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Bettacchioli E, Chiche L, Chaussabel D, Cornec D, Jourde-Chiche N, Rinchai D. An interactive web application for exploring systemic lupus erythematosus blood transcriptomic diversity. Database (Oxford) 2024; 2024:baae045. [PMID: 38805754 PMCID: PMC11131423 DOI: 10.1093/database/baae045] [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: 10/02/2023] [Revised: 04/07/2024] [Accepted: 05/22/2024] [Indexed: 05/30/2024]
Abstract
In the field of complex autoimmune diseases such as systemic lupus erythematosus (SLE), systems immunology approaches have proven invaluable in translational research settings. Large-scale datasets of transcriptome profiling have been collected and made available to the research community in public repositories, but remain poorly accessible and usable by mainstream researchers. Enabling tools and technologies facilitating investigators' interaction with large-scale datasets such as user-friendly web applications could promote data reuse and foster knowledge discovery. Microarray blood transcriptomic data from the LUPUCE cohort (publicly available on Gene Expression Omnibus, GSE49454), which comprised 157 samples from 62 adult SLE patients, were analyzed with the third-generation (BloodGen3) module repertoire framework, which comprises modules and module aggregates. These well-characterized samples corresponded to different levels of disease activity, different types of flares (including biopsy-proven lupus nephritis), different auto-antibody profiles and different levels of interferon signatures. A web application was deployed to present the aggregate-level, module-level and gene-level analysis results from LUPUCE dataset. Users can explore the similarities and heterogeneity of SLE samples, navigate through different levels of analysis, test hypotheses and generate custom fingerprint grids and heatmaps, which may be used in reports or manuscripts. This resource is available via this link: https://immunology-research.shinyapps.io/LUPUCE/. This web application can be employed as a stand-alone resource to explore changes in blood transcript profiles in SLE, and their relation to clinical and immunological parameters, to generate new research hypotheses.
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Affiliation(s)
- Eléonore Bettacchioli
- B Lymphocytes, Autoimmunity and Immunotherapies, UMR 1227, Univ Brest, Inserm, Brest 29200, France
- Brest University Hospital, Brest 29200, France
| | - Laurent Chiche
- Department of Internal Medicine, Hôpital Européen, Marseille 13003, France
| | - Damien Chaussabel
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha 26999, Qatar
- Computational Sciences Department, The Jackson Laboratory, Farmington, CT 06032, USA
| | - Divi Cornec
- B Lymphocytes, Autoimmunity and Immunotherapies, UMR 1227, Univ Brest, Inserm, Brest 29200, France
- Brest University Hospital, Brest 29200, France
| | - Noémie Jourde-Chiche
- Department of Nephrology, AP-HM, Marseille 13003, France
- C2VN, INSERM, INRAE, Aix-Marseille Universite, Marseille 13003, France
| | - Darawan Rinchai
- Translational Medicine Division, Research Branch, Sidra Medicine, Doha 26999, Qatar
- Department of Infectious Diseases, St Jude’s Children Research Hospital, TN, Memphis 38105, USA
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Rinchai D, Brummaier T, A Marr A, Habib T, Toufiq M, Kino T, Nosten F, Al Khodor S, Terranegra A, McGready R, Kabeer BSA, Chaussabel D. A data browsing application for accessing gene and module-level blood transcriptome profiles of healthy pregnant women from high- and low-resource settings. Database (Oxford) 2024; 2024:baae021. [PMID: 38564425 PMCID: PMC10986794 DOI: 10.1093/database/baae021] [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: 05/30/2023] [Revised: 11/08/2023] [Accepted: 03/06/2024] [Indexed: 04/04/2024]
Abstract
Transcriptome profiling data, generated via RNA sequencing, are commonly deposited in public repositories. However, these data may not be easily accessible or usable by many researchers. To enhance data reuse, we present well-annotated, partially analyzed data via a user-friendly web application. This project involved transcriptome profiling of blood samples from 15 healthy pregnant women in a low-resource setting, taken at 6 consecutive time points beginning from the first trimester. Additional blood transcriptome profiles were retrieved from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public repository, representing a cohort of healthy pregnant women from a high-resource setting. We analyzed these datasets using the fixed BloodGen3 module repertoire. We deployed a web application, accessible at https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/which displays the module-level analysis results from both original and public pregnancy blood transcriptome datasets. Users can create custom fingerprint grid and heatmap representations via various navigation options, useful for reports and manuscript preparation. The web application serves as a standalone resource for exploring blood transcript abundance changes during pregnancy. Alternatively, users can integrate it with similar applications developed for earlier publications to analyze transcript abundance changes of a given BloodGen3 signature across a range of disease cohorts. Database URL: https://thejacksonlaboratory.shinyapps.io/BloodGen3_Pregnancy/.
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Affiliation(s)
- Darawan Rinchai
- Research Branch, Sidra Medicine, Al Gharrafa St, Doha 26999, Qatar
- Department of Infectious Diseases, St Jude’s Children Research Hospital, 262 Danny Thomas Pl, Memphis, TN 38105, USA
| | - Tobias Brummaier
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, 78, 1, Mae Ramat 63140, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, New Richards Building, Roosevelt Dr, Oxford OX3 7BN, UK
- Swiss Tropical and Public Health Institute, Basel 4123, Switzerland
- Faculty of Medicine, University of Basel, Basel 4001, Switzerland
| | - Alexandra A Marr
- Research Branch, Sidra Medicine, Al Gharrafa St, Doha 26999, Qatar
| | - Tanwir Habib
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha 24144, Qatar
| | - Mohammed Toufiq
- The Jackson Laboratory for Genomic Medicine, 10, Discovery Dr, Farmington, CT 06032, USA
| | - Tomoshigue Kino
- Research Branch, Sidra Medicine, Al Gharrafa St, Doha 26999, Qatar
| | - François Nosten
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, 78, 1, Mae Ramat 63140, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, New Richards Building, Roosevelt Dr, Oxford OX3 7BN, UK
| | | | | | - Rose McGready
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, 78, 1, Mae Ramat 63140, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, New Richards Building, Roosevelt Dr, Oxford OX3 7BN, UK
| | | | - Damien Chaussabel
- Research Branch, Sidra Medicine, Al Gharrafa St, Doha 26999, Qatar
- The Jackson Laboratory for Genomic Medicine, 10, Discovery Dr, Farmington, CT 06032, USA
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3
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Brummaier T, Rinchai D, Toufiq M, Karim MY, Habib T, Utzinger J, Paris DH, McGready R, Marr AK, Kino T, Terranegra A, Al Khodor S, Chaussabel D, Syed Ahamed Kabeer B. Design of a targeted blood transcriptional panel for monitoring immunological changes accompanying pregnancy. Front Immunol 2024; 15:1319949. [PMID: 38352867 PMCID: PMC10861739 DOI: 10.3389/fimmu.2024.1319949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2023] [Accepted: 01/11/2024] [Indexed: 02/16/2024] Open
Abstract
Background Immunomodulatory processes exert steering functions throughout pregnancy. Detecting diversions from this physiologic immune clock may help identify pregnant women at risk for pregnancy-associated complications. We present results from a data-driven selection process to develop a targeted panel of mRNAs that may prove effective in detecting pregnancies diverting from the norm. Methods Based on a de novo dataset from a resource-constrained setting and a dataset from a resource-rich area readily available in the public domain, whole blood gene expression profiles of uneventful pregnancies were captured at multiple time points during pregnancy. BloodGen3, a fixed blood transcriptional module repertoire, was employed to analyze and visualize gene expression patterns in the two datasets. Differentially expressed genes were identified by comparing their abundance to non-pregnant postpartum controls. The selection process for a targeted gene panel considered (i) transcript abundance in whole blood; (ii) degree of correlation with the BloodGen3 module; and (iii) pregnancy biology. Results We identified 176 transcripts that were complemented with eight housekeeping genes. Changes in transcript abundance were seen in the early stages of pregnancy and similar patterns were observed in both datasets. Functional gene annotation suggested significant changes in the lymphoid, prostaglandin and inflammation-associated compartments, when compared to the postpartum controls. Conclusion The gene panel presented here holds promise for the development of predictive, targeted, transcriptional profiling assays. Such assays might become useful for monitoring of pregnant women, specifically to detect potential adverse events early. Prospective validation of this targeted assay, in-depth investigation of functional annotations of differentially expressed genes, and assessment of common pregnancy-associated complications with the aim to identify these early in pregnancy to improve pregnancy outcomes are the next steps.
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Affiliation(s)
- Tobias Brummaier
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Darawan Rinchai
- Research Department, Sidra Medicine, Doha, Qatar
- Department of Infectious Diseases, St. Jude Children Research Hospital, Memphis, TN, United States
| | | | | | - Tanwir Habib
- Research Department, Sidra Medicine, Doha, Qatar
- Bioinformatics Core, Weill Cornell Medicine-Qatar, Education City, Doha, Qatar
| | - Jürg Utzinger
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Daniel H. Paris
- Swiss Tropical and Public Health Institute, Allschwil, Switzerland
- University of Basel, Basel, Switzerland
| | - Rose McGready
- Shoklo Malaria Research Unit, Mahidol-Oxford Tropical Medicine Research Unit, Faculty of Tropical Medicine, Mahidol University, Mae Sot, Thailand
- Centre for Tropical Medicine and Global Health, Nuffield Department of Medicine, University of Oxford, Oxford, United Kingdom
| | | | | | | | | | - Damien Chaussabel
- Research Department, Sidra Medicine, Doha, Qatar
- Computational Sciences Department, The Jackson Laboratory, Farmington, CT, United States
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Toufiq M, Rinchai D, Bettacchioli E, Kabeer BSA, Khan T, Subba B, White O, Yurieva M, George J, Jourde-Chiche N, Chiche L, Palucka K, Chaussabel D. Harnessing large language models (LLMs) for candidate gene prioritization and selection. J Transl Med 2023; 21:728. [PMID: 37845713 PMCID: PMC10580627 DOI: 10.1186/s12967-023-04576-8] [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] [Received: 08/28/2023] [Accepted: 09/28/2023] [Indexed: 10/18/2023] Open
Abstract
BACKGROUND Feature selection is a critical step for translating advances afforded by systems-scale molecular profiling into actionable clinical insights. While data-driven methods are commonly utilized for selecting candidate genes, knowledge-driven methods must contend with the challenge of efficiently sifting through extensive volumes of biomedical information. This work aimed to assess the utility of large language models (LLMs) for knowledge-driven gene prioritization and selection. METHODS In this proof of concept, we focused on 11 blood transcriptional modules associated with an Erythroid cells signature. We evaluated four leading LLMs across multiple tasks. Next, we established a workflow leveraging LLMs. The steps consisted of: (1) Selecting one of the 11 modules; (2) Identifying functional convergences among constituent genes using the LLMs; (3) Scoring candidate genes across six criteria capturing the gene's biological and clinical relevance; (4) Prioritizing candidate genes and summarizing justifications; (5) Fact-checking justifications and identifying supporting references; (6) Selecting a top candidate gene based on validated scoring justifications; and (7) Factoring in transcriptome profiling data to finalize the selection of the top candidate gene. RESULTS Of the four LLMs evaluated, OpenAI's GPT-4 and Anthropic's Claude demonstrated the best performance and were chosen for the implementation of the candidate gene prioritization and selection workflow. This workflow was run in parallel for each of the 11 erythroid cell modules by participants in a data mining workshop. Module M9.2 served as an illustrative use case. The 30 candidate genes forming this module were assessed, and the top five scoring genes were identified as BCL2L1, ALAS2, SLC4A1, CA1, and FECH. Researchers carefully fact-checked the summarized scoring justifications, after which the LLMs were prompted to select a top candidate based on this information. GPT-4 initially chose BCL2L1, while Claude selected ALAS2. When transcriptional profiling data from three reference datasets were provided for additional context, GPT-4 revised its initial choice to ALAS2, whereas Claude reaffirmed its original selection for this module. CONCLUSIONS Taken together, our findings highlight the ability of LLMs to prioritize candidate genes with minimal human intervention. This suggests the potential of this technology to boost productivity, especially for tasks that require leveraging extensive biomedical knowledge.
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Affiliation(s)
- Mohammed Toufiq
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Eleonore Bettacchioli
- INSERM UMR1227, Lymphocytes B et Autoimmunité, Université de Bretagne Occidentale, Brest, France
- Service de Rhumatologie, CHU de Brest, Brest, France
| | | | - Taushif Khan
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Bishesh Subba
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Olivia White
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Marina Yurieva
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Joshy George
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | | | - Laurent Chiche
- Service de Médecine Interne, Hôpital Européen, Marseille, France
| | - Karolina Palucka
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
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Multi-objective optimization identifies a specific and interpretable COVID-19 host response signature. Cell Syst 2022; 13:989-1001.e8. [PMID: 36549275 DOI: 10.1016/j.cels.2022.11.008] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2022] [Revised: 08/05/2022] [Accepted: 11/22/2022] [Indexed: 12/24/2022]
Abstract
The identification of a COVID-19 host response signature in blood can increase the understanding of SARS-CoV-2 pathogenesis and improve diagnostic tools. Applying a multi-objective optimization framework to both massive public and new multi-omics data, we identified a COVID-19 signature regulated at both transcriptional and epigenetic levels. We validated the signature's robustness in multiple independent COVID-19 cohorts. Using public data from 8,630 subjects and 53 conditions, we demonstrated no cross-reactivity with other viral and bacterial infections, COVID-19 comorbidities, or confounders. In contrast, previously reported COVID-19 signatures were associated with significant cross-reactivity. The signature's interpretation, based on cell-type deconvolution and single-cell data analysis, revealed prominent yet complementary roles for plasmablasts and memory T cells. Although the signal from plasmablasts mediated COVID-19 detection, the signal from memory T cells controlled against cross-reactivity with other viral infections. This framework identified a robust, interpretable COVID-19 signature and is broadly applicable in other disease contexts. A record of this paper's transparent peer review process is included in the supplemental information.
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Wu J, Yang W, Li H. An artificial neural network model based on autophagy-related genes in childhood systemic lupus erythematosus. Hereditas 2022; 159:34. [PMID: 36114579 PMCID: PMC9479435 DOI: 10.1186/s41065-022-00248-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2022] [Accepted: 09/02/2022] [Indexed: 11/10/2022] Open
Abstract
Abstract
Background
Childhood systemic lupus erythematosus (cSLE) is a multisystemic, life-threatening autoimmune disease. Compared to adults, SLE in childhood is more active, can cause multisystem involvement including renal, neurological and hematological, and can cause cumulative damage across systems more rapidly. Autophagy, one of the core functions of cells, is involved in almost every process of the immune response and has been shown to be associated with many autoimmune diseases, being a key factor in the interplay between innate and adaptive immunity. Autophagy influences the onset, progression and severity of SLE. This paper identifies new biomarkers for the diagnosis and treatment of childhood SLE based on an artificial neural network of autophagy-related genes.
Methods
We downloaded dataset GSE100163 from the Gene Expression Omnibus database and used Protein–protein Interaction Network (PPI) and Least Absolute Shrinkage and Selection Operator (LASSO) to screen the signature genes of autophagy-related genes in cSLE. A new artificial neural network model for cSLE diagnosis was constructed using the signature genes. The predictive efficiency of the model was also validated using the dataset GSE65391. Finally, "CIBERSORT" was used to calculate the infiltration of immune cells in cSLE and to analyze the relationship between the signature genes and the infiltration of immune cells.
Results
We identified 37 autophagy-related genes that differed in cSLE and normal samples, and finally obtained the seven most relevant signature genes for cSLE (DDIT3, GNB2L1, CTSD, HSPA8, ULK1, DNAJB1, CANX) by PPI and LASOO regression screening, and constructed an artificial neural network diagnostic model for cSLE. Using this model, we plotted the ROC curves for the training and validation group diagnoses with the area under the curve of 0.976 and 0.783, respectively. Finally, we performed immunoassays on cSLE samples, and the results showed that Plasma cells, Macrophages M0, Dendritic cells activated and Neutrophils were significantly infiltrated in cSLE.
Conclusion
We constructed an artificial neural network diagnostic model of seven autophagy-related genes that can be used for the diagnosis of cSLE. Meanwhile, the characteristic genes affect the immune infiltration of cSLE, which may provide new perspectives for the exploration of cSLE treatment and related mechanisms.
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Meng XW, Cheng ZL, Lu ZY, Tan YN, Jia XY, Zhang M. MX2: Identification and systematic mechanistic analysis of a novel immune-related biomarker for systemic lupus erythematosus. Front Immunol 2022; 13:978851. [PMID: 36059547 PMCID: PMC9433551 DOI: 10.3389/fimmu.2022.978851] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Accepted: 08/01/2022] [Indexed: 11/26/2022] Open
Abstract
Background Systemic lupus erythematosus (SLE) is an autoimmune disease that involves multiple organs. However, the current SLE-related biomarkers still lack sufficient sensitivity, specificity and predictive power for clinical application. Thus, it is significant to explore new immune-related biomarkers for SLE diagnosis and development. Methods We obtained seven SLE gene expression profile microarrays (GSE121239/11907/81622/65391/100163/45291/49454) from the GEO database. First, differentially expressed genes (DEGs) were screened using GEO2R, and SLE biomarkers were screened by performing WGCNA, Random Forest, SVM-REF, correlation with SLEDAI and differential gene analysis. Receiver operating characteristic curves (ROCs) and AUC values were used to determine the clinical value. The expression level of the biomarker was verified by RT‒qPCR. Subsequently, functional enrichment analysis was utilized to identify biomarker-associated pathways. ssGSEA, CIBERSORT, xCell and ImmuCellAI algorithms were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Finally, the transcriptional regulatory network of the biomarker was constructed, and the corresponding therapeutic drugs were predicted. Results Multiple algorithms were screened together for a unique marker gene, MX2, and expression analysis of multiple datasets revealed that MX2 was highly expressed in SLE compared to the normal group (all P < 0.05), with the same trend validated by RT‒qPCR (P = 0.026). Functional enrichment analysis identified the main pathway of MX2 promotion in SLE as the NOD-like receptor signaling pathway (NES=2.492, P < 0.001, etc.). Immuno-infiltration analysis showed that MX2 was closely associated with neutrophils, and single-cell and transcriptomic data revealed that MX2 was specifically expressed in neutrophils. The NOD-like receptor signaling pathway was also remarkably correlated with neutrophils (r >0.3, P < 0.001, etc.). Most of the MX2-related interacting proteins were associated with SLE, and potential transcription factors of MX2 and its related genes were also significantly associated with the immune response. Conclusion Our study found that MX2 can serve as an immune-related biomarker for predicting the diagnosis and disease activity of SLE. It activates the NOD-like receptor signaling pathway and promotes neutrophil infiltration to aggravate SLE.
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Affiliation(s)
- Xiang-Wen Meng
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Zhi-Luo Cheng
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Zhi-Yuan Lu
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Ya-Nan Tan
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
| | - Xiao-Yi Jia
- School of Pharmacy, Anhui University of Chinese Medicine, Hefei, China
- Anhui Province Key Laboratory of Chinese Medicinal Formula, Hefei, China
- Anhui Province Key Laboratory of Research and Development of Chinese Medicine, Hefei, China
- *Correspondence: Xiao-Yi Jia, ; Min Zhang,
| | - Min Zhang
- Department of Rheumatology and Immunology, The First Affiliated Hospital of USTC, Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, China
- *Correspondence: Xiao-Yi Jia, ; Min Zhang,
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8
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Huang SSY, Rinchai D, Toufiq M, Kabeer BSA, Roelands J, Hendrickx W, Boughorbel S, Bedognetti D, Van Panhuys N, Chaussabel D, Garand M. Transcriptomic profile investigations highlight a putative role for NUDT16 in sepsis. J Cell Mol Med 2022; 26:1714-1721. [PMID: 35174610 PMCID: PMC8899167 DOI: 10.1111/jcmm.17240] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 01/08/2022] [Accepted: 02/01/2022] [Indexed: 12/20/2022] Open
Abstract
Sepsis is an aberrant systemic inflammatory response mediated by the acute activation of the innate immune system. Neutrophils are important contributors to the innate immune response that controls the infection, but harbour the risk of collateral tissue damage such as thrombosis and organ dysfunction. A better understanding of the modulations of cellular processes in neutrophils and other blood cells during sepsis is needed and can be initiated via transcriptomic profile investigations. To that point, the growing repertoire of publicly accessible transcriptomic datasets serves as a valuable resource for discovering and/or assessing the robustness of biomarkers. We employed systematic literature mining, reductionist approach to gene expression profile and empirical in vitro work to highlight the role of a Nudix hydrolase family member, NUDT16, in sepsis. The relevance and implication of the expression of NUDT16 under septic conditions and the putative functional roles of this enzyme are discussed.
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Affiliation(s)
- Susie Shih Yin Huang
- Division of Pediatric Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, St Louis, Missouri, USA.,Sidra Medicine, Doha, Qatar
| | | | | | | | | | - Wouter Hendrickx
- Sidra Medicine, Doha, Qatar.,College of Health and Life Sciences, Hamad Bin Khalifa University, Doha, Qatar
| | | | | | | | | | - Mathieu Garand
- Division of Pediatric Cardiothoracic Surgery, Department of Surgery, Washington University School of Medicine, St Louis, Missouri, USA.,Sidra Medicine, Doha, Qatar
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9
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Huang SSY, Toufiq M, Saraiva LR, Van Panhuys N, Chaussabel D, Garand M. Transcriptome and Literature Mining Highlight the Differential Expression of ERLIN1 in Immune Cells during Sepsis. BIOLOGY 2021; 10:755. [PMID: 34439987 PMCID: PMC8389572 DOI: 10.3390/biology10080755] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Revised: 07/24/2021] [Accepted: 07/27/2021] [Indexed: 12/20/2022]
Abstract
Sepsis results from the dysregulation of the host immune system. This highly variable disease affects 19 million people globally, and accounts for 5 million deaths annually. In transcriptomic datasets curated from public repositories, we observed a consistent upregulation (3.26-5.29 fold) of ERLIN1-a gene coding for an ER membrane prohibitin and a regulator of inositol 1, 4, 5-trisphosphate receptors and sterol regulatory element-binding proteins-under septic conditions in healthy neutrophils, monocytes, and whole blood. In vitro expression of the ERLIN1 gene and proteins was measured by stimulating the whole blood of healthy volunteers to a combination of lipopolysaccharide and peptidoglycan. Septic stimulation induced a significant increase in ERLIN1 expression; however, ERLIN1 was differentially expressed among the immune blood cell subsets. ERLIN1 was uniquely increased in whole blood neutrophils, and confirmed in the differentiated HL60 cell line. The scarcity of ERLIN1 in sepsis literature indicates a knowledge gap between the functions of ERLIN1, calcium homeostasis, and cholesterol and fatty acid biosynthesis, and sepsis. In combination with experimental data, we bring forth the hypothesis that ERLIN1 is variably modulated among immune cells in response to cellular perturbations, and has implications for ER functions and/or ER membrane protein components during sepsis.
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Affiliation(s)
- Susie S. Y. Huang
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (L.R.S.); (N.V.P.); (D.C.)
| | - Mohammed Toufiq
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (L.R.S.); (N.V.P.); (D.C.)
| | - Luis R. Saraiva
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (L.R.S.); (N.V.P.); (D.C.)
- College of Health and Life Sciences, Hamad Bin Khalifa University, Doha 34110, Qatar
| | - Nicholas Van Panhuys
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (L.R.S.); (N.V.P.); (D.C.)
| | - Damien Chaussabel
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (L.R.S.); (N.V.P.); (D.C.)
| | - Mathieu Garand
- Research Department, Sidra Medicine, Doha 26999, Qatar; (M.T.); (L.R.S.); (N.V.P.); (D.C.)
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10
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Altman MC, Rinchai D, Baldwin N, Toufiq M, Whalen E, Garand M, Syed Ahamed Kabeer B, Alfaki M, Presnell SR, Khaenam P, Ayllón-Benítez A, Mougin F, Thébault P, Chiche L, Jourde-Chiche N, Phillips JT, Klintmalm G, O'Garra A, Berry M, Bloom C, Wilkinson RJ, Graham CM, Lipman M, Lertmemongkolchai G, Bedognetti D, Thiebaut R, Kheradmand F, Mejias A, Ramilo O, Palucka K, Pascual V, Banchereau J, Chaussabel D. Development of a fixed module repertoire for the analysis and interpretation of blood transcriptome data. Nat Commun 2021; 12:4385. [PMID: 34282143 PMCID: PMC8289976 DOI: 10.1038/s41467-021-24584-w] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2020] [Accepted: 06/21/2021] [Indexed: 01/21/2023] Open
Abstract
As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles. Taken together, this well-characterized and well-supported transcriptional module repertoire can be employed for the interpretation and benchmarking of blood transcriptome profiles within and across patient cohorts. Blood transcriptome fingerprints for the 16 reference cohorts can be accessed interactively via: https://drinchai.shinyapps.io/BloodGen3Module/ .
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Affiliation(s)
- Matthew C Altman
- Systems Immunology, Benaroya Research Institute, Seattle, WA, USA.
- Division of Allergy and Infectious Diseases, University of Washington, Seattle, WA, USA.
| | | | - Nicole Baldwin
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX, USA
| | | | - Elizabeth Whalen
- Systems Immunology, Benaroya Research Institute, Seattle, WA, USA
| | | | | | | | - Scott R Presnell
- Systems Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Prasong Khaenam
- Systems Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Aaron Ayllón-Benítez
- Inserm U1219 Bordeaux Population Health Research Center, Bordeaux University, Bordeaux, France
| | - Fleur Mougin
- Inserm U1219 Bordeaux Population Health Research Center, Bordeaux University, Bordeaux, France
| | | | - Laurent Chiche
- Department of Internal Medicine, Hopital Européen, Marseille, France
| | | | - J Theodore Phillips
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX, USA
| | - Goran Klintmalm
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX, USA
| | - Anne O'Garra
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK
- National Heart and Lung Institute, Imperial College London, London, UK
| | | | - Chloe Bloom
- National Heart and Lung Institute, Imperial College London, London, UK
| | - Robert J Wilkinson
- The Francis Crick Institute, London, UK
- Department of Infectious Disease, Imperial College, London, UK
- Wellcome Center for Infectious Diseases Research in Africa and Department of Medicine, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town Observatory, 7925, Cape Town, Republic of South Africa
| | - Christine M Graham
- Laboratory of Immunoregulation and Infection, The Francis Crick Institute, London, UK
| | - Marc Lipman
- UCL Respiratory, Division of Medicine, University College London, London, UK
| | - Ganjana Lertmemongkolchai
- Centre for Research and Development of Medical Diagnostic Laboratories, Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand
| | | | - Rodolphe Thiebaut
- Inserm U1219 Bordeaux Population Health Research Center, Bordeaux University, Bordeaux, France
| | - Farrah Kheradmand
- Baylor College of Medicine & Center for Translational Research on Inflammatory Diseases, Michael E. DeBakey VAMC, Houston, TX, USA
| | - Asuncion Mejias
- Abigail Wexner Research Institute at Nationwide Children's Hospital and the Ohio State University School of Medicine, Columbus, OH, USA
| | - Octavio Ramilo
- Abigail Wexner Research Institute at Nationwide Children's Hospital and the Ohio State University School of Medicine, Columbus, OH, USA
| | - Karolina Palucka
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Virginia Pascual
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX, USA
- Weill Cornell Medicine, New York, NY, USA
| | - Jacques Banchereau
- Baylor Institute for Immunology Research, Baylor Research Institute, Dallas, TX, USA
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, USA
| | - Damien Chaussabel
- Systems Immunology, Benaroya Research Institute, Seattle, WA, USA.
- Research Branch, Sidra Medicine, Doha, Qatar.
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11
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Rinchai D, Verzoni E, Huber V, Cova A, Squarcina P, De Cecco L, de Braud F, Ratta R, Dugo M, Lalli L, Vallacchi V, Rodolfo M, Roelands J, Castelli C, Chaussabel D, Procopio G, Bedognetti D, Rivoltini L. Integrated transcriptional-phenotypic analysis captures systemic immunomodulation following antiangiogenic therapy in renal cell carcinoma patients. Clin Transl Med 2021; 11:e434. [PMID: 34185403 PMCID: PMC8214860 DOI: 10.1002/ctm2.434] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2020] [Revised: 05/09/2021] [Accepted: 05/12/2021] [Indexed: 12/11/2022] Open
Abstract
BACKGROUND The combination of immune checkpoint blockade (ICB) with standard therapies is becoming a common approach for overcoming resistance to cancer immunotherapy in most human malignancies including metastatic renal cell carcinoma (mRCC). In this regard, insights into the immunomodulatory properties of antiangiogenic agents may help designing multidrug schedules based on specific immune synergisms. METHODS We used orthogonal transcriptomic and phenotyping platforms combined with functional analytic pipelines to elucidate the immunomodulatory effect of the antiangiogenic agent pazopanib in mRCC patients. Nine patients were studied longitudinally over a period of 6 months. We also analyzed transcriptional data from The Cancer Genome Atlas (TCGA) RCC cohort (N = 571) to assess the prognostic implications of our findings. The effect of pazopanib was assessed in vitro on NK cells and T cells. Additionally, myeloid-derived suppressor (MDSC)-like cells were generated from CD14+ monocytes transfected with mimics of miRNAs associated with MDSC function in the presence or absence of pazopanib. RESULTS Pazopanib administration caused a rapid and dramatic reshaping in terms of frequency and transcriptional activity of multiple blood immune cell subsets, with a downsizing of MDSC and regulatory T cells in favor of a strong enhancement in PD-1 expressing cytotoxic T and Natural Killer effectors. These changes were paired with an increase of the expression of transcripts reflecting activation of immune-effector functions. This immunomodulation was marked but transient, peaking at the third month of treatment. Moreover, the intratumoral expression level of a MDSC signature (MDSC INT) was strongly associated with poor prognosis in RCC patients. In vitro experiments indicate that the observed immunomodulation might be due to an inhibitory effect on MDSC-mediated suppression, rather than a direct effect on NK and T cells. CONCLUSIONS The marked but transient nature of this immunomodulation, peaking at the third month of treatment, provides the rationale for the use of antiangiogenics as a preconditioning strategy to improve the efficacy of ICB.
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Affiliation(s)
| | - Elena Verzoni
- Medical Oncology DepartmentFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Veronica Huber
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Agata Cova
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Paola Squarcina
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Loris De Cecco
- Platform of Integrated BiologyFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Filippo de Braud
- Medical Oncology DepartmentFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | | | - Matteo Dugo
- Platform of Integrated BiologyFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Luca Lalli
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Viviana Vallacchi
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Monica Rodolfo
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | | | - Chiara Castelli
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | | | - Giuseppe Procopio
- Medical Oncology DepartmentFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
| | - Davide Bedognetti
- Cancer Research DepartmentSidra MedicineDohaQatar
- Dipartimento di Medicina Interna e Specialità MedicheUniversità degli Studi di GenovaGenovaItaly
- College of Health and Life SciencesHamad Bin Khalifa UniversityDohaQatar
| | - Licia Rivoltini
- Unit of Immunotherapy of Human TumorsFondazione IRCCS Istituto Nazionale dei TumoriMilanItaly
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12
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Rinchai D, Roelands J, Toufiq M, Hendrickx W, Altman MC, Bedognetti D, Chaussabel D. BloodGen3Module: Blood transcriptional module repertoire analysis and visualization using R. Bioinformatics 2021; 37:2382-2389. [PMID: 33624743 PMCID: PMC8388021 DOI: 10.1093/bioinformatics/btab121] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 01/14/2021] [Accepted: 02/23/2021] [Indexed: 11/28/2022] Open
Abstract
Motivation We previously described the construction and characterization of fixed reusable blood transcriptional module repertoires. More recently we released a third iteration (‘BloodGen3’ module repertoire) that comprises 382 functionally annotated modules and encompasses 14 168 transcripts. Custom bioinformatic tools are needed to support downstream analysis, visualization and interpretation relying on such fixed module repertoires. Results We have developed and describe here an R package, BloodGen3Module. The functions of our package permit group comparison analyses to be performed at the module-level, and to display the results as annotated fingerprint grid plots. A parallel workflow for computing module repertoire changes for individual samples rather than groups of samples is also available; these results are displayed as fingerprint heatmaps. An illustrative case is used to demonstrate the steps involved in generating blood transcriptome repertoire fingerprints of septic patients. Taken together, this resource could facilitate the analysis and interpretation of changes in blood transcript abundance observed across a wide range of pathological and physiological states. Availability and implementation The BloodGen3Module package and documentation are freely available from Github: https://github.com/Drinchai/BloodGen3Module. Supplementary information Supplementary data are available at Bioinformatics online.
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Affiliation(s)
| | | | | | | | - Matthew C Altman
- Division of Allergy and Infectious Diseases, University of Washington, Seattle, Washington, USA.,Systems Immunology, Benaroya Research Institute, Seattle, Washington, USA
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