Proteome Science最新文献

Background: Acute kidney injury is a common complication of sepsis, and its mechanism is very complicated. The purpose of this study was to investigate the mechanism of key differentially expressed proteins and their related signaling pathways in the occurrence and development of acute kidney injury in sepsis through proteomics.

Methods: Acute kidney injury was induced by intraperitoneal injection of lipopolysaccharide at 10 mg/kg. Renal tissues were analyzed by TMT quantitative proteomic analysis. Differentially expressed proteins (DEPs) were screened. Gene Ontology (GO) function analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis and protein-protein interaction (PPI) network analysis were performed.

Results: We obtained 530 DEPs. GO analysis showed that the biological process of DEPs was mainly stress response. The molecular functions of DEPs mainly focus on catalytic activity. The cellular components of DEPs were mainly located in the intracellular and cytoplasm. KEGG analysis showed that DEPs were mainly involved in metabolic pathways. Ten key proteins with interaction degree, such as Isg15, Irf7, Oasl2, Ifit3, Apob, Oasl, Ube2l6, Ifit2, Ifih1 and Ifit1 were identified. Irf7 was significantly up-regulated in rat kidney tissues.

Conclusion: The upregulation of Irf7 plays an important role in the mechanism of acute renal injury induced by sepsis.

Background: Metabolomics, a burgeoning field within systems biology, focuses on the comprehensive study of small molecules present in biological systems. Mass spectrometry (MS) has emerged as a powerful tool for metabolomic analysis due to its high sensitivity, resolution, and ability to characterize a wide range of metabolites thus offering deep insights into the metabolic profiles of living systems.

Aim of review: This review provides an overview of the methodologies, workflows, strategies, data analysis techniques, and applications associated with mass spectrometry-based metabolomics.

Key scientific concepts of review: We discuss workflows, key strategies, experimental procedures, data analysis techniques, and diverse applications of metabolomics in various research domains. Nuances of sample preparation, metabolite extraction, separation using chromatographic techniques, mass spectrometry analysis, and data processing are elaborated. Moreover, standards, quality controls, metabolite annotation, software for statistical and pathway analysis are also covered. In conclusion, this review aims to facilitate the understanding and adoption of mass spectrometry-based metabolomics by newcomers and researchers alike by providing a foundational understanding and insights into the current state and future directions of this dynamic field.

The risk of cardiovascular disease (CVD) in patients with rheumatoid arthritis (RA) is much higher than that in the general population. As its etiology is not fully understood, we performed a pilot study using a shotgun proteomic approach to investigate whether the plasma signature in RA patients with CVD might show an altered profile. Subjects with RA were compared to a group of RA patients with a previous cardiovascular event (CVE). The cohort consisted of an RA control group (n = 10) and a group (n = 10) of RA patients with a history of CVD. Samples were collected at least 6 months before the CVE and 3-6 months after the CVE. All subjects were matched to controls for age, sex, and medication use. Plasma depletion of the 14 most abundant proteins was followed by bottom-up shotgun proteomics analysis (LC‒MS/MS). Relative changes in protein/peptide abundance were investigated using classical statistical analyses with Perseus and XG-Boost machine learning to compare between groups and to determine the relative importance of identified proteins, respectively. Principal component analysis (PCA) revealed no difference in the global protein and peptide signatures between the control and CVE groups. A total of 150, 239 and 74 protein ID's showed in comparison between Post Event vs. controls, Event vs. no Event and Pre event vs. Post Event respectively a statistically difference in relative abundance (p < 0.05). Remarkedly a total of 236 proteins ID's showed a statistical significant difference in relative abundance in the PRE-Event group compared to the control group which could also be confirmed by XGboost machine learning. Here, we demonstrated potential differences in the plasma proteome signature of rheumatic patients with cardiovascular events. Interestingly, this signature may be present prior to CVE's. However the conclusions must be drawn with caution, since this is a pilot study and further investigation with larger cohorts is warranted to identify potential risk markers that may predict the relative risk of CVEs in rheumatic diseases.

Background: Heart failure with preserved ejection fraction (HFpEF) is a complex syndrome that currently lacks effective biomarkers for early diagnosis and treatment. This study seeks to identify new potential biomarkers for HFpEF using proteomics and machine learning.

Methods: Plasma samples were collected from 20 patients newly diagnosed age, sex, BMI matched HFpEF and 20 healthy controls (HCs). Proteomic analysis was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) in data-independent acquisition mode. Differentially expressed proteins (DEPs) were identified and analyzed through enrichment analyses and protein-protein interaction (PPI) network construction. Machine learning methods, including LASSO regression and the Boruta algorithm were used to select candidate biomarkers. The diagnostic value of these proteins was assessed using receiver operating characteristic (ROC) curves and nomogram construction. Expression of candidate proteins was analyzed in immune cells and tissues. Finally, enzyme-linked immunosorbent assay (ELISA) was used to validate the plasma levels of selected proteins.

Results: A total of 34 DEPs were identified between HFpEF patients and HCs. Enrichment analyses revealed involvement in acute-phase response and immune pathways. PPI network analysis identified nine hub proteins. Machine learning methods narrowed the candidates to four potential biomarkers: SERPINA1, AFM, SERPINA3, and ITIH4. Among these, SERPINA3 showed the highest diagnostic value with an area under the ROC curve (AUC) of 0.835. ELISA validation confirmed that plasma SERPINA3 levels were significantly elevated in HFpEF patients compared to HCs (p < 0.0001).

Conclusions: Our findings suggest that SERPINA3 could serve as a biomarker for HFpEF, Elevated plasma levels of SERPINA3 in HFpEF patients suggest its utility in early diagnosis and may provide insights into the disease's pathogenesis.

Lysine crotonylation (Kcr) is a novel post-translational modification that is important in functional studies. However, our understanding of Kcr in the developing human fetus brain, heart, kidney, liver, and lung remains restricted. In this study, we used high-resolution LC-MS/MS and high-sensitivity immunoaffinity purification to analyze Kcr in the brain, heart, kidney, liver, and lung of 17-week fetus. A total of 24,947 Kcr modification sites were identified in 5,102 proteins, resulting in the most diverse Kcr proteome of fetus organs ever reported. We investigated the universality and specificity of Kcr during the development of several organs in 17-week fetus using bioinformatics analysis. Kcr proteins were found to be closely associated with the synthesis, transcription and translation of genetic material, energy production and metabolic processes. Importantly, the expression of Kcr proteins in each organ was closely related to the organs' developmental functions. Furthermore, several highly modified Kcr proteins may be important targets during fetus organ development. This discovery advances our understanding of fetus organ development and establishes the groundwork for future research into the regulatory mechanisms of crotonylation in fetus organ development.

Background: Diabetic encephalopathy (DE) is considered as one of the complications of diabetes,which is associated with cognitive impairment in the pathological process of development. Up to now, phospholipid phosphatase related 4 (Plppr4), also known as plasticity related gene 1 (PRG-1) has been revealed its important role in neuroplasticity. However, the underlying mechanisms of Plppr4 on the basis of diabetic-induced cognitive dysfunction (DCD) are still unknown. The aim of current study was to provide insight into molecular mechanism and cellular heterogeneity underlying DCD, and investigate the functional role of PRG-1 involved in this process.

Methods: Combined Single-cell RNA sequencing (scRNA-seq) and RNA transcriptome analysis, the distinct sub-populations, functional heterogeneity as well as potential enriched signaling pathways of hippocampal cells could be elucidated.

Results: We identified the sub-cluster of type I spiral ganglion neurons expressed marker gene as Amigo2 in cluster8 and Cnr1 in cluster 9 of hippocampal cells from DCD and the effect of those on neuronal cells interaction. We also found that PRG-1 was involved in the synaptic plasticity regulation of hippocampus via NGFBDNF-Trkb signaling pathway. In high glucose induced HT22 cells injury model in vitro, we investigated that down-regulated PRG-1 along with down-regulated BDNF and also decreased expression of synapsin-1, PSD-95, SYN which are related to synaptic plasticity; Meanwhile, the Prg-1 targeted miR-18a-LncRNA NONRATG-022419 pairs related with significantly down-regulated expression of PRG-1.

Conclusion: This study revealed the synaptic plasticity regulation of PRG-1 in DCD, and might provide the therapeutic target and potential biomarkers for early interventions in DCD patients.

Objective: The study aimed to explore the role of metabolism-related proteins and their correlation with clinical data in predicting the prognosis of polycystic ovary syndrome (PCOS).

Methods: This research involves a secondary analysis of proteomic data derived from endometrial samples collected from our study group, which includes 33 PCOS patients and 7 control subjects. A comprehensive identification and analysis of 4425 proteins were conducted to screened differentially expressed proteins (DEPs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently performed on the DEPs. To identify independent prognostic metabolism-related proteins, univariate Cox regression and LASSO regression were applied. The expression levels of these proteins were then used to develop a prognostic model, with their predictive accuracy evaluated through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. Furthermore, we also investigate the correlation between clinical data and prognostic proteins.

Results: The study identified 285 DEPs between the PCOS and control groups. GO enrichment analysis revealed significant involvement in metabolic processes, while KEGG pathway analysis highlighted pathways such as glycolysis/gluconeogenesis and glucagon signaling. Ten key metabolism-related proteins (ACSL5, ANPEP, CYB5R3, ENOPH1, GLS, GLUD1, LDHB, PLCD1, PYCR2, and PYCR3) were identified as significant predictors of PCOS prognosis. Patients were separated into high and low-risk groups according to the risk score. The ROC curves for predicting outcomes at 6, 28, and 37 weeks demonstrated excellent predictive performance, with AUC values of 0.98, 1.0, and 1.0, respectively. The nomogram constructed from these proteins provided a reliable tool for predicting pregnancy outcomes. DCA indicated a net benefit of the model across various risk thresholds, and the calibration curve confirmed the model's accuracy. Additionally, we also found BMI exhibited a significant negative correlation with the expression of GLS (r =-0.44, p = 0.01) and CHO showed a significant positive correlation with the expression of LDHB (r = 0.35, p = 0.04).

Conclusion: The identified metabolism-related proteins provide valuable insights into the prognosis of PCOS. The protein based prognostic model offers a robust and reliable tool for risk stratification and personalized management of PCOS patients.

Tuberculosis drug resistance contributes to the spread of tuberculosis. Immunotherapy is an effective strategy for treating tuberculosis, with the regulation of macrophage-mediated anti-tuberculosis immunity being crucial. Norcantharidin (NCTD), a drug used in tumor immunotherapy, has significant immunomodulatory effects. Thus, NCTD may have an anti-tuberculosis role by regulating immunity. Understanding how NCTD affects the proteome of Mtb-infected macrophages can provide valuable insights into potential treatments. This study aimed to investigate the impact of NCTD (10 μg/mL) on the proteome of macrophages infected with Mtb H37Ra using liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. A total of 69 differentially regulated proteins (DRPs) were identified, with 28 up-regulated and 41 down-regulated in the NCTD-treated group. Validation of six DRPs (CLTCL1, VAV1, SP1, TRIM24, MYO1G, and WDR70) by Western blot analysis confirmed the accuracy of the LC-MS/MS method used in this study. NCTD modulates various protein expressions involved in chromatin-modifying enzymes, RHO GTPases activating PAKs, Fc gamma R-mediated phagocytosis, T cell receptor signaling pathway, and antigen processing and presentation. Overall, the research provides new insights into the effects of NCTD on the proteome of Mtb-infected macrophages. The identified changes highlight potential targets for future therapeutic interventions aimed at enhancing host immunity against Mtb infection or developing new anti-TB drugs based on these findings.

Introduction: Hypertensive intracerebral hemorrhage (HICH) stands out as a critical complication of primary hypertension. Consequently, investigating messenger RNA (mRNA) biomarkers becomes imperative, offering potential targets. This study is conducted for elucidating the expression profile of blood mRNA biomarkers in HICH.

Methods: Twenty-five HICH patients were constituted the HICH group.Twenty-two healthy volunteers recruited and comprised the control group. Peripheral blood cells were extracted to identify candidate mRNA. The identified differential expressions of genes between the two groups were validated, and the potential associations between these differentially expressed genes and adverse events were analyzed. GO and KEGG enrichment of DEGs, Weighted Gene Co-expression Network and Protein Interaction Network were established. target mRNA was screened.

Results: The study identified 3163 differentially expressed genes in HICH. 8 candidate mRNA (SPI1, HK3, HCK, SYK, CD14, FCER1G, CYBB, FGR) were pinpointed. Associations with pathways affecting HICH development included HIF-1 signaling, NF-kappa B signaling, and C-type lectin receptor signaling. In the HICH group, higher expressions of HK3, HCK, SYK, CD14, FCER1G, CYBB, and FGR, and lower SPI1 expression compared to the control group. HICH patients experienced high rates of complications: pulmonary infection (84%), epilepsy (16%), enlarged hematoma (20%), gastrointestinal bleeding (48%), malnutrition (84%), and lower limb deep vein thrombosis (DVT) (12%). Factors contributing to pulmonary infection included age and elevated expression of HCK, SYK, CD14, and FGR. SPI1 was associated with epilepsy, while its lower expression correlated with hematoma enlargement. Gastrointestinal bleeding was linked to increased cerebral hemorrhage. Malnutrition was associated with higher age, and expressions of HK3, HCK, SYK, CD14, FCER1G, CYBB, and FGR. Patients with lower limb DVT had elevated expressions of the identified genes.

Conclusion: In hypertensive intracerebral hemorrhage, there are elevated expressions of HK3, HCK, SYK, CD14, FCER1G, CYBB, and FGR, along with reduced expression of SPI1. Furthermore, age, along with elevated expressions of HCK, SYK, CD14, and FGR, serves as influencing factors contributing to pulmonary infection in patients.

Background: The complexity of delayed cerebral ischemia (DCI) after aneurysmal subarachnoid hemorrhage (aSAH) may require the simultaneous analysis of variant types of protein biomarkers to describe it more accurately. In this study, we analyzed for the first time the alterations of cerebrospinal fluid (CSF) proteins in patients with aSAH by multi-targeted Olink proteomics, aiming to reveal the pathophysiology of DCI and provide insights into the diagnosis and treatment of aSAH.

Methods: Six aSAH patients and six control patients were selected, and CSF samples were analyzed by Olink Proteomics (including 96-neurology panel and 96-inflammation panel) based on Proximity Extension Assay (PEA). Differentially expressed proteins (DEPs) were acquired and bioinformatics analysis was performed.

Results: PCA analysis revealed better intra- and inter-group reproducibility of CSF samples in the control and aSAH groups. 23 neurology-related and 31 inflammation-relevant differential proteins were identified. In the neurology panel, compared to controls, the up-regulated proteins in the CSF of SAH patients predominantly included macrophage scavenger receptor 1 (MSR1), siglec-1, siglec-9, cathepsin C (CTSC), cathepsin S (CTSS), etc. Meanwhile, in the inflammation group, the incremental proteins mainly contained interleukin-6 (IL-6), MCP-1, CXCL10, CXCL-9, TRAIL, etc. Cluster analysis exhibited significant differences in differential proteins between the two groups. GO function enrichment analysis hinted that the differential proteins pertinent to neurology in the CSF of SAH patients were mainly involved in the regulation of defense response, vesicle-mediated transport and regulation of immune response; while the differential proteins related to inflammation were largely connected with the cellular response to chemokine, response to chemokine and chemokine-mediated signaling pathway. Additionally, in the neurology panel, KEGG enrichment analysis indicated that the differential proteins were significantly enriched in the phagosome, apoptosis and microRNAs in cancer pathway. And in the inflammation panel, the differential proteins were mainly enriched in the chemokine signaling pathway, viral protein interaction with cytokine and cytokine receptor and toll-like receptor signaling pathway.

Conclusions: These identified differential proteins reveal unique pathophysiological characteristics secondary to aSAH. Further characterization of these proteins and aberrant pathways in future research could enable their application as potential therapeutic targets and biomarkers for DCI after aSAH.