Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Matters Arising
  • Published:

Revisiting the intrinsic mycobiome in pancreatic cancer

Nature volume 620, pages E1–E6 (2023)Cite this article

Subjects

Matters Arising to this article was published on 02 August 2023

The Original Article was published on 02 October 2019

arising from: B. Aykut et al. Nature https://doi.org/10.1038/s41586-019-1608-2 (2019)

A growing body of literature suggests that alterations in the human microbiome are causative of disease initiation and progression. Aykut et al.1 have provided evidence that alterations in the gut fungal microbiome (the ‘mycobiome’), along with the presence of fungal elements within pancreatic tissue (specifically those of the genus Malassezia), are associated with pancreatic oncogenesis. On analysing the human sequencing data presented in the original article, we found few fungal reads in pancreatic tissue samples and did not identify differences in pancreatic or gut mycobiome composition between healthy participants and patients with pancreatic ductal adenocarcinoma (PDAC). Our reanalysis of these data does not support an association between an intrinsic pancreatic mycobiome and the development of human PDAC, and illustrates the challenges in analysing microbiome sequencing data from low-biomass samples.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: QIIME2 reanalysis of human pancreatic ITS sequencing data made publicly available by Aykut et al.
Fig. 2: QIIME2 reanalysis of human faecal ITS sequencing data made publicly available by Aykut et al.

Data availability

The sequencing dataset generated in the experiments conducted by Aykut et al. is available in the NCBI SRA (https://www.ncbi.nlm.nih.gov/sra; PRJNA557226). Raw count and taxonomy tables generated by QIIME2 and DADA2 as part of our reanalysis of these original sequencing data are available in a public GitHub repository (https://github.com/afletch00/Fungi-Nature-2022).

Code availability

The scripts for the QIIME2 and DADA2 pipelines and downstream analyses performed in R are available in a public GitHub repository (https://github.com/afletch00/Fungi-Nature-2022).

References

  1. Aykut, B. et al. The fungal mycobiome promotes pancreatic oncogenesis via activation of MBL. Nature 574, 264–267 (2019).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  2. Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Prodan, A. et al. Comparing bioinformatic pipelines for microbial 16S rRNA amplicon sequencing. PLoS ONE 15, e0227434 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Edgar, R. C. Accuracy of microbial community diversity estimated by closed- and open-reference OTUs. PeerJ 5, e3889 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Callahan, B. J., McMurdie, P. J. & Holmes, S. P. Exact sequence variants should replace operational taxonomic units in marker-gene data analysis. ISME J. 11, 2639–2643 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Caruso, V., Song, X., Asquith, M. & Karstens, L. Performance of microbiome sequence inference methods in environments with varying biomass. mSystems 4, e00163-18 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  8. Narayan, N. R. et al. Piphillin predicts metagenomic composition and dynamics from DADA2-corrected 16S rDNA sequences. BMC Genomics 21, 56 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Selway, C. A., Eisenhofer, R. & Weyrich, L. S. Microbiome applications for pathology: challenges of low microbial biomass samples during diagnostic testing. J. Pathol. Clin. Res. 6, 97–106 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Eisenhofer, R. et al. Contamination in low microbial biomass microbiome studies: issues and recommendations. Trends Microbiol. 27, 105–117 (2019).

    Article  CAS  PubMed  Google Scholar 

  11. McMurdie, P. J. & Holmes, S. phyloseq: An R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).

    Article  ADS  CAS  PubMed  PubMed Central  Google Scholar 

  12. Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).

    Article  Google Scholar 

Download references

Acknowledgements

We thank the Duke University School of Medicine for use of the Sequencing and Genomic Technologies Shared Resource, which provided services for sequence analysis; and A. N. Fletcher for providing graphical consultation and colour palette development. This work was funded by the Duke University School of Medicine through a grant from the Duke Microbiome Center. M.S.K. was supported by a National Institutes of Health Career Development Award (K23-AI135090). A.M.E. was supported by a National Institutes of Health T-32 grant (T32-CA093245) for translational research in surgical oncology.

Author information

Authors and Affiliations

  1. Department of Surgery, Division of Surgical Oncology, Duke University School of Medicine, Durham, NC, USA

    Ashley A. Fletcher, Austin M. Eckhoff & Peter J. Allen

  2. Division of Pediatric Infectious Diseases, Associate Director of the Duke Microbiome Center, Duke University, Durham, NC, USA

    Matthew S. Kelly

Authors
  1. Ashley A. Fletcher
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Matthew S. Kelly
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Austin M. Eckhoff
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Peter J. Allen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

A.A.F. was primarily responsible for sequencing data analyses, interpretation of findings and preparation of the manuscript. M.S.K. assisted with sequencing data analyses, interpretation of the results and preparation of the manuscript. A.M.E. assisted in data analyses and review of the manuscript. P.J.A. contributed to the interpretation of the study findings, and reviewed and revised the manuscript. All authors reviewed and approved submission of the final manuscript.

Corresponding author

Correspondence to Peter J. Allen.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data figures and tables

Extended Data Fig. 1 DADA2 analysis by Fletcher et al. of ITS sequencing data from Aykut et al. human pancreatic tissue samples.

a, Quality plot of raw sequencing reads. The y-axis represents the Phred quality score and the x-axis represents the cycle, which corresponds to the base position of sequencing reads. The mean quality score at each base position is shown by a green line and the quartiles of the quality score distribution are shown by orange lines. The number of sequencing reads in each sample is shown in red font. The red line shows the scaled proportion of reads that extend to at least that position. b, Box plots depicting fungal reads in normal pancreas tissue (n = 5 biologically independent samples) and pancreatic ductal adenocarcinoma (PDAC) tissue (n = 13 biologically independent samples) c, Box plots depicting sequencing reads assigned to the fungal genus Malassezia in normal pancreas (n = 5 biologically independent samples) and PDAC tissue (n = 13 biologically independent samples). d, Relative abundances, and e, read counts of the top ten fungal genera in pancreatic tissue samples from healthy individuals and patients with PDAC. Box plot minima and maxima bounds represent the 25th and 75th percentiles, respectively; the centre bound represents the median. Whiskers extend to 1.5 times the interquartile range (IQR). P values were estimated using two-sided Wilcoxon rank-sum tests (b,c). Individual data points are shown.

Extended Data Fig. 2 DADA2 analysis by Duke Genomic Analysis and Bioinformatics Core of ITS sequencing data from Aykut et al. human pancreatic tissue samples.

a, Box plots depicting fungal reads in normal pancreas tissue (n = 5 biologically independent samples) and pancreatic ductal adenocarcinoma (PDAC) tissue (n = 13 biologically independent samples) b, Box plots depicting sequencing reads assigned to the fungal genus Malassezia in normal pancreas tissue (n = 5 biologically independent samples) and PDAC tissue (n = 13 biologically independent samples). c, Relative abundances, and d, read counts of the top ten fungal genera in pancreatic tissue samples from healthy individuals and patients with PDAC. Box plot minima and maxima bounds represent the 25th and 75th percentiles, respectively; the centre bound represents the median. Whiskers extend to 1.5 times the interquartile range (IQR). P values were estimated using two-sided Wilcoxon rank-sum tests (a,b). Individual data points are shown.

Supplementary information

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fletcher, A.A., Kelly, M.S., Eckhoff, A.M. et al. Revisiting the intrinsic mycobiome in pancreatic cancer. Nature 620, E1–E6 (2023). https://doi.org/10.1038/s41586-023-06292-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-023-06292-1

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer