Prediction of anastomotic insufficiency based on the mucosal microbiome prior to colorectal surgery: a proof-of-principle study.
Humans
Male
Female
Aged
Anastomotic Leak
/ etiology
Middle Aged
Gastrointestinal Microbiome
/ genetics
Colorectal Neoplasms
/ surgery
RNA, Ribosomal, 16S
/ genetics
Colorectal Surgery
/ adverse effects
Intestinal Mucosa
/ microbiology
Bacteria
/ classification
Colon
/ microbiology
Proof of Concept Study
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
03 Jul 2024
03 Jul 2024
Historique:
received:
13
12
2023
accepted:
19
06
2024
medline:
4
7
2024
pubmed:
4
7
2024
entrez:
3
7
2024
Statut:
epublish
Résumé
Anastomotic leakage (AL) is a potentially life-threatening complication following colorectal cancer (CRC) resection. In this study, we aimed to unravel longitudinal changes in microbial structure before, during, and after surgery and to determine if microbial alterations may be predictive for risk assessment between sufficient anastomotic healing (AS) and AL prior surgery. We analysed the microbiota of 134 colon mucosal biopsies with 16S rRNA V1-V2 gene sequencing. Samples were collected from three location sites before, during, and after surgery, and patients received antibiotics after the initial collection and during surgery. The microbial structure showed dynamic surgery-related changes at different time points. Overall bacterial diversity and the abundance of some genera such as Faecalibacterium or Alistipes decreased over time, while the genera Enterococcus and Escherichia_Shigella increased. The distribution of taxa between AS and AL revealed significant differences in the abundance of genera such as Prevotella, Faecalibacterium and Phocaeicola. In addition to Phocaeicola, Ruminococcus2 and Blautia showed significant differences in abundance between preoperative sample types. ROC analysis of the predictive value of these genera for AL revealed an AUC of 0.802 (p = 0.0013). In summary, microbial composition was associated with postoperative outcomes, and the abundance of certain genera may be predictive of postoperative complications.
Identifiants
pubmed: 38961176
doi: 10.1038/s41598-024-65320-w
pii: 10.1038/s41598-024-65320-w
doi:
Substances chimiques
RNA, Ribosomal, 16S
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
15335Subventions
Organisme : European Regional Development Fund
ID : ZS/2018/11/95324
Informations de copyright
© 2024. The Author(s).
Références
Arnold, M. et al. Global patterns and trends in colorectal cancer incidence and mortality. Gut 66, 683–691 (2017).
pubmed: 26818619
doi: 10.1136/gutjnl-2015-310912
Chan, D. S. M. et al. Red and processed meat and colorectal cancer incidence: meta-analysis of prospective studies. PLoS ONE 6, e20456 (2011).
pubmed: 21674008
pmcid: 3108955
doi: 10.1371/journal.pone.0020456
Krämer, H. U., Schöttker, B., Raum, E. & Brenner, H. Type 2 diabetes mellitus and colorectal cancer: Meta-analysis on sex-specific differences. Eur. J. Cancer 48, 1269–1282 (2012).
pubmed: 21889332
doi: 10.1016/j.ejca.2011.07.010
Kyrgiou, M. et al. Adiposity and cancer at major anatomical sites: Umbrella review of the literature. BMJ. https://doi.org/10.1136/bmj.j477 (2017).
doi: 10.1136/bmj.j477
pubmed: 29074629
pmcid: 5656976
Wiegering, A. et al. Improved survival of patients with colon cancer detected by screening colonoscopy. Int. J. Colorectal Dis. 31, 1039–1045 (2016).
pubmed: 26763006
doi: 10.1007/s00384-015-2501-6
Benson, A. B. et al. Rectal cancer, Version 2.2022, NCCN clinical practice guidelines in oncology. J. Natl. Compreh. Cancer Netw. 20, 1139–1167 (2022).
doi: 10.6004/jnccn.2022.0051
Tsalikidis, C. et al. Predictive factors for anastomotic leakage following colorectal cancer surgery: Where are we and where are we going?. Curr. Oncol. 30, 3111–3137 (2023).
pubmed: 36975449
pmcid: 10047700
doi: 10.3390/curroncol30030236
Sciuto, A. et al. Predictive factors for anastomotic leakage after laparoscopic colorectal surgery. World J. Gastroenterol. 24, 2247–2260 (2018).
pubmed: 29881234
pmcid: 5989239
doi: 10.3748/wjg.v24.i21.2247
Vilchez-Vargas, R. et al. Gut microbial similarity in twins is driven by shared environment and aging. EBioMedicine 79, 104011 (2022).
pubmed: 35490553
pmcid: 9062754
doi: 10.1016/j.ebiom.2022.104011
Arumugam, M. et al. Enterotypes of the human gut microbiome. Nature 473, 174–180 (2011).
pubmed: 21508958
pmcid: 3728647
doi: 10.1038/nature09944
Vasapolli, R. et al. Analysis of transcriptionally active bacteria throughout the gastrointestinal tract of healthy individuals. Gastroenterology 157, 1081-1092.e3 (2019).
pubmed: 31175864
doi: 10.1053/j.gastro.2019.05.068
Cani, P. D. Human gut microbiome: Hopes, threats and promises. Gut 67, 1716–1725 (2018).
pubmed: 29934437
doi: 10.1136/gutjnl-2018-316723
Heintz-Buschart, A. & Wilmes, P. Human gut microbiome: Function Matters. Trends Microbiol 26, 563–574 (2018).
pubmed: 29173869
doi: 10.1016/j.tim.2017.11.002
Mima, K. et al. Fusobacterium nucleatum in colorectal carcinoma tissue and patient prognosis. Gut 65, 1973–1980 (2016).
pubmed: 26311717
doi: 10.1136/gutjnl-2015-310101
Bullman, S. et al. Analysis of fusobacterium persistence and antibiotic response in colorectal cancer. Science 358, 1443–1448 (2017).
pubmed: 29170280
pmcid: 5823247
doi: 10.1126/science.aal5240
Gershuni, V. M. & Friedman, E. S. The microbiome-host interaction as a potential driver of anastomotic leak. Curr. Gastroenterol. Rep. 21, 4 (2019).
pubmed: 30684121
pmcid: 9041531
doi: 10.1007/s11894-019-0668-7
Williamson, A. J. & Alverdy, J. C. Influence of the microbiome on anastomotic leak. Clin. Colon. Rectal. Surg. 34, 439–446 (2021).
pubmed: 34853567
pmcid: 8610638
doi: 10.1055/s-0041-1735276
Schulz, C. et al. The active bacterial assemblages of the upper Gi tract in individuals with and without Helicobacter infection. Gut 67, 216–225 (2018).
pubmed: 27920199
doi: 10.1136/gutjnl-2016-312904
Lehr, K. et al. Microbial composition of tumorous and adjacent gastric tissue is associated with prognosis of gastric cancer. Sci. Rep. 13, 4640 (2023).
pubmed: 36944721
pmcid: 10030820
doi: 10.1038/s41598-023-31740-3
Ohigashi, S. et al. Significant changes in the intestinal environment after surgery in patients with colorectal cancer. J. Gastrointest. Surg. 17, 1657–1664 (2013).
pubmed: 23807702
doi: 10.1007/s11605-013-2270-x
Lee, D.-S. et al. Risk factors for acquisition of multidrug-resistant bacteria in patients with anastomotic leakage after colorectal cancer surgery. Int. J. Colorectal Dis. 30, 497–504 (2015).
pubmed: 25735927
doi: 10.1007/s00384-015-2161-6
Akter, T. et al. Virulence and antibiotic-resistance genes in Enterococcus faecalis associated with streptococcosis disease in fish. Sci. Rep. 13, 1551 (2023).
pubmed: 36707682
pmcid: 9883459
doi: 10.1038/s41598-022-25968-8
Tett, A. et al. The Prevotella copri complex comprises four distinct clades underrepresented in westernized populations. Cell. Host. Microbe 26, 666-679.e7 (2019).
pubmed: 31607556
pmcid: 6854460
doi: 10.1016/j.chom.2019.08.018
Jernberg, C., Löfmark, S., Edlund, C. & Jansson, J. K. Long-term ecological impacts of antibiotic administration on the human intestinal microbiota. ISME J. 1, 56–66 (2007).
pubmed: 18043614
doi: 10.1038/ismej.2007.3
Van Praagh, J. B., De Goffau, M. C., Harmsen, H. J. M. & Havenga, K. Response to comment on ‘mucus microbiome of anastomotic tissue during surgery has predictive value for colorectal anastomotic leakage’. Ann. Surg. 269, E69–E71 (2019).
pubmed: 30985361
doi: 10.1097/SLA.0000000000002857
Agarwala, R. et al. Database resources of the national center for biotechnology information. Nucl. Acids Res. 44, D7–D19 (2016).
doi: 10.1093/nar/gkv1290
Levy, R. et al. Longitudinal analysis reveals transition barriers between dominant ecological states in the gut microbiome. Proc. Natl. Acad. Sci. USA 117, 13839–13845 (2020).
pubmed: 32471946
pmcid: 7306764
doi: 10.1073/pnas.1922498117
Johnson, E. L., Heaver, S. L., Walters, W. A. & Ley, R. E. Microbiome and metabolic disease: Revisiting the bacterial phylum Bacteroidetes. J. Mol. Med. 95, 1–8 (2017).
pubmed: 27900395
doi: 10.1007/s00109-016-1492-2
Komen, N. et al. Polymerase chain reaction for Enterococcus faecalis in drain fluid: The first screening test for symptomatic colorectal anastomotic leakage. The appeal-study: Analysis of parameters predictive for evident anastomotic leakage. Int. J. Colorectal Dis. 29, 15–21 (2014).
pubmed: 24122105
doi: 10.1007/s00384-013-1776-8
Huisman, D. E. et al. LekCheck: A prospective study to identify perioperative modifiable risk factors for anastomotic leakage in colorectal surgery. Ann. Surg. 275, e189–e197 (2022).
pubmed: 32511133
doi: 10.1097/SLA.0000000000003853
World Medical Association. World medical association declaration of Helsinki. JAMA 310, 2191 (2013).
doi: 10.1001/jama.2013.281053
Vilchez-Vargas, R. et al. Profiling of the bacterial microbiota along the murine alimentary tract. Int. J. Mol. Sci. 23, 1783 (2022).
pubmed: 35163705
pmcid: 8836272
doi: 10.3390/ijms23031783
Lane, D. J. 16S/23S rRNA Sequencing. in Nucleic Acid Techniques in Bacterial Systematic (eds. Stackebrandt, E. & Goodfellow, M.) 115–175 (Wiley, New York, 1991).
Camarinha-Silva, A. et al. Comparing the anterior nare bacterial community of two discrete human populations using Illumina amplicon sequencing. Environ. Microbiol. 16, 2939–2952 (2014).
pubmed: 24354520
doi: 10.1111/1462-2920.12362
Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).
pubmed: 27214047
pmcid: 4927377
doi: 10.1038/nmeth.3869
McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One 8, e61217 (2013).
pubmed: 23630581
pmcid: 3632530
doi: 10.1371/journal.pone.0061217
Maidak, B. L. et al. The RDP (Ribosomal Database Project). Nucl. Acids Res. 25, 109–110 (1997).
pubmed: 9016515
pmcid: 146422
doi: 10.1093/nar/25.1.109
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naïve Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
pubmed: 17586664
pmcid: 1950982
doi: 10.1128/AEM.00062-07
Clarke, K. R. Non-parametric multivariate analyses of changes in community structure. Austral. Ecol. 18, 117–143 (1993).
doi: 10.1111/j.1442-9993.1993.tb00438.x
Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26, 32–46 (2001).
Clarke, K. R., Gorley, R., Sommerfield, P. J. & Warwick, R. M. Change in marine communities—An approach to statistical analysis and interpretation. (PRIMER-E Ltd, Plymouth, 2014).
Hothorn, T. & Hornik, K. exactRankTests: Exact Distributions for Rank and Permutation Tests. R Package Version 0.8–35 Preprint at https://cran.r-project.org/package=exactRankTests (2022).
Kodikara, S., Ellul, S. & Lê-Cao, K.-A. Statistical challenges in longitudinal microbiome data analysis. Brief Bioinform 23, 273 (2022).
doi: 10.1093/bib/bbac273
Shields-Cutler, R. R., Al-Ghalith, G. A., Yassour, M. & Knights, D. SplinectomeR enables group comparisons in longitudinal microbiome studies. Front. Microbiol. 9, 785 (2018).
pubmed: 29740416
pmcid: 5924793
doi: 10.3389/fmicb.2018.00785
Oksanen, J. et al. Vegan: Community ecology package. R Package Version 2.6–2 Preprint at https://cran.r-project.org/package=vegan (2015).
Goslee, S. C. & Urban, D. L. The ecodist package for dissimilarity-based analysis of ecological data. J. Stat. Softw. 22, 1–19 (2007).
doi: 10.18637/jss.v022.i07
Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. Cluster: Cluster analysis basics and extensions. R package version 2.1.3 Preprint at https://cran.r-project.org/package=cluster (2022).
Galtier, N., Gouy, M. & Gautier, C. SEAVIEW and PHYLO_WIN: Two graphic tools for sequence alignment and molecular phylogeny. Comput. Appl. Biosci. 12, 543–548 (1996).
pubmed: 9021275
Letunic, I. & Bork, P. Interactive tree of life (iTOL) v5: An online tool for phylogenetic tree display and annotation. Nucleic Acids Res. 49, W293–W296 (2021).
pubmed: 33885785
pmcid: 8265157
doi: 10.1093/nar/gkab301
Kartal, E. et al. A faecal microbiota signature with high specificity for pancreatic cancer. Gut 71, 1359–1372 (2022).
pubmed: 35260444
doi: 10.1136/gutjnl-2021-324755