Improved Integrated Whole Proteomic and Phosphoproteomic Profiles of Severe Acute Pancreatitis

The role of proteomics in acute pancreatitis: new and old knowledge

INTRODUCTION

Around 20% of individuals diagnosed with acute pancreatitis (AP) may develop severe acute pancreatitis (SAP), possibly resulting in a mortality rate ranging from 15% to 35%. There is an urgent need to thoroughly understand the molecular phenotypes of SAP resulting from diverse etiologies. The field of translational research on AP has seen the use of several innovative proteomic methodologies via the ongoing improvement of isolation, tagging, and quantification methods.

AREAS COVERED

This paper provides a comprehensive overview of differentially abundant proteins (DAPs) identified in AP by searching the PubMed/MEDLINE database (2003-2023) and adds significantly to the current theoretical framework.

EXPERT OPINION

DAPs for potentially diagnosing AP based on proteomic identification need to be confirmed by multi-center studies that include larger samples. The discovery of DAPs in various organs at different AP stages via proteomic technologies is essential better to understand the pathophysiology of AP-related multiple organ dysfunction syndrome. Regarding the translational research of AP, novel approaches like single-cell proteomics and imaging using mass spectrometry may be used as soon as they become available.

Temporal Proteomic and Lipidomic Profiles of Cerulein-Induced Acute Pancreatitis Reveal Novel Insights for Metabolic Alterations in the Disease Pathogenesis

The pathophysiological mechanisms of acute pancreatitis (AP) are complex and have remained a mystery to date, but metabolism is gradually recognized as an important driver of AP onset and development. We used a cerulein-induced AP mouse model to conduct liquid chromatography-mass spectrometry (LC-MS/MS)-based time-course proteomics and lipidomics in order to better understand the underlying metabolic alterations linked with AP. Results showed that a series of significant changes in proteins over time with a boost in expression were enriched in lipase activity, lipoprotein, and lipids absorption and transport regulation. Furthermore, 16 proteins associated with lipid metabolism and signaling pathways together with the whole lipid species changing profile led to the vital identification of changing law in glycerides, phosphoglycerides, and free fatty acids. In addition to lipid metabolism and regulation-associated proteins, several digestive enzymes and adaptive anti-trypsin, stress response, and energy metabolism-related proteins showed an increment in abundance. Notably, central carbon and branched chain amino acid metabolism were enhanced during 0-24 h from the first cerulein stimulation. Taken together, this integrated proteomics and lipidomics revealed a novel metabolic insight into metabolites transforming rules that might be relevant to their function and drug targets investigation. (Created with Biorender.com.).

Predicting the Need for Therapeutic Intervention and Mortality in Acute Pancreatitis: A Two-Center International Study Using Machine Learning

Background: Current approaches to predicting intervention needs and mortality have reached 65–85% accuracy, which falls below clinical decision-making requirements in patients with acute pancreatitis (AP). We aimed to accurately predict therapeutic intervention needs and mortality on admission, in AP patients, using machine learning (ML). Methods: Data were obtained from three databases of patients admitted with AP: one retrospective (Chengdu) and two prospective (Liverpool and Chengdu) databases. Intervention and mortality differences, as well as potential predictors, were investigated. Univariate analysis was conducted, followed by a random forest ML algorithm used in multivariate analysis, to identify predictors. The ML performance matrix was applied to evaluate the model’s performance. Results: Three datasets of 2846 patients included 25 potential clinical predictors in the univariate analysis. The top ten identified predictors were obtained by ML models, for predicting interventions and mortality, from the training dataset. The prediction of interventions includes death in non-intervention patients, validated with high accuracy (96%/98%), the area under the receiver-operating-characteristic curve (0.90/0.98), and positive likelihood ratios (22.3/69.8), respectively. The post-test probabilities in the test set were 55.4% and 71.6%, respectively, which were considerably superior to existing prognostic scores. The ML model, for predicting mortality in intervention patients, performed better or equally with prognostic scores. Conclusions: ML, using admission clinical predictors, can accurately predict therapeutic interventions and mortality in patients with AP.

Data-Independent Acquisition-Based Proteome and Phosphoproteome Profiling Reveals Early Protein Phosphorylation and Dephosphorylation Events in Arabidopsis Seedlings upon Cold Exposure

Protein phosphorylation plays an important role in mediating signal transduction in cold response in plants. To better understand how plants sense and respond to the early temperature drop, we performed data-independent acquisition (DIA) method-based mass spectrometry analysis to profile the proteome and phosphoproteome of Arabidopsis seedlings upon cold stress in a time-course manner (10, 30 and 120 min of cold treatments). Our results showed the rapid and extensive changes at the phosphopeptide levels, but not at the protein abundance levels, indicating cold-mediated protein phosphorylation and dephosphorylation events. Alteration of over 1200 proteins at phosphopeptide levels were observed within 2 h of cold treatment, including over 140 kinases, over 40 transcriptional factors and over 40 E3 ligases, revealing the complexity of regulation of cold adaption. We summarized cold responsive phosphoproteins involved in phospholipid signaling, cytoskeleton reorganization, calcium signaling, and MAPK cascades. Cold-altered levels of 73 phosphopeptides (mostly novel cold-responsive) representing 62 proteins were validated by parallel reaction monitoring (PRM). In summary, this study furthers our understanding of the molecular mechanisms of cold adaption in plants and strongly supports that DIA coupled with PRM are valuable tools in uncovering early signaling events in plants.

Transcriptome and proteome analysis of the antitumor activity of maslinic acid against pancreatic cancer cells

Maslinic acid (MA) is a triterpenoid compound of natural abundance in olive plants possessing numerous biological activities. The effect and molecular mechanism of MA on pancreatic cancer cells remain elusive. Here, we explored the anti-tumor activity of MA on human pancreatic cancer cells and the potential underlying molecular mechanism. The anti-cancer effects of MA on whole-cell processes, including proliferation, migration, and invasion in pancreatic cancer cells, were systematically assessed by colony formation, transwell, and migration assays. The search for potential therapeutic targets was achieved via transcriptomics and proteomics analyses. MA was demonstrated to inhibit the proliferation, migration, and invasion of PANC-1 and Patu-8988 cells, but induced apoptosis of these cells. Several key candidate genes and proteins of functional relevance for the anti-tumor activity of MA were identified through the association analysis of transcriptomics and proteomics. To our knowledge, this is the first transcription and proteomics-based comprehensive analysis of the mechanism of MA against pancreatic cancer. The findings demonstrate that MA holds promise as a therapeutic drug for managing pancreatic cancer.