DOI: 10.5061/dryad.z08kprrnn
title: Overcoming nutritional immunity by engineering iron-scavenging bacteria for cancer therapy

description: 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.
description: # Overcoming nutritional immunity by engineering iron-scavenging bacteria
 for 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.