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. 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. 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. The upregulation of Irf7 plays an important role in the mechanism of acute renal injury induced by sepsis.
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. This review provides an overview of the methodologies, workflows, strategies, data analysis techniques, and applications associated with mass spectrometry-based metabolomics. 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.
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. 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. 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). 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.
