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Biology subjects

Suva, M.

Publications and source records attributed to Suva, M..

2 recordsLinked to original sources

Oxidized protein states define metastatic fitness in lung cancer

AbstractReactive oxygen species (ROS) are a pervasive feature of human cancers, yet the protein targets through which ROS-regulated cell states shape tumor biology remains poorly understood. Here, using cysteine chemical proteomics, we define signatures of protein states under distinct cellular ROS environments that capture protein oxidation and conformational changes. Quantifying these signatures in primary lung tumors and brain metastases revealed a surprising enrichment of oxidative states in metastasis. To determine how these states support fitness, we performed genome-wide CRISPR screens and identified the mitochondrial Complex I subunit NDUFA10 as a key oxidation-dependent vulnerability. Oxidation of NDUFA10*Cys253 supports Complex I function through a previously unrecognized nucleotide kinase activity that maintains mitochondrial DNA levels. Enforcing a reduced conformation in NDUFA10 disrupts brain metastatic colonization in vivo. These findings establish ROS regulated protein states as a functional layer of tumor fitness, providing a framework for identifying redox-dependent mechanisms that support cancer progression.

Cancer Biology↗

A MAP of tumor-host interactions in glioma at single cell resolution

Single-cell RNA sequencing is the reference technique to characterize the heterogeneity of tumor microenvironment and can be efficiently used to discover cross-talk mechanisms between immune cells and cancer cells. We present a novel method, single cell Tumor-Host Interaction tool (scTHI), to identify significantly activated ligand-receptor interactions across clusters of cells from single-cell RNA sequencing data. We apply our approach to uncover the ligand-receptor interactions in glioma using six publicly available human glioma datasets encompassing 71 patients. We provide a comprehensive map of the signalling mechanisms between malignant cells and non-malignant cells in glioma uncovering potential novel therapeutic targets.

bioinformatics↗