Plasma Metabolomics Study on the Impact of Different CRF Levels on MetS Risk Factors.

To investigate the metabolomic mechanisms by which changes in cardiorespiratory fitness (CRF) levels affect metabolic syndrome (MetS) risk factors and to provide a theoretical basis for the improvement of body metabolism via CRF in people with MetS risk factors, a comparative blood metabolomics study of individuals with varying levels of CRF and varying degrees of risk factors for MetS was conducted. METHODS: Ninety subjects between the ages of 40 and 45 were enrolled, and they were categorized into low-MetS (LM ≤ two items) and high MetS (HM > three items) groups, as well as low- and high-CRF (LC, HC) and LCLM, LCLM, LCHM, and HCHM groups. Plasma was taken from the early morning abdominal venous blood. LC-MS was conducted using untargeted metabolomics technology, and the data were statistically and graphically evaluated using SPSS26.0 and R language. RESULTS: (1) There were eight common differential metabolites in the HC vs. LC group as follows: methionine (↓), γ-aminobutyric acid (↑), 2-oxoglutatic acid (↑), arginine (↑), serine (↑), cis-aconitic acid (↑), glutamine (↓), and valine (↓); the HM vs. LM group are contrast. (2) In the HCHM vs. LCLM group, trends were observed in 2-oxoglutatic acid (↑), arginine (↑), serine (↑), cis-aconitic acid (↑), glutamine (↓), and valine (↓). (3) CRF and MetS risk factors jointly affect biological metabolic pathways such as arginine biosynthesis, TCA cycle, cysteine and methionine metabolism, glycine, serine, and threonine metabolism, arginine and proline metabolism, and alanine, aspartate, and glutamate metabolism. CONCLUSION: The eight common differential metabolites can serve as potential biomarkers for distinguishing individuals with different CRF levels and varying degrees of MetS risk factors. Increasing CRF levels may potentially mitigate MetS risk factors, as higher CRF levels are associated with reduced MetS risk.

Super-hydrophilic sulfonate-modified covalent organic framework nanosheets for efficient separation and enrichment of glycopeptides.

Highly efficient extraction of glycopeptides prior to mass spectrometry detection is extremely crucial for glycoproteomic research, especially in disease biomarker research. Reported here is the first time by applying two-dimensional (2D) covalent organic framework (COFs) nanosheets for highly efficient enrichment of glycopeptides. Particularly, by incorporating hydrophilic monomers through a bottom-up strategy, the 2D COF nanosheets (denoted as NUS-9) displayed an ultra-high graft density of sulfonic groups and super-hydrophilicity. In addition, because of the large surface area, low steric hindrance, high chemical stability, and abundant accessibility sites of 2D COF nanosheets, NUS-9 exhibited remarkable efficiency for glycopeptide enrichment, involving excellent detection sensitivity (0.01 fmol µL-1), outstanding enrichment capability, and good enrichment selectivity (1:1500, horseradish peroxidase (HRP) tryptic digest to bovine serum albumin (BSA) tryptic digest), and recovery (92.2 ± 2.0%). Moreover, the NUS-9 was able to unambiguously detect 631 endogenous glycopeptides from human saliva, demonstrating an unparalleled high efficiency in glycopeptide enrichment. Gene ontology analyses of proteins from human saliva enriched by NUS-9 demonstrated its potential for comprehensive glycoproteome analysis.

MRE: A translational knowledge graph completion model based on multiple relation embedding.

Knowledge graph completion (KGC) has attracted significant research interest in applying knowledge graphs (KGs). Previously, many works have been proposed to solve the KGC problem, such as a series of translational and semantic matching models. However, most previous methods suffer from two limitations. First, current models only consider the single form of relations, thus failing to simultaneously capture the semantics of multiple relations (direct, multi-hop and rule-based). Second, the data-sparse problem of knowledge graphs would make part of relations challenging to embed. This paper proposes a novel translational knowledge graph completion model named multiple relation embedding (MRE) to address the above limitations. We attempt to embed multiple relations to provide more semantic information for representing KGs. To be more specific, we first leverage PTransE and AMIE+ to extract multi-hop and rule-based relations. Then, we propose two specific encoders to encode extracted relations and capture semantic information of multiple relations. We note that our proposed encoders can achieve interactions between relations and connected entities in relation encoding, which is rarely considered in existing methods. Next, we define three energy functions to model KGs based on the translational assumption. At last, a joint training method is adopted to perform KGC. Experimental results illustrate that MRE outperforms other baselines on KGC, demonstrating the effectiveness of embedding multiple relations for advancing knowledge graph completion.

Facile synthesis of core-shell structured magnetic covalent organic framework composite nanospheres for selective enrichment of peptides with simultaneous exclusion of proteins.

Selective enrichment of peptides from complex biosamples is essential for mass spectrometry-based proteomics but still remains a challenge. In this work, a facile approach was developed for rapid room-temperature synthesis of core-shell structured magnetic covalent organic framework composite nanospheres (denoted as Fe3O4@TbBd) by using monodisperse Fe3O4 nanoparticles as the magnetic core and 1,3,5-triformylbenzene (Tb) and benzidine (Bd) as two building blocks in the presence of dimethyl sulfoxide (DMSO). The resultant core-shell structured Fe3O4@TbBd nanospheres exhibited high adsorption capacity (28.5 mg g-1), fast adsorption kinetics (<5 min) and excellent reusability (more than 30 times) for peptides, owing to their specific properties of high surface area (196.21 m2 g-1), large pore volume (0.63 cm3 g-1), narrow pore size distribution (∼2.8 nm), strong magnetic response (41.5 emu g-1), as well as good thermal and chemical stability. Moreover, the Fe3O4@TbBd nanospheres also showed good selectivity towards hydrophobic peptides and a size-exclusion effect against proteins due to the inherent π-π stacking interaction and interconnected mesoporous channels of covalent organic framework (COF) shells. Taking advantage of these composite nanospheres, selective extraction and efficient enrichment of low abundance hydrophobic peptides from human serum in the presence of high abundance proteins were achieved. Based on the HPLC-Q-TOF/MS results, 29 hydrophobic peptides assigned to 12 proteins were clearly identified in 5 ng µL-1 human serum digestion upon treatment with Fe3O4@TbBd nanospheres, much better than those obtained without treatment, confirming the outstanding performance of the Fe3O4@TbBd nanospheres in proteome analysis.

One-pot preparation of glutathione-silica hybrid monolith for mixed-mode capillary liquid chromatography based on "thiol-ene" click chemistry.

A novel glutathione (GSH)-silica hybrid monolithic column synthesized via a combination of thiol-ene click reaction and one-pot process was described, where thiol-end GSH organic monomer and 2,2-azobisisobutyronitrile (AIBN) were mixed with hydrolyzed tetramethyloxysilane (TMOS) and γ-methacryloxypropyltrimethoxysilane (γ-MAPS) and then introduced into a fused-silica capillary for simultaneous polycondensation and "thiol-ene" click reaction to form the GSH-silica hybrid monolith. The effects of the molar ratio of TMOS/γ-MAPS, the amount of GSH, and the volume of porogen on the morphology, permeability and pore properties of the prepared GSH-silica hybrid monoliths were studied in detail. A uniform monolithic network with high porosity was obtained. A series of test compounds including alkylbenzenes, amides, and anilines were used to evaluate the retention behaviors of the GSH-silica hybrid monolithic column. The results demonstrated that the prepared GSH-silica hybrid monolith exhibited multiple interactions including hydrophobicity, hydrophilicity, as well as cation exchange interaction. The run-to-run, column-to-column and batch-to-batch reproducibilities of the GSH-silica hybrid monolith for phenols' retention were satisfactory with the relative standard deviations (RSDs) less than 1.3% (n=5), 2.6% (n=3) and 3.2% (n=3), respectively, indicating the effectiveness and practicability of the proposed method. In addition, the GSH-silica hybrid monolith was applied to the separation of nucleotides, peptides and protein tryptic digests, respectively. The successful applications suggested the potential of the GSH-silica hybrid monolith in complex sample analysis.