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      Microbiota-derived 3-IAA influences chemotherapy efficacy in pancreatic cancer

      research-article
      1 , 2 , , 3 , 4 , 1 , 2 , 2 , 3 , 3 , 5 , 6 , 5 , 6 , 7 , 7 , 8 , 4 , 9 , 10 , 3 , 11 , 11 , 4 , 3 , 12 , 13 , 12 , 13 , 9 , 14 , 15 , 16 , 16 , 4 , 17 , 3 , 3 , 1 , 18 , 3 , 1 , 1 , 19 , 5 , 6 , 3 , 5 , 6 ,
      Nature
      Nature Publishing Group UK
      Cancer therapeutic resistance, Tumour heterogeneity, Predictive markers, Chemotherapy

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          Abstract

          Pancreatic ductal adenocarcinoma (PDAC) is expected to be the second most deadly cancer by 2040, owing to the high incidence of metastatic disease and limited responses to treatment 1, 2 . Less than half of all patients respond to the primary treatment for PDAC, chemotherapy 3, 4 , and genetic alterations alone cannot explain this 5 . Diet is an environmental factor that can influence the response to therapies, but its role in PDAC is unclear. Here, using shotgun metagenomic sequencing and metabolomic screening, we show that the microbiota-derived tryptophan metabolite indole-3-acetic acid (3-IAA) is enriched in patients who respond to treatment. Faecal microbiota transplantation, short-term dietary manipulation of tryptophan and oral 3-IAA administration increase the efficacy of chemotherapy in humanized gnotobiotic mouse models of PDAC. Using a combination of loss- and gain-of-function experiments, we show that the efficacy of 3-IAA and chemotherapy is licensed by neutrophil-derived myeloperoxidase. Myeloperoxidase oxidizes 3-IAA, which in combination with chemotherapy induces a downregulation of the reactive oxygen species (ROS)-degrading enzymes glutathione peroxidase 3 and glutathione peroxidase 7. All of this results in the accumulation of ROS and the downregulation of autophagy in cancer cells, which compromises their metabolic fitness and, ultimately, their proliferation. In humans, we observed a significant correlation between the levels of 3-IAA and the efficacy of therapy in two independent PDAC cohorts. In summary, we identify a microbiota-derived metabolite that has clinical implications in the treatment of PDAC, and provide a motivation for considering nutritional interventions during the treatment of patients with cancer.

          Abstract

          Indole-3-acetic acid (3-IAA), a tryptophan metabolite derived from the gut microbiota, is associated with a better response to chemotherapy in pancreatic ductal adenocarcinoma (PDAC), and dietary interventions could have a role in the treatment of PDAC.

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          Most cited references49

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          Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2

          In comparative high-throughput sequencing assays, a fundamental task is the analysis of count data, such as read counts per gene in RNA-seq, for evidence of systematic changes across experimental conditions. Small replicate numbers, discreteness, large dynamic range and the presence of outliers require a suitable statistical approach. We present DESeq2, a method for differential analysis of count data, using shrinkage estimation for dispersions and fold changes to improve stability and interpretability of estimates. This enables a more quantitative analysis focused on the strength rather than the mere presence of differential expression. The DESeq2 package is available at http://www.bioconductor.org/packages/release/bioc/html/DESeq2.html. Electronic supplementary material The online version of this article (doi:10.1186/s13059-014-0550-8) contains supplementary material, which is available to authorized users.
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            STAR: ultrafast universal RNA-seq aligner.

            Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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              DADA2: High resolution sample inference from Illumina amplicon data

              We present DADA2, a software package that models and corrects Illumina-sequenced amplicon errors. DADA2 infers sample sequences exactly, without coarse-graining into OTUs, and resolves differences of as little as one nucleotide. In several mock communities DADA2 identified more real variants and output fewer spurious sequences than other methods. We applied DADA2 to vaginal samples from a cohort of pregnant women, revealing a diversity of previously undetected Lactobacillus crispatus variants.
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                Author and article information

                Contributors
                j.tintelnot@uke.de
                n.gagliani@uke.de
                Journal
                Nature
                Nature
                Nature
                Nature Publishing Group UK (London )
                0028-0836
                1476-4687
                22 February 2023
                22 February 2023
                2023
                : 615
                : 7950
                : 168-174
                Affiliations
                [1 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, II. Department of Medicine, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [2 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Mildred Scheel Cancer Career Center HaTriCS4, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [3 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Department of General, Visceral and Thoracic Surgery, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [4 ]GRID grid.7490.a, ISNI 0000 0001 2238 295X, Research Group Microbial Immune Regulation, , Helmholtz Centre for Infection Research, ; Braunschweig, Germany
                [5 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, I. Department of Medicine, , University Medical Center Hamburg- Eppendorf, ; Hamburg, Germany
                [6 ]Hamburg Center for Translational Immunology (HCTI), Hamburg, Germany
                [7 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, III. Department of Medicine, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [8 ]GRID grid.7048.b, ISNI 0000 0001 1956 2722, Department of Clinical Medicine, , Aarhus University, ; Aarhus, Denmark
                [9 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Institute of Clinical Chemistry and Laboratory Medicine, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [10 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Newborn Screening and Metabolic Laboratory, , Department of Pediatrics, University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [11 ]GRID grid.13648.38, ISNI 0000 0001 2180 3484, Research Department Cell and Gene Therapy, Department of Stem Cell Transplantation, , University Medical Center Hamburg-Eppendorf, ; Hamburg, Germany
                [12 ]GRID grid.410718.b, ISNI 0000 0001 0262 7331, Bridge Institute of Experimental Tumor Therapy, West German Cancer Center, , University Hospital Essen, University Duisburg-Essen, ; Essen, Germany
                [13 ]GRID grid.7497.d, ISNI 0000 0004 0492 0584, Division of Solid Tumor Translational Oncology, German Cancer Consortium (DKTK Partner Site Essen) and German Cancer Research Center (DKFZ), ; Heidelberg, Germany
                [14 ]GRID grid.4912.e, ISNI 0000 0004 0488 7120, Irish Centre for Vascular Biology, School of Pharmacy and Biomolecular Sciences, , Royal College of Surgeons in Ireland, ; Dublin, Ireland
                [15 ]GRID grid.410607.4, Center for Thrombosis and Hemostasis (CTH), , Johannes Gutenberg University Medical Center, ; Mainz, Germany
                [16 ]GRID grid.5252.0, ISNI 0000 0004 1936 973X, Department of Internal Medicine III, , Ludwig-Maximilians-University (LMU) Hospital, ; Munich, Germany
                [17 ]GRID grid.10423.34, ISNI 0000 0000 9529 9877, Hannover Medical School (MHH), ; Hannover, Germany
                [18 ]GRID grid.412315.0, Hematology–Oncology Practice Hamburg (HOPE), , University Cancer Center Hamburg, ; Hamburg, Germany
                [19 ]GRID grid.137628.9, ISNI 0000 0004 1936 8753, Department of Radiation Oncology, Perlmutter Cancer Center, , New York University Grossman School of Medicine, ; New York, NY USA
                Author information
                http://orcid.org/0000-0003-4619-9433
                http://orcid.org/0000-0002-1010-0975
                http://orcid.org/0000-0001-7674-1276
                http://orcid.org/0000-0002-7735-5462
                http://orcid.org/0000-0001-9050-6766
                http://orcid.org/0000-0002-5066-7140
                http://orcid.org/0000-0002-8772-4778
                http://orcid.org/0000-0001-6048-4711
                http://orcid.org/0000-0002-1922-2127
                http://orcid.org/0000-0003-0185-1459
                http://orcid.org/0000-0002-5173-9492
                http://orcid.org/0000-0001-9325-8227
                http://orcid.org/0000-0001-8514-1395
                Article
                5728
                10.1038/s41586-023-05728-y
                9977685
                36813961
                cb0dfd3c-8c2d-4d44-b483-fa664011d81a
                © The Author(s) 2023

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 February 2022
                : 12 January 2023
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                © The Author(s), under exclusive licence to Springer Nature Limited 2023

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                cancer therapeutic resistance,tumour heterogeneity,predictive markers,chemotherapy

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