Proteomes最新文献

Background: Membrane proteins are preferentially solubilized with sodium dodecyl sulfate (SDS), which necessitates a purification protocol to deplete the surfactant prior to mass spectrometry analysis. However, maintaining solubility of intact membrane proteins is challenged in an SDS-free environment. SDS precipitation with potassium salts (KCl) offers a potentially viable workflow to deplete SDS and permit proteoform analysis. The purpose of this study is to devise a robust detergent-based protocol applicable for processing and analysis of intact membrane-associated proteoforms.

Methods: The precipitation conditions impacting SDS removal from spinach chloroplasts and liver membrane proteome preparations were evaluated, capitalizing on optimization of pH (highly basic), addition of MS-compatible solubilizing additives (urea) and adjustment of the KCl to SDS ratio to maximize recovery and purity.

Results: Characterization of the SDS-solubilized, KCl-precipitated spinach membrane preparation revealed multiple charge envelope MS spectra displaying high signal to noise, free of SDS adducts. Precipitation at pH 12 or with urea improved protein recovery and purity. Bottom-up analysis identified 1826 distinct liver protein groups from four independent SDS precipitation conditions. While precipitation at pH 8 without urea revealed a greater number of protein identifications by mass spectrometry, precipitation under highly basic conditions (pH 12) with urea provided higher membrane protein recovery and achieved the greatest number (732 of 1056) and largest percentage (69.3%) of membrane proteins identified in the SDS removal workflow.

Conclusion: This workflow provides new opportunities for MS-based proteoform analysis by capitalizing on the benefits of SDS for protein extraction while maintaining high solubility and purity of intact proteins though KCl precipitation of the surfactant.

Background: Snake envenomation is a major public health issue in Nigeria, primarily due to bites from Echis ocellatus, Naja nigricollis, and Bitis arietans. Understanding their venom composition is essential for effective antivenom development. This study characterizes and compares the venom proteomes of these snakes using iTRAQ-based proteomics, focusing on key toxin families and their relative abundances. Methods: Venom samples were ethically collected from adult snakes, pooled by species, lyophilized, and stored for proteomic analysis. Proteins were extracted, digested with trypsin, and labeled with iTRAQ. Peptides were analyzed via mass spectrometry, and data were processed using Mascot and IQuant for protein identification and quantification. Results:E. ocellatus and B. arietans venoms had similar profiles, rich in C-type lectins, serine proteases, and phospholipase A₂s. These comprised 17%, 11%, and 5% in E. ocellatus and 47%, 10%, and 7% in B. arietans, with metalloproteinases dominating both (53% and 47%). In N. nigricollis, three-finger toxins (9%) were most abundant, followed by metalloproteinases (3%). All species shared four core protein families, with N. nigricollis also containing four uncharacterized proteins. Conclusions: This study highlights venom compositional differences, advancing snake venom biology and informing targeted antivenom development.

Background: Alterations in protein abundance profiles in yeast deletion strains are frequently utilized to gain insights into cellular functions and regulatory networks, most of which are conserved in higher eukaryotes.

Methods: This study investigates the impact of protein extraction methodologies on the whole proteome analysis of S. cerevisiae, comparing detergent-based lysis versus mechanical lysis with silica beads. We evaluated the proteomic profiles of wild-type and two yeast deletion strains, siz1Δ and nfi1Δ (siz2Δ), which are SUMO E3 ligases. Combining isobaric TMTpro-labeling with mass spectrometry using real-time search MS3, we profiled over 4700 proteins, covering approximately 80% of the yeast proteome.

Results: Hierarchical clustering and principal component analyses revealed that the choice of protein extraction method significantly influenced the proteomic data, overshadowing the genetic variances among these strains. Notably, the detergent-based lysis showed superior performance in extracting proteins compared to mechanical lysis. Despite minimal proteomic alterations among strains, we observed consistent changes regardless of the lysis strategy in proteins such as Ino1, Rep1, Rep2, Snz1, and Fdh1 in both SUMO E3 ligase deletion strains, implying potential redundant mechanisms of control for these proteins.

Conclusion: These findings underscore the importance of method selection at each step of sample preparation in proteomic studies and enhance our comprehension of cellular adaptations to genetic perturbations.

Background: Human proteins exist in numerous modifications-proteoforms-which are promising targets for biomarker studies. In this study, we aimed to generate comparative proteomics data, including proteoform patterns, from hepatocellular carcinoma (HCC) and nonmalignant liver tissues.

Methods: To investigate protein profiles and proteoform patterns, we employed a panoramic, integrative top-down proteomics approach: two-dimensional gel electrophoresis (2DE) coupled with liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS).

Results: We visualized over 2500 proteoform patterns per sample type, enabling the identification of distinct protein signatures and common patterns differentiating nonmalignant and malignant liver cells. Among these, 1270 protein patterns were uniformly observed across all samples. Additionally, 38 proteins-including pyruvate kinase PKM (KPYM), annexin A2 (ANXA2), and others-exhibited pronounced differences in proteoform patterns between nonmalignant and malignant tissues.

Conclusions: Most proteoform patterns of the same protein were highly similar, with the dominant peak corresponding to theoretical (unmodified) protein parameters. However, certain proteins displayed altered proteoform patterns and additional proteoforms in cancer compared to controls. These proteins were prioritized for further characterization.

Background: Type 2 diabetes mellitus (T2DM) is an epidemic chronic disease that affects millions of people worldwide. This study aims to explore the impact of T2DM on the tear proteome, specifically investigating whether alterations occur before the development of diabetic retinopathy.

Methods: Flush tear samples were collected from healthy subjects and subjects with preDM and T2DM. Tear proteins were processed and analyzed by mass spectrometry-based shotgun proteomics using a data-independent acquisition parallel acquisition serial fragmentation (diaPASEF) approach. Machine learning algorithms, including random forest, lasso regression, and support vector machine, and statistical tools were used to identify potential biomarkers.

Results: Machine learning models identified 17 proteins with high importance in classification. Among these, five proteins (cystatin-S, S100-A11, submaxillary gland androgen-regulated protein 3B, immunoglobulin lambda variable 3-25, and lambda constant 3) exhibited differential abundance across these three groups. No correlations were identified between proteins and clinical assessments of the ocular surface. Notably, the 17 important proteins showed superior prediction accuracy in distinguishing all three groups (healthy, preDM, and T2DM) compared to the five proteins that were statistically significant.

Conclusions: Alterations in the tear proteome profile were observed in adults with preDM and T2DM before the clinical diagnosis of ocular abnormality, including retinopathy.

Peroxidases are essential enzymes that catalyze redox reactions, with wide-ranging biological implications. Among these, an enhanced ascorbate peroxidase (APEX) has emerged as a valuable tool for studying intricate intracellular events with spatiotemporal precision, particularly in protein-protein, protein-RNA, and protein-DNA interaction networks in living cells. This review discusses APEX's structural and functional attributes, its evolution through genetic engineering, and its transformative applications in high-resolution mapping used for proteomic and transcriptomic studies. Furthermore, it highlights recent advancements in substrate innovation and addresses current challenges and future directions in leveraging APEX for cutting-edge biological research.

Radiotherapy resistance represents a critical aspect of cancer treatment, and molecular characterization is needed to explore the pathways and mechanisms involved. DNA repair, hypoxia, metabolic reprogramming, apoptosis, tumor microenvironment modulation, and activation of cancer stem cells are the primary mechanisms that regulate radioresistance, and understanding their complex interactions is essential for planning the correct therapeutic strategy. Proteomics has emerged as a key approach in precision medicine to study tumor heterogeneity and treatment response in cancer patients. The integration of mass spectrometry-based techniques with bioinformatics has enabled high-throughput, quantitative analyses to identify biomarkers, pathways, and new potential therapeutic targets. This review highlights recent advances in proteomic technologies and their application in identifying biomarkers predictive of radiosensitivity and radioresistance in different tumors, including head and neck, breast, lung, and prostate cancers. Sample variability, data interpretation, and the translation of findings into clinical practice remain challenging elements of proteomics. However, technological advancements support its application in a wide range of topics, allowing a comprehensive approach to radiobiology, which helps overcome radiation resistance. Ultimately, incorporating proteomics into the radiotherapy workflow offers significant potential for enhancing treatment efficacy, minimizing toxicity, and guiding precision oncology strategies.

Protein deamidation, a nonenzymatic post-translational modification that converts asparagine and glutamine residues into their acidic forms, such as aspartic acid, iso-aspartic acid, or glutamic acid, has emerged as a pivotal process affecting protein stability and function. Once considered a minor biochemical occurrence, deamidation is now recognized for its significant role in aging, age-associated diseases, disease progression, cancer, and therapeutic efficacy. This review explores the recent advances in understanding protein deamidation, its impact on cellular homeostasis, protein misfolding, and age-related and chronic diseases including neurodegeneration and cancer. The study also highlights the challenges posed by deamidation in biopharmaceuticals, where it compromises therapeutic stability and efficacy. Advancements in state-of-the-art analytical techniques and computational approaches for identifying deamidation sites and predicting deamidation-prone regions are discussed, along with deeper insights into how deamidation affects protein structure and function. Based on the current insights, this review underscores the dual role of deamidation as both a natural regulatory process and a contributor to pathological states, providing a roadmap for future research in aging biology, disease mechanisms, and therapeutics.

Background: Esophageal adenocarcinoma (EAC) diagnosis involves invasive and expensive endoscopy with biopsy, but rising EAC incidence has not been reduced by increased surveillance. This study aimed to develop and clinically validate a novel glycoprotein biomarker blood test for EAC, named PromarkerEso.

Methods: Serum glycoprotein relative concentrations were measured using a lectin-based magnetic bead array pulldown method, with multiple reaction monitoring mass spectrometry in 259 samples across three independent cohorts. A panel of glycoproteins: alpha-1-antitrypsin, alpha-1-antichymotrypsin, complement C9 and plasma kallikrein, were combined with clinical factors (age, sex and BMI) in an algorithm to categorize the samples by the risk of EAC.

Results: PromarkerEso demonstrated a strong discrimination of EAC from the controls (area under the curve (AUC) of 0.91 in the development cohort and 0.82 and 0.98 in the validation cohorts). The test exhibited a high sensitivity for EAC (98% in the development cohort, and 99.9% and 91% in the validation cohorts) and a high specificity (88% in the development cohort, and 86% and 99% in the validation cohorts). PromarkerEso identified individuals with and without EAC (96% and 95% positive and negative predictive values).

Conclusions: This less invasive approach for EAC detection with the novel combination of these glycoprotein biomarkers and clinical factors coalesces in a potential step toward improved diagnosis.

Background: Previously, we found that an uncharacterized protein CXorf38 is significantly downregulated in human ZIP8-knockout (KO) cells. Given that ZIP8 regulates essential micronutrients linked to diseases including cancer, this study aims to characterize CXorf38 and evaluate its role in lung adenocarcinoma. Methods: iTRAQ-based proteomics was previously used to identify the abundance of proteins in ZIP8-KO cells. Cell proliferation and colony formation assays were used to examine the function of CXorf38 by overexpressing the gene in lung adenocarcinoma cell lines. Kaplan-Meier survival analysis was used to assess the prognostic value of CXorf38, while TCGA clinical database analysis was used to evaluate its expression in lung cancer tissues, particularly in smokers. Bioinformatics analyses (GO, KEGG, PPI, and ICI) were performed on CXorf38-coexpressed genes derived from patients with lung cancer. Results: CXorf38 overexpression suppressed lung cancer cell proliferation and colony formation, suggesting a tumor-suppressive role. Higher CXorf38 expression correlated with improved survival in patients with lung adenocarcinoma, but not in lung squamous cell carcinoma. Clinical data showed CXorf38 downregulation with lung cancer tissues of smokers, indicating a potential role in smoking-induced cancer progression and treatment. Functional analysis using bioinformatics linked CXorf38 to immune response regulation, suggesting involvement in the tumor immune microenvironment. Conclusions: Our study reveals for the first time that CXorf38 is a potential tumor suppressor, prognostic biomarker, and/or tumor immune regulator in lung adenocarcinoma-further research is warranted to explore its role in tumor immunity and its therapeutic potential.