Response between SARS-CoV-2 and Other Viral Infections

2020 ◽  
Vol 7 (Supplement_1) ◽  
pp. S326-S327
Author(s):  
Simone A Thair ◽  
Yudong He ◽  
Yehudit Hasin-Brumshtein ◽  
Suraj Sakaram ◽  
Rushika R Pandya ◽  
...  
Keyword(s):  
Immune Response ◽  
B Cells ◽  
Nk Cells ◽  
Pathway Analysis ◽  
Host Response ◽  
Viral Infections ◽  
Cell Types ◽  
Gene Signature ◽  
Gene Signatures ◽  
Similarities And Differences

Abstract Background COVID-19 is a pandemic caused by the SARS-CoV-2 virus that shares and differs in clinical characteristics of known viral infections. Methods We obtained RNAseq profiles of 62 prospectively enrolled COVID-19 patients and 24 healthy controls (HC). We collected 23 independent studies profiling 1,855 blood samples from patients covering six viruses (influenza, RSV, HRV, Ebola, Dengue and SARS-CoV-1). We studied host whole-blood transcriptomic responses in COVID-19 compared to non-COVID-19 viral infections to understand similarities and differences in host response. Gene signature threshold was absolute effect size ≥1, FDR ≤ 0.05%. Results Differential gene expression of COVID-19 vs HC are highly correlated with non-COVID-19 vs HC (r=0.74, p&lt; 0.001). We discovered two gene signatures: COVID-19 vs HC (2002 genes) (COVIDsig) and non-COVID-19 vs HC (635 genes) (nonCOVIDsig). Pathway analysis of over-expressed signature genes in COVIDsig or nonCOVIDsig identified similar pathways including neutrophil activation, innate immune response, immune response to viral infection and cytokine production. Conversely, for under-expressed genes, pathways indicated repression of lymphocyte differentiation and activation (Fig1). Intersecting the two gene signatures found two genes significantly oppositely regulated (ACO1, ATL3). We derived a third gene signature using COCONUT to compare COVID-19 to non-COVID-19 viral infections (416 genes) (Fig2). Pathway analysis did not result in significant enrichment, suggesting identification of novel biology (Fig1). Statistical deconvolution of bulk transcriptomic data found M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells and total B cells changed in the same direction across COVID-19 and non-COVID-19 infections. Cell types that increased in COVID-19 relative to non-COVID-19 were CD56bright NK cells, M2 macrophages and total NK cells. Those that decreased in non-COVID-19 relative to COVID-19 were CD56dim NK cells & memory B cells and eosinophils (Fig3). Figure 1 Figure 2 Figure 3 Conclusion The concordant and discordant responses mapped here provide a window to explore the pathophysiology of COVID-19 vs other viral infections and show clear differences in signaling pathways and cellularity as part of the host response to SARS-CoV-2. Disclosures Simone A. Thair, PhD, Inflammatix, Inc. (Employee, Shareholder) Yudong He, PhD, Inflammatix Inc. (Employee) Yehudit Hasin-Brumshtein, PhD, Inflammatix (Employee, Shareholder) Suraj Sakaram, MS in Biochemistry and Molecular Biology, Inflammatix (Employee, Other Financial or Material Support, stock options) Rushika R. Pandya, MS, Inflammatix Inc. (Employee, Shareholder) David C. Rawling, PhD, Inflammatix Inc. (Employee, Shareholder) Purvesh Khatri, PhD, Inflammatix Inc. (Shareholder) Timothy Sweeney, MD, PHD, Inflammatix, Inc. (Employee, Shareholder)

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