DNA nanomachines reveal an adaptive energy mode in confinement-induced amoeboid migration powered by polarized mitochondrial distribution.

Energy metabolism is highly interdependent with adaptive cell migration in vivo. Mechanical confinement is a critical physical cue that induces switchable migration modes of the mesenchymal-to-amoeboid transition (MAT). However, the energy states in distinct migration modes, especially amoeboid-like stable bleb (A2) movement, remain unclear. In this report, we developed multivalent DNA framework-based nanomachines to explore strategical mitochondrial trafficking and differential ATP levels during cell migration in mechanically heterogeneous microenvironments. Through single-particle tracking and metabolomic analysis, we revealed that fast A2-moving cells driven by biomimetic confinement recruited back-end positioning of mitochondria for powering highly polarized cytoskeletal networks, preferentially adopting an energy-saving mode compared with a mesenchymal mode of cell migration. We present a versatile DNA nanotool for cellular energy exploration and highlight that adaptive energy strategies coordinately support switchable migration modes for facilitating efficient metastatic escape, offering a unique perspective for therapeutic interventions in cancer metastasis.

Multi-Omic Profiling of Multi-Biosamples Reveals the Role of Amino Acid and Nucleotide Metabolism in Endometrial Cancer.

Background: Endometrial cancer (EC) is one of the most common gynecological cancers. The traditional diagnosis of EC relies on histopathology, which, however, is invasive and may arouse tumor spread. There have been many studies aiming to find the metabolomic biomarkers of EC to improve the early diagnosis of cancer in a non-invasive or minimally invasive way, which can also provide valuable information for understanding the disease. However, most of these studies only analyze a single type of sample by metabolomics, and cannot provide a comprehensive view of the altered metabolism in EC patients. Our study tries to gain a pathway-based view of multiple types of samples for understanding metabolomic disorders in EC by combining metabolomics and proteomics. Methods: Forty-four EC patients and forty-three controls were recruited for the research. We collected endometrial tissue, urine, and intrauterine brushing samples. Untargeted metabolomics and untargeted proteomics were both performed on the endometrial tissue samples, while only untargeted metabolomics was performed on the urine and intrauterine brushing samples. Results: By integrating the differential metabolites and proteins between EC patients and controls detected in the endometrial tissue samples, we identified several EC-related significant pathways, such as amino acid metabolism and nucleotide metabolism. The significance of these pathways and the potential of metabolite biomarker-based diagnosis were then further verified by using urine and intrauterine brushing samples. It was found that the regulation of metabolites involved in the significant pathways showed similar trends in the intrauterine brushings and the endometrial tissue samples, while opposite trends in the urine and the endometrial tissue samples. Conclusions: With multi-omics characterization of multi-biosamples, the metabolomic changes related to EC are illustrated in a pathway-based way. The network of altered metabolites and related proteins provides a comprehensive view of altered metabolism in the endometrial tissue samples. The verification of these critical pathways by using urine and intrauterine brushing samples provides evidence for the possible non-invasive or minimally invasive biopsy for EC diagnosis in the future.