Tsuchida, N. | bioRxiv Science

bioRxiv2026

Iron-dependent reprogramming of damage-associated peptide receptor signaling coordinates immunity and phosphate stress adaptation

Plant adaptation to inorganic phosphate (Pi) limitation entails extensive developmental and metabolic reprogramming, collectively termed the phosphate starvation response (PSR). How plants balance nutrient acquisition with immune competence remains unresolved. Here, we show that PSR selectively remodels pattern-recognition receptor (PRR) signaling in Arabidopsis thaliana. Membrane proteomics reveals increased abundance of the Pep receptor PEPR1 under Pi deficiency, whereas most receptor kinases, including the microbial PRRs FLS2 and CERK1, are reduced. Immunoblotting further shows increased PEPR2 abundance. Consistently, Pep-triggered responses are strongly potentiated under Pi depletion, while flagellin- and chitin-induced responses are not, indicating selective PEPR sensitization rather than global immune suppression. This sensitization requires the PSR-associated ferroxidases LPR1/LPR2, NADPH oxidases RBOHD/RBOHF, and transcription factor WRKY33, linking iron-dependent redox remodeling to immune amplification. Beyond inducible defense, PEPRs promote basal PSR transcriptional reprogramming and growth under Pi limitation. Loss of PEPR1/PEPR2 compromises bacterial resistance and alters root-associated microbiota assembly under nutrient deficiency. Together, Pi depletion selectively sensitizes specific PRR pathways. PEPRs exhibit context-dependent dual functionality, promoting either immunity or nutrient stress adaptation, potentially in response to distinct PROPEP-derived ligands, thereby supporting nutrient acquisition without compromising immune vigilance. These findings provide a framework for how nutritional status selectively remodels PRR signaling to coordinate immunity with nutrient stress adaptation.

plant biology↗

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↗