Serum proteomic patterns as a predictor of severity in 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.

GlycA: Evaluation of a New Biomarker of Acute Pancreatitis

BACKGROUND

Acute pancreatitis (AP) is a leading cause of gastrointestinal hospital admissions, with up to 40% mortality in patients with moderate-severe AP. Glycoprotein acetylation (GlycA) is measured as a nuclear magnetic resonance signal (NMR) of the post-translational modification of glycosylated acute-phase proteins released during inflammation. We aimed to investigate the role of GlycA as an inflammatory biomarker of AP.

METHODS

We prospectively enrolled 20 AP patients and 22 healthy controls and collected EDTA plasma samples at admission and discharge. NMR spectra were acquired from these samples using a 400 MHz Vantera® Clinical Analyzer, and GlycA concentrations were calculated (normal = 400 μmol/L). The GlycA NMR signal, at 2.00 ± 0.01 ppm in the NMR spectrum, is derived from the N-acetyl methyl group protons within the carbohydrate side chains of circulating glycoproteins such as α1-acid glycoprotein, haptoglobin, α1-antitrypsin, α1-antichymotrypsin, and transferrin. GlycA levels were then compared between AP patients and controls, as well as within the AP group, based on etiology and severity.

RESULTS

Demographic comparisons were similar, except for a higher BMI in AP patients compared to healthy controls (29.9 vs. 24.8 kg/m2; p < 0.001). AP was mild in 10 patients, moderate in 7, and severe in 3. GlycA levels were higher in AP patients than healthy controls on admission (578 vs. 376 μmol/L, p < 0.001) and at discharge (655 vs. 376 μmol/L, p < 0.001). GlycA levels were significantly higher in patients with moderate-severe AP than in those with mild AP at discharge (533 vs. 757 μmol/L, p = 0.023) but not at admission. After adjusting for BMI, multivariable regression indicated that patients with GlycA levels > 400 μmol/L had significantly higher odds of having AP of any severity (OR = 6.88; 95% CI, 2.07-32.2; p = 0.004) and mild AP (OR = 6.12; 95% CI, 1.48-42.0; p = 0.025) than controls.

CONCLUSION

Our pilot study highlights the use of GlycA as a novel diagnostic biomarker of inflammation in patients with AP. Our study shows that GlycA levels were significantly higher in hospitalized AP patients compared to healthy controls. Patients with moderate-to-severe AP had higher GlycA levels compared to patients with mild AP at the time of their hospital discharge, suggesting persistent inflammation in patients with severe disease.

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.).

DIA-Based Proteomic Analysis of Plasma Protein Profiles in Patients with Severe Acute Pancreatitis

Acute pancreatitis (AP) is a pancreatic inflammatory disease that varies greatly in course and severity. To further the understanding of the pathology of AP, we carried out data-independent acquisition-based proteomic analyses using proteins extracted from the plasma of patients with severe acute pancreatitis (SAP) (experimental group) and healthy volunteers (control group). Compared to the control group, there were 35 differentially expressed proteins (DEPs) in the plasma of patients with SAP. Of those, the expression levels for 6 proteins were significantly increased, and 29 proteins were significantly decreased. Moreover, six candidate biomarkers—VWF, ORM2, CD5L, CAT, IGLV3-10, and LTF—were matched as candidate biomarkers of the disease severity of AP. The area under the receiver operating characteristic of 0.903 (95% CI: 0.839, 0.967) indicated that this combination of these six candidate biomarkers had a good prediction accuracy for predicting the severity of AP. Our study provides specific DEPs that may be useful in the diagnosis and prognosis of SAP, which suggests new theoretical bases for the occurrence and development of SAP and offers potential novel treatment strategies for SAP.

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.

Multifactorial Scores and Biomarkers of Prognosis of Acute Pancreatitis: Applications to Research and Practice

Acute pancreatitis (AP) is a severe inflammation of the pancreas presented with sudden onset and severe abdominal pain with a high morbidity and mortality rate, if accompanied by severe local and systemic complications. Numerous studies have been published about the pathogenesis of AP; however, the precise mechanism behind this pathology remains unclear. Extensive research conducted over the last decades has demonstrated that the first 24 h after symptom onset are critical for the identification of patients who are at risk of developing complications or death. The identification of these subgroups of patients is crucial in order to start an aggressive approach to prevent mortality. In this sense and to avoid unnecessary overtreatment, thereby reducing the financial implications, the proper identification of mild disease is also important and necessary. A large number of multifactorial scoring systems and biochemical markers are described to predict the severity. Despite recent progress in understanding the pathophysiology of AP, more research is needed to enable a faster and more accurate prediction of severe AP. This review provides an overview of the available multifactorial scoring systems and biochemical markers for predicting severe AP with a special focus on their advantages and limitations.

Identification of cysteinylated transthyretin, a predictive biomarker of treatment response to partially hydrolyzed guar gum in type 2 diabetes rats, by surface-enhanced laser desorption/ionization time-of-flight mass spectrometry

Recent evidence has indicated that total fiber intake is inversely related to type 2 diabetes risk. The present study aimed to investigate the effects of chronic administration of partially hydrolyzed guar gum (PHGG), a water-soluble dietary fiber, on the occurrence of diabetes and its complications, fatty liver and nephropathy. We also identified predictive serum biomarkers of treatment response to PHGG by mass spectroscopy-based proteomic analysis using Otsuka Long-Evans Tokushima Fatty (OLETF) rats, a good model of human non-insulin-dependent diabetes mellitus. In this study, at 5 weeks of age, OLETF rats and control strain Long-Evans Tokushima Otsuka (LETO) rats were fed a control diet or a high-fiber diet (5% PHGG) for 57 weeks. Body weight, food intake, oral glucose tolerance test, plasma insulin levels, and urine glucose and protein levels were regularly measured. Oral glucose tolerance tests (OGTT) and storage of serum in a deep freezer were conducted at the beginning of the experiment and every 4 weeks after overnight fasting during the experiments. PHGG treatment affected neither meal patterns nor the body weight of OLETF and LETO rats. Repeated measure analysis of variance revealed significant differences in fasting plasma glucose and plasma glucose at 2 h after OGTT between control OLETF (OLETF-C) rats and OLETF rats treated with PHGG (OLETF-F). The glucose response determined by the area under the curve of OGTT was significantly greater in OLETF-C rats than that in OLETF-F rats at 25 weeks of age. HOMA-IR, an index of insulin resistance, increased at 25 weeks of age in OLETF-C rats, while this increase was significantly inhibited in OLETF-F rats. At 62 weeks of age, PHGG treatment significantly improved hepatic steatosis as well as renal mesangial matrix accumulation in OLETF rats. To identify the risk marker for diabetes mellitus by SELDI-TOF MS, we collected sera from 21-week-old individuals. Among the 12 specific peaks that were risk marker candidates for diabetes mellitus, the m/z 13,720 peak was identified as that of cysteinylated transthyretin by sequencing of four tryptic peptides using tandem mass spectrometry and peak distribution around the m/z 13,720 peak in the SELDI-TOF spectra. In conclusion, we found that chronic treatment with PHGG improved insulin resistance, delayed the onset of diabetes, and inhibited the development of diabetic complications, as well as identified cysteinylated transthyretin as a predictive biomarker of treatment response to PHGG in OLETF rats.

Role of Biomarkers in Diagnosis and Prognostic Evaluation of Acute Pancreatitis

Acute pancreatitis is a potentially life threatening disease. The spectrum of severity of the illness ranges from mild self-limiting disease to a highly fatal severe necrotizing pancreatitis. Despite intensive research and improved patient care, overall mortality still remains high, reaching up to 30–40% in cases with infected pancreatic necrosis. Although little is known about the exact pathogenesis, it has been widely accepted that premature activation of digestive enzymes within the pancreatic acinar cell is the trigger that leads to autodigestion of pancreatic tissue which is followed by infiltration and activation of leukocytes. Extensive research has been done over the past few decades regarding their role in diagnosis and prognostic evaluation of severe acute pancreatitis. Although many standalone biochemical markers have been studied for early assessment of severity, C-reactive protein still remains the most frequently used along with Interleukin-6. In this review we have discussed briefly the pathogenesis and the role of different biochemical markers in the diagnosis and severity evaluation in acute pancreatitis.

Prognostic markers in acute pancreatitis

Acute pancreatitis has a mortality rate of 5-10%. Early deaths are mainly due to multiorgan failure and late deaths are due to septic complications from pancreatic necrosis. The recently described 2012 Revised Atlanta Classification and the Determinant Classification both provide a more accurate description of edematous and necrotizing pancreatitis and local complications. The 2012 Revised Atlanta Classification uses the modified Marshall scoring system for assessing organ dysfunction. The Determinant Classification uses the sepsis-related organ failure assessment scoring system for organ dysfunction and, unlike the 2012 Revised Atlanta Classification, includes infected necrosis as a criterion of severity. These scoring systems are used to assess systemic complications requiring intensive therapy unit support and intra-abdominal complications requiring minimally invasive interventions. Numerous prognostic systems and markers have been evaluated but only the Glasgow system and serum CRP levels provide pragmatic prognostic accuracy early on. Novel concepts using genetic, transcriptomic and proteomic profiling and also functional imaging for the identification of specific disease patterns are now required.

Proteomics as a systems approach to pancreatitis

Proteomics is an approach to looking at the identity, amount, proteolysis, compartmentalization, and posttranslational modification of a large number of proteins simultaneously in a cell or tissue. Recently, proteomics has begun to be applied to the study of pancreatitis to ascertain mechanisms of disease and search for biomarkers of disease. Most mechanistic work has been carried out in animal models of acute pancreatitis. In 8 studies, 97 proteins have been reported to increase, 55 to decrease, and 23 to undergo proteolysis. Proteins showing increases are most often related to stress, inflammation, or the cytoskeleton, whereas decreases are seen in digestive enzymes and proteins related to metabolism. Many protein changes however, are not consistent between studies and only the most recent studies are rigorous and quantitative. By contrast, biomarker studies have focused on pancreatic juice and plasma of humans with disease and often are directed at distinguishing chronic pancreatitis from cancer. Chronic pancreatitis has also been investigated in tissue sections of histological samples. In this review, the results of studies to date are described as well as coverage of the methods used and special issues that must be considered. Areas are pointed out that are worthy of future study.

Serum proteome profiling of pancreatitis using recombinant antibody microarrays reveals disease-associated biomarker signatures

PURPOSE

Pancreatitis is an inflammatory state of the pancreas, for which high-performing serological biomarkers are lacking. The aim of the present study was to evaluate the use of affinity proteomics for identifying potential markers of disease and stratifying pancreatitis subtypes.

EXPERIMENTAL DESIGN

High-content, recombinant antibody microarrays were applied for serum protein expression profiling of 113 serum samples from patients with chronic, acute, and autoimmune pancreatitis, as well as healthy controls. The sample groups were compared using supervised classification based on support vector machine analysis.

RESULTS

This discovery study showed that pancreatitis subtypes could be discriminated with high accuracy. Using unfiltered data, the individual subtypes, as well as the combined pancreatitis cohort, were distinguished from healthy controls with high AUC values (0.96-1.00). Moreover, characteristic protein patterns and AUC values in the range of 0.69-0.95 were observed for the individual pancreatitis entities when compared to each other, and to all other samples combined.

CONCLUSIONS AND CLINICAL RELEVANCE

This study demonstrated the potential of the antibody microarray approach for stratification of pancreatitis. Distinct candidate multiplex serum biomarker signatures for chronic, acute, and autoimmune pancreatitis were defined, which could enhance our fundamental knowledge of the underlying molecular mechanisms, and potentially lead to improved diagnosis.

Diagnosis of pancreatic adenocarcinoma using protein chip technology

BACKGROUND

To develop a serum-specific protein fingerprint which is capable of differentiating samples from patients with pancreatic cancer and those with other pancreatic conditions.

METHODS

We used SELDI-TOF-MS coupled with CM10 chips and bioinformatics tools to analyze a total of 118 serum samples in this study; 78 serum samples were analyzed to establish the diagnostic models and the other 40 samples were analyzed on the second day as an independent test set.

RESULTS

The analysis of this independent test set yielded a specificity of 91.6% and a sensitivity of 91.6% for pattern 1, which distinguished pancreatic adenocarcinoma (PC) from healthy individuals and a specificity of 80.0% and a sensitivity of 90.9% for pattern 2, which distinguished PC from chronic pancreatitis.

CONCLUSION

This study indicated that the SELDI-TOF-MS technique can facilitate the discovery of better serum tumor biomarkers and a combination of specific models is more accurate than a single model in diagnosis of PC.

Application of a global proteomic approach to archival precursor lesions: deleted in malignant brain tumors 1 and tissue transglutaminase 2 are upregulated in pancreatic cancer precursors

BACKGROUND

Pancreatic cancer is an almost uniformly fatal disease, and early detection is a critical determinant of improved survival. A variety of noninvasive precursor lesions of pancreatic adenocarcinoma have been identified, which provide a unique opportunity for intervention prior to onset of invasive cancer. Biomarker discovery in precursor lesions has been hampered by the ready availability of fresh specimens, and limited yields of proteins suitable for large scale screening.

METHODS

We utilized Liquid Tissue, a novel technique for protein extraction from archival formalin-fixed material, and mass spectrometry to conduct a global proteomic analysis of an intraductal papillary mucinous neoplasm (IPMN). Tissue microarrays comprised of 38 IPMNs were used for validation of candidate proteins.

RESULTS

The proteomic analysis of the IPMN Liquid Tissue lysate resulted in identification of 1,534 peptides corresponding to 523 unique proteins. A subset of 25 proteins was identified that had previously been reported as upregulated in pancreatic cancer. Immunohistochemical analysis for two of these, deleted in malignant brain tumors 1 (DMBT1) and tissue transglutaminase 2 (TGM2), confirmed their overexpression in IPMNs.

CONCLUSION

Global proteomics analysis using the Liquid Tissue workflow is a feasible approach for unbiased biomarker discovery in limited archival material, particularly applicable to precursor lesions of cancer.

Peritoneal fluid proteome in women with different ASRM stages of endometriosis

OBJECTIVE

Endometriosis has a wide range of severity but molecular factors associated with variable extension of the disease have not been widely investigated. The present study compares the peritoneal fluid (PF) proteome of 109 women with endometriosis and different disease stage as defined by the American Society for Reproductive Medicine (ASRM).

METHODS

PF samples were subjected to two-dimensional gel electrophoresis; protein spots of interest were identified by liquid chromatography tandem mass spectrometry.

RESULTS

Over 470 protein spots were analyzed. One isoform of haptoglobin alpha chain, alpha-1b-glycoprotein and one unknown protein had higher expression in PF of women with ASRM stage I-II endometriosis. Four isoforms of alpha1-antitrypsin, three isoforms of alpha-1b-glycoprotein, one isoform of S100-A8 and serotransferrin had higher expression in PF of women with ASRM stage III-IV disease.

CONCLUSIONS

Several protein isoforms have different expression in PF of women with ASRM stage I-II endometriosis than in those with ASRM stage III-IV disease; most of these molecules are involved in inflammation and immune response.

Severe acute pancreatitis: Pathogenetic aspects and prognostic factors

Approximately 20% of patients with acute pancreatitis develop a severe disease associated with complications and high risk of mortality. The purpose of this study is to review pathogenesis and prognostic factors of severe acute pancreatitis (SAP). An extensive medline search was undertaken with focusing on pathogenesis, complications and prognostic evaluation of SAP. Cytokines and other inflammatory markers play a major role in the pathogenesis and course of SAP and can be used as prognostic markers in its early phase. Other markers such as simple prognostic scores have been found to be as effective as multifactorial scoring systems (MFSS) at 48 h with the advantage of simplicity, efficacy, low cost, accuracy and early prediction of SAP. Recently, several laboratory markers including hematocrit, blood urea nitrogen (BUN), creatinine, matrix metalloproteinase-9 (MMP-9) and serum amyloid A (SAA) have been used as early predictors of severity within the first 24 h. The last few years have witnessed a tremendous progress in understanding the pathogenesis and predicting the outcome of SAP. In this review we classified the prognostic markers into predictors of severity, pancreatic necrosis (PN), infected PN (IPN) and mortality.