Summary of Study ST002217

This data is available at the NIH Common Fund's National Metabolomics Data Repository (NMDR) website, the Metabolomics Workbench, https://www.metabolomicsworkbench.org, where it has been assigned Project ID PR001416. The data can be accessed directly via it's Project DOI: 10.21228/M8PM6K This work is supported by NIH grant, U2C- DK119886.

See: https://www.metabolomicsworkbench.org/about/howtocite.php

This study contains a large results data set and is not available in the mwTab file. It is only available for download via FTP as data file(s) here.

Study ID	ST002217
Study Title	Non-targeted metabolomics screen comparing metabolite profiles of serum from PDAC-bearing mice that received 1% choline supplementation or control diet using high-resolution, high-performance LC-MS/MS analysis.
Study Summary	The composition of the gut microbiome controls innate and adaptive immunity and has emerged as a key regulator of tumor growth and the success of immune checkpoint blockade (ICB) therapy. However, the underlying mechanisms remain unclear. Pancreatic ductal adenocarcinoma (PDAC) tends to be refractory to therapy, including ICB. We found that the gut microbe-derived metabolite trimethylamine N-oxide (TMAO) enhances anti-tumor immunity to PDAC. Delivery of TMAO given intraperitoneally or via dietary choline supplement to PDAC-bearing mice reduces tumor growth and is associated with an immunostimulatory tumor-associated macrophage (TAM) phenotype and activated effector T cell response in the tumor microenvironment. Mechanistically, TMAO signals through potentiating type-I interferon (IFN) pathway and confers anti-tumor effects in a type-I IFN dependent manner. Notably, delivering TMAOprimed macrophages alone produced similar anti-tumor effects. Combining TMAO with ICB (anti-PD1 and/or anti-Tim3) significantly reduced tumor burden and improved survival beyond TMAO or ICB alone. Finally, the levels of trimethylamine (TMA)- producing bacteria and of CutC gene expression correlate with improved survivorship and response to anti-PD1 in cancer patients. Together, our study identifies the gut microbial metabolite TMAO as an important driver of anti-tumor immunity and lays the groundwork for new therapeutic strategies.
Institute	The Wistar Institute
Last Name	Shinde
First Name	Rahul
Address	3601 Spruce St, Philadelphia, PA 19104
Email	rshinde@wistar.org
Phone	215-898-3717
Submit Date	2022-07-11
Raw Data Available	Yes
Raw Data File Type(s)	raw(Thermo)
Analysis Type Detail	LC-MS
Release Date	2022-07-22
Release Version	1

Select appropriate tab below to view additional metadata details:

Project:

Project ID:	PR001416
Project DOI:	doi: 10.21228/M8PM6K
Project Title:	The microbiome-derived metabolite TMAO drives immune activation and boosts response to immune checkpoint blockade in pancreatic cancer
Project Summary:	The composition of the gut microbiome controls innate and adaptive immunity and has emerged as a key regulator of tumor growth and the success of immune checkpoint blockade (ICB) therapy. However, the underlying mechanisms remain unclear. Pancreatic ductal adenocarcinoma (PDAC) tends to be refractory to therapy, including ICB. We found that the gut microbe-derived metabolite trimethylamine N-oxide (TMAO) enhances anti-tumor immunity to PDAC. Delivery of TMAO given intraperitoneally or via dietary choline supplement to PDAC-bearing mice reduces tumor growth and is associated with an immunostimulatory tumor-associated macrophage (TAM) phenotype and activated effector T cell response in the tumor microenvironment. Mechanistically, TMAO signals through potentiating type-I interferon (IFN) pathway and confers anti-tumor effects in a type-I IFN dependent manner. Notably, delivering TMAOprimed macrophages alone produced similar anti-tumor effects. Combining TMAO with ICB (anti-PD1 and/or anti-Tim3) significantly reduced tumor burden and improved survival beyond TMAO or ICB alone. Finally, the levels of trimethylamine (TMA)- producing bacteria and of CutC gene expression correlate with improved survivorship and response to anti-PD1 in cancer patients. Together, our study identifies the gut microbial metabolite TMAO as an important driver of anti-tumor immunity and lays the groundwork for new therapeutic strategies.
Institute:	The Wistar Institute
Last Name:	Shinde
First Name:	Rahul
Address:	3601 Spruce St, Philadelphia, PA 19104
Email:	rshinde@wistar.org
Phone:	215-898-3717

Subject:

Subject ID:	SU002303
Subject Type:	Mammal
Subject Species:	Mus musculus
Taxonomy ID:	10090

Factors:

Subject type: Mammal; Subject species: Mus musculus (Factor headings shown in green)

mb_sample_id	local_sample_id	Treatment
SA211917	1% Choline diet 1	1% Choline
SA211918	1% Choline diet 2	1% Choline
SA211919	1% Choline diet 3	1% Choline
SA211920	1% Choline diet 4	1% Choline
SA211921	1% Choline diet 5	1% Choline
SA211922	Control diet 3	Control
SA211923	Control diet 4	Control
SA211924	Control diet 5	Control
SA211925	Control diet 2	Control
SA211926	Control diet 1	Control
SA211927	QC-8	n/a
SA211928	QC-7	n/a
SA211929	QC-6	n/a
SA211930	QC-5	n/a
SA211931	QC-9	n/a
SA211932	QC-10	n/a
SA211933	QC-13	n/a
SA211934	QC-12	n/a
SA211935	QC-11	n/a
SA211936	QC-4	n/a

Showing results 1 to 20 of 20

Showing results 1 to 20 of 20

Collection:

Collection ID:	CO002296
Collection Summary:	Three weeks after treatment, mice were euthanized for the assessment of serum for non-targeted metabolomics screen by LC-MS/MS analysis.
Sample Type:	Blood (serum)

Treatment:

Treatment ID:	TR002315
Treatment Summary:	C57BL6/J (B6) mice received orthotopic tumor implant of 1.5 X 10^5 PDAC cells into the pancreas and provided with a 1% choline diet (Envigo Teklad Diets, TD.130328) or ingredients matched normal chow control diet (Envigo Teklad Diets, TD.00588).

Sample Preparation:

Sampleprep ID:	SP002309
Sampleprep Summary:	Briefly, polar metabolites were extracted from serum samples with 10 times volume of ice-cold 80% methanol. Deproteinated supernatants were stored at -80 °C prior to analysis. A quality control (QC) sample was generated by pooling equal volumes of all samples immediately before LC-MS analysis.

Combined analysis:

Analysis ID	AN003626
Analysis type	MS
Chromatography type	HILIC
Chromatography system	Thermo Vanquish
Column	SeQuant ZIC-HILIC (150 x 2.1mm,5um)
MS Type	ESI
MS instrument type	Orbitrap
MS instrument name	Thermo Q Exactive HF-X Orbitrap
Ion Mode	UNSPECIFIED
Units	Normalized Peak Area

Chromatography:

Chromatography ID:	CH002681
Chromatography Summary:	Hydrophilic interaction liquid chromatography (HILIC) was performed at 0.2 ml/min on a ZIC-pHILIC column (2.1 mm × 150 mm, EMD Millipore) at 45 °C. Solvent A was 20 mM ammonium carbonate, 0.1% ammonium hydroxide, pH 9.2, and solvent B was acetonitrile. The gradient was 85% B for 2 min, 85% B to 20% B over 15 min, 20% B to 85% B over 0.1 min, and 85% B for 8.9 min. The autosampler was held at 4 °C. For each analysis, 4 µl of sample was injected.
Instrument Name:	Thermo Vanquish
Column Name:	SeQuant ZIC-HILIC (150 x 2.1mm,5um)
Flow Gradient:	85% B for 2 min, 85% B to 20% B over 15 min, 20% B to 85% B over 0.1 min, and 85% B for 8.9 min.
Solvent A:	100% water; 20 mM ammonium carbonate; 0.1% ammonium hydroxide, pH 9.6
Solvent B:	100% acetonitrile
Chromatography Type:	HILIC

MS:

MS ID:	MS003377
Analysis ID:	AN003626
Instrument Name:	Thermo Q Exactive HF-X Orbitrap
Instrument Type:	Orbitrap
MS Type:	ESI
MS Comments:	The following parameters were used for the MS analysis: sheath gas flow rate, 40; auxiliary gas flow rate, 10; sweep gas flow rate, 2; auxiliary gas heater temperature, 350 °C; spray voltage, 3.5 kV for positive mode and 3.2 kV for negative mode; capillary temperature, 325 °C; and funnel RF level, 40. All samples were analyzed by full MS with polarity switching. The QC sample was analyzed at the start of the sample sequence and after every 3-4 samples. The QC sample was also analyzed by data-dependent MS/MS with separate runs for positive and negative ion modes. Full MS scans were acquired at 120,000 resolution with a scan range of 50-750 m/z in positive mode and 65-975 m/z in negative mode. Data-dependent MS/MS scans were acquired for the top 10 highest intensity ions at 15,000 resolution with an isolation width of 1.0 m/z and stepped normalized collision energy of 20-40-60. Data analysis was performed using Compound Discoverer 3.1 (ThermoFisher Scientific) with separate analyses for positive and negative polarities. Retention time alignment used the adaptative curve model with 0.3 min maximum shift, 5 ppm mass tolerance, and 3 S/N threshold. Peak detection required less than 5 ppm mass error for extracted ion chromatograms with a 100,000 minimum peak intensity. [M+H]+1 and [M-H]-1 adducts were considered. Peaks were required to have a width at half height less than 1.0 min and a minimum of 5 scans. Components that had only a monoisotopic peak and no further isotopes were discarded. The maximum element count for isotope pattern modeling was C90H190N10O20P3S5. Compounds were grouped across samples with 5 ppm mass error and 0.2 min retention time shift. Peaks not detected initially in a given sample were determined using the fill gaps algorithm with 5 ppm mass error and 1.5 S/N threshold with real peak detection. The gap function uses a priority system to determine missing values: 1) matching detected ions based on expected m/z and retention time regardless of adduct assignment, 2) re-detecting peaks at lower thresholds, 3) simulating peaks based on expected m/z, and 4) imputing spectrum noise based on detection limit values. Compound quantifications were corrected for instrument drift by QC areas using the cubic spline regression model. Each compound was required to be detected in all QC runs with an RSD less than 40%. Metabolites were identified by accurate mass (5 ppm mass error) and retention time (0.3 min shift) using a database generated from pure standards or by accurate mass and MS2 spectra using the mzCloud spectral database (mzCloud.org), specifically the ‘Endogenous Metabolites’ and ‘Steroids/Vitamins/Hormones’ compound classes, and selecting the best matches with HighChem HighRes identity search match factors of 50 or greater. Results were manually processed to remove entries with apparent peak mis-integrations and correct commonly misannotated metabolites. Positive and negative data sets of identified compounds were merged, and the preferred polarity was selected for compounds identified in both polarities. For compounds identified multiple times at different retention times, a single entry was selected with priority given to standards database matches followed by greater mzCloud match factors and peak areas.
Ion Mode:	UNSPECIFIED