Daisuke Saigusa

Ferroptosis is a redox-driven form of regulated cell death with therapeutic potential in glioblastoma, but the adaptive mechanisms that allow tumor cells to survive ferroptotic stress remain incompletely understood. Here, we investigated whether translational stress surveillance contributes to ferroptosis resistance. Parallel genome-wide CRISPR-Cas9 knockout screens using the mechanistically distinct ferroptosis inducers erastin and RSL3 identified endothelial differentiation-related factor 1 (EDF1), an established ribosome-collision sensor, as a leading determinant of glioblastoma-cell survival. EDF1 loss markedly sensitized glioblastoma cells to ferroptosis, increased lipid peroxidation, and impaired proliferation, colony formation, and spheroid growth. Integrated RNA sequencing and ribosome profiling revealed extensive transcriptional–translational discordance during ferroptotic stress. EDF1 deficiency caused widespread codon-specific ribosome pausing, suppression of nascent protein synthesis, reduced ribotoxic stress signaling, and failure to activate the stress-associated G/C-ending codon-biased translational program. These effects occurred without major changes in the abundance of the canonical ferroptosis regulators GPX4, SLC7A11, and FSP1 or in cellular glutathione levels, indicating that EDF1 supports ferroptosis resistance through a distinct adaptive pathway. EDF1 loss was also associated with altered mitochondrial ultrastructure, increased mitochondrial respiration, reduced spare respiratory capacity, and suppression of androgen receptor signaling. Restoration of androgen receptor expression rescued ferroptosis sensitivity in EDF1-deficient cells. Collectively, these findings identify EDF1-dependent translational adaptation as a previously unrecognized component of ferroptosis resistance and establish a mechanistic link between ribosome surveillance and redox-stress tolerance in glioblastoma.

This study investigates the mass spectrometric analysis of 10 novel amidoamine oxide compounds, which are innovative hydrogelators for polar solvents. This research aims to identify characteristic fragment patterns for these amide compounds using high-resolution mass spectrometry. Methanol solutions of the compounds were analyzed in positive and negative ion modes, and MS1 and MS2 spectra at 6 collision energy levels were obtained via electrospray ionization and hybrid tandem mass spectrometry. The importance of low-intensity peaks in structure elucidation was emphasized because low-intensity fragments could provide crucial structural information, especially for compounds with similar structures. Chain-length-dependent fragmentation patterns were observed, which could aid in predicting the structures of related compounds. This research highlights the challenges of balancing informative low-intensity peaks with accurate spectral matching in databases. Based on our results, combining mass spectrometry with separation techniques, such as liquid chromatography, could enhance structural elucidation for unknown compounds. This study contributes to the broader field of mass spectrometry and structural chemistry, particularly in the analysis of amide compounds, and future directions are proposed for developing robust algorithms for selecting and interpreting low-intensity peaks to improve compound identification in complex mixtures.

The matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) technique was used to obtain the molecular images of cryosections without labeling. Although MALDI-MSI has been widely used to detect small molecules from biological tissues, issues remain due to the technical process of cryosectioning and limited mass spectrometry parameters. The use of a conductive adhesive film is a unique method to obtain high-quality sections from cutting tissue, such as bone, muscle, adipose tissue, and whole body of mice or fish, and we have reported the utilization of the film for MALDI-MSI in previous. However, some signal of the small molecules using the conductive adhesive films was still lower than on the indium tin oxide (ITO) glass slide. Here, the sample preparation and analytical conditions for MALDI-MSI using an advanced conductive adhesive film were optimized to obtain strong signals from whole mice heads. The effects of tissue thickness and laser ionization power on signal intensity were verified using MALDI-MSI. The phospholipid signal intensity was measured for samples with three tissue thicknesses (5, 10, and 20 μm); compared to the signals from the samples on the ITO glass slides, the signals with conductive adhesive films exhibited significantly higher intensities when a laser with a higher range of power was used to ionize the small molecules. Thus, the technique using the advanced conductive adhesive film showed an improvement in MALDI-MSI analysis.

Supplementary figure 1. Dynamic range plot of quantified Te-EV proteome. The X axis indicates abundance rank of qualified Te-EV proteome, and the Y axis indicates the relative abundance by the LC/MS analysis. Supplementary figure 2. GO analysis of up-regulated (fold change > 5, adjusted p < 0.05) and down-regulated (fold change < 0.02, adjusted p < 0.05) proteins in tumor Te-EVs. For biological process, cellular component and molecular function, data of GOTERM_BP_FAT, GOTERM_CC_FAT and GOTERM_MF_FAT were used from Gene_Ontology in Functional Annotation Tool on the website, respectively. The Y axis indicates the percentage of the annotated proteins. Supplementary figure 3. Scatter plot showing EV-CAT1 sandwich ELISA (unit) on the X axis and preoperative CEA (ng / ml) on the Y axis (left). Magnified view of the plot is also shown (right). Coefficient of correlation value was 8.5 Ã- 10-3. Supplementary figure 4. Volcano plot indicating p-value (Te-EVs vs. tissues) on the Y axis and log ratio (Te-EVs / tissue) of LC/MS intensity on the X axis. Blue dots indicate the top 100 Te-EV proteins which are significantly up-regulated than tissue proteins, and orange dots indicate the top 100 proteins which are significantly down-regulated than tissue proteins. Supplementary figure 5. The analyses of subcellular proteome localisations are shown. The subcellular locations of tissues and Te-EVs are obtained on the Uniprot website. The distribution of the two proteome is similar with each other. The numbers show percentages.

Table S1. Clinical information and protein abundances in LC/MS analysis of the CRC patients and the healthy donors. Table S2: The list of identified 8,565 CRC tissue proteins. Table S3: The list of identified 6,307 CRC Te-EV proteins. Table S3: The list of identified 6,307 CRC Te-EV proteins. Table S4: The 487 EV biomarker candidates for CRC (adjusted p < 0.05 and Fold-change > 5.0, paired t-test). Table S5. The focused 11 EV biomarker candidates for CRC selected as surface antigens for EV-sandwich ELISA (adjusted p < 0.05 and Fold-change > 5.0, paired t-test). Table S6. Enriched GO terms of biological process (up-regulated 487 proteins in tumor Te-EVs). Table S7. Top 100 of up-regulated and down-regulated Te-EV proteins compared with tissue proteins. Table S8a. Enriched KEGG pathways (up-regulated 487 proteins in tumor Te-EVs). Table 8b: Enriched KEGG pathways (down-regulated 88 proteins in tumor Te-EVs).