Summary of Study ST002085

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 PR001324. The data can be accessed directly via it's Project DOI: 10.21228/M8K989 This work is supported by NIH grant, U2C- DK119886.

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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.

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Study IDST002085
Study TitleA genome-scale gain-of-function CRISPR screen in CD8 T cells identifies proline metabolism as a means to enhance CAR-T therapy(Part 1)
Study SummaryChimeric antigen receptor (CAR)-T cell-based immunotherapy for cancer and immunological diseases has made great strides, but it still faces multiple hurdles. Finding the right molecular targets to engineer T cells toward a desired function has broad implications for the armamentarium of T cell-centered therapies. Here, we developed a dead-guide RNA (dgRNA)-based CRISPR activation screen in primary CD8+ T cells, and identified gain-of-function (GOF) targets for CAR-T engineering. Targeted knock-in or overexpression of a lead target, PRODH2, enhanced CAR-T-based killing and in vivo efficacy in multiple cancer models. Transcriptomics and metabolomics in CAR-T cells revealed that augmenting PRODH2 expression re-shaped broad and distinct gene expression and metabolic programs. Mitochondrial, metabolic and immunological analyses showed that PRODH2 engineering enhances the metabolic and immune functions of CAR-T cells against cancer. Together these findings provide a system for identification of GOF immune boosters, and demonstrate PRODH2 as a target to enhance CAR-T efficacy.
Institute
Yale University
Last NameYe
First NameLupeng
Address850 West campus drive
Emaillupeng.ye@yale.edu
Phone2035436568
Submit Date2022-02-12
Raw Data AvailableYes
Raw Data File Type(s)d
Analysis Type DetailLC-MS
Release Date2022-02-28
Release Version1
Lupeng Ye Lupeng Ye
https://dx.doi.org/10.21228/M8K989
ftp://www.metabolomicsworkbench.org/Studies/ application/zip

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Combined analysis:

Analysis ID AN003402 AN003403
Analysis type MS MS
Chromatography type HILIC HILIC
Chromatography system Agilent 6490 Agilent 6550
Column Phenomenex Kinetex C18 (150 x 2.1mm,2.6um) Phenomenex Kinetex C18 (150 x 2.1mm,2.6um)
MS Type ESI ESI
MS instrument type Triple quadrupole QTOF
MS instrument name Agilent 6490 QQQ Agilent 6550 QTOF
Ion Mode POSITIVE POSITIVE
Units ppm ppm

MS:

MS ID:MS003169
Analysis ID:AN003402
Instrument Name:Agilent 6490 QQQ
Instrument Type:Triple quadrupole
MS Type:ESI
MS Comments:Two metabolomics strategies were adopted, i.e. untargeted metabolomics (aiming to unbiasedly detect all detectable metabolites) and targeted approaches (aiming to detect specifically defined metabolites, such as related metabolites in the proline metabolism and T cell metabolism). For untargeted metabolomics analysis, the optimized workflow consists of automated peak detection and integration, peak alignment, background noise subtraction, and multivariate data analysis. These steps were carried out for comprehensive metabolite phenotyping of the two groups using Agilent Mass Hunter Qualitative Analysis Software (Version B.07.0.0, build 7.0.7024.0) and Agilent Mass Profiler Professional (Version 14.5-Build 2772). The metabolites were first putatively identified based on accurate mass match (accurate mass ± 30 ppm error) and fragmentation pattern match. Putative structural annotation was carried out by searching the metabolite databases HMDB (http://www.hmdb.ca/) and METLIN (http:// metlin.scripps.edu) using the mass-to-charge ratio of the metabolic features.
Ion Mode:POSITIVE
  
MS ID:MS003170
Analysis ID:AN003403
Instrument Name:Agilent 6550 QTOF
Instrument Type:QTOF
MS Type:ESI
MS Comments:Two metabolomics strategies were adopted, i.e. untargeted metabolomics (aiming to unbiasedly detect all detectable metabolites) and targeted approaches (aiming to detect specifically defined metabolites, such as related metabolites in the proline metabolism and T cell metabolism). For untargeted metabolomics analysis, the optimized workflow consists of automated peak detection and integration, peak alignment, background noise subtraction, and multivariate data analysis. These steps were carried out for comprehensive metabolite phenotyping of the two groups using Agilent Mass Hunter Qualitative Analysis Software (Version B.07.0.0, build 7.0.7024.0) and Agilent Mass Profiler Professional (Version 14.5-Build 2772). The metabolites were first putatively identified based on accurate mass match (accurate mass ± 30 ppm error) and fragmentation pattern match. Putative structural annotation was carried out by searching the metabolite databases HMDB (http://www.hmdb.ca/) and METLIN (http:// metlin.scripps.edu) using the mass-to-charge ratio of the metabolic features.
Ion Mode:POSITIVE
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