COVID-19; Gut microbiome; Metagenomics; Metatranscriptomics; SARS-CoV-2
Abstract :
[en] BACKGROUND: Infections with SARS-CoV-2 have a pronounced impact on the gastrointestinal tract and its resident microbiome. Clear differences between severe cases of infection and healthy individuals have been reported, including the loss of commensal taxa. We aimed to understand if microbiome alterations including functional shifts are unique to severe cases or a common effect of COVID-19. We used high-resolution systematic multi-omic analyses to profile the gut microbiome in asymptomatic-to-moderate COVID-19 individuals compared to a control group. RESULTS: We found a striking increase in the overall abundance and expression of both virulence factors and antimicrobial resistance genes in COVID-19. Importantly, these genes are encoded and expressed by commensal taxa from families such as Acidaminococcaceae and Erysipelatoclostridiaceae, which we found to be enriched in COVID-19-positive individuals. We also found an enrichment in the expression of a betaherpesvirus and rotavirus C genes in COVID-19-positive individuals compared to healthy controls. CONCLUSIONS: Our analyses identified an altered and increased infective competence of the gut microbiome in COVID-19 patients. Video Abstract.
Research center :
- Luxembourg Centre for Systems Biomedicine (LCSB): Bioinformatics Core (R. Schneider Group) - Luxembourg Centre for Systems Biomedicine (LCSB): Eco-Systems Biology (Wilmes Group) - Luxembourg Centre for Systems Biomedicine (LCSB): Clinical & Experimental Neuroscience (Krüger Group)
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Bibliography
Merad M, Blish CA, Sallusto F, Iwasaki A. The immunology and immunopathology of COVID-19. Science. 2022;375:1122–7.
WHO Coronavirus (COVID-19) dashboard. https://covid19.who.int/. Accessed 10 Sept 2022.
Fischer A, et al. Long COVID symptomatology after 12 months and its impact on quality of life according to initial coronavirus disease 2019 disease severity. Open Forum Infect Dis. 2022;9:ofac397.
Lamers MM, et al. SARS-CoV-2 productively infects human gut enterocytes. Science. 2020;369:50–4.
Wu Y, et al. Prolonged presence of SARS-CoV-2 viral RNA in faecal samples. Lancet Gastroenterol Hepatol. 2020;5:434–5.
Chen Y, et al. Six-month follow-up of gut microbiota richness in patients with COVID-19. Gut. 2022;71:222–5.
Yeoh YK, et al. Gut microbiota composition reflects disease severity and dysfunctional immune responses in patients with COVID-19. Gut. 2021;70:698–706.
Sorbara MT, Pamer EG. Interbacterial mechanisms of colonization resistance and the strategies pathogens use to overcome them. Mucosal Immunol. 2019;12:1–9.
Devi P, et al. Co-infections as Modulators of Disease Outcome: Minor Players or Major Players? Front Microbiol. 2021;12:664386.
Kim D, Quinn J, Pinsky B, Shah NH, Brown I. Rates of co-infection between SARS-CoV-2 and other respiratory pathogens. JAMA. 2020;323:2085–6.
Lansbury L, Lim B, Baskaran V, Lim WS. Co-infections in people with COVID-19: a systematic review and meta-analysis. J Infect. 2020;81:266–75.
Garcia-Vidal C, et al. Incidence of co-infections and superinfections in hospitalized patients with COVID-19: a retrospective cohort study. Clin Microbiol Infect. 2021;27:83–8.
Rutsaert L, et al. COVID-19-associated invasive pulmonary aspergillosis. Ann Intensive Care. 2020;10:71.
Silva DL, et al. Fungal and bacterial coinfections increase mortality of severely ill COVID-19 patients. J Hosp Infect. 2021;113:145–54.
Santoso P, et al. MDR pathogens organisms as risk factor of mortality in secondary pulmonary bacterial infections among COVID-19 patients: observational studies in two referral hospitals in West Java. Indonesia Int J Gen Med. 2022;15:4741–51.
D’Costa VM, et al. Antibiotic resistance is ancient. Nature. 2011;477:457–61.
Pelfrene E, Botgros R, Cavaleri M. Antimicrobial multidrug resistance in the era of COVID-19: a forgotten plight? Antimicrob Resist Infect Control. 2021;10:21.
Beceiro A, Tomás M, Bou G. Antimicrobial resistance and virulence: a successful or deleterious association in the bacterial world? Clin Microbiol Rev. 2013;26:185–230.
Martínez JL, Baquero F. Interactions among strategies associated with bacterial infection: pathogenicity, epidemicity, and antibiotic resistance. Clin Microbiol Rev. 2002;15:647–79.
Burrus V, Waldor MK. Shaping bacterial genomes with integrative and conjugative elements. Res Microbiol. 2004;155:376–86.
Rybak B, et al. Antibiotic resistance, virulence, and phylogenetic analysis of Escherichia coli strains isolated from free-living birds in human habitats. PLoS ONE. 2022;17:e0262236.
Masri L, et al. Host-pathogen coevolution: the selective advantage of Bacillus thuringiensis virulence and its cry toxin genes. PLoS Biol. 2015;13:e1002169.
Wu Y, et al. Resident bacteria contribute to opportunistic infections of the respiratory tract. PLoS Pathog. 2021;17:e1009436.
de Nies L, et al. PathoFact: a pipeline for the prediction of virulence factors and antimicrobial resistance genes in metagenomic data. Microbiome. 2021;9:49.
Narayanasamy S, et al. IMP: a pipeline for reproducible reference-independent integrated metagenomic and metatranscriptomic analyses. Genome Biol. 2016;17:260.
Sanchez-Ramirez DC, Normand K, Zhaoyun Y, Torres-Castro R. Long-term impact of COVID-19: a systematic review of the literature and meta-analysis. Biomedicines. 2021;9(8):900.
Hazan S, et al. Lost microbes of COVID-19: Bifidobacterium, Faecalibacterium depletion and decreased microbiome diversity associated with SARS-CoV-2 infection severity. BMJ Open Gastroenterol. 2022;9(1):e000871.
Lymberopoulos E, Gentili GI, Budhdeo S, Sharma N. COVID-19 severity is associated with population-level gut microbiome variations. Front Cell Infect Microbiol. 2022;12:963338.
Yamamoto S, et al. The human microbiome and COVID-19: A systematic review. PLoS ONE. 2021;16:e0253293.
Zuo T, et al. Alterations in gut microbiota of patients with COVID-19 during time of hospitalization. Gastroenterology. 2020;159:944-955.e8.
Anderson EJ, Weber SG. Rotavirus infection in adults. Lancet Infect Dis. 2004;4:91–9.
Wang L-P, et al. The changing pattern of enteric pathogen infections in China during the COVID-19 pandemic: a nation-wide observational study. Lancet Reg Health West Pac. 2021;16:100268.
Wong SH, Lui RN, Sung JJ. Covid-19 and the digestive system. J Gastroenterol Hepatol. 2020;35:744–8.
D’Amico F, Baumgart DC, Danese S, Peyrin-Biroulet L. Diarrhea during COVID-19 infection: pathogenesis, epidemiology, prevention, and management. Clin Gastroenterol Hepatol. 2020;18:1663–72.
Teklemariam AD, Hashem AM, Saber SH, Almuhayawi MS, Haque S, Abujamel TS, et al. Bacterial coinfections and antimicrobial resistance associated with the Coronavirus Disease 2019 infection. Biotechnol Genet Eng Rev. 2022;19:1–22. https://doi.org/10.1080/02648725.2022.2122297. Epub ahead of print. PMID: 36123822.
Islam MR, Rahman MM, Ahasan MT, Sarkar N, Akash S, Islam M, et al. The impact of mucormycosis (black fungus) on SARSCoV-2-infected patients: at a glance. Environ Sci Pollut Res Int. 2022;29(46):69341–66. 10.1007/s11356-022-22204-8.
Manor O, et al. Health and disease markers correlate with gut microbiome composition across thousands of people. Nat Commun. 2020;11:5206.
Milosavljevic MN, et al. Antimicrobial treatment of Erysipelatoclostridium ramosum invasive infections: a systematic review. Rev Inst Med Trop Sao Paulo. 2021;63:e30.
Candela M, et al. Inflammation and colorectal cancer, when microbiota-host mutualism breaks. World J Gastroenterol. 2014;20:908–22.
Chandra H, Sharma KK, Tuovinen OH, Sun X, Shukla P. Pathobionts: mechanisms of survival, expansion, and interaction with host with a focus on Clostridioides difficile. Gut Microbes. 2021;13:1979882.
Chedid M, et al. Antibiotics in treatment of COVID-19 complications: a review of frequency, indications, and efficacy. J Infect Public Health. 2021;14:570–6.
WHO Regional Office for Europe/European Centre for Disease Prevention and Control. Antimicrobial resistance surveillance in Europe 2022 – 2020 data. Copenhagen: WHO Regional Office for Europe; 2022. https://www.ecdc.europa.eu/en/publications-data/antimicrobial-resistance-surveillance-europe-2022-2020-data. Accessed 10 Sept 2022.
Kariyawasam RM, et al. Antimicrobial resistance (AMR) in COVID-19 patients: a systematic review and meta-analysis (November 2019-June 2021). Antimicrob Resist Infect Control. 2022;11:45.
Kang Y, et al. Alterations of fecal antibiotic resistome in COVID-19 patients after empirical antibiotic exposure. Int J Hyg Environ Health. 2022;240:113882.
Peng Y, et al. Gut microbiome and resistome changes during the first wave of the COVID-19 pandemic in comparison with pre-pandemic travel-related changes. J Travel Med. 2021;28(7):067.
Cao J, et al. Integrated gut virome and bacteriome dynamics in COVID-19 patients. Gut Microbes. 2021;13:1–21.
Bassetti M, Righi E, Carnelutti A, Graziano E, Russo A. Multidrug-resistant Klebsiella pneumoniae: challenges for treatment, prevention and infection control. Expert Rev Anti Infect Ther. 2018;16:749–61.
Ke S, Weiss ST, Liu Y-Y. Dissecting the role of the human microbiome in COVID-19 via metagenome-assembled genomes. Nat Commun. 2022;13:5235.
Fagherazzi G, et al. Protocol for a prospective, longitudinal cohort of people with COVID-19 and their household members to study factors associated with disease severity: the Predi-COVID study. BMJ Open. 2020;10:e041834.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–2.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25:1754–60.
Kopylova E, Noé L, Touzet H. SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data. Bioinformatics. 2012;28:3211–7.
Rodriguez-R LM, Konstantinidis KT. Nonpareil: a redundancy-based approach to assess the level of coverage in metagenomic datasets. Bioinformatics. 2014;30:629–35.
Andrews S. FastQC: A Quality Control Tool for High Throughput Sequence Data [Online]. 2010. Available online at: http://www.bioinformatics.babraham.ac.uk/projects/fastqc, https://qubeshub.org/resources/fastqc.
Ewels P, Magnusson M, Lundin S, Käller M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics. 2016;32:3047–8.
Wood DE, Lu J, Langmead B. Improved metagenomic analysis with Kraken 2. Genome Biol. 2019;20:257.
Bushnell B. BBMap: A fast, accurate, splice-aware aligner. 2014. https://www.osti.gov/biblio/1241166.
Ondov BD, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17:132.
Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.
Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics. 2014;30:2068–9.
Kang DD, et al. MetaBAT 2: an adaptive binning algorithm for robust and efficient genome reconstruction from metagenome assemblies. PeerJ. 2019;7:e7359.
Wu Y-W, Simmons BA, Singer SW. MaxBin 2.0: an automated binning algorithm to recover genomes from multiple metagenomic datasets. Bioinformatics. 2016;32:605–7.
Heintz-Buschart A, et al. Integrated multi-omics of the human gut microbiome in a case study of familial type 1 diabetes. Nat Microbiol. 2016;2:16180.
Sieber CMK, et al. Recovery of genomes from metagenomes via a dereplication, aggregation and scoring strategy. Nat Microbiol. 2018;3:836–43.
Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
Chaumeil P-A, Mussig AJ, Hugenholtz P, Parks DH. GTDB-Tk: a toolkit to classify genomes with the Genome Taxonomy Database. Bioinformatics. 2019;36:1925–7.
Mikheenko A, Saveliev V, Gurevich A. MetaQUAST: evaluation of metagenome assemblies. Bioinformatics. 2016;32:1088–90.
Kieft K, Zhou Z, Anantharaman K. VIBRANT: automated recovery, annotation and curation of microbial viruses, and evaluation of viral community function from genomic sequences. Microbiome. 2020;8:90.
Johansen J, et al. Genome binning of viral entities from bulk metagenomics data. Nat Commun. 2022;13:965.
Roux S, et al. IMG/VR v3: an integrated ecological and evolutionary framework for interrogating genomes of uncultivated viruses. Nucleic Acids Res. 2021;49:D764–75.
Chen S, He C, Li Y, Li Z, Melançon CE. A computational toolset for rapid identification of SARS-CoV-2, other viruses and microorganisms from sequencing data. Brief Bioinform. 2021;22:924–35.
Beghini F, et al. Integrating taxonomic, functional, and strain-level profiling of diverse microbial communities with bioBakery 3. Elife. 2021;10:e65088.
Alcock BP, et al. CARD 2020: antibiotic resistome surveillance with the comprehensive antibiotic resistance database. Nucleic Acids Res. 2020;48:D517–25.
Arango-Argoty G, et al. DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data. Microbiome. 2018;6:23.
Ren J, et al. Identifying viruses from metagenomic data by deep learning. arXiv [q-bio.GN]. 2018.
Roux S, Enault F, Hurwitz BL, Sullivan MB. VirSorter: mining viral signal from microbial genomic data. PeerJ. 2015;3:e985.
Krawczyk PS, Lipinski L, Dziembowski A. PlasFlow: predicting plasmid sequences in metagenomic data using genome signatures. Nucleic Acids Res. 2018;46:e35.
Chen L, et al. VFDB: a reference database for bacterial virulence factors. Nucleic Acids Res. 2005;33:D325–8.
R Core Team (2020). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. https://www.R-project.org/, https://www.organizingcreativity.com/2020/08/citing-r-and-rstudio/.
Mallick H, et al. Multivariable association discovery in population-scale meta-omics studies. PLoS Comput Biol. 2021;17:e1009442.
Lin H, Peddada SD. Analysis of compositions of microbiomes with bias correction. Nat Commun. 2020;11:3514.