Histidine phosphorylation (pHis), which plays a key role in signal transduction in bacteria and lower eukaryotes, has been shown to be involved in tumorigenesis. Due to its chemical instability, substoichiometric properties, and lack of specific enrichment reagents, there is a lack of approaches for specific and unbiased enrichment of pHis-proteins/peptides. In this study, an integrated strategy was established and evaluated as an unbiased tool for exploring the histidine phosphoproteome. First, taking advantage of the lower charge states of pHis-peptides versus the non-modified naked peptides at weak acid solution (∼pH 2.7), strong cation exchange (SCX) chromatography was used to differentiate modified and non-modified naked peptides. Furthermore, selective enrichment of the pHis-peptide was performed by applying Cu-IDA beads enrichment. Finally, stable isotope dimethyl labeling was introduced to guarantee high-confidence assignment of pHis-peptides. Using this integrated strategy, 563 different pHis-peptides (H = 1) in 385 proteins were identified from HeLa lysates. Motif analysis revealed that pHis prefers hydrophobic amino acids and has the consensus motif-HxxK, which covered the reports from different approaches. Thus, our method may provide an unbiased and effective tool to reveal histidine phosphoproteome and to study the biological process and function of histidine phosphorylation.
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http://dx.doi.org/10.1021/acs.analchem.1c03374 | DOI Listing |
Anal Chem
January 2025
School of Physics and Optoelectronics, Xiangtan University, Xiangtan 411105, PR China.
Low humidity detection down to the parts per million level is urgently demanded in various industrial applications. The hardly detected tiny electrical signal variations caused by a very small amount of water adsorption are one of the intrinsic reasons that restrain the detection limit of the humidity sensors. Herein, a carbon-based field-effect transistor (FET) humidity sensor utilizing adsorbed water as the dual function of a sensing gate and analyte was proposed.
View Article and Find Full Text PDFJ Biophotonics
January 2025
Nanjing University of Chinese Medicine, Nanjing, China.
Liver malignancies, particularly hepatocellular carcinoma (HCC), pose a formidable global health challenge. Conventional diagnostic techniques frequently fall short in precision, especially at advanced HCC stages. In response, we have developed a novel diagnostic strategy that integrates hyperspectral imaging with deep learning.
View Article and Find Full Text PDFIn Vitro Model
June 2024
3B's Research Group, European Institute of Excellence in Tissue Engineering and Regenerative Medicine Headquarters, Parque de Ciência e Tecnologia, I3Bs - Research Institute on Biomaterials, Biodegradable and Biomimetics - University of Minho, Zona Industrial da Gandra - Avepark, Barco, Guimaraes, 4805-017 Portugal.
Soft microfluidic systems play a pivotal role in personalized medicine, particularly in in vitro diagnostics tools and disease modeling. These systems offer unprecedented precision and versatility, enabling the creation of intricate three-dimensional (3D) tissue models that can closely emulate both physiological and pathophysiological conditions. By leveraging innovative biomaterials and bioinks, soft microfluidic systems can circumvent the current limitations involving the use of polydimethylsiloxane (PDMS), thus facilitating the development of customizable systems capable of sustaining the functions of encapsulated cells and mimicking complex biological microenvironments.
View Article and Find Full Text PDFBiomater Transl
November 2024
Guangdong Provincial Key Laboratory of Construction and Detection in Tissue Engineering, School of Basic Medical Science, Southern Medical University, Guangzhou, Guangdong Province, China.
Cardiovascular diseases are a leading cause of death worldwide, and effective treatment for cardiac disease has been a research focal point. Although the development of new drugs and strategies has never ceased, the existing drug development process relies primarily on rodent models such as mice, which have significant shortcomings in predicting human responses. Therefore, human-based in vitro cardiac tissue models are considered to simulate physiological and functional characteristics more effectively, advancing disease treatment and drug development.
View Article and Find Full Text PDFFront Cardiovasc Med
January 2025
Department of Artificial Intelligence and Informatics, Mayo Clinic, Jacksonville, FL, United States.
Background: Effective management of dual antiplatelet therapy (DAPT) following drug-eluting stent (DES) implantation is crucial for preventing adverse events. Traditional prognostic tools, such as rule-based methods or Cox regression, despite their widespread use and ease, tend to yield moderate predictive accuracy within predetermined timeframes. This study introduces a new contrastive learning-based approach to enhance prediction efficacy over multiple time intervals.
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