TIMAHAC: Streamlined Tandem IMAC-HILIC Workflow for Simultaneous and High-Throughput Plant Phosphoproteomics and N-glycoproteomics.

Protein post-translational modifications (PTMs) are crucial in plant cellular processes, particularly in protein folding and signal transduction. N-glycosylation and phosphorylation are notably significant PTMs, playing essential roles in regulating plant responses to environmental stimuli. However, current sequential enrichment methods for simultaneous analysis of phosphoproteome and N-glycoproteome are labor-intensive and time-consuming, limiting their throughput. Addressing this challenge, this study introduces a novel tandem S-Trap-IMAC-HILIC (S-Trap: suspension trapping; IMAC: immobilized metal ion affinity chromatography; HILIC: hydrophilic interaction chromatography) strategy, termed TIMAHAC, for simultaneous analysis of plant phosphoproteomics and N-glycoproteomics. This approach integrates IMAC and HILIC into a tandem tip format, streamlining the enrichment process of phosphopeptides and N-glycopeptides. The key innovation lies in the use of a unified buffer system and an optimized enrichment sequence to enhance efficiency and reproducibility. The applicability of TIMAHAC was demonstrated by analyzing the Arabidopsis phosphoproteome and N-glycoproteome in response to abscisic acid (ABA) treatment. Up to 1954 N-glycopeptides and 11,255 phosphopeptides were identified from Arabidopsis, indicating its scalability for plant tissues. Notably, distinct perturbation patterns were observed in the phosphoproteome and N-glycoproteome, suggesting their unique contributions to ABA response. Our results reveal that TIMAHAC offers a comprehensive approach to studying complex regulatory mechanisms and PTM interplay in plant biology, paving the way for in-depth investigations into plant signaling networks.

DIA-Based Phosphoproteomics Identifies Early Phosphorylation Events in Response to EGTA and Mannitol in Arabidopsis.

Osmotic stress significantly hampers plant growth and crop yields, emphasizing the need for a thorough comprehension of the underlying molecular responses. Previous research has demonstrated that osmotic stress rapidly induces calcium influx and signaling, along with the activation of a specific subset of protein kinases, notably the Raf-like protein (RAF)-sucrose nonfermenting-1-related protein kinase 2 (SnRK2) kinase cascades within minutes. However, the intricate interplay between calcium signaling and the activation of RAF-SnRK2 kinase cascades remains elusive. Here, in this study, we discovered that Raf-like protein (RAF) kinases undergo hyperphosphorylation in response to osmotic shocks. Intriguingly, treatment with the calcium chelator EGTA robustly activates RAF-SnRK2 cascades, mirroring the effects of osmotic treatment. Utilizing high-throughput data-independent acquisition-based phosphoproteomics, we unveiled the global impact of EGTA on protein phosphorylation. Beyond the activation of RAFs and SnRK2s, EGTA treatment also activates mitogen-activated protein kinase cascades, Calcium-dependent protein kinases, and receptor-like protein kinases, etc. Through overlapping assays, we identified potential roles of mitogen-activated protein kinase kinase kinase kinases and receptor-like protein kinases in the osmotic stress-induced activation of RAF-SnRK2 cascades. Our findings illuminate the regulation of phosphorylation and cellular events by Ca2+ signaling, offering insights into the (exocellular) Ca2+ deprivation during early hyperosmolality sensing and signaling.

An automated ICU agitation monitoring system for video streaming using deep learning classification.

OBJECTIVE: To address the challenge of assessing sedation status in critically ill patients in the intensive care unit (ICU), we aimed to develop a non-contact automatic classifier of agitation using artificial intelligence and deep learning. METHODS: We collected the video recordings of ICU patients and cut them into 30-second (30-s) and 2-second (2-s) segments. All of the segments were annotated with the status of agitation as "Attention" and "Non-attention". After transforming the video segments into movement quantification, we constructed the models of agitation classifiers with Threshold, Random Forest, and LSTM and evaluated their performances. RESULTS: The video recording segmentation yielded 427 30-s and 6405 2-s segments from 61 patients for model construction. The LSTM model achieved remarkable accuracy (ACC 0.92, AUC 0.91), outperforming other methods. CONCLUSION: Our study proposes an advanced monitoring system combining LSTM and image processing to ensure mild patient sedation in ICU care. LSTM proves to be the optimal choice for accurate monitoring. Future efforts should prioritize expanding data collection and enhancing system integration for practical application.

Development of a Predictive Model for Optimization of Embryo Transfer Timing Using Blood-Based microRNA Expression Profile.

MicroRNAs (miRNAs) can regulate the expression of genes involved in the establishment of the window of implantation (WOI) in the endometrium. Recent studies indicated that cell-free miRNAs in uterine fluid and blood samples could act as alternative and non-invasive sample types for endometrial receptivity analysis. In this study, we attempt to systematically evaluate whether the expression levels of cell-free microRNAs in blood samples could be used as non-invasive biomarkers for assessing endometrial receptivity status. We profiled the miRNA expression levels of 111 blood samples using next-generation sequencing to establish a predictive model for the assessment of endometrial receptivity status. This model was validated with an independent dataset (n = 73). The overall accuracy is 95.9%. Specifically, we achieved accuracies of 95.9%, 95.9%, and 100.0% for the pre-receptive group, the receptive group, and the post-respective group, respectively. Additionally, we identified a set of differentially expressed miRNAs between different endometrial receptivity statuses using the following criteria: p-value < 0.05 and fold change greater than 1.5 or less than -1.5. In conclusion, the expression levels of cell-free miRNAs in blood samples can be utilized in a non-invasive manner to distinguish different endometrial receptivity statuses.

Regulation of primary cilia disassembly through HUWE1-mediated TTBK2 degradation plays a crucial role in cerebellar development and medulloblastoma growth.

Development of the cerebellum requires precise regulation of granule neuron progenitor (GNP) proliferation. Although it is known that primary cilia are necessary to support GNP proliferation, the exact molecular mechanism governing primary cilia dynamics within GNPs remains elusive. Here, we establish the pivotal roles for the centrosomal kinase TTBK2 (Tau tubulin kinase-2) and the E3 ubiquitin ligase HUWE1 in GNP proliferation. We show that TTBK2 is highly expressed in proliferating GNPs under Sonic Hedgehog (SHH) signaling, coinciding with active GNP proliferation and the presence of primary cilia. TTBK2 stabilizes primary cilia by inhibiting their disassembly, thereby promoting GNP proliferation in response to SHH. Mechanistically, we identify HUWE1 as a novel centrosomal E3 ligase that facilitates primary cilia disassembly by targeting TTBK2 degradation. Disassembly of primary cilia serves as a trigger for GNP differentiation, allowing their migration from the external granule layer (EGL) of the cerebellum to the internal granule layer (IGL) for subsequent maturation. Moreover, we have established a link between TTBK2 and SHH-type medulloblastoma (SHH-MB), a tumor characterized by uncontrolled GNP proliferation. TTBK2 depletion inhibits SHH-MB proliferation, indicating that TTBK2 may be a potential therapeutic target for this cancer type. In summary, our findings reveal the mechanism governing cerebellar development and highlight a potential anti-cancer strategy for SHH-MB.