Proteomes最新文献

In recent years, avocados have gained worldwide popularity as a nutritive food. This trend is causing a rise in the production of this fruit, which is accompanied by several problems associated with monocultural practices. Despite massive economic gains, limited molecular and structural information has been generated about avocado ripening. In fact, limited studies have attempted to unravel the proteome complexity dynamics of avocado fruit. We therefore conducted a comparative proteomics study on avocado peel and pulp during the postharvest shelf life using tandem mass tag synchronous precursor selection triple-stage mass spectrometry. We identified 3161 and 1128 proteins in the peel and pulp, respectively. Peels exhibited major over-accumulation of proteins associated with water deprivation and oxidative stress, along with abscisic acid biosynthesis. Ethylene, jasmonic acid, phenylpropanoid, and flavonoid biosynthesis pathways were activated. Structurally, we observed the accumulation of lignin and a reduction in cuticular thickness, which coincides with the reduction in the levels of long-chain acyl-coenzyme A synthetase and a marginal increase in 10,16-dihydroxyhexadecanoic acid. Our study sheds light on the association of proteome modulation with the structural features of Hass avocado. Its detailed characterization will provide an alternative for better preservation during the postharvest period.

Muscle tissue is one of the most dynamic and plastic tissues of the mammalian body and covers different roles, such as force generation and metabolic control. Muscular proteomics provides an important opportunity to reveal the molecular mechanisms behind muscle pathophysiology. To ensure successful proteomic analysis, it is necessary to have an efficient and reproducible protein extraction method. This study aimed to evaluate the efficacy of two different extraction protocols of muscle samples for two-dimensional gel electrophoresis. In particular, mouse muscle proteins were extracted by an SDS-based buffer (Method A) and by a UREA/CHAPS/DTE/TRIS solution (Method B). The efficacies of the methods were assessed by performing an image analysis of the 2DE gels and by statistical and multivariate analyses. The 2DE gels in both preparations showed good resolution and good spot overlapping. Methods A and B produced 2DE gels with different means of total spots, higher for B. Image analysis showed different patterns of protein abundance between the protocols. The results showed that the two methods extract and solubilize proteins with different chemical-physical characteristics and different cellular localizations. These results attest the efficacy and reproducibility of both protein extraction methods, which can be parallelly applied for comprehensive proteomic profiling of muscle tissue.

Despite the advances in modern cancer therapy, malignant diseases are still a leading cause of morbidity and mortality worldwide. Conventional treatment methods frequently lead to side effects and drug resistance in patients, highlighting the need for novel therapeutic approaches. Recent findings have identified the existence of non-canonical micropeptides, an additional layer of the proteome complexity, also called the microproteome. These small peptides are a promising class of therapeutic agents with the potential to address the limitations of current cancer treatments. The microproteome is encoded by regions of the genome historically annotated as non-coding, and its existence has been revealed thanks to recent advances in proteomic and bioinformatic technology, which dramatically improved the understanding of proteome complexity. Micropeptides have been shown to be biologically active in several cancer types, indicating their therapeutic role. Furthermore, they are characterized by low toxicity and high target specificity, demonstrating their potential for the development of better tolerated drugs. In this review, we survey the current landscape of known micropeptides with a role in cancer progression or treatment, discuss their potential as anticancer agents, and describe the methodological challenges facing the proteome field of research.

High-throughput omics technologies have dramatically changed biological research, providing unprecedented insights into the complexity of living systems. This review presents a comprehensive examination of the current landscape of high-throughput omics pipelines, covering key technologies, data integration techniques and their diverse applications. It looks at advances in next-generation sequencing, mass spectrometry and microarray platforms and highlights their contribution to data volume and precision. In addition, this review looks at the critical role of bioinformatics tools and statistical methods in managing the large datasets generated by these technologies. By integrating multi-omics data, researchers can gain a holistic understanding of biological systems, leading to the identification of new biomarkers and therapeutic targets, particularly in complex diseases such as cancer. The review also looks at the integration of omics data into electronic health records (EHRs) and the potential for cloud computing and big data analytics to improve data storage, analysis and sharing. Despite significant advances, there are still challenges such as data complexity, technical limitations and ethical issues. Future directions include the development of more sophisticated computational tools and the application of advanced machine learning techniques, which are critical for addressing the complexity and heterogeneity of omics datasets. This review aims to serve as a valuable resource for researchers and practitioners, highlighting the transformative potential of high-throughput omics technologies in advancing personalized medicine and improving clinical outcomes.

Soluble interleukin 1 receptor-like 1 (ST2) is a circulating protein demonstrated to be associated with cardiovascular diseases; however, it has not been studied as a biomarker for peripheral artery disease (PAD). Using a prospectively recruited cohort of 476 patients (312 with PAD and 164 without PAD), we conducted a prognostic study of PAD using clinical/biomarker data. Plasma concentrations of three circulating proteins [ST2, cytokine-responsive gene-2 (CRG-2), vascular endothelial growth factor (VEGF)] were measured at baseline and the cohort was followed for 2 years. The outcome of interest was a 2-year major adverse limb event (MALE; composite of major amputation, vascular intervention, or acute limb ischemia). Using 10-fold cross-validation, a random forest model was trained using clinical characteristics and plasma ST2 levels. The primary model evaluation metric was the F1 score. Out of the three circulating proteins analyzed, ST2 was the only one that was statistically significantly higher in individuals with PAD compared to patients without PAD (mean concentration in plasma of 9.57 [SD 5.86] vs. 11.39 [SD 6.43] pg/mL, p < 0.001). Over a 2-year period, 28 (9%) patients with PAD experienced MALE. Our predictive model, incorporating clinical features and plasma ST2 levels, achieved an F1 score of 0.713 for forecasting 2-year MALE outcomes. Patients identified as high-risk by this model showed a significantly increased likelihood of developing MALE (HR 1.06, 95% CI 1.02-1.13, p = 0.003). By combining clinical characteristics and plasma ST2 levels, our proposed predictive model offers accurate risk assessment for 2-year MALE in PAD patients. This algorithm supports risk stratification in PAD, guiding clinical decisions regarding further vascular evaluation, specialist referrals, and appropriate medical or surgical interventions, thereby potentially enhancing patient outcomes.

Chronic obstructive pulmonary disease (COPD) ranks as the third leading cause of global illness and mortality. It is commonly triggered by exposure to respiratory irritants like cigarette smoke or biofuel pollutants. This multifaceted condition manifests through an array of symptoms and lung irregularities, characterized by chronic inflammation and reduced lung function. Present therapies primarily rely on maintenance medications to alleviate symptoms, but fall short in impeding disease advancement. COPD's diverse nature, influenced by various phenotypes, complicates diagnosis, necessitating precise molecular characterization. Omics-driven methodologies, including biomarker identification and therapeutic target exploration, offer a promising avenue for addressing COPD's complexity. This analysis underscores the critical necessity of improving molecular profiling to deepen our comprehension of COPD and identify potential therapeutic targets. Moreover, it advocates for tailoring treatment strategies to individual phenotypes. Through comprehensive exploration-based molecular characterization and the adoption of personalized methodologies, innovative treatments may emerge that are capable of altering the trajectory of COPD, instilling optimism for efficacious disease-modifying interventions.

Capsular contracture (CC) is one of the most common postoperative complications associated with breast implant-associated infections. The mechanisms that lead to CC remain poorly understood. Plasma is an ideal biospecimen for early proteomics biomarker discovery. However, as high-abundance proteins mask signals from low-abundance proteins, identifying novel or specific proteins as biomarkers for a particular disease has been hampered. Here, we employed depletion of high-abundance plasma proteins followed by Tandem Mass Tag (TMT)-based quantitative proteomics to compare 10 healthy control patients against 10 breast implant CC patients. A total of 450 proteins were identified from these samples. Among them, 16 proteins were significantly differentially expressed in which 5 proteins were upregulated and 11 downregulated in breast implant CC patients compared to healthy controls. Gene Ontology enrichment analysis revealed that proteins related to cell, cellular processes and catalytic activity were highest in the cellular component, biological process, and molecular function categories, respectively. Further, pathway analysis revealed that inflammatory responses, focal adhesion, platelet activation, and complement and coagulation cascades were enriched pathways. The differentially abundant proteins from TMT-based quantitative proteomics have the potential to provide important information for future mechanistic studies and in the development of breast implant CC biomarkers.

Examining the composition of the typical urinary peptidome and identifying the enzymes responsible for its formation holds significant importance, as it mirrors the normal physiological state of the human body. Any deviation from this normal profile could serve as an indicator of pathological processes occurring in vivo. Consequently, this study focuses on characterizing the normal urinary peptidome and investigating the various catalytic enzymes that are involved in generating these native peptides in urine. Our findings reveal that 1503 endogenous peptides, corresponding to 436 precursor proteins, were consistently identified robustly in at least 10 samples out of a total of 19 samples. Notably, the liver and kidneys exhibited the highest number of tissue-enriched or enhanced genes in the analyzed urinary peptidome. Furthermore, among the catalytic types, CTSD (cathepsin D) and MMP2 (matrix metalloproteinase-2) emerged as the most prominent peptidases in the aspartic and metallopeptidases categories, respectively. A comparison of our dataset with two of the most comprehensive urine peptidome datasets to date indicates a consistent relative abundance of core endogenous peptides for different proteins across all three datasets. These findings can serve as a foundational reference for the discovery of biomarkers in various human diseases.

This perspective article is concerned with the question of how proteomics, which is a core technique of systems biology that is deeply embedded in the multi-omics field of modern bioresearch, can help us better understand the molecular pathogenesis of complex diseases. As an illustrative example of a monogenetic disorder that primarily affects the neuromuscular system but is characterized by a plethora of multi-system pathophysiological alterations, the muscle-wasting disease Duchenne muscular dystrophy was examined. Recent achievements in the field of dystrophinopathy research are described with special reference to the proteome-wide complexity of neuromuscular changes and body-wide alterations/adaptations. Based on a description of the current applications of top-down versus bottom-up proteomic approaches and their technical challenges, future systems biological approaches are outlined. The envisaged holistic and integromic bioanalysis would encompass the integration of diverse omics-type studies including inter- and intra-proteomics as the core disciplines for systematic protein evaluations, with sophisticated biomolecular analyses, including physiology, molecular biology, biochemistry and histochemistry. Integrated proteomic findings promise to be instrumental in improving our detailed knowledge of pathogenic mechanisms and multi-system dysfunction, widening the available biomarker signature of dystrophinopathy for improved diagnostic/prognostic procedures, and advancing the identification of novel therapeutic targets to treat Duchenne muscular dystrophy.