The antibiotic resistance of Edwardsiella tarda is becoming increasingly prevalent, and thus novel antimicrobial strategies are being sought. Lysine acylation has been demonstrated to play an important role in bacterial physiological functions, while its role in bacterial antibiotic resistance remains largely unclear. In this study, we investigated the lysine acetylation and succinylation profiles of E. tarda strain EIB202 using affinity antibody purification combined with LC-MS/MS. A total of 1511 lysine-acetylation sites were identified on 589 proteins, and 2346 lysine-succinylation sites were further identified on 692 proteins of this pathogen. Further bioinformatic analysis showed that both post-translational modifications (PTMs) were enriched in the tricarboxylic acid (TCA) cycle, pyruvate metabolism, biosynthesis, and carbon metabolism. In addition, 948 peptides of 437 proteins had overlapping associations with multiple metabolic pathways. Moreover, both acetylation and succinylation were found in many antimicrobial resistance (AMR) proteins, suggesting their potentially vital roles in antibiotic resistance. In general, our work provides insights into the acetylome and succinylome features responsible for the antibiotic resistance mechanism of E. tarda, and the results may facilitate future investigations into the pathogenesis of this bacterium.

Mass spectrometry (MS) has become a prominent choice for large-scale absolute protein quantification, but its quantification accuracy still has substantial room for improvement. A crucial issue is the bias between the peptide MS intensity and the actual peptide abundance, i.e., the fact that peptides with equal abundance may have different MS intensities. This bias is mainly caused by the diverse physicochemical properties of peptides. Here, we propose a novel algorithm for label-free absolute protein quantification, LFAQ, which can correct the biased MS intensities by using the predicted peptide quantitative factors for all identified peptides. When validated on datasets produced by different MS instruments and data acquisition modes, LFAQ presented accuracy and precision superior to those of existing methods. In particular, it reduced the quantification error by an average of 46% for low-abundance proteins.

Introduction: Osteosarcoma (OS) is the most common bone tumor, characterized by a high incidence, rapid progression, and frequent metastases. The implementation of chemotherapy has made important progress, while the necrosis rate is limited and the survival rates remain unsatisfactory, therefore novel approaches are needed.Methods: We used proteomic analysis to characterize the molecular landscape of patients exhibiting different levels of chemotherapy-induced necrosis.Results: Patients with low necrosis rate (≤70%) showed distinct expression patterns, with significant upregulation of proteins involved in DNA replication, metabolism, and mitochondrial pathway. The Runx1-related signaling pathway was also identified as potentially involved in disease progression. Remarkably, Mitochondrial Ribosomal Protein L4 (MRPL4) and Macrophage Erythroblast Attacher, E3 Ubiquitin Ligase (MEMA) were identified as hub proteins in MEGENA analysis and the public database. By integrating with immunohistochemistry, the higher expression level was verified in samples of OS patients compared to those of healthy people. Discussion: Overall, our project improves the knowledge of the expression pattern with different necrosis rates of OS samples, and the findings of MRPL4 and MAEA indicate the potential role in chemoresistance and provide new targets for the therapeutic strategy for OS patients with a low necrosis rate.

Background: Prefrailty is associated with anomalies in protein metabolism; however, the serum proteomic signatures remain unclear. This study investigated protein profiles across different health statuses and evaluated their potential for the early identification of prefrailty. Methods: Older adults were categorized as robust, prefrail, and frail. Untargeted proteomic screening in a discovery cohort (n = 30) was followed by parallel reaction monitoring (PRM) validation (n = 99). Multidimensional clinical parameters and differentially expressed proteins were integrated within machine learning pipelines to refine the search for characteristic features of prefrailty. Results: 166 proteins were found to be differentially expressed across frailty statuses, with 15 significantly prefrailty-associated proteins subsequently confirmed by PRM validation. These proteins were functionally enriched in pathways related to cell signaling, protein metabolism, immune regulation, and skeletal muscle function maintenance. A Random Forest model, further assembled from gait speed, skeletal muscle mass, E3-independent E2 ubiquitin-conjugating enzyme (UBE2O), Timed Up and Go test time, alpha-actinin-3 (ACTN3), and Mini-Mental State Examination score, exhibited the most robust performance for early frailty identification among multiple algorithms compared. Conclusion: This exploratory study identified candidate serum protein biomarkers associated with prefrailty. Preliminary machine learning models incorporating UBE2O and ACTN3 suggested the feasibility of discriminating prefrailty from robust status, reflecting underlying proteomic heterogeneity among community-dwelling older adults.

Introduction: With the increasing identification of multidrug resistance in Aeromonas hydrophila isolated from diverse food sources, there is an urgent need to investigate its resistance mechanism. Previous studies have demonstrated that OmpR/PhoB-type response regulators (RRs) play critical roles in mediating bacterial tolerance to various environmental stresses. Methods and results: In this study, we constructed an AHA_4052 gene knockout strain, a member of the OmpR/PhoB family of DNA-binding response regulators, and studied its phenotypic characteristics. Deletion of the AHA_4052 gene rendered the bacteria significantly more sensitive to high temperature, osmotic stress, and aminoglycoside antibiotics compared to the wild-type A. hydrophila . Label-free quantitative proteomic analysis revealed 131 differentially expressed proteins in the ΔAHA_4052 mutant strain. These proteins were predominantly associated with ribosome, butanoate metabolism, and glycerophospholipid metabolism pathways, with ribosome-related proteins accounting for 17.56% of the total. Additionally, seven antibiotic resistance-related proteins exhibited significant expression changes in the mutant strain. Chromatin immunoprecipitation assay–polymerase chain reaction (ChIP-PCR) validation further demonstrated that the transcriptional regulator AHA_4052 directly binds to the promoters of two resistance genes, AHA_2114 and AHA_3488. Discussion: Collectively, these findings indicate that the transcriptional regulatory factor AHA_4052 plays a critical role in stress tolerance, particularly against aminoglycosides, providing insights into the resistance mechanisms of A. hydrophila and potentially informing the development of new therapeutic strategies.