Hijikata, A. | Search bioRxiv

bioRxiv2025

Discovery of a Selective Inhibitor of ZIP14 with Therapeutic Potential for Cancer-associated Cachexia

ZIP14/SLC39A14, a membrane-bound metal transporter, is essential for systemic metal homeostasis and has been implicated in inflammatory and metabolic disorders, including cancer-associated cachexia. Despite its biological and therapeutic significance, no selective inhibitors have been identified. Here, we identify 1-phenyl-8-(2-phenylethyl)-1,3,8-triazaspiro[4.5]decan-4-one (PPTD) as the first selective small-molecule inhibitor of ZIP14. PPTD efficiently blocks ZIP14-mediated uptake of zinc, iron, manganese, and cadmium, while sparing the closely related transporter ZIP8/SLC39A8. Mechanistically, PPTD binds specifically to a pocket formed at the dimer interface of ZIP14, as revealed by AlphaFold3 structural prediction, ligand-interaction profiling, structure-activity analyses, and site-directed mutagenesis, providing direct evidence for a targeted inhibition mechanism. ZIP14-driven metal influx promotes reactive oxygen species and lipid peroxidation, leading to cytotoxicity, which PPTD effectively reverses. In vivo, PPTD ameliorates major features of cancer cachexia in mice, including weight loss, reduced survival, muscle wasting, impaired locomotor activity, and disease progression. PPTD thus provides both a chemical probe to dissect ZIP14 function and a potential therapeutic candidate for cancer cachexia, establishing a foundation for the development of therapies targeting ZIP14-mediated metal dysregulation.

biochemistry↗

bioRxiv2022

Exome-wide benchmark of difficult-to-sequence regions using short-read next-generation DNA sequencing

Next-generation DNA sequencing (NGS) in short-read mode has been recently used for genetic testing in various clinical settings. NGS data accuracy is crucial in clinical settings, and several reports regarding quality control of NGS data, focusing mostly on establishing NGS sequence read accuracy, have been published thus far. Variant calling is another critical source of NGS errors that remains mostly unexplored despite its established significance. In this study, we used a machine-learning-based method to establish an exome-wide benchmark of difficult-to-sequence regions using 10 genome sequence features on the basis of real-world NGS data accumulated in The Genome Aggregation Database (gnomAD) of the human reference genome sequence (GRCh38/hg38). We used the obtained metrics, designated "UNMET score," along with other lines of structural information of the human genome to identify difficult-to-sequence genomic regions using conventional NGS. Thus, the UNMET score could provide appropriate caveats to address potential sequential errors in protein-coding exons of the human reference genome sequence GRCh38/hg38 in clinical sequencing.

genomics↗