Bifidobacterium bifidum strains synergize with immune checkpoint inhibitors to reduce tumour burden in mice.
Animals
Bifidobacterium bifidum
/ classification
Carcinoma, Non-Small-Cell Lung
/ drug therapy
Drug Therapy, Combination
Gastrointestinal Microbiome
Humans
Immune Checkpoint Inhibitors
/ therapeutic use
Interferon-gamma
/ genetics
Lung Neoplasms
/ drug therapy
Metabolome
/ drug effects
Mice
Neoplasms, Experimental
/ drug therapy
Probiotics
/ administration & dosage
Species Specificity
Transcriptome
/ drug effects
Tryptophan
/ metabolism
Tumor Burden
/ drug effects
Journal
Nature microbiology
ISSN: 2058-5276
Titre abrégé: Nat Microbiol
Pays: England
ID NLM: 101674869
Informations de publication
Date de publication:
03 2021
03 2021
Historique:
received:
05
01
2020
accepted:
13
11
2020
pubmed:
13
1
2021
medline:
13
5
2021
entrez:
12
1
2021
Statut:
ppublish
Résumé
The gut microbiome can influence the development of tumours and the efficacy of cancer therapeutics
Identifiants
pubmed: 33432149
doi: 10.1038/s41564-020-00831-6
pii: 10.1038/s41564-020-00831-6
doi:
Substances chimiques
Immune Checkpoint Inhibitors
0
Interferon-gamma
82115-62-6
Tryptophan
8DUH1N11BX
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
277-288Commentaires et corrections
Type : CommentIn
Références
Viaud, S. et al. The intestinal microbiota modulates the anticancer immune effects of cyclophosphamide. Science 342, 971–976 (2013).
pubmed: 24264990
pmcid: 4048947
doi: 10.1126/science.1240537
Daillère, R. et al. Enterococcus hirae and Barnesiella intestinihominis facilitate cyclophosphamide-induced therapeutic immunomodulatory effects. Immunity 45, 931–943 (2016).
pubmed: 27717798
doi: 10.1016/j.immuni.2016.09.009
Iida, N. et al. Commensal bacteria control cancer response to therapy by modulating the tumor microenvironment. Science 342, 967–970 (2013).
pubmed: 24264989
pmcid: 6709532
doi: 10.1126/science.1240527
Sivan, A. et al. Commensal Bifidobacterium promotes antitumor immunity and facilitates anti-PD-L1 efficacy. Science 350, 1084–1089 (2015).
pubmed: 26541606
pmcid: 4873287
doi: 10.1126/science.aac4255
Gopalakrishnan, V. et al. Gut microbiome modulates response to anti-PD-1 immunotherapy in melanoma patients. Science 359, 97–103 (2018).
pubmed: 29097493
doi: 10.1126/science.aan4236
Thaiss, C. A., Zmora, N., Levy, M. & Elinav, E. The microbiome and innate immunity. Nature 535, 65–74 (2016).
pubmed: 27383981
doi: 10.1038/nature18847
Honda, K. & Littman, D. R. The microbiota in adaptive immune homeostasis and disease. Nature 535, 75–84 (2016).
pubmed: 27383982
doi: 10.1038/nature18848
Rosshart, S. P. et al. Wild mouse gut microbiota promotes host fitness and improves disease resistance. Cell 171, 1015–1028 (2017).
pubmed: 29056339
pmcid: 6887100
doi: 10.1016/j.cell.2017.09.016
Vétizou, M. et al. Anticancer immunotherapy by CTLA-4 blockade relies on the gut microbiota. Science 350, 1079–1084 (2015).
pubmed: 26541610
pmcid: 4721659
doi: 10.1126/science.aad1329
Routy, B. et al. Gut microbiome influences efficacy of PD-1-based immunotherapy against epithelial tumors. Science 359, 91–97 (2018).
doi: 10.1126/science.aan3706
pubmed: 29097494
Matson, V. et al. The commensal microbiome is associated with anti-PD-1 efficacy in metastatic melanoma patients. Science 359, 104–108 (2018).
pubmed: 29302014
pmcid: 6707353
doi: 10.1126/science.aao3290
Eisenhauer, E. A. et al. New response evaluation criteria in solid tumours: revised RECIST guideline (version 1.1). Eur. J. Cancer 45, 228–247 (2009).
pubmed: 19097774
doi: 10.1016/j.ejca.2008.10.026
Guerrero-Ros, I. et al. The negative effect of lipid challenge on autophagy inhibits T cell responses. Autophagy 16, 223–238 (2020).
pubmed: 30982401
doi: 10.1080/15548627.2019.1606635
Gao, J. et al. Loss of IFN-γ pathway genes in tumor cells as a mechanism of resistance to anti-CTLA-4 therapy. Cell 167, 397–404.e9 (2016).
pubmed: 27667683
pmcid: 5088716
doi: 10.1016/j.cell.2016.08.069
Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).
pubmed: 28783722
pmcid: 5870757
doi: 10.1038/nature23477
Medzhitov, R. Toll-like receptors and innate immunity. Nat. Rev. Immunol. 1, 135–145 (2001).
pubmed: 11905821
doi: 10.1038/35100529
Kohwi, Y., Imai, K., Tamura, Z. & Hashimoto, Y. Antitumor effect of Bifidobacterium infantis in mice. GANN 69, 613–618 (1978).
pubmed: 729960
Rafter, J. Probiotics and colon cancer. Best. Pract. Res. Clin. Gastroenterol. 17, 849–859 (2003).
pubmed: 14507593
doi: 10.1016/S1521-6918(03)00056-8
Zhang, L. S. & Davies, S. S. Microbial metabolism of dietary components to bioactive metabolites: opportunities for new therapeutic interventions. Genome Med. 8, 46 (2016).
pubmed: 27102537
pmcid: 4840492
doi: 10.1186/s13073-016-0296-x
Desbonnet, L., Garrett, L., Clarke, G., Bienenstock, J. & Dinan, T. G. The probiotic Bifidobacteria infantis: an assessment of potential antidepressant properties in the rat. J. Psychiatr. Res. 43, 164–174 (2008).
pubmed: 18456279
doi: 10.1016/j.jpsychires.2008.03.009
Xiao, J. et al. Effects of milk products fermented by Bifidobacterium longum on blood lipids in rats and healthy adult male volunteers. J. Dairy Sci. 86, 2452–2461 (2003).
pubmed: 12906063
doi: 10.3168/jds.S0022-0302(03)73839-9
An, H. M. et al. Antiobesity and lipid-lowering effects of Bifidobacterium spp. in high fat diet-induced obese rats. Lipids Health Dis. 10, 116 (2011).
pubmed: 21745411
pmcid: 3146849
doi: 10.1186/1476-511X-10-116
Wang, K. et al. Bifidobacterium bifidum TMC3115 can characteristically influence glucose and lipid profile and intestinal microbiota in the middle-aged and elderly. Probiotics Antimicrob. Proteins 11, 1182–1194 (2019).
pubmed: 29974409
doi: 10.1007/s12602-018-9441-8
Howie, D., Ten Bokum, A., Necula, A. S., Cobbold, S. P. & Waldmann, H. The role of lipid metabolism in T lymphocyte differentiation and survival. Front. Immunol. 8, 1949 (2018).
pubmed: 29375572
pmcid: 5770376
doi: 10.3389/fimmu.2017.01949
Long, J. et al. Lipid metabolism and carcinogenesis, cancer development. Am. J. Cancer Res. 8, 778–791 (2018).
pubmed: 29888102
pmcid: 5992506
Cruceriu, D., Baldasici, O., Balacescu. & Berindan-Neagoe, I. The dual role of tumor necrosis factor-alpha (TNF-α) in breast cancer: molecular insights and therapeutic approaches. Cell. Oncol. 43, 1–18 (2020).
doi: 10.1007/s13402-019-00489-1
Chen, X., Bäumel, M., Männel, D. N., Howard, O. Z. & Oppenheim, J. J. Interaction of TNF with TNF receptor type 2 promotes expansion and function of mouse CD4
pubmed: 17579033
doi: 10.4049/jimmunol.179.1.154
Schioppa, T. et al. B regulatory cells and the tumor-promoting actions of TNF-α during squamous carcinogenesis. Proc. Natl Acad. Sci. USA 108, 10662–10667 (2011).
pubmed: 21670304
doi: 10.1073/pnas.1100994108
pmcid: 3127875
Zhao, X. et al. TNF signaling drives myeloid-derived suppressor cell accumulation. J. Clin. Invest. 122, 4094–4104 (2012).
pubmed: 23064360
pmcid: 3484453
doi: 10.1172/JCI64115
Zheng, L. et al. Induction of apoptosis in mature T cells by tumour necrosis factor. Nature 377, 348–351 (1995).
pubmed: 7566090
doi: 10.1038/377348a0
Bertrand, F. et al. Blocking tumor necrosis factor α enhances CD8 T-cell-dependent immunity in experimental melanoma. Cancer Res. 75, 2619–2628 (2015).
pubmed: 25977337
doi: 10.1158/0008-5472.CAN-14-2524
Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).
pubmed: 22933715
doi: 10.1093/nar/gks808
Davis, M. P., van Dongen, S., Abreu-Goodger, C., Bartonicek, N. & Enright, A. J. Kraken: a set of tools for quality control and analysis of high-throughput sequence data. Methods 63, 41–49 (2013).
pubmed: 23816787
pmcid: 3991327
doi: 10.1016/j.ymeth.2013.06.027
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet.journal 17, 10–12 (2011).
doi: 10.14806/ej.17.1.200
Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37, 852–857 (2019).
pubmed: 31341288
pmcid: 7015180
doi: 10.1038/s41587-019-0209-9
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
DeSantis, T. Z. et al. Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl. Environ. Microbiol. 72, 5069–5072 (2006).
pubmed: 16820507
pmcid: 1489311
doi: 10.1128/AEM.03006-05
Segata, N. et al. Metagenomic biomarker discovery and explanation. Genome Biol. 12, R60 (2011).
pubmed: 21702898
pmcid: 3218848
doi: 10.1186/gb-2011-12-6-r60
Hill, D. A. et al. Metagenomic analyses reveal antibiotic-induced temporal and spatial changes in intestinal microbiota with associated alterations in immune cell homeostasis. Mucosal Immunol. 3, 148–158 (2010).
pubmed: 19940845
doi: 10.1038/mi.2009.132
Segata, N. et al. Metagenomic microbial community profiling using unique clade-specific marker genes. Nat. Methods 9, 811–814 (2012).
pubmed: 22688413
pmcid: 3443552
doi: 10.1038/nmeth.2066
Abubucker, S. et al. Metabolic reconstruction for metagenomic data and its application to the human microbiome. PLoS Comput. Biol. 8, e1002358 (2012).
pubmed: 22719234
pmcid: 3374609
doi: 10.1371/journal.pcbi.1002358
Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).
pubmed: 25061070
pmcid: 4609014
doi: 10.1093/bioinformatics/btu494
Ferrer-Font, L. et al. High-dimensional analysis of intestinal immune cells during helminth infection. eLife 9, e51678 (2020).
pubmed: 32041687
pmcid: 7012606
doi: 10.7554/eLife.51678
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
pubmed: 23104886
doi: 10.1093/bioinformatics/bts635
Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinformatics 12, 323 (2011).
pubmed: 21816040
pmcid: 3163565
doi: 10.1186/1471-2105-12-323
Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).
pubmed: 25516281
pmcid: 4302049
doi: 10.1186/s13059-014-0550-8
Bindea, G. et al. ClueGO: a Cytoscape plug-in to decipher functionally grouped gene ontology and pathway annotation networks. Bioinformatics 25, 1091–1093 (2009).
pubmed: 19237447
pmcid: 2666812
doi: 10.1093/bioinformatics/btp101
Bang, D. Y., Byeon, S. K. & Moon, M. H. Rapid and simple extraction of lipids from blood plasma and urine for liquid chromatography–tandem mass spectrometry. J. Chromatogr. A 1331, 19–26 (2014).
pubmed: 24491523
doi: 10.1016/j.chroma.2014.01.024
Lim, S., Byeon, S. K., Lee, J. Y. & Moon, M. H. Computational approach to structural identification of phospholipids using raw mass spectra from nanoflow liquid chromatography–electrospray ionization–tandem mass spectrometry. J. Mass Spectrom. 47, 1004–1014 (2012).
pubmed: 22899509
doi: 10.1002/jms.3033
Horai, H. et al. MassBank: a public repository for sharing mass spectral data for life sciences. J. Mass Spectrom. 45, 703–714 (2010).
pubmed: 20623627
doi: 10.1002/jms.1777
Ivanova, P. T., Milne, S. B., Myers, D. S. & Brown, H. A. Lipidomics: a mass spectrometry based systems level analysis of cellular lipids. Curr. Opin. Chem. Biol. 13, 526–531 (2009).
pubmed: 19744877
pmcid: 2787871
doi: 10.1016/j.cbpa.2009.08.011
Bankevich, A. et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J. Comput. Biol. 19, 455–477 (2012).
pubmed: 22506599
pmcid: 3342519
doi: 10.1089/cmb.2012.0021
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075 (2013).
pubmed: 23422339
pmcid: 3624806
doi: 10.1093/bioinformatics/btt086
Seemann, T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 30, 2068–2069 (2014).
pubmed: 24642063
doi: 10.1093/bioinformatics/btu153
Page, A. J. et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 31, 3691–3693 (2015).
pubmed: 26198102
pmcid: 4817141
doi: 10.1093/bioinformatics/btv421
Pritchard, L., Glover, R. H., Humphris, S., Elphinstone, J. G. & Toth, I. K. Genomics and taxonomy in diagnostics for food security: soft-rotting enterobacterial plant pathogens. Anal. Methods 8, 12–24 (2016).
doi: 10.1039/C5AY02550H
Bardou, P., Mariette, J., Escudié, F., Djemiel, C. & Klopp, C. jvenn: an interactive Venn diagram viewer. BMC Bioinformatics 15, 293 (2014).
pubmed: 25176396
pmcid: 4261873
doi: 10.1186/1471-2105-15-293
Förstner, K. U., Vogel, J. & Sharma, C. M. READemption—a tool for the computational analysis of deep-sequencing-based transcriptome data. Bioinformatics 30, 3421–3423 (2014).
pubmed: 25123900
doi: 10.1093/bioinformatics/btu533
Robinson, M. D., McCarthy, D. J. & Smyth, G. K. edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics 26, 139–140 (2010).
doi: 10.1093/bioinformatics/btp616
pubmed: 19910308