Whole genome sequencing and characterization of Corynebacterium isolated from the healthy and dry eye ocular surface.
Corynebacterium
Dry eye disease
Ocular isolates
Phenotypic characterization
Whole genome sequencing
Journal
BMC microbiology
ISSN: 1471-2180
Titre abrégé: BMC Microbiol
Pays: England
ID NLM: 100966981
Informations de publication
Date de publication:
28 Sep 2024
28 Sep 2024
Historique:
received:
21
04
2024
accepted:
11
09
2024
medline:
29
9
2024
pubmed:
29
9
2024
entrez:
28
9
2024
Statut:
epublish
Résumé
The purpose of this study was to characterize Corynebacterium isolated from the ocular surface of dry eye disease patients and healthy controls. We aimed to investigate the pathogenic potential of these isolates in relation to ocular surface health. To this end, we performed whole genome sequencing in combination with biochemical, enzymatic, and antibiotic susceptibility tests. In addition, we employed deferred growth inhibition assays to examine how Corynebacterium isolates may impact the growth of potentially competing microorganisms including the ocular pathogens Pseudomonas aeruginosa and Staphylococcus aureus, as well as other Corynebacterium present on the eye. The 23 isolates were found to belong to 8 different species of Corynebacterium with genomes ranging from 2.12 mega base pairs in a novel Corynebacterium sp. to 2.65 mega base pairs in C. bovis. Whole genome sequencing revealed the presence of a range of antimicrobial targets present in all isolates. Pangenome analysis showed the presence of 516 core genes and that the pangenome is open. Phenotypic characterization showed variously urease, lipase, mucinase, protease and DNase activity in some isolates. Attention was particularly drawn to a potentially new or novel Corynebacterium species which had the smallest genome, and which produced a range of hydrolytic enzymes. Strikingly the isolate inhibited in vitro the growth of a range of possible pathogenic bacteria as well as other Corynebacterium isolates. The majority of Corynebacterium species included in this study did not seem to possess canonical pathogenic activity. This study is the first reported genomic and biochemical characterization of ocular Corynebacterium. A number of potential virulence factors were identified which may have direct relevance for ocular health and contribute to the finding of our previous report on the ocular microbiome, where it was shown that DNA libraries were often dominated by members of this genus. Particularly interesting in this regard was the observation that some Corynebacterium, particularly new or novel Corynebacterium sp. can inhibit the growth of other ocular Corynebacterium as well as known pathogens of the eye.
Sections du résumé
BACKGROUND
BACKGROUND
The purpose of this study was to characterize Corynebacterium isolated from the ocular surface of dry eye disease patients and healthy controls. We aimed to investigate the pathogenic potential of these isolates in relation to ocular surface health. To this end, we performed whole genome sequencing in combination with biochemical, enzymatic, and antibiotic susceptibility tests. In addition, we employed deferred growth inhibition assays to examine how Corynebacterium isolates may impact the growth of potentially competing microorganisms including the ocular pathogens Pseudomonas aeruginosa and Staphylococcus aureus, as well as other Corynebacterium present on the eye.
RESULTS
RESULTS
The 23 isolates were found to belong to 8 different species of Corynebacterium with genomes ranging from 2.12 mega base pairs in a novel Corynebacterium sp. to 2.65 mega base pairs in C. bovis. Whole genome sequencing revealed the presence of a range of antimicrobial targets present in all isolates. Pangenome analysis showed the presence of 516 core genes and that the pangenome is open. Phenotypic characterization showed variously urease, lipase, mucinase, protease and DNase activity in some isolates. Attention was particularly drawn to a potentially new or novel Corynebacterium species which had the smallest genome, and which produced a range of hydrolytic enzymes. Strikingly the isolate inhibited in vitro the growth of a range of possible pathogenic bacteria as well as other Corynebacterium isolates. The majority of Corynebacterium species included in this study did not seem to possess canonical pathogenic activity.
CONCLUSIONS
CONCLUSIONS
This study is the first reported genomic and biochemical characterization of ocular Corynebacterium. A number of potential virulence factors were identified which may have direct relevance for ocular health and contribute to the finding of our previous report on the ocular microbiome, where it was shown that DNA libraries were often dominated by members of this genus. Particularly interesting in this regard was the observation that some Corynebacterium, particularly new or novel Corynebacterium sp. can inhibit the growth of other ocular Corynebacterium as well as known pathogens of the eye.
Identifiants
pubmed: 39342108
doi: 10.1186/s12866-024-03517-9
pii: 10.1186/s12866-024-03517-9
doi:
Substances chimiques
Anti-Bacterial Agents
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
368Informations de copyright
© 2024. The Author(s).
Références
Tauch A, Fernández-Natal I, Soriano F. A microbiological and clinical review on corynebacterium kroppenstedtii. Int J Infect Dis. 2016;48:33–9.
pubmed: 27155209
doi: 10.1016/j.ijid.2016.04.023
Bernard K. The genus corynebacterium and other medically relevant coryneform-like bacteria. J Clin Microbiol. 2012;50(10):3152–8.
pubmed: 22837327
pmcid: 3457441
doi: 10.1128/JCM.00796-12
Parte AC, Sardà Carbasse J, Meier-Kolthoff JP, Reimer LC, Göker M. List of prokaryotic names with standing in nomenclature (LPSN) moves to the DSMZ. Int J Syst Evol MicroBiol. 2020;70(11):5607–12.
pubmed: 32701423
pmcid: 7723251
doi: 10.1099/ijsem.0.004332
Tauch A, Kaiser O, Hain T, Goesmann A, Weisshaar B, Albersmeier A, et al. Complete genome sequence and analysis of the multiresistant nosocomial pathogen Corynebacterium jeikeium K411, a lipid-requiring bacterium of the human skin flora. J Bacteriol. 2005;187(13):4671–82.
pubmed: 15968079
pmcid: 1151758
doi: 10.1128/JB.187.13.4671-4682.2005
Poetsch A, Haußmann U, Burkovski A. Proteomics of corynebacteria: from biotechnology workhorses to pathogens. Proteomics. 2011;11(15):3244–55.
pubmed: 21674800
doi: 10.1002/pmic.201000786
Sagerfors S, Poehlein A, Afshar M, Lindblad BE, Brüggemann H, Söderquist B. Clinical and genomic features of Corynebacterium macginleyi-associated infectious keratitis. Sci Rep. 2021;11(1):6015.
pubmed: 33727638
pmcid: 7966771
doi: 10.1038/s41598-021-85336-w
Aoki T, Kitazawa K, Deguchi H, Sotozono C. Current evidence for Corynebacterium on the ocular surface. Microorganisms. 2021;9(2):254.
pubmed: 33513871
pmcid: 7912348
doi: 10.3390/microorganisms9020254
Petrillo F, Pignataro D, Lavano MA, Santella B, Folliero V, Zannella C et al. Current evidence on the Ocular Surface Microbiota and Related diseases. Microorganisms. 2020;8(7):1033.
Peter VG, Morandi SC, Herzog EL, Zinkernagel MS, Zysset-Burri DC. Investigating the ocular surface microbiome: what can it tell us? Clin Ophthalmol. 2023;17:259–71.
pubmed: 36698849
pmcid: 9870096
doi: 10.2147/OPTH.S359304
St Leger AJ, Desai JV, Drummond RA, Kugadas A, Almaghrabi F, Silver P, et al. An ocular commensal protects against corneal infection by driving an Interleukin-17 response from mucosal γδ T cells. Immunity. 2017;47(1):148-e585.
pubmed: 28709803
doi: 10.1016/j.immuni.2017.06.014
Hardy BL, Dickey SW, Plaut RD, Riggins DP, Stibitz S, Otto M et al. Corynebacterium pseudodiphtheriticum exploits Staphylococcus aureus Virulence Components in a Novel Polymicrobial Defense Strategy. mBio. 2019;10(1):e02491–18.
Bomar L, Brugger SD, Yost BH, Davies SS, Lemon KP. Corynebacterium accolens releases antipneumococcal free fatty acids from human nostril and skin surface triacylglycerols. mBio. 2016;7(1). https://doi.org/10.1128/mbio.01725-15 .
Menberu MA, Liu S, Cooksley C, Hayes AJ, Psaltis AJ, Wormald PJ, et al. Corynebacterium accolens has antimicrobial activity against staphylococcus aureus and methicillin-resistant s. aureus pathogens isolated from the sinonasal niche of chronic rhinosinusitis patients. Pathogens. 2021;10(2):207.
pubmed: 33672855
pmcid: 7918835
doi: 10.3390/pathogens10020207
Szabo D, Ostorhazi E, Stercz B, Makra N, Penzes K, Kristof K, et al. Specific nasopharyngeal Corynebacterium strains serve as gatekeepers against SARS-CoV-2 infection. GeroScience. 2023;45(5):2927–38.
pubmed: 37338780
pmcid: 10643471
doi: 10.1007/s11357-023-00850-1
Naqvi M, Fineide F, Utheim TP, Charnock C. Culture- and non-culture-based approaches reveal unique features of the ocular microbiome in dry eye patients. Ocul Surf. 2024:123–9.
Shamsuzzaman M, Dahal RH, Kim S, Kim J. Genome insight and probiotic potential of three novel species of the genus Corynebacterium. Front Microbiol. 2023;14:14.
doi: 10.3389/fmicb.2023.1225282
Moran JC, Crank EL, Ghabban HA, Horsburgh MJ. Deferred growth inhibition assay to quantify the effect of Bacteria-derived antimicrobials on competition. J Vis Exp. 2016;(115):e54437.
Chen S, Zhou Y, Chen Y, Gu J. Fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics. 2018;34(17):i884-90.
pubmed: 30423086
pmcid: 6129281
doi: 10.1093/bioinformatics/bty560
Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M, Kulikov AS, et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol. 2012;19(5):455–77.
pubmed: 22506599
pmcid: 3342519
doi: 10.1089/cmb.2012.0021
Bushnell B. Normalizes read depth based on kmer counts. Can also error-correct, bin reads by kmer depth, and generate a kmer depth histogram. bbnorm. 2017. Available from: https://sourceforge.net/projects/bbmap/ .
Gurevich A, Saveliev V, Vyahhi N, Tesler G. QUAST: quality assessment tool for genome assemblies. Bioinformatics. 2013;29(8):1072–5.
pubmed: 23422339
pmcid: 3624806
doi: 10.1093/bioinformatics/btt086
Olson RD, Assaf R, Brettin T, Conrad N, Cucinell C, Davis James J, et al. Introducing the bacterial and viral bioinformatics resource center (BV-BRC): a resource combining PATRIC, IRD and ViPR. Nucleic Acids Res. 2022;51(D1):D678-89.
pmcid: 9825582
doi: 10.1093/nar/gkac1003
Brettin T, Davis JJ, Disz T, Edwards RA, Gerdes S, Olsen GJ, et al. RASTtk: a modular and extensible implementation of the RAST algorithm for building custom annotation pipelines and annotating batches of genomes. Sci Rep. 2015;5: 8365.
pubmed: 25666585
pmcid: 4322359
doi: 10.1038/srep08365
Wattam AR, Davis JJ, Assaf R, Boisvert S, Brettin T, Bun C, et al. Improvements to PATRIC, the all-bacterial bioinformatics database and analysis resource center. Nucleic Acids Res. 2017;45(D1):D535-42.
pubmed: 27899627
doi: 10.1093/nar/gkw1017
Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene ontology: tool for the unification of biology. The gene ontology consortium. Nat Genet. 2000;25(1):25–9.
pubmed: 10802651
pmcid: 3037419
doi: 10.1038/75556
Schomburg I, Chang A, Ebeling C, Gremse M, Heldt C, Huhn G, et al. BRENDA, the enzyme database: updates and major new developments. Nucleic Acids Res. 2004;32(Database issue):D431-433.
pubmed: 14681450
pmcid: 308815
doi: 10.1093/nar/gkh081
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44(D1):D457-62.
pubmed: 26476454
doi: 10.1093/nar/gkv1070
Bortolaia V, Kaas RS, Ruppe E, Roberts MC, Schwarz S, Cattoir V, et al. ResFinder 4.0 for predictions of phenotypes from genotypes. J Antimicrob Chemother. 2020;75(12):3491–500.
pubmed: 32780112
pmcid: 7662176
doi: 10.1093/jac/dkaa345
Nasim F, Dey A, Qureshi IA. Comparative genome analysis of Corynebacterium species: the underestimated pathogens with high virulence potential. Infect Genet Evol. 2021;93: 104928.
pubmed: 34022437
doi: 10.1016/j.meegid.2021.104928
Chaudhari NM, Gupta VK, Dutta C. BPGA- an ultra-fast pan-genome analysis pipeline. Sci Rep. 2016;6: 24373.
pubmed: 27071527
pmcid: 4829868
doi: 10.1038/srep24373
Meier-Kolthoff JP, Göker M. TYGS is an automated high-throughput platform for state-of-the-art genome-based taxonomy. Nat Commun. 2019;10(1):2182.
pubmed: 31097708
pmcid: 6522516
doi: 10.1038/s41467-019-10210-3
Meier-Kolthoff JP, Carbasse JS, Peinado-Olarte RL, Göker M. TYGS and LPSN: a database tandem for fast and reliable genome-based classification and nomenclature of prokaryotes. Nucleic Acids Res. 2021;50(D1):D801-7.
pmcid: 8728197
doi: 10.1093/nar/gkab902
Ondov BD, Treangen TJ, Melsted P, Mallonee AB, Bergman NH, Koren S, et al. Mash: fast genome and metagenome distance estimation using MinHash. Genome Biol. 2016;17(1):132.
pubmed: 27323842
pmcid: 4915045
doi: 10.1186/s13059-016-0997-x
Lagesen K, Hallin P, Rødland EA, Stærfeldt H-H, Rognes T, Ussery DW. RNAmmer: consistent and rapid annotation of ribosomal RNA genes. Nucleic Acids Res. 2007;35(9):3100–8.
pubmed: 17452365
pmcid: 1888812
doi: 10.1093/nar/gkm160
Camacho C, Coulouris G, Avagyan V, Ma N, Papadopoulos J, Bealer K, et al. BLAST+: architecture and applications. BMC Bioinformatics. 2009;10(1): 421.
pubmed: 20003500
pmcid: 2803857
doi: 10.1186/1471-2105-10-421
Meier-Kolthoff JP, Auch AF, Klenk H-P, Göker M. Genome sequence-based species delimitation with confidence intervals and improved distance functions. BMC Bioinformatics. 2013;14(1): 60.
pubmed: 23432962
pmcid: 3665452
doi: 10.1186/1471-2105-14-60
Lefort V, Desper R, Gascuel O. FastME 2.0: a comprehensive, accurate, and fast distance-based phylogeny inference program. Mol Biol Evol. 2015;32(10):2798–800.
pubmed: 26130081
pmcid: 4576710
doi: 10.1093/molbev/msv150
Farris JS. Estimating phylogenetic trees from distance matrices. Am Nat. 1972;106(951):645–68.
doi: 10.1086/282802
Kreft Ł, Botzki A, Coppens F, Vandepoele K, Van Bel M. PhyD3: a phylogenetic tree viewer with extended phyloXML support for functional genomics data visualization. Bioinformatics. 2017;33(18):2946–7.
pubmed: 28525531
doi: 10.1093/bioinformatics/btx324
Richter M, Rosselló-Móra R, Oliver Glöckner F, Peplies J. JSpeciesWS: a web server for prokaryotic species circumscription based on pairwise genome comparison. Bioinformatics. 2015;32(6):929–31.
pubmed: 26576653
pmcid: 5939971
doi: 10.1093/bioinformatics/btv681
Johnson M, Zaretskaya I, Raytselis Y, Merezhuk Y, McGinnis S, Madden TL. NCBI BLAST: a better web interface. Nucleic Acids Res. 2008;36(suppl2):W5-9.
pubmed: 18440982
pmcid: 2447716
doi: 10.1093/nar/gkn201
Madeira F, Madhusoodanan N, Lee J, Eusebi A, Niewielska A, Tivey ARN, et al. The EMBL-EBI Job dispatcher sequence analysis tools framework in 2024. Nucleic Acids Res. 2024;52(W1):W521-525.
pubmed: 38597606
pmcid: 11223882
doi: 10.1093/nar/gkae241
Minh BQ, Schmidt HA, Chernomor O, Schrempf D, Woodhams MD, von Haeseler A, et al. IQ-TREE 2: new models and efficient methods for phylogenetic inference in the genomic era. Mol Biol Evol. 2020;37(5):1530–4.
pubmed: 32011700
pmcid: 7182206
doi: 10.1093/molbev/msaa015
Trifinopoulos J, Nguyen LT, von Haeseler A, Minh BQ. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 2016;44(W1):W232-5.
pubmed: 27084950
pmcid: 4987875
doi: 10.1093/nar/gkw256
Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7.
pubmed: 15034147
pmcid: 390337
doi: 10.1093/nar/gkh340
Capella-Gutiérrez S, Silla-Martínez JM, Gabaldón T. trimAl: a tool for automated alignment trimming in large-scale phylogenetic analyses. Bioinformatics. 2009;25(15):1972–3.
pubmed: 19505945
pmcid: 2712344
doi: 10.1093/bioinformatics/btp348
Kalyaanamoorthy S, Minh BQ, Wong TKF, von Haeseler A, Jermiin LS. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat Methods. 2017;14(6):587–9.
pubmed: 28481363
pmcid: 5453245
doi: 10.1038/nmeth.4285
Minh BQ, Nguyen MAT, von Haeseler A. Ultrafast approximation for phylogenetic bootstrap. Mol Biol Evol. 2013;30(5):1188–95.
pubmed: 23418397
pmcid: 3670741
doi: 10.1093/molbev/mst024
Hoang DT, Chernomor O, von Haeseler A, Minh BQ, Vinh LS. UFBoot2: improving the Ultrafast bootstrap approximation. Mol Biol Evol. 2018;35(2):518–22.
pubmed: 29077904
doi: 10.1093/molbev/msx281
Anisimova M, Gascuel O. Approximate likelihood-ratio test for branches: a fast, accurate, and powerful alternative. Syst Biol. 2006;55(4):539–52.
pubmed: 16785212
doi: 10.1080/10635150600755453
Letunic I, Bork P. Interactive tree of life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 2021;49(W1):W293-6.
pubmed: 33885785
pmcid: 8265157
doi: 10.1093/nar/gkab301
Jensen MG, Svraka L, Baez E, Lund M, Poehlein A, Brüggemann H. Species- and strain-level diversity of Corynebacteria isolated from human facial skin. BMC Microbiol. 2023;23(1):366.
pubmed: 38017392
pmcid: 10683109
doi: 10.1186/s12866-023-03129-9
Jain C, Rodriguez-R LM, Phillippy AM, Konstantinidis KT, Aluru S. High throughput ANI analysis of 90K prokaryotic genomes reveals clear species boundaries. Nat Commun. 2018;9(1):5114.
pubmed: 30504855
pmcid: 6269478
doi: 10.1038/s41467-018-07641-9
Huang Y, Song MH, Li SG, Yu Shen H, Qu PH, Zhang DF. Preliminary comparative genomics analysis among Corynebacterium kroppenstedtii complex necessitates a reassessment of precise species associated with mastitis. J Appl Microbiol. 2023;135(1):lxad314.
Jang YJ, Qin Q-Q, Huang S-Y, Peter ATJ, Ding X-M, Kornmann B. Accurate prediction of protein function using statistics-informed graph networks. Nat Commun. 2024;15(1):6601.
pubmed: 39097570
pmcid: 11297950
doi: 10.1038/s41467-024-50955-0
Craig JP, Nichols KK, Akpek EK, Caffery B, Dua HS, Joo C-K, et al. TFOS DEWS II definition and classification report. Ocul Surf. 2017;15(3):276–83.
pubmed: 28736335
doi: 10.1016/j.jtos.2017.05.008
Ozkan J, Nielsen S, Diez-Vives C, Coroneo M, Thomas T, Willcox M. Temporal Stability and Composition of the ocular surface Microbiome. Sci Rep. 2017;7(1):9880.
pubmed: 28852195
pmcid: 5575025
doi: 10.1038/s41598-017-10494-9
Ozkan J, Majzoub ME, Coroneo M, Thomas T, Willcox M. Ocular microbiome changes in dry eye disease and meibomian gland dysfunction. Exp Eye Res. 2023;235: 109615.
pubmed: 37586456
doi: 10.1016/j.exer.2023.109615
Stapleton F, Alves M, Bunya VY, Jalbert I, Lekhanont K, Malet F, et al. TFOS DEWS II Epidemiology Report. Ocul Surf. 2017;15(3):334–65.
pubmed: 28736337
doi: 10.1016/j.jtos.2017.05.003
Weinert LA, Welch JJ. Why might bacterial pathogens have small genomes? Trends Ecol Evol. 2017;32(12):936–47.
pubmed: 29054300
doi: 10.1016/j.tree.2017.09.006
Rouli L, Merhej V, Fournier PE, Raoult D. The bacterial pangenome as a new tool for analysing pathogenic bacteria. New Microbes New Infect. 2015;7:72–85.
pubmed: 26442149
pmcid: 4552756
doi: 10.1016/j.nmni.2015.06.005
Wall DM, Duffy PS, Dupont C, Prescott JF, Meijer WG. Isocitrate lyase activity is required for virulence of the intracellular pathogen Rhodococcus equi. Infect Immun. 2005;73(10):6736–41.
pubmed: 16177351
pmcid: 1230931
doi: 10.1128/IAI.73.10.6736-6741.2005
Vernhardsdottir RR, Magno MS, Hynnekleiv L, Lagali N, Dartt DA, Vehof J, et al. Antibiotic treatment for dry eye disease related to meibomian gland dysfunction and blepharitis – a review. Ocul Surf. 2022;26:211–21.
pubmed: 36210626
doi: 10.1016/j.jtos.2022.08.010
Magiorakos AP, Srinivasan A, Carey RB, Carmeli Y, Falagas ME, Giske CG, et al. Multidrug-resistant, extensively drug-resistant and pandrug-resistant bacteria: an international expert proposal for interim standard definitions for acquired resistance. Clin Microbiol Infect. 2012;18(3):268–81.
pubmed: 21793988
doi: 10.1111/j.1469-0691.2011.03570.x
Haas B, Bonifait L, Vaillancourt K, Charette SJ, Gottschalk M, Grenier D. Characterization of DNase activity and gene in Streptococcus suis and evidence for a role as virulence factor. BMC Res Notes. 2014;7(1): 424.
pubmed: 24996230
pmcid: 4094637
doi: 10.1186/1756-0500-7-424
Pimenta FP, Souza MC, Pereira GA, Hirata R Jr, Camello TCF, Mattos-Guaraldi AL. DNase test as a novel approach for the routine screening of Corynebacterium diphtheriae. Lett Appl Microbiol. 2008;46(3):307–11.
pubmed: 18290809
doi: 10.1111/j.1472-765X.2007.02310.x
Corfield AP, Carrington SD, Hicks SJ, Berry M, Ellingham R. Ocular mucins: purification, metabolism and functions. Prog Retin Eye Res. 1997;16(4):627–56.
doi: 10.1016/S1350-9462(96)00039-0
Salem N, Salem L, Saber S, Ismail G, Bluth MH. Corynebacterium urealyticum: a comprehensive review of an understated organism. Infection and drug resistance. 2015:129 – 45.
Jäger K, Kielstein H, Dunse M, Nass N, Paulsen F, Sel S. Enzymes of urea synthesis are expressed at the ocular surface, and decreased urea in the tear fluid is associated with dry-eye syndrome. Graefes Arch Clin Exp Ophthalmol. 2013;251(8):1995–2002.
pubmed: 23740519
doi: 10.1007/s00417-013-2391-7
Gladysheva IV, Cherkasov SV, Khlopko YA, Plotnikov AO. Genome characterization and probiotic potential of Corynebacterium amycolatum human vaginal isolates. Microorganisms. 2022;10(2): 249.
pubmed: 35208706
pmcid: 8878833
doi: 10.3390/microorganisms10020249