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Xie L, Zhu G, Long S, Wang M, Cheng X, Dong Y, Wang C, Wang G. Identification of MORN3 and LLGL2 as novel diagnostic biomarkers for latent tuberculosis infection using machine learning strategies and experimental verification. Ann Med 2024; 56:2380797. [PMID: 39054612 DOI: 10.1080/07853890.2024.2380797] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 05/10/2024] [Accepted: 05/18/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Current diagnostic methods cannot effectively distinguish between latent tuberculosis infection (LTBI) and active tuberculosis (ATB). This study aims to explore novel non-invasive diagnostic biomarkers for LTBI and to elucidate possible molecular mechanisms of LTBI pathogenesis. METHODS Three GEO datasets (GSE19439, GSE19444, and GSE62525) were utilized to analyze the differentially expressed genes (DEGs). Functional enrichment studies were then performed on these DEGs. To ascertain potential diagnostic biomarkers, we utilized two different machine learning techniques: LASSO and RF. ROC curves were constructed in both the training and validation datasets to assess the diagnostic efficacy. The expression of identified biomarkers was verified by RT-qPCR in our own Chinese cohort. Using CIBERSORT, we estimated the abundances of 22 immune cell types in LTBI group, and subsequently analyzed the relationship between biomarker expression and immune cell infiltration. RESULTS 166 DEGs were identified between ATB and LTBI groups, which are primarily associated with immune responses, inflammatory signaling pathways, and infection factors. Following that, 22 candidate diagnostic biomarkers for LTBI were selected in the machine learning process. Three up-regulated genes, MORN3, LLGL2, and IFT140, whose expression levels were not previously reported in TB, were validated using the training and validation cohort datasets. In our own Chinese cohort, we also found that MORN3 and LLGL2 showed good diagnostic effect using RT-qPCR method. Finally, we revealed the specific infiltration features of immune cells in LTBI and observed a notable correlation between potential marker expression and immune cells. CONCLUSIONS MORN3 and LLGL2 emerged as candidate diagnostic biomarkers for LTBI, following the elucidation of the key immune cell types involved. Our findings will contribute to providing a potential target for early noninvasive diagnosis of LTBI patients.
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Affiliation(s)
- Longxiang Xie
- Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng, Henan, China
- School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Gaoya Zhu
- School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Sibo Long
- Department of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, China
| | - Mengna Wang
- School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Xinxin Cheng
- School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Yuzhe Dong
- School of Basic Medical Sciences, Henan University, Kaifeng, Henan, China
| | - Chaoyang Wang
- Department of General Surgery, Huaihe Hospital of Henan University, Henan University, Kaifeng, Henan, China
| | - Guirong Wang
- Department of Clinical Laboratory, Beijing Chest Hospital, Capital Medical University, Beijing Tuberculosis and Thoracic Tumor Institute, Beijing, China
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2
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Channon-Wells S, Habgood-Coote D, Vito O, Galassini R, Wright VJ, Brent AJ, Heyderman RS, Anderson ST, Eley B, Martinón-Torres F, Levin M, Kaforou M, Herberg JA. Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease. J Transl Med 2024; 22:802. [PMID: 39210372 PMCID: PMC11360490 DOI: 10.1186/s12967-024-05241-4] [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: 01/09/2024] [Accepted: 04/27/2024] [Indexed: 09/04/2024] Open
Abstract
BACKGROUND Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression. METHODS We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics. RESULTS Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%). CONCLUSIONS Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.
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Affiliation(s)
- Samuel Channon-Wells
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Dominic Habgood-Coote
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Ortensia Vito
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Rachel Galassini
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Victoria J Wright
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Andrew J Brent
- Oxford University Hospitals NHS Foundation Trust, Headley Way, Headington, Oxford, UK
- Nuffield Department of Medicine, University of Oxford, Oxford, UK
| | - Robert S Heyderman
- Research Department of Infection, Division of Infection and Immunity, University College London, London, UK
| | | | - Brian Eley
- Department of Paediatrics and Child Health, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa
| | - Federico Martinón-Torres
- Translational Pediatrics and Infectious Diseases, Department of Pediatrics, Hospital Clínico Universitario de Santiago de Compostela, Santiago de Compostela, Galicia, Spain
- Genetics, Vaccines, Infections and Pediatrics Research Group (GENVIP), Instituto de Investigación Santiaria de Santiago, Universidade de Santiago de Compostela, Santiago de Compostela, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Respiratorias (CIBER-ES), Instituto de Salud Carlos III, Madrid, Spain
| | - Michael Levin
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Myrsini Kaforou
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK
- Centre for Paediatrics and Child Health, Imperial College London, London, UK
| | - Jethro A Herberg
- Section of Paediatric Infectious Disease, Department of Infectious Disease, Imperial College London, London, UK.
- Centre for Paediatrics and Child Health, Imperial College London, London, UK.
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3
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Millar JE, Docherty AB. It ain't what you do (it's the way that you do it): modulating the host response in sepsis. Anaesthesia 2024; 79:792-796. [PMID: 38747302 DOI: 10.1111/anae.16314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 04/29/2024] [Indexed: 07/16/2024]
Affiliation(s)
- Jonathan E Millar
- Baillie-Gifford Pandemic Science Hub, Centre for Inflammation Research, University of Edinburgh, Edinburgh, UK
- Intensive Care Unit, Queen Elizabeth University Hospital, Glasgow, UK
| | - Annemarie B Docherty
- Centre for Medical Informatics, Usher Institute, University of Edinburgh, Edinburgh, UK
- Intensive Care Unit, Royal Infirmary of Edinburgh, Edinburgh, UK
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4
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Shinde P, Soldevila F, Reyna J, Aoki M, Rasmussen M, Willemsen L, Kojima M, Ha B, Greenbaum JA, Overton JA, Guzman-Orozco H, Nili S, Orfield S, Gygi JP, da Silva Antunes R, Sette A, Grant B, Olsen LR, Konstorum A, Guan L, Ay F, Kleinstein SH, Peters B. A multi-omics systems vaccinology resource to develop and test computational models of immunity. CELL REPORTS METHODS 2024; 4:100731. [PMID: 38490204 PMCID: PMC10985234 DOI: 10.1016/j.crmeth.2024.100731] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 01/04/2024] [Accepted: 02/20/2024] [Indexed: 03/17/2024]
Abstract
Systems vaccinology studies have identified factors affecting individual vaccine responses, but comparing these findings is challenging due to varying study designs. To address this lack of reproducibility, we established a community resource for comparing Bordetella pertussis booster responses and to host annual contests for predicting patients' vaccination outcomes. We report here on our experiences with the "dry-run" prediction contest. We found that, among 20+ models adopted from the literature, the most successful model predicting vaccination outcome was based on age alone. This confirms our concerns about the reproducibility of conclusions between different vaccinology studies. Further, we found that, for newly trained models, handling of baseline information on the target variables was crucial. Overall, multiple co-inertia analysis gave the best results of the tested modeling approaches. Our goal is to engage community in these prediction challenges by making data and models available and opening a public contest in August 2024.
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Affiliation(s)
- Pramod Shinde
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Ferran Soldevila
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Joaquin Reyna
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, San Diego, CA, USA
| | - Minori Aoki
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mikkel Rasmussen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lisa Willemsen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mari Kojima
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Brendan Ha
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jason A Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - James A Overton
- Knocean Inc., 107 Quebec Avenue, Toronto, Ontario M6P 2T3, Canada
| | - Hector Guzman-Orozco
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Somayeh Nili
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Shelby Orfield
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jeremy P Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Ricardo da Silva Antunes
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Barry Grant
- Department of Molecular Biology, School of Biological Sciences, University of California, San Diego, La Jolla, CA, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Anna Konstorum
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ferhat Ay
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Steven H Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA; Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA; Department of Medicine, University of California, San Diego, San Diego, CA, USA.
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5
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Wu C, Guo D. Identification of Two Flip-Over Genes in Grass Family as Potential Signature of C4 Photosynthesis Evolution. Int J Mol Sci 2023; 24:14165. [PMID: 37762466 PMCID: PMC10531853 DOI: 10.3390/ijms241814165] [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: 07/09/2023] [Revised: 09/05/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023] Open
Abstract
In flowering plants, C4 photosynthesis is superior to C3 type in carbon fixation efficiency and adaptation to extreme environmental conditions, but the mechanisms behind the assembly of C4 machinery remain elusive. This study attempts to dissect the evolutionary divergence from C3 to C4 photosynthesis in five photosynthetic model plants from the grass family, using a combined comparative transcriptomics and deep learning technology. By examining and comparing gene expression levels in bundle sheath and mesophyll cells of five model plants, we identified 16 differentially expressed signature genes showing cell-specific expression patterns in C3 and C4 plants. Among them, two showed distinctively opposite cell-specific expression patterns in C3 vs. C4 plants (named as FOGs). The in silico physicochemical analysis of the two FOGs illustrated that C3 homologous proteins of LHCA6 had low and stable pI values of ~6, while the pI values of LHCA6 homologs increased drastically in C4 plants Setaria viridis (7), Zea mays (8), and Sorghum bicolor (over 9), suggesting this protein may have different functions in C3 and C4 plants. Interestingly, based on pairwise protein sequence/structure similarities between each homologous FOG protein, one FOG PGRL1A showed local inconsistency between sequence similarity and structure similarity. To find more examples of the evolutionary characteristics of FOG proteins, we investigated the protein sequence/structure similarities of other FOGs (transcription factors) and found that FOG proteins have diversified incompatibility between sequence and structure similarities during grass family evolution. This raised an interesting question as to whether the sequence similarity is related to structure similarity during C4 photosynthesis evolution.
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Affiliation(s)
| | - Dianjing Guo
- State Key Laboratory of Agrobiotechnology, School of Life Sciences, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong SAR, China;
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6
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Shinde P, Soldevila F, Reyna J, Aoki M, Rasmussen M, Willemsen L, Kojima M, Ha B, Greenbaum JA, Overton JA, Guzman-Orozco H, Nili S, Orfield S, Gygi JP, da Silva Antunes R, Sette A, Grant B, Olsen LR, Konstorum A, Guan L, Ay F, Kleinstein SH, Peters B. A systems vaccinology resource to develop and test computational models of immunity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.28.555193. [PMID: 37693565 PMCID: PMC10491180 DOI: 10.1101/2023.08.28.555193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/12/2023]
Abstract
Computational models that predict an individual's response to a vaccine offer the potential for mechanistic insights and personalized vaccination strategies. These models are increasingly derived from systems vaccinology studies that generate immune profiles from human cohorts pre- and post-vaccination. Most of these studies involve relatively small cohorts and profile the response to a single vaccine. The ability to assess the performance of the resulting models would be improved by comparing their performance on independent datasets, as has been done with great success in other areas of biology such as protein structure predictions. To transfer this approach to system vaccinology studies, we established a prototype platform that focuses on the evaluation of Computational Models of Immunity to Pertussis Booster vaccinations (CMI-PB). A community resource, CMI-PB generates experimental data for the explicit purpose of model evaluation, which is performed through a series of annual data releases and associated contests. We here report on our experience with the first such 'dry run' for a contest where the goal was to predict individual immune responses based on pre-vaccination multi-omic profiles. Over 30 models adopted from the literature were tested, but only one was predictive, and was based on age alone. The performance of new models built using CMI-PB training data was much better, but varied significantly based on the choice of pre-vaccination features used and the model building strategy. This suggests that previously published models developed for other vaccines do not generalize well to Pertussis Booster vaccination. Overall, these results reinforced the need for comparative analysis across models and datasets that CMI-PB aims to achieve. We are seeking wider community engagement for our first public prediction contest, which will open in early 2024.
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Affiliation(s)
- Pramod Shinde
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Ferran Soldevila
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Joaquin Reyna
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Bioinformatics and Systems Biology Graduate Program, University of California, San Diego, CA, USA
| | - Minori Aoki
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mikkel Rasmussen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Lisa Willemsen
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Mari Kojima
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Brendan Ha
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jason A Greenbaum
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - James A Overton
- Knocean Inc., 107 Quebec Ave. Toronto, Ontario, M6P 2T3, Canada
| | - Hector Guzman-Orozco
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Somayeh Nili
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Shelby Orfield
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Jeremy P. Gygi
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
| | - Ricardo da Silva Antunes
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
| | - Alessandro Sette
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Barry Grant
- Department of Molecular Biology, School of Biological Sciences, University of California San Diego, La Jolla, California, USA
| | - Lars Rønn Olsen
- Department of Health Technology, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Anna Konstorum
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Ferhat Ay
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
| | - Steven H. Kleinstein
- Program in Computational Biology & Bioinformatics, Yale University, New Haven, CT, USA
- Department of Pathology, Yale University School of Medicine, New Haven, CT, USA
| | - Bjoern Peters
- Center for Infectious Disease and Vaccine Research, La Jolla Institute for Immunology, La Jolla, CA, USA
- Department of Medicine, University of California, San Diego, San Diego, CA, USA
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7
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Satam H, Joshi K, Mangrolia U, Waghoo S, Zaidi G, Rawool S, Thakare RP, Banday S, Mishra AK, Das G, Malonia SK. Next-Generation Sequencing Technology: Current Trends and Advancements. BIOLOGY 2023; 12:997. [PMID: 37508427 PMCID: PMC10376292 DOI: 10.3390/biology12070997] [Citation(s) in RCA: 93] [Impact Index Per Article: 93.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Revised: 07/09/2023] [Accepted: 07/11/2023] [Indexed: 07/30/2023]
Abstract
The advent of next-generation sequencing (NGS) has brought about a paradigm shift in genomics research, offering unparalleled capabilities for analyzing DNA and RNA molecules in a high-throughput and cost-effective manner. This transformative technology has swiftly propelled genomics advancements across diverse domains. NGS allows for the rapid sequencing of millions of DNA fragments simultaneously, providing comprehensive insights into genome structure, genetic variations, gene expression profiles, and epigenetic modifications. The versatility of NGS platforms has expanded the scope of genomics research, facilitating studies on rare genetic diseases, cancer genomics, microbiome analysis, infectious diseases, and population genetics. Moreover, NGS has enabled the development of targeted therapies, precision medicine approaches, and improved diagnostic methods. This review provides an insightful overview of the current trends and recent advancements in NGS technology, highlighting its potential impact on diverse areas of genomic research. Moreover, the review delves into the challenges encountered and future directions of NGS technology, including endeavors to enhance the accuracy and sensitivity of sequencing data, the development of novel algorithms for data analysis, and the pursuit of more efficient, scalable, and cost-effective solutions that lie ahead.
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Affiliation(s)
- Heena Satam
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Kandarp Joshi
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Upasana Mangrolia
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Sanober Waghoo
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Gulnaz Zaidi
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Shravani Rawool
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Ritesh P. Thakare
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
| | - Shahid Banday
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
| | - Alok K. Mishra
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
| | - Gautam Das
- miBiome Therapeutics, Mumbai 400102, India; (H.S.); (K.J.); (U.M.); (S.W.); (G.Z.); (S.R.)
| | - Sunil K. Malonia
- Department of Molecular Cell and Cancer Biology, UMass Chan Medical School, Worcester, MA 01605, USA; (R.P.T.); (S.B.); (A.K.M.)
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8
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Fu J, Zhu F, Xu CJ, Li Y. Metabolomics meets systems immunology. EMBO Rep 2023; 24:e55747. [PMID: 36916532 PMCID: PMC10074123 DOI: 10.15252/embr.202255747] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2022] [Revised: 12/24/2022] [Accepted: 02/24/2023] [Indexed: 03/16/2023] Open
Abstract
Metabolic processes play a critical role in immune regulation. Metabolomics is the systematic analysis of small molecules (metabolites) in organisms or biological samples, providing an opportunity to comprehensively study interactions between metabolism and immunity in physiology and disease. Integrating metabolomics into systems immunology allows the exploration of the interactions of multilayered features in the biological system and the molecular regulatory mechanism of these features. Here, we provide an overview on recent technological developments of metabolomic applications in immunological research. To begin, two widely used metabolomics approaches are compared: targeted and untargeted metabolomics. Then, we provide a comprehensive overview of the analysis workflow and the computational tools available, including sample preparation, raw spectra data preprocessing, data processing, statistical analysis, and interpretation. Third, we describe how to integrate metabolomics with other omics approaches in immunological studies using available tools. Finally, we discuss new developments in metabolomics and its prospects for immunology research. This review provides guidance to researchers using metabolomics and multiomics in immunity research, thus facilitating the application of systems immunology to disease research.
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Affiliation(s)
- Jianbo Fu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Feng Zhu
- College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China
| | - Cheng-Jian Xu
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Yang Li
- Centre for Individualised Infection Medicine (CiiM), a joint venture between the Helmholtz Centre for Infection Research (HZI) and Hannover Medical School (MHH), Hannover, Germany.,TWINCORE Centre for Experimental and Clinical Infection Research, a joint venture between the Helmholtz Centre for Infection Research (HZI) and the Hannover Medical School (MHH), Hannover, Germany.,Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, Nijmegen, The Netherlands
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Maher MC, Soriaga LB, Gupta A, Chen YP, di Iulio J, Ledoux S, Smithey MJ, Cathcart AL, McKusick K, Sun D, Aldinger M, Alexander E, Purcell L, Ding X, Peppercorn A, Austin D, Mogalian E, Yeh WW, Shapiro AE, Corti D, Virgin HW, Pang PS, Telenti A. Antibody therapy reverses biological signatures of COVID-19 progression. Cell Rep Med 2022; 3:100721. [PMID: 35977462 PMCID: PMC9380250 DOI: 10.1016/j.xcrm.2022.100721] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 05/21/2022] [Accepted: 07/20/2022] [Indexed: 01/19/2023]
Abstract
Understanding who is at risk of progression to severe coronavirus disease 2019 (COVID-19) is key to clinical decision making and effective treatment. We study correlates of disease severity in the COMET-ICE clinical trial that randomized 1:1 to placebo or to sotrovimab, a monoclonal antibody for the treatment of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection (ClinicalTrials.gov04545060). Laboratory parameters identify study participants at greater risk of severe disease, including a high neutrophil-to-lymphocyte ratio (NLR), a negative SARS-CoV-2 serologic test, and whole-blood transcriptome profiles. Sotrovimab treatment is associated with normalization of NLR and the transcriptomic profile and with a decrease of viral RNA in nasopharyngeal samples. Transcriptomics provides the most sensitive detection of participants who would go on to be hospitalized or die. To facilitate timely measurement, we identify a 10-gene signature with similar predictive accuracy. We identify markers of risk for disease progression and demonstrate that normalization of these parameters occurs with antibody treatment of established infection.
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Affiliation(s)
- M. Cyrus Maher
- Vir Biotechnology, San Francisco, CA, USA,Corresponding author
| | | | - Anil Gupta
- Albion Finch Medical, William Osler Health Centre, Toronto, ON, Canada
| | | | | | | | | | | | | | - David Sun
- Vir Biotechnology, San Francisco, CA, USA
| | | | | | | | - Xiao Ding
- Vir Biotechnology, San Francisco, CA, USA
| | | | | | | | | | - Adrienne E. Shapiro
- Departments of Global Health and Medicine, University of Washington and Fred Hutchinson Cancer Research Center, Seattle, WA, USA
| | - Davide Corti
- Humabs Biomed SA, a subsidiary of Vir Biotechnology, Bellinzona, Switzerland
| | - Herbert W. Virgin
- Vir Biotechnology, San Francisco, CA, USA,Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO 63110, USA,Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX 75390, USA
| | | | - Amalio Telenti
- Vir Biotechnology, San Francisco, CA, USA,Corresponding author
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10
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Kircher M, Chludzinski E, Krepel J, Saremi B, Beineke A, Jung K. Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin. Int J Mol Sci 2022; 23:ijms23052481. [PMID: 35269624 PMCID: PMC8910329 DOI: 10.3390/ijms23052481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2021] [Revised: 02/17/2022] [Accepted: 02/21/2022] [Indexed: 02/01/2023] Open
Abstract
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.
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Affiliation(s)
- Magdalena Kircher
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
| | - Elisa Chludzinski
- Department of Pathology, University of Veterinary Medicine Hannover, Buenteweg 17, 30559 Hannover, Germany; (E.C.); (A.B.)
| | - Jessica Krepel
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
| | - Babak Saremi
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
| | - Andreas Beineke
- Department of Pathology, University of Veterinary Medicine Hannover, Buenteweg 17, 30559 Hannover, Germany; (E.C.); (A.B.)
| | - Klaus Jung
- Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, Germany; (M.K.); (J.K.); (B.S.)
- Correspondence: ; Tel.: +49-511-953-8878
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