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      Plasma and urine proteomics and gut microbiota analysis reveal potential factors affecting COVID-19 vaccination response

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          Summary

          The efficacy of COVID-19 vaccination relies on the induction of neutralizing antibodies, which can vary among vaccine recipients. In this study, we investigated the potential factors affecting the neutralizing antibody response by combining plasma and urine proteomics and gut microbiota analysis. We found that activation of the LXR/FXR pathway in plasma was associated with the production of ACE2-RBD-inhibiting antibodies, while urine proteins related to complement system, acute phase response signaling, LXR/FXR, and STAT3 pathways were correlated with neutralizing antibody production. Moreover, we observed a correlation between the gut microbiota and plasma and urine proteins, as well as the vaccination response. Based on the above data, we built a predictive model for vaccination response (AUC = 0.85). Our study provides insights into characteristic plasma and urine proteins and gut microbiota associated with the ACE2-RBD-inhibiting antibodies, which could benefit our understanding of the host response to COVID-19 vaccination.

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          Highlights

          • •

            Plasma LXR/FXR activation is associated with ACE2-RBD-inhibiting antibody titer

          • •

            Urine proteins correlate with neutralizing antibodies production

          • •

            Gut microbiota is related with plasma and urine proteins, and vaccination response

          • •

            Integration of plasma, urine data, and clinical features predicts vaccination response

          Abstract

          Health sciences; Immunity; Microbiology; Experimental models in systems biology; Proteomics

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          Most cited references61

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          Causal analysis approaches in Ingenuity Pathway Analysis

          John Osborne, Andreas Kramer, Jeff Green … (2015)
          Motivation: Prior biological knowledge greatly facilitates the meaningful interpretation of gene-expression data. Causal networks constructed from individual relationships curated from the literature are particularly suited for this task, since they create mechanistic hypotheses that explain the expression changes observed in datasets. Results: We present and discuss a suite of algorithms and tools for inferring and scoring regulator networks upstream of gene-expression data based on a large-scale causal network derived from the Ingenuity Knowledge Base. We extend the method to predict downstream effects on biological functions and diseases and demonstrate the validity of our approach by applying it to example datasets. Availability: The causal analytics tools ‘Upstream Regulator Analysis', ‘Mechanistic Networks', ‘Causal Network Analysis' and ‘Downstream Effects Analysis' are implemented and available within Ingenuity Pathway Analysis (IPA, http://www.ingenuity.com). Supplementary information: Supplementary material is available at Bioinformatics online.
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            Proteomic and Metabolomic Characterization of COVID-19 Patient Sera

            Bo Shen, Xiao Yi, Yaoting Sun … (2020)
            Summary Early detection and effective treatment of severe COVID-19 patients remain major challenges. Here, we performed proteomic and metabolomic profiling of sera from 46 COVID-19 and 53 control individuals. We then trained a machine learning model using proteomic and metabolomic measurements from a training cohort of 18 non-severe and 13 severe patients. The model was validated using ten independent patients, seven of which were correctly classified. Targeted proteomics and metabolomics assays were employed to further validate this molecular classifier in a second test cohort of 19 new COVID-19 patients, leading to 16 correct assignments. We identified molecular changes in the sera of COVID-19 patients compared to other groups implicating dysregulation of macrophage, platelet degranulation and complement system pathways, and massive metabolic suppression. This study revealed characteristic protein and metabolite changes in the sera of severe COVID-19 patients, which might be used in selection of potential blood biomarkers for severity evaluation.
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              Potent neutralizing antibodies against SARS-CoV-2 identified by high-throughput single-cell sequencing of convalescent patients’ B cells

              Yunlong Cao, Bin Su, Xianghua Guo … (2020)
              Summary The COVID-19 pandemic urgently needs therapeutic and prophylactic interventions. Here we report the rapid identification of SARS-CoV-2 neutralizing antibodies by high-throughput single-cell RNA and VDJ sequencing of antigen-enriched B cells from 60 convalescent patients. From 8,558 antigen-binding IgG1+ clonotypes, 14 potent neutralizing antibodies were identified with the most potent one, BD-368-2, exhibiting an IC50 of 1.2 ng/mL and 15 ng/mL against pseudotyped and authentic SARS-CoV-2, respectively. BD-368-2 also displayed strong therapeutic and prophylactic efficacy in SARS-CoV-2-infected hACE2-transgenic mice. Additionally, the 3.8Å Cryo-EM structure of a neutralizing antibody in complex with the spike-ectodomain trimer revealed the antibody’s epitope overlaps with the ACE2 binding site. Moreover, we demonstrated that SARS-CoV-2 neutralizing antibodies could be directly selected based on similarities of their predicted CDR3H structures to those of SARS-CoV neutralizing antibodies. Altogether, we showed that human neutralizing antibodies could be efficiently discovered by high-throughput single B-cell sequencing in response to pandemic infectious diseases.
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                Author and article information

                Contributors
                Zhen Dong
                Zhongjun Li
                Tiannan Guo
                Shiming Yang
                Journal
                Journal ID (nlm-ta): iScience
                Journal ID (iso-abbrev): iScience
                Title: iScience
                Publisher: Elsevier
                ISSN (Electronic): 2589-0042
                Publication date PMC-release: 08 January 2024
                Publication date Collection: 16 February 2024
                Publication date (Electronic): 08 January 2024
                Volume: 27
                Issue: 2
                Electronic Location Identifier: 108851
                Affiliations
                [1 ]Department of Gastroenterology, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
                [2 ]iMarkerlab, Westlake Laboratory of Life Sciences and Biomedicine, Key Laboratory of Structural Biology of Zhejiang Province, School of Life Sciences, Westlake University, Hangzhou, Zhejiang Province, China
                [3 ]Institute of Basic Medical Sciences, Westlake Institute for Advanced Study, Hangzhou, Zhejiang Province, China
                [4 ]Research Center for Industries of the Future, Westlake University, 600 Dunyu Road, Hangzhou 310030, Zhejiang, China
                [5 ]Center for Infectious Disease Research, Westlake University, 18 Shilongshan Road, Hangzhou 310024, Zhejiang, China
                [6 ]Laboratory Medicine Center, Xinqiao Hospital, Third Military Medical University, Chongqing 400037, China
                Author notes
                [∗ ]Corresponding author dongzhen@ 123456westlake.edu.cn
                [∗∗ ]Corresponding author johnneyusc@ 123456gmail.com
                [∗∗∗ ]Corresponding author guotiannan@ 123456westlake.edu.cn
                [∗∗∗∗ ]Corresponding author yangshiming@ 123456tmmu.edu.cn
                [7]

                These authors contributed equally

                [8]

                Lead contact

                Article
                Publisher Item ID: S2589-0042(24)00072-5 Publisher ID: 108851
                DOI: 10.1016/j.isci.2024.108851
                PMC ID: 10838952
                PubMed ID: 38318387
                SO-VID: 7fddbb06-fc26-4ba9-8fc0-6caffaaeaf1d
                Copyright © © 2024 The Author(s)
                License:

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

                History
                Date received : 14 April 2023
                Date revision received : 15 October 2023
                Date accepted : 3 January 2024
                Categories
                Subject: Article

                Keywords: health sciences,immunity,microbiology,experimental models in systems biology,proteomics
                Data availability:
                Keywords: health sciences, immunity, microbiology, experimental models in systems biology, proteomics

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