Shuaijian Dai | PubMed Health

PubMed2026

Deciphering the Protein Phosphorylation Dynamics Triggered by Seconds of Force Stimulation.

Plants perceive mechanical forces through phosphosignaling networks, but their relationship with gravity signaling remains elusive. To dissect gravity force signaling components, we performed SILIA-based phosphoproteomics on Arabidopsis aerial organs subjected to 20-s inversion or 30-s gravistimulation, identifying 2,733 and 2,878 phosphoproteins, respectively. Quantitative analysis revealed 34 significantly regulated phosphoproteins specific to inversion and 52 specific to gravistimulation. Inversion-specific phosphoproteins, associated with the initial calcium code, likely mediate calcium signals through EF-hand proteins, CPK1, and calmodulin-interacting proteins, potentially intersecting with receptor-like kinase-initiated MAPK cascades via RAF15 and MKK1/2 to induce gravitropic responses. Gravistimulation-specific phosphoproteins, linked to the secondary calcium code, function in calcium signaling/homeostasis (ACA8, ZAC, IQD2, ANNAT1), membrane vesicle trafficking (ABCG36, ARF-GAP8), and lipid signaling (PIP5K8/9), supporting auxin transport and stress signal transduction. Immunoblot validation confirmed treatment-associated phosphosites pS108-PATL3 and pS107-TREPH2, along with inversion-specific pS1145-ATEH2, exhibiting stem-specific phosphorylation enhancement and force-discriminatory responses. Functional analysis identified the integrin-like protein GREPH1 as a key gravitropism regulator, with greph1 mutants displaying reduced inflorescence stem gravicurvature. Notably, hyperphosphorylation of pS107-TREPH2 and pS1145-ATEH2 peaked at 20 to 50 s in greph1 mutants but persisted from 20 s to 2 h in WT plants. These findings establish a stem-enriched phosphorylation code for gravity force discrimination, with GREPH1 modulating spatiotemporal phosphoprotein dynamics and shoot gravicurvature, potentially functioning as a receptor reminiscent of sedimenting plastids.

Arabidopsis↗

PubMed2026

NanoSSL: attention mechanism-based self-supervised learning method for protein identification using nanopores.

MOTIVATION: Nanopores are cutting-edge interdisciplinary tools that can analyze biomolecules at the single-molecule level for many applications, e.g. DNA sequencing. Efforts are underway to extend nanopores to proteomics, including the development of machine learning algorithms for protein sequencing and identification. However, single-molecule data are intrinsically noisy and hard to process. Moreover, the development and performance of machine learning for nanopore is jeopardized by data scarcity. Self-supervised learning is an emerging method that may yield advantages in nanopore scenarios. RESULTS: We propose and experimentally validate Nanopore analysis using Self-Supervised Learning (NanoSSL), a generative self-supervised learning framework based on attention mechanisms for the identification of protein signals from nanopores. Leveraging a two-step approach consisting of self-supervised pre-training and supervised fine-tuning, NanoSSL learns useful feature representations from empirical data to facilitate downstream classification tasks. Inspired by the concept of fragmentation in conventional protein sequencing technologies, during pretraining each translocation event is split into multiple non-overlapping fragments of equal size, some of which are randomly masked and reconstructed using a masked autoencoder. Learning the feature representations of the reconstructed nanopore events facilitates molecular identification in fine-tuning. In this study, we retested a publicly available nanopore multiplexed protein sensing dataset for model iteration, and subsequently measured Alzheimer's disease biomarker Aβ1-42 using homemade solid-state nanopores. Empirical results indicated NanoSSL achieved an unprecedented performance across four metrics: accuracy, precision, recall, and F1 score, when classifying two mutated Aβ1-42, E22G and G37R. The self-supervised learning and attention mechanism were verified as the source of performance gains. AVAILABILITY AND IMPLEMENTATION: The main program is available at https://doi.org/10.5281/zenodo.17172822.

Nanopores↗