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Suryadevara R, Gregory A, Lu R, Xu Z, Masoomi A, Lutz SM, Berman S, Yun JH, Saferali A, Ryu MH, Moll M, Sin DD, Hersh CP, Silverman EK, Dy J, Pratte KA, Bowler RP, Castaldi PJ, Boueiz A. Blood-based Transcriptomic and Proteomic Biomarkers of Emphysema. Am J Respir Crit Care Med 2024; 209:273-287. [PMID: 37917913 PMCID: PMC10840768 DOI: 10.1164/rccm.202301-0067oc] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Accepted: 11/02/2023] [Indexed: 11/04/2023] Open
Abstract
Rationale: Emphysema is a chronic obstructive pulmonary disease phenotype with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and development of targeted therapies. Objectives: To discover blood omics biomarkers for chest computed tomography-quantified emphysema and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training sample of 2,370 COPDGene participants with available blood RNA sequencing, plasma proteomics, and clinical data. Internal validation was conducted in a COPDGene testing sample (n = 1,016), and external validation was done in the ECLIPSE study (n = 526). Because low body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Elastic net models with bootstrapping were also developed in the training sample sequentially using clinical, blood cell proportions, RNA-sequencing, and proteomic biomarkers to predict quantitative emphysema. Model accuracy was assessed by the area under the receiver operating characteristic curves for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: Totals of 3,829 genes, 942 isoforms, 260 exons, and 714 proteins were significantly associated with emphysema (false discovery rate, 5%) and yielded 11 biological pathways. Seventy-four percent of these genes and 62% of these proteins showed mediation by BMI. Our prediction models demonstrated reasonable predictive performance in both COPDGene and ECLIPSE. The highest-performing model used clinical, blood cell, and protein data (area under the receiver operating characteristic curve in COPDGene testing, 0.90; 95% confidence interval, 0.85-0.90). Conclusions: Blood transcriptome and proteome-wide analyses revealed key biological pathways of emphysema and enhanced the prediction of emphysema.
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Affiliation(s)
| | | | - Robin Lu
- Channing Division of Network Medicine
| | | | - Aria Masoomi
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | - Sharon M. Lutz
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | | | - Jeong H. Yun
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | | | | | - Matthew Moll
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
- Pulmonary, Critical Care, Allergy, and Sleep Medicine Section, Veterans Affairs Boston Healthcare System, West Roxbury, Massachusetts
| | - Don D. Sin
- Centre for Heart Lung Innovation, St. Paul’s Hospital, Vancouver, British Columbia, Canada
- Respiratory Division, Department of Medicine, University of British Columbia, Vancouver, British Columbia, Canada; and
| | - Craig P. Hersh
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Edwin K. Silverman
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
| | - Jennifer Dy
- Department of Electrical and Computer Engineering, Northeastern University, Boston, Massachusetts
| | | | - Russell P. Bowler
- Division of Pulmonary, Critical Care and Sleep Medicine, National Jewish Health, Denver, Colorado
| | - Peter J. Castaldi
- Channing Division of Network Medicine
- Division of General Medicine and Primary Care, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts
| | - Adel Boueiz
- Channing Division of Network Medicine
- Division of Pulmonary and Critical Care Medicine, and
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2
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Ricker CA, Meli K, Van Allen EM. Historical perspective and future directions: computational science in immuno-oncology. J Immunother Cancer 2024; 12:e008306. [PMID: 38191244 PMCID: PMC10826578 DOI: 10.1136/jitc-2023-008306] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/07/2023] [Indexed: 01/10/2024] Open
Abstract
Immuno-oncology holds promise for transforming patient care having achieved durable clinical response rates across a variety of advanced and metastatic cancers. Despite these achievements, only a minority of patients respond to immunotherapy, underscoring the importance of elucidating molecular mechanisms responsible for response and resistance to inform the development and selection of treatments. Breakthroughs in molecular sequencing technologies have led to the generation of an immense amount of genomic and transcriptomic sequencing data that can be mined to uncover complex tumor-immune interactions using computational tools. In this review, we discuss existing and emerging computational methods that contextualize the composition and functional state of the tumor microenvironment, infer the reactivity and clonal dynamics from reconstructed immune cell receptor repertoires, and predict the antigenic landscape for immune cell recognition. We further describe the advantage of multi-omics analyses for capturing multidimensional relationships and artificial intelligence techniques for integrating omics data with histopathological and radiological images to encapsulate patterns of treatment response and tumor-immune biology. Finally, we discuss key challenges impeding their widespread use and clinical application and conclude with future perspectives. We are hopeful that this review will both serve as a guide for prospective researchers seeking to use existing tools for scientific discoveries and inspire the optimization or development of novel tools to enhance precision, ultimately expediting advancements in immunotherapy that improve patient survival and quality of life.
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Affiliation(s)
- Cora A Ricker
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
| | - Kevin Meli
- Medical Oncology, Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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3
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Blengio F, Hocini H, Richert L, Lefebvre C, Durand M, Hejblum B, Tisserand P, McLean C, Luhn K, Thiebaut R, Levy Y. Identification of early gene expression profiles associated with long-lasting antibody responses to the Ebola vaccine Ad26.ZEBOV/MVA-BN-Filo. Cell Rep 2023; 42:113101. [PMID: 37691146 DOI: 10.1016/j.celrep.2023.113101] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2023] [Revised: 07/24/2023] [Accepted: 08/21/2023] [Indexed: 09/12/2023] Open
Abstract
Ebola virus disease is a severe hemorrhagic fever with a high fatality rate. We investigate transcriptome profiles at 3 h, 1 day, and 7 days after vaccination with Ad26.ZEBOV and MVA-BN-Filo. 3 h after Ad26.ZEBOV injection, we observe an increase in genes related to antigen presentation, sensing, and T and B cell receptors. The highest response occurs 1 day after Ad26.ZEBOV injection, with an increase of the gene expression of interferon-induced antiviral molecules, monocyte activation, and sensing receptors. This response is regulated by the HESX1, ATF3, ANKRD22, and ETV7 transcription factors. A plasma cell signature is observed on day 7 post-Ad26.ZEBOV vaccination, with an increase of CD138, MZB1, CD38, CD79A, and immunoglobulin genes. We have identified early expressed genes correlated with the magnitude of the antibody response 21 days after the MVA-BN-Filo and 364 days after Ad26.ZEBOV vaccinations. Our results provide early gene signatures that correlate with vaccine-induced Ebola virus glycoprotein-specific antibodies.
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Affiliation(s)
- Fabiola Blengio
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Hakim Hocini
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Laura Richert
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France; University Bordeaux, Department of Public Health, INSERM Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, Bordeaux, France; CHU de Bordeaux, Pôle de Santé Publique, Service d'Information Médicale, Bordeaux, France
| | - Cécile Lefebvre
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Mélany Durand
- University Bordeaux, Department of Public Health, INSERM Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, Bordeaux, France; CHU de Bordeaux, Pôle de Santé Publique, Service d'Information Médicale, Bordeaux, France
| | - Boris Hejblum
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France; University Bordeaux, Department of Public Health, INSERM Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, Bordeaux, France; CHU de Bordeaux, Pôle de Santé Publique, Service d'Information Médicale, Bordeaux, France
| | - Pascaline Tisserand
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Chelsea McLean
- Janssen Vaccines & Prevention, B.V. Archimediesweg, Leiden, the Netherlands
| | - Kerstin Luhn
- Janssen Vaccines & Prevention, B.V. Archimediesweg, Leiden, the Netherlands
| | - Rodolphe Thiebaut
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France; University Bordeaux, Department of Public Health, INSERM Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, Bordeaux, France; CHU de Bordeaux, Pôle de Santé Publique, Service d'Information Médicale, Bordeaux, France.
| | - Yves Levy
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France; Assistance Publique-Hôpitaux de Paris, Groupe Henri-Mondor Albert-Chenevier, Service Immunologie Clinique, Créteil, France.
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4
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Hocini H, Wiedemann A, Blengio F, Lefebvre C, Cervantes-Gonzalez M, Foucat E, Tisserand P, Surenaud M, Coléon S, Prague M, Guillaumat L, Krief C, Fenwick C, Laouénan C, Bouadma L, Ghosn J, Pantaleo G, Thiébaut R, Lévy Y. Neutrophil Activation and Immune Thrombosis Profiles Persist in Convalescent COVID-19. J Clin Immunol 2023; 43:882-893. [PMID: 36943669 PMCID: PMC10029801 DOI: 10.1007/s10875-023-01459-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2022] [Accepted: 02/24/2023] [Indexed: 03/23/2023]
Abstract
PURPOSE Following a severe COVID-19 infection, a proportion of individuals develop prolonged symptoms. We investigated the immunological dysfunction that underlies the persistence of symptoms months after the resolution of acute COVID-19. METHODS We analyzed cytokines, cell phenotypes, SARS-CoV-2 spike-specific and neutralizing antibodies, and whole blood gene expression profiles in convalescent severe COVID-19 patients 1, 3, and 6 months following hospital discharge. RESULTS We observed persistent abnormalities until month 6 marked by (i) high serum levels of monocyte/macrophage and endothelial activation markers, chemotaxis, and hematopoietic cytokines; (ii) a high frequency of central memory CD4+ and effector CD8+ T cells; (iii) a decrease in anti-SARS-CoV-2 spike and neutralizing antibodies; and (iv) an upregulation of genes related to platelet, neutrophil activation, erythrocytes, myeloid cell differentiation, and RUNX1 signaling. We identified a "core gene signature" associated with a history of thrombotic events, with upregulation of a set of genes involved in neutrophil activation, platelet, hematopoiesis, and blood coagulation. CONCLUSION The lack of restoration of gene expression to a normal profile after up to 6 months of follow-up, even in asymptomatic patients who experienced severe COVID-19, signals the need to carefully extend their clinical follow-up and propose preventive measures.
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Affiliation(s)
- Hakim Hocini
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Aurélie Wiedemann
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Fabiola Blengio
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Cécile Lefebvre
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Minerva Cervantes-Gonzalez
- Département Épidémiologie Biostatistiques Et Recherche Clinique, AP-HP, Hôpital Bichat, INSERM, Centre d'Investigation Clinique-Epidémiologie Clinique 1425, 75018, Paris, France
- UMR 1137, Université de Paris, INSERM, IAME, 75018, Paris, France
- APHP- Hôpital Bichat - Médecine Intensive et Réanimation des Maladies Infectieuses, Paris, France
| | - Emile Foucat
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Pascaline Tisserand
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Mathieu Surenaud
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Séverin Coléon
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Mélanie Prague
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
- Department of Public Health, Univ. Bordeaux, Inserm Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, Bordeaux, France
| | - Lydia Guillaumat
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Corinne Krief
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
| | - Craig Fenwick
- Service of Immunology and Allergy, Department of Medicine, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cédric Laouénan
- Département Épidémiologie Biostatistiques Et Recherche Clinique, AP-HP, Hôpital Bichat, INSERM, Centre d'Investigation Clinique-Epidémiologie Clinique 1425, 75018, Paris, France
- UMR 1137, Université de Paris, INSERM, IAME, 75018, Paris, France
| | - Lila Bouadma
- UMR 1137, Université de Paris, INSERM, IAME, 75018, Paris, France
- APHP- Hôpital Bichat - Médecine Intensive et Réanimation des Maladies Infectieuses, Paris, France
| | - Jade Ghosn
- UMR 1137, Université de Paris, INSERM, IAME, 75018, Paris, France
- AP-HP, Hôpital Bichat, Service de Maladies Infectieuses Et Tropicales, 75018, Paris, France
| | - Giuseppe Pantaleo
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
- Swiss Vaccine Research Institute, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Rodolphe Thiébaut
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France
- Department of Public Health, Univ. Bordeaux, Inserm Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, Bordeaux, France
- CHU de Bordeaux, Pôle de Santé Publique, Service d'Information Médicale, Bordeaux, France
| | - Yves Lévy
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Créteil, France.
- Assistance Publique-Hôpitaux de Paris, Service Immunologie Clinique, Groupe Henri-Mondor Albert-Chenevier, Créteil, France.
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5
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Munsch G, Proust C, Labrouche-Colomer S, Aïssi D, Boland A, Morange PE, Roche A, de Chaisemartin L, Harroche A, Olaso R, Deleuze JF, James C, Emmerich J, Smadja DM, Jacqmin-Gadda H, Trégouët DA. Genome-wide association study of a semicontinuous trait: illustration of the impact of the modeling strategy through the study of Neutrophil Extracellular Traps levels. NAR Genom Bioinform 2023; 5:lqad062. [PMID: 37388819 PMCID: PMC10304785 DOI: 10.1093/nargab/lqad062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2023] [Revised: 05/10/2023] [Accepted: 06/07/2023] [Indexed: 07/01/2023] Open
Abstract
Over the last years, there has been a considerable expansion of genome-wide association studies (GWAS) for discovering biological pathways underlying pathological conditions or disease biomarkers. These GWAS are often limited to binary or quantitative traits analyzed through linear or logistic models, respectively. In some situations, the distribution of the outcome may require more complex modeling, such as when the outcome exhibits a semicontinuous distribution characterized by an excess of zero values followed by a non-negative and right-skewed distribution. We here investigate three different modeling for semicontinuous data: Tobit, Negative Binomial and Compound Poisson-Gamma. Using both simulated data and a real GWAS on Neutrophil Extracellular Traps (NETs), an emerging biomarker in immuno-thrombosis, we demonstrate that Compound Poisson-Gamma was the most robust model with respect to low allele frequencies and outliers. This model further identified the MIR155HG locus as significantly (P = 1.4 × 10-8) associated with NETs plasma levels in a sample of 657 participants, a locus recently highlighted to be involved in NETs formation in mice. This work highlights the importance of the modeling strategy for GWAS of a semicontinuous outcome and suggests Compound Poisson-Gamma as an elegant but neglected alternative to Negative Binomial for modeling semicontinuous outcome in the context of genomic investigations.
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Affiliation(s)
| | - Carole Proust
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000 Bordeaux, France
| | - Sylvie Labrouche-Colomer
- UMR1034, Inserm, Biology of Cardiovascular Diseases, University of Bordeaux, Pessac, France
- Laboratoire d’Hématologie, CHU de Bordeaux, Pessac, France
| | - Dylan Aïssi
- Univ. Bordeaux, Inserm, Bordeaux Population Health Research Center, UMR 1219, F-33000 Bordeaux, France
| | - Anne Boland
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057 Evry, France
| | - Pierre-Emmanuel Morange
- Cardiovascular and Nutrition Research Center (C2VN), INSERM, INRAE, Aix-Marseille University, Marseille, France
| | - Anne Roche
- Service pneumologie hôpital Bicêtre, France
| | - Luc de Chaisemartin
- Service Auto-immunité, Hypersensibilité et Biothérapies, Hôpital Bichat, Assistance Publique-Hôpitaux de Paris, Paris, France
- Université Paris-Saclay, INSERM, Inflammation, Microbiome, Immunosurveillance, Orsay, France
| | - Annie Harroche
- Service d’Hématologie Clinique Centre de Traitement de l’Hémophilie Hôpital Necker Enfants Malades, France
| | - Robert Olaso
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057 Evry, France
- Centre d’Etude du Polymorphisme Humain, Fondation Jean Dausset, Paris, France
| | - Jean-François Deleuze
- Université Paris-Saclay, CEA, Centre National de Recherche en Génomique Humaine (CNRGH), 91057 Evry, France
- Centre d’Etude du Polymorphisme Humain, Fondation Jean Dausset, Paris, France
| | - Chloé James
- UMR1034, Inserm, Biology of Cardiovascular Diseases, University of Bordeaux, Pessac, France
- Laboratoire d’Hématologie, CHU de Bordeaux, Pessac, France
| | - Joseph Emmerich
- Department of vascular medicine, Paris Saint-Joseph Hospital Group, University of Paris, UMR1153, INSERM, CRESS, 185 rue Raymond Losserand, Cité, 75674, France
| | - David M Smadja
- Innovative Therapies in Hemostasis, Université de Paris, INSERM, F-75006 Paris, France
- Hematology Department and Biosurgical Research Lab (Carpentier Foundation), Assistance Publique Hôpitaux de Paris, Centre-Université de Paris (APHP-CUP), F-75015 Paris, France
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6
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Dill-McFarland KA, Mitchell K, Batchu S, Segnitz RM, Benson B, Janczyk T, Cox MS, Mayanja-Kizza H, Boom WH, Benchek P, Stein CM, Hawn TR, Altman MC. Kimma: flexible linear mixed effects modeling with kinship covariance for RNA-seq data. Bioinformatics 2023; 39:btad279. [PMID: 37140544 PMCID: PMC10182851 DOI: 10.1093/bioinformatics/btad279] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 05/05/2023] Open
Abstract
MOTIVATION The identification of differentially expressed genes (DEGs) from transcriptomic datasets is a major avenue of research across diverse disciplines. However, current bioinformatic tools do not support covariance matrices in DEG modeling. Here, we introduce kimma (Kinship In Mixed Model Analysis), an open-source R package for flexible linear mixed effects modeling including covariates, weights, random effects, covariance matrices, and fit metrics. RESULTS In simulated datasets, kimma detects DEGs with similar specificity, sensitivity, and computational time as limma unpaired and dream paired models. Unlike other software, kimma supports covariance matrices as well as fit metrics like Akaike information criterion (AIC). Utilizing genetic kinship covariance, kimma revealed that kinship impacts model fit and DEG detection in a related cohort. Thus, kimma equals or outcompetes current DEG pipelines in sensitivity, computational time, and model complexity. AVAILABILITY AND IMPLEMENTATION Kimma is freely available on GitHub https://github.com/BIGslu/kimma with an instructional vignette at https://bigslu.github.io/kimma_vignette/kimma_vignette.html.
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Affiliation(s)
- Kimberly A Dill-McFarland
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
| | - Kiana Mitchell
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
- Department of Biology, University of California San Diego, 9500 Gilman Dr, La Jolla, CA 92093, United States
| | - Sashank Batchu
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
| | - Richard Max Segnitz
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
| | - Basilin Benson
- Systems Immunology Division, Benaroya Research Institute, 1201 Ninth Avenue, Seattle, CA 98101, United States
| | - Tomasz Janczyk
- Systems Immunology Division, Benaroya Research Institute, 1201 Ninth Avenue, Seattle, CA 98101, United States
| | - Madison S Cox
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
| | - Harriet Mayanja-Kizza
- Department of Medicine, School of Medicine, Makerere University, PO Box 7072, Kampala, Uganda
| | - William Henry Boom
- Department of Medicine, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Penelope Benchek
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Catherine M Stein
- Department of Population & Quantitative Health Sciences, Case Western Reserve University, 10900 Euclid Ave, Cleveland, OH 44106, United States
| | - Thomas R Hawn
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
| | - Matthew C Altman
- Division of Allergy and Infectious Diseases, Department of Medicine, University of Washington, 750 Republican St, Seattle, WA 98109, United States
- Systems Immunology Division, Benaroya Research Institute, 1201 Ninth Avenue, Seattle, CA 98101, United States
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7
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Calgaro M, Romualdi C, Risso D, Vitulo N. benchdamic: benchmarking of differential abundance methods for microbiome data. Bioinformatics 2023; 39:6881076. [PMID: 36477500 PMCID: PMC9825737 DOI: 10.1093/bioinformatics/btac778] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2022] [Revised: 11/21/2022] [Accepted: 12/06/2022] [Indexed: 12/13/2022] Open
Abstract
SUMMARY Recently, an increasing number of methodological approaches have been proposed to tackle the complexity of metagenomics and microbiome data. In this scenario, reproducibility and replicability have become two critical issues, and the development of computational frameworks for the comparative evaluations of such methods is of utmost importance. Here, we present benchdamic, a Bioconductor package to benchmark methods for the identification of differentially abundant taxa. AVAILABILITY AND IMPLEMENTATION benchdamic is available as an open-source R package through the Bioconductor project at https://bioconductor.org/packages/benchdamic/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Matteo Calgaro
- Department of Biotechnology, University of Verona, Verona 37134, Italy
| | - Chiara Romualdi
- Department of Biology, University of Padova, Padova 35131, Italy
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8
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Richert L, Lelièvre JD, Lacabaratz C, Hardel L, Hocini H, Wiedemann A, Lucht F, Poizot-Martin I, Bauduin C, Diallo A, Rieux V, Rouch E, Surenaud M, Lefebvre C, Foucat E, Tisserand P, Guillaumat L, Durand M, Hejblum B, Launay O, Thiébaut R, Lévy Y. T Cell Immunogenicity, Gene Expression Profile, and Safety of Four Heterologous Prime-Boost Combinations of HIV Vaccine Candidates in Healthy Volunteers: Results of the Randomized Multi-Arm Phase I/II ANRS VRI01 Trial. JOURNAL OF IMMUNOLOGY (BALTIMORE, MD. : 1950) 2022; 208:2663-2674. [PMID: 35613727 DOI: 10.4049/jimmunol.2101076] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Accepted: 04/03/2022] [Indexed: 06/15/2023]
Abstract
Heterologous prime-boost strategies are of interest for HIV vaccine development. The order of prime-boost components could be important for the induction of T cell responses. In this phase I/II multi-arm trial, three vaccine candidates were used as prime or boost: modified vaccinia Ankara (MVA) HIV-B (coding for Gag, Pol, Nef); HIV LIPO-5 (five lipopeptides from Gag, Pol, Nef); DNA GTU-MultiHIV B (coding for Rev, Nef, Tat, Gag, Env gp160 clade B). Healthy human volunteers (n = 92) were randomized to four groups: 1) MVA at weeks 0/8 + LIPO-5 at weeks 20/28 (M/L); 2) LIPO-5 at weeks 0/8 + MVA at weeks 20/28 (L/M); 3) DNA at weeks 0/4/12 + LIPO-5 at weeks 20/28 (G/L); 4) DNA at weeks 0/4/12 + MVA at weeks 20/28 (G/M). The frequency of IFN-γ-ELISPOT responders at week 30 was 33, 43, 0, and 74%, respectively. Only MVA-receiving groups were further analyzed (n = 62). Frequency of HIV-specific cytokine-positive (IFN-γ, IL-2, or TNF-α) CD4+ T cells increased significantly from week 0 to week 30 (median change of 0.06, 0.11, and 0.10% for M/L, L/M, and G/M, respectively), mainly after MVA vaccinations, and was sustained until week 52. HIV-specific CD8+ T cell responses increased significantly at week 30 in M/L and G/M (median change of 0.02 and 0.05%). Significant whole-blood gene expression changes were observed 2 wk after the first MVA injection, regardless of its use as prime or boost. An MVA gene signature was identified, including 86 genes mainly related to cell cycle pathways. Three prime-boost strategies led to CD4+ and CD8+ T cell responses and to a whole-blood gene expression signature primarily due to their MVA HIV-B component.
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Affiliation(s)
- Laura Richert
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Inria SISTM Team, Talence, France
- CHU de Bordeaux, Service d'Information Médicale, Bordeaux, France
- Vaccine Research Institute, Créteil, France
| | - Jean-Daniel Lelièvre
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
- Groupe Henri-Mondor Albert-Chenevier, AP-HP, Créteil, France
| | - Christine Lacabaratz
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Lucile Hardel
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Vaccine Research Institute, Créteil, France
| | - Hakim Hocini
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Aurélie Wiedemann
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Frédéric Lucht
- CHU de Saint Etienne, Saint-Priest-en-Jarez, France
- Université Jean Monnet and Université de Lyon, Saint-Etienne, France
| | - Isabelle Poizot-Martin
- Aix-Marseille Université, APHM, INSERM, IRD, SESSTIM, Sciences Economiques & Sociales de la Santé & Traitement de l'Information Médicale, ISSPAM, APHM Sainte-Marguerite, Service d'Immuno-Hématologie Clinique, Marseille, France
| | - Claire Bauduin
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Vaccine Research Institute, Créteil, France
| | | | - Véronique Rieux
- Vaccine Research Institute, Créteil, France
- INSERM-ANRS, Paris, France
| | - Elodie Rouch
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Vaccine Research Institute, Créteil, France
| | - Mathieu Surenaud
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Cécile Lefebvre
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Emile Foucat
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Pascaline Tisserand
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Lydia Guillaumat
- Vaccine Research Institute, Créteil, France
- INSERM U955, Université Paris-Est Créteil, Créteil, France
| | - Mélany Durand
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Inria SISTM Team, Talence, France
- Vaccine Research Institute, Créteil, France
| | - Boris Hejblum
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Inria SISTM Team, Talence, France
- Vaccine Research Institute, Créteil, France
| | - Odile Launay
- CIC 1417 F-CRIN I-REIVAC, INSERM, Hôpital Cochin, AP-HP, Paris, France; and
- Université Paris Descartes, Paris, France
| | - Rodolphe Thiébaut
- University of Bordeaux, INSERM, Bordeaux Population Health Research Center, UMR1219, Bordeaux, France
- Inria SISTM Team, Talence, France
- CHU de Bordeaux, Service d'Information Médicale, Bordeaux, France
- Vaccine Research Institute, Créteil, France
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9
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Li Y, Ge X, Peng F, Li W, Li JJ. Exaggerated false positives by popular differential expression methods when analyzing human population samples. Genome Biol 2022; 23:79. [PMID: 35292087 PMCID: PMC8922736 DOI: 10.1186/s13059-022-02648-4] [Citation(s) in RCA: 86] [Impact Index Per Article: 43.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Accepted: 03/07/2022] [Indexed: 12/05/2022] Open
Abstract
When identifying differentially expressed genes between two conditions using human population RNA-seq samples, we found a phenomenon by permutation analysis: two popular bioinformatics methods, DESeq2 and edgeR, have unexpectedly high false discovery rates. Expanding the analysis to limma-voom, NOISeq, dearseq, and Wilcoxon rank-sum test, we found that FDR control is often failed except for the Wilcoxon rank-sum test. Particularly, the actual FDRs of DESeq2 and edgeR sometimes exceed 20% when the target FDR is 5%. Based on these results, for population-level RNA-seq studies with large sample sizes, we recommend the Wilcoxon rank-sum test.
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Affiliation(s)
- Yumei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA
| | - Xinzhou Ge
- Department of Statistics, University of California, Los Angeles, CA, 90095, USA
| | - Fanglue Peng
- Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, TX, 77030, USA
| | - Wei Li
- Division of Computational Biomedicine, Department of Biological Chemistry, School of Medicine, University of California, Irvine, Irvine, CA, 92697, USA.
| | - Jingyi Jessica Li
- Department of Statistics, University of California, Los Angeles, CA, 90095, USA. .,Interdepartmental Program in Bioinformatics, University of California, Los Angeles, CA, 90095, USA. .,Department of Human Genetics, University of California, Los Angeles, CA, 90095, USA. .,Department of Computational Medicine, University of California, Los Angeles, CA, 90095, USA. .,Department of Biostatistics, University of California, Los Angeles, CA, 90095, USA.
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10
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Lévy Y, Wiedemann A, Hejblum BP, Durand M, Lefebvre C, Surénaud M, Lacabaratz C, Perreau M, Foucat E, Déchenaud M, Tisserand P, Blengio F, Hivert B, Gauthier M, Cervantes-Gonzalez M, Bachelet D, Laouénan C, Bouadma L, Timsit JF, Yazdanpanah Y, Pantaleo G, Hocini H, Thiébaut R. CD177, a specific marker of neutrophil activation, is associated with coronavirus disease 2019 severity and death. iScience 2021; 24:102711. [PMID: 34127958 PMCID: PMC8189740 DOI: 10.1016/j.isci.2021.102711] [Citation(s) in RCA: 73] [Impact Index Per Article: 24.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/26/2021] [Accepted: 06/08/2021] [Indexed: 01/03/2023] Open
Abstract
The identification of patients with coronavirus disease 2019 and high risk of severe disease is a challenge in routine care. We performed cell phenotypic, serum, and RNA sequencing gene expression analyses in severe hospitalized patients (n = 61). Relative to healthy donors, results showed abnormalities of 27 cell populations and an elevation of 42 cytokines, neutrophil chemo-attractants, and inflammatory components in patients. Supervised and unsupervised analyses revealed a high abundance of CD177, a specific neutrophil activation marker, contributing to the clustering of severe patients. Gene abundance correlated with high serum levels of CD177 in severe patients. Higher levels were confirmed in a second cohort and in intensive care unit (ICU) than non-ICU patients (P < 0.001). Longitudinal measurements discriminated between patients with the worst prognosis, leading to death, and those who recovered (P = 0.01). These results highlight neutrophil activation as a hallmark of severe disease and CD177 assessment as a reliable prognostic marker for routine care.
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Affiliation(s)
- Yves Lévy
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France,Assistance Publique-Hôpitaux de Paris, Groupe Henri-Mondor Albert-Chenevier, Service Immunologie Clinique, Créteil, France,Corresponding author
| | - Aurélie Wiedemann
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Boris P. Hejblum
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France,Univ. Bordeaux, Department of Public Health, INSERM U1219 Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, 146 Rue Leo Saignat, 33076 Bordeaux, France
| | - Mélany Durand
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France,Univ. Bordeaux, Department of Public Health, INSERM U1219 Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, 146 Rue Leo Saignat, 33076 Bordeaux, France
| | - Cécile Lefebvre
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Mathieu Surénaud
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Christine Lacabaratz
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Matthieu Perreau
- Swiss Vaccine Research Institute, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Emile Foucat
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Marie Déchenaud
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Pascaline Tisserand
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Fabiola Blengio
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Benjamin Hivert
- Univ. Bordeaux, Department of Public Health, INSERM U1219 Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, 146 Rue Leo Saignat, 33076 Bordeaux, France
| | - Marine Gauthier
- Univ. Bordeaux, Department of Public Health, INSERM U1219 Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, 146 Rue Leo Saignat, 33076 Bordeaux, France
| | - Minerva Cervantes-Gonzalez
- AP-HP, Hôpital Bichat, Département Épidémiologie Biostatistiques et Recherche Clinique, INSERM, Centre d’Investigation clinique-Epidémiologie Clinique 1425, F-75018 Paris, France,AP-HP, Hôpital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France,Université de Paris, INSERM, IAME UMR 1137, F-75018 Paris, France
| | - Delphine Bachelet
- AP-HP, Hôpital Bichat, Département Épidémiologie Biostatistiques et Recherche Clinique, INSERM, Centre d’Investigation clinique-Epidémiologie Clinique 1425, F-75018 Paris, France,Université de Paris, INSERM, IAME UMR 1137, F-75018 Paris, France
| | - Cédric Laouénan
- AP-HP, Hôpital Bichat, Département Épidémiologie Biostatistiques et Recherche Clinique, INSERM, Centre d’Investigation clinique-Epidémiologie Clinique 1425, F-75018 Paris, France,Université de Paris, INSERM, IAME UMR 1137, F-75018 Paris, France
| | - Lila Bouadma
- APHP- Hôpital Bichat – Médecine Intensive et Réanimation des Maladies Infectieuses, Paris, France
| | - Jean-François Timsit
- APHP- Hôpital Bichat – Médecine Intensive et Réanimation des Maladies Infectieuses, Paris, France
| | - Yazdan Yazdanpanah
- AP-HP, Hôpital Bichat, Service de Maladies Infectieuses et Tropicales, F-75018 Paris, France,Université de Paris, INSERM, IAME UMR 1137, F-75018 Paris, France
| | - Giuseppe Pantaleo
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France,Swiss Vaccine Research Institute, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland,Immunology and Allergy Service, Department of Medicine, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland
| | - Hakim Hocini
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France
| | - Rodolphe Thiébaut
- Vaccine Research Institute, Université Paris-Est Créteil, Faculté de Médecine, INSERM U955, Team 16, Hopital Henri Mondor, 51 Av Marechal de Lattre de Tassigny, 94010 Créteil, France,Univ. Bordeaux, Department of Public Health, INSERM U1219 Bordeaux Population Health Research Centre, Inria SISTM, UMR 1219, 146 Rue Leo Saignat, 33076 Bordeaux, France,CHU de Bordeaux, Pôle de Santé Publique, Service d’Information Médicale, Bordeaux, France,Corresponding author
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