Low beauvericin concentrations promote PC-12 cell survival under oxidative stress by regulating lipid metabolism and PI3K/AKT/mTOR signaling.

Beauvericin (BEA), a naturally occurring cyclic peptide with good pharmacological activity, has been widely explored in anticancer research. Although BEA is toxic, studies have demonstrated its antioxidant activity. However, to date, the antioxidant mechanisms of BEA remain unclear. Herein, we conducted a comprehensive and detailed study of the antioxidant mechanism of BEA using an untargeted metabolomics approach, subsequently validating the results. BEA concentrations of 0.5 and 1 µM significantly inhibited H2O2-induced oxidative stress (OS), decreased reactive oxygen species levels in PC-12 cells, and restored the mitochondrial membrane potential. Untargeted metabolomics indicated that BEA was primarily involved in lipid-related metabolism, suggesting its role in resisting OS in PC-12 cells by participating in lipid metabolism. BEA combated OS damage by increasing phosphatidylcholine, phosphatidylethanolamine, and sphingolipid levels. In the current study, BEA upregulated proteins related to the PI3K/AKT/mTOR pathway, thereby promoting cell survival. These findings support the antioxidant activity of BEA at low concentrations, warranting further research into its pharmacological effects.

Combination of automated sample preparation and micro-flow LC-MS for high-throughput plasma proteomics.

BACKGROUND: Non-invasive detection of blood-based markers is a critical clinical need. Plasma has become the main sample type for clinical proteomics research because it is easy to obtain and contains measurable protein biomarkers that can reveal disease-related physiological and pathological changes. Many efforts have been made to improve the depth of its identification, while there is an increasing need to improve the throughput and reproducibility of plasma proteomics analysis in order to adapt to the clinical large-scale sample analysis. METHODS: We have developed and optimized a robust plasma analysis workflow that combines an automated sample preparation platform with a micro-flow LC-MS-based detection method. The stability and reproducibility of the workflow were systematically evaluated and the workflow was applied to a proof-of-concept plasma proteome study of 30 colon cancer patients from three age groups. RESULTS: This workflow can analyze dozens of samples simultaneously with high reproducibility. Without protein depletion and prefractionation, more than 300 protein groups can be identified in a single analysis with micro-flow LC-MS system on a Orbitrap Exploris 240 mass spectrometer, including quantification of 35 FDA approved disease markers. The quantitative precision of the entire workflow was acceptable with median CV of 9%. The preliminary proteomic analysis of colon cancer plasma from different age groups could be well separated with identification of potential colon cancer-related biomarkers. CONCLUSIONS: This workflow is suitable for the analysis of large-scale clinical plasma samples with its simple and time-saving operation, and the results demonstrate the feasibility of discovering significantly changed plasma proteins and distinguishing different patient groups.

Robust Capillary- and Micro-Flow Liquid Chromatography-Tandem Mass Spectrometry Methods for High-Throughput Proteome Profiling.

Capillary- and micro-flow liquid chromatography-tandem mass spectrometry (capLC-MS/MS and µLC-MS/MS) is becoming a valuable alternative to nano-flow LC-MS/MS due to its high robustness and throughput. The systematic comparison of capLC-MS/MS and µLC-MS/MS systems for global proteome profiling has not been reported yet. Here, the capLC-MS/MS (150 µm i.d. column, 1 µL/min) and µLC-MS/MS (1 mm i.d. column, 50 µL/min) systems were both established based on UltiMate 3000 RSLCnano coupled to an Orbitrap Exploris 240 by integrating with different flowmeters. We evaluated both systems in terms of sensitivity, analysis throughput, separation efficiency, and robustness. capLC-MS/MS was about 10 times more sensitive than µLC-MS/MS at different gradient lengths. Compared with capLC-MS/MS, µLC-MS/MS was able to achieve higher analysis throughput and separation efficiency. During the 7 days' long-term performance test, both systems showed good reproducibility of chromatographic full width (RSD < 3%), retention time (RSD < 0.4%), and protein identification (RSD < 3%). These results demonstrate that capLC-MS/MS is more suitable for high-throughput analysis of clinical samples with a limited starting material. When enough samples are available, µLC-MS/MS is preferred. Together, capLC and µLC coupled to Orbitrap Exploris 240 with moderate sensitivity should well meet the needs of large-cohort clinical proteomic analysis.

Exosomal lncRNA-p21 derived from mesenchymal stem cells protects epithelial cells during LPS-induced acute lung injury by sponging miR-181.

Long noncoding RNAs (lncRNAs) act as essential regulators of various diseases. However, the functions of lncRNAs in sepsis-induced acute lung injury (SALI) remain unclear. Here, we found that lipopolysaccharide could upregulate lncRNA-p21 expression in mesenchymal stem cells (MSCs) in a time- and dose-dependent manner and that lncRNA-p21 was packaged into exosomes. Furthermore, we demonstrated that treatment with exosomal lncRNA-p21 could increase the expression of sirtuin 1 (SIRT1) to protect MLE-12 cells from apoptosis during sepsis. Moreover, we identified SIRT1 as a direct target of miR-181 and found that the level of SIRT1 was negatively correlated with the level of miR-181. The luciferase reporter assay also confirmed the negative correlation between the levels of miR-181 and lncRNA-p21. Our results showed that the lncRNA-p21-induced downregulation of miR-181 might suppress epithelial cell apoptosis and alleviate lung tissue injury by upregulating SIRT1 expression, suggesting the potential therapeutic effects of lncRNA-p21 on SALI. In conclusion, we found that the novel lncRNA-p21/miR-181/SIRT1 pathway may play an important role in the progression of SALI, and MSC-derived exosomes may be a new therapeutic strategy for this disease.

Endothelial progenitor cell transplantation attenuates lipopolysaccharide-induced acute lung injury via regulating miR-10a/b-5p.

BACKGROUND: Bone marrow-derived endothelial progenitor cells (EPCs) are shown to attenuate lipopolysaccharide- (LPS-) induced acute lung injury (ALI) in animal models. However, the molecular mechanism is largely unknown. MATERIALS AND METHODS: The animal model of ALI was induced by intratracheal instillation of purified LPS with 2.5 mg/ml/kg. The expression of microRNAs and ADAM15 in lung tissues and LPS-induced mouse pulmonary microvascular endothelial cells (MPMVECs) was determined by quantitative real-time PCR and western blot analysis. The target relationship between miR-10a/b-5p and ADAM15 was confirmed by luciferase reporter assay and RNA interference. The effect of EPCs on MPMVEC proliferation was detected by MTT assay. RESULTS: EPCs increased the expression of miR-10a/b-5p and reduced ADAM15 protein level in LPS-induced ALI lung tissues and MPMVECs (p < 0.05), and promoted LPS-induced MPMVEC proliferation (p < 0.05). ADAM15 was confirmed to be a downstream target of miR-10a/b-5p. Additionally, EPCs promoted LPS-induced MPMVEC proliferation and exerted the therapeutic effect of ALI via regulating miR-10a/b-5p/ADAM15 axis. CONCLUSION: EPC transplantation exerted its therapeutic effect of ALI via increasing miR-10a/b-5p and reducing ADAM15, thus providing a novel insight into the molecular mechanism of EPC transplantation in treating ALI.

Integration of metabolomics and transcriptomics reveals the therapeutic mechanism underlying Chelidonium majus L. in the treatment of allergic asthma.

BACKGROUND: Chelidonium majus is a well-known traditional Chinese medicine, and has been reported of the effect in relieving cough and asthma. However, the mechanism of action is still unknown. METHODS: Asthmatic SD rats were first sensitized and established through ovalbumin (OVA) motivation. Subsequently, Hematoxylin and eosin (H&E) staining, Masson's trichrome (Masson) staining, Periodic acid-Schiff (PAS) staining and inflammatory cytokines assay of interleukin (IL)-4, IL-6, IL-17 were implemented to evaluate the protective effects of Chelidonium majus on asthma. Then, the effects of Chelidonium majus and their molecular mechanisms of action on asthma were detected based on the integration of transcriptomics and metabolomics analyses. RESULTS: After administration with Chelidonium majus, the histological injuries of inflammation, collagen deposition and mucus secretion in lungs were attenuated and the serum inflammatory cytokines perturbations were also converted. Furthermore, integrated analysis revealed that after Chelidonium majus treatment, 7 different expression genes (DEGs) (Alox15, P4ha1, Pla2g16, Pde3a, Nme1, Entpd8 and Adcy9) and 9 metabolic biomarkers (ADP, Xanthosine, Hypoxanthine, Inosine, prostaglandin E2 (PGE2), prostaglandin F2a (PGF2a), phosphatidylserine, Creatine and LysoPC (10:0)) were discovered to be connected with the enrichment metabolic pathways, including Purine metabolism, Arachidonic acid metabolism, Arginine and proline metabolism and Glycerophospholipid metabolism. The obtained metabolic biomarkers and DEGs were mainly related to energy metabolism and inflammation, and may be potential therapeutic targets. CONCLUSION: Chelidonium majus relieved OVA-induced asthma in rats by regulating the Alox15, P4ha1, Pla2g16, Pde3a, Nme1, Entpd8 and Adcy9 genes expression to restore the disorders in energy metabolism and inflammation.

High-throughput drug target discovery using a fully automated proteomics sample preparation platform.

Drug development is plagued by inefficiency and high costs due to issues such as inadequate drug efficacy and unexpected toxicity. Mass spectrometry (MS)-based proteomics, particularly isobaric quantitative proteomics, offers a solution to unveil resistance mechanisms and unforeseen side effects related to off-targeting pathways. Thermal proteome profiling (TPP) has gained popularity for drug target identification at the proteome scale. However, it involves experiments with multiple temperature points, resulting in numerous samples and considerable variability in large-scale TPP analysis. We propose a high-throughput drug target discovery workflow that integrates single-temperature TPP, a fully automated proteomics sample preparation platform (autoSISPROT), and data independent acquisition (DIA) quantification. The autoSISPROT platform enables the simultaneous processing of 96 samples in less than 2.5 hours, achieving protein digestion, desalting, and optional TMT labeling (requires an additional 1 hour) with 96-channel all-in-tip operations. The results demonstrated excellent sample preparation performance with >94% digestion efficiency, >98% TMT labeling efficiency, and >0.9 intra- and inter-batch Pearson correlation coefficients. By automatically processing 87 samples, we identified both known targets and potential off-targets of 20 kinase inhibitors, affording over a 10-fold improvement in throughput compared to classical TPP. This fully automated workflow offers a high-throughput solution for proteomics sample preparation and drug target/off-target identification.

[Advances in high-throughput proteomic analysis].

Proteomic analysis aims at characterizing proteins on a large scale, including their relative abundance, post-translational modifications, protein-protein interactions and so on. Proteomic profiling helps to elucidate the mechanisms of disease occurrence and to discover new diagnostic markers and therapeutic targets. Mass spectrometry (MS)-based proteomic technologies have advanced to allow comprehensive qualitative and quantitative proteome profiling across a myriad proteins in cells and tissues. High-throughput proteomics is the core technique for large-scale protein characterization. With the increased demand for large cohort proteomic analysis in the biomedical research field, high-throughput proteomic analysis has become a critical issue that needs to be urgently addressed. The standard shotgun proteomic workflow comprises four steps, including sample preparation, peptide separation, MS acquisition, and data analysis. Advances in these four steps have contributed to the development of high-throughput proteomics. In this review, we aimed at summarizing the current information on the state-of-the-art development of high-throughput proteomic analysis, mainly including the following topics: (1) High-throughput, automatic proteomic sample preparation methods based on liquid-handling workstations. The automation of the proteomic sample preparation steps is essential for high-throughput proteomic analysis, which will significantly reduce variation of manual operation and sample loss by multistep sample processing. The commercial liquid handling workstations, including King FisherTM Flex, Agilent Bravo, AssayMAP Bravo, and Biomek® NXP, perform the handling steps of 96- or 384-channel microplate formats using a mechanical arm that increases the throughput and robustness of sample preparation. (2) High-throughput proteomic detection methods based on microliter-flow-rate liquid chromatography coupled with mass spectrometry (micro-flow LC-MS/MS). Nanoliter-flow-rate liquid chromatography coupled with mass spectrometry (Nano-flow LC-MS/MS) is widely used in classic proteomic research due to its excellent sensitivity, which often comes at the expense of robustness. Owing to the improved robustness and decreased injection-to-injection overheads, micro-flow LC-MS/MS has become increasingly popular in high-throughput proteomic analysis. (3) Using MS instrumentation with high sensitivity and fast scanning speed to realize in-depth proteomic analysis coupled with short chromatographic gradient separation. In recent years, new MS instrumentation continues to exhibit speed of analysis and sensitivity enables the large-scale profiling of hundreds of samples. In particular, ion mobility-based MS, such as timsTOF Pro and Exploris 480 equipped with a front-end high field asymmetric waveform ion mobility spectrometry (FAIMS), which provides fast, sensitive, and robust proteome profiling, thus shifting proteomics to the high-throughput era. (4) Artificial intelligence-, deep neural network-, and machine learning-based proteome data analysis methods. These approaches have improved comprehensive proteomic analysis efficiency. Specifically, the emergence of new algorithms and the up gradation of search engines accelerate the process of high-throughput data analysis. Additionally, the challenges and future development of high-throughput proteomics are prospected. In conclusion, high-throughput proteomic technologies are expected to gradually "transform" and become powerful tools for large cohort proteomic analysis in the near future.

Exosomes derived from LPS-induced MHs cells prompted an inflammatory response in sepsis-induced acute lung injury.

Exosome is a novel tool with an essential role in cell communication. However, its role in the pathogenesis of sepsis-induced acute lung injury is currently unknown. Here, we first found that lipopolysaccharide (LPS) could up-regulate the expression of pro-inflammatory cytokines and promote exosomes release in the murine alveolar macrophage cell line (MHs cells). Moreover, we found MHs cells derived exosomes also maintain the pro-inflammatory effect after LPS stimulation. Treating with hydrochloride hydrate (GW4869) could dose-dependently downregulated the release of exosomes and inhibited the upregulation of inflammatory cytokines in MHs cells with LPS treatment. Also, we further identified GW4869 administration induced the remission of histopathologic changes, the reduction of pro-inflammatory cytokines in lung tissue, and inhibit serum exosomes release. These results indicate that the downregulation of exosome release by GW4869 might protect lung tissue from LPS induced injury through the suppression of excessive inflammatory responses, suggesting its potential therapeutic effects on sepsis-induced acute lung injury.