Overcoming nutritional immunity by engineering iron-scavenging bacteria for cancer therapy
Data files
May 21, 2024 version files 8.48 GB
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In_vitro-1-1.raw
815.05 MB
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In_vitro-1-2.raw
767.05 MB
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In_vitro-2-1.raw
812.84 MB
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In_vitro-2-2.raw
813.81 MB
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Intratumor-1-1.raw
828.96 MB
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Intratumor-1-2.raw
842.06 MB
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Intratumor-2-1.raw
1.01 GB
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Intratumor-2-2.raw
1.01 GB
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Proteomics_data.xlsx
514.57 KB
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README.md
3.76 KB
Abstract
Certain bacteria demonstrate the ability to target and colonize the tumor microenvironment, a characteristic that positions them as innovative carriers for delivering various therapeutic agents in cancer therapy. Nevertheless, our understanding of how bacteria adapt their physiological condition to the tumor microenvironment remains elusive. In this work, we employed liquid chromatography-tandem mass spectrometry to examine the proteome of E. coli colonized in murine tumors. Compared to E. coli cultivated in the rich medium, we found that E. coli colonized in tumors notably upregulated the processes related to ferric ions, including enterobactin biosynthesis and iron homeostasis. This finding indicated that the tumor is an iron-deficient environment to E. coli. We also found that the colonization of E. coli in the tumor led to an increased expression of lipocalin 2 (LCN2), a host protein that can sequester enterobactin. We therefore engineered E. coli to evade the nutritional immunity provided by LCN2. By introducing the IroA cluster, the E. coli synthesizes the glycosylated enterobactin, which creates steric hindrance to avoid the LCN2 sequestration. The IroA-E. coli showed enhanced resistance to LCN2 and significantly improved the anti-tumor activity in mice. Moreover, the mice were cured by the IroA-E. coli treatment became resistant to the tumor re-challenge, indicating the establishment of immunological memory. Overall, our study underscores the crucial role of bacteria's ability to acquire ferric ions within the tumor microenvironment for effective cancer therapy.
We have submitted our raw data sets, including “In vivo.raw” (Mass spectrometry-based label-free quantification proteomics analysis of intratumoral E. coli), “In LB.raw” (Mass spectrometry-based label-free quantification proteomics analysis of E. coli in LB medium), and “Proteomics data.xlsx”.
Fie Descriptions
Proteomics data.xlsx_data:
l Significant: The observed protein abundance difference between samples is statistically significant.
l -LOG(P-value): The negative logarithm of the p-value, representing the significance of the protein abundance difference, where higher values indicate higher significance.
l Difference: The fold change or difference in protein abundance between the compared conditions or samples.
l T: Protein IDs: The unique identifiers for the proteins detected in the experiment.
l T: Majority protein IDs: The identifiers for the proteins that are most commonly identified peptides mapped to.
l T: Protein names: The names of the proteins as identified or annotated from a database.
l T: Gene names: The gene names corresponding to the identified proteins.
l T: id: An internal or reference identifier used within the dataset.
l C: Only identified by site: Indicates proteins identified only by specific post-translational modification sites.
l C: Reverse: Indicates if the protein sequence was from a decoy (reverse) database used in false discovery rate analysis.
l C: Potential contaminant: Marks proteins that are potential contaminants.
l N: Peptides: The number of peptides identified for each protein.
l N: Razor + unique peptides: Count of unique peptides plus those shared peptides (razor peptides) assigned to the protein with the most peptides.
l N: Unique peptides: The number of peptides uniquely identifying a protein.
l N: Sequence coverage [%]: The percentage of the protein sequence covered by identified peptides.
l N: Unique + razor sequence coverage [%]: The percentage of the protein sequence covered by both unique and razor peptides.
l N: Unique sequence coverage [%]: The percentage of the protein sequence covered only by unique peptides.
l N: Mol. weight [kDa]: The molecular weight of the protein in kilodaltons.
l N: Q-value: The minimum false discovery rate at which the protein is identified.
l N: Score: A score indicating confidence in protein identification.
l N: Intensity: The intensity of the signal derived from the protein.
l N: MS/MS count: The number of MS/MS spectra identifying peptides from the protein.
l LFQ intensity BL21-WT_1 Day_in vivo_1: Label-free quantification intensity for the sample from intratumoral E. coli, replicate 1.
l LFQ intensity BL21-WT_1 Day_in vivo_2: Label-free quantification intensity for the sample from intratumoral E. coli, replicate 2.
l LFQ intensity BL21-WT_3 hours_in LB_1: Label-free quantification intensity for the sample from E. coli in LB medium, replicate 1.
l LFQ intensity BL21-WT_3 hours_in LB_2: Label-free quantification intensity for the sample from E. coli in LB medium, replicate 2.
Key Information Sources
Mass spectrometry-based proteomics data:
l UniProt
Code/Software
Mass spectrometry-based proteomics data:
l MaxQuant version 2.0.1 is required to run Raw MS data; then proceed to Perseus version 1.6.15.0 to conduct the statistical analysis.
l mzmine is another recommended open-source software for examining the spectra in the RAW files.