Type 1 diabetes cadaveric human pancreata exhibit a unique exocrine tissue proteomic profile

The pathogenic "symphony" in type 1 diabetes: A disorder of the immune system, β cells, and exocrine pancreas

Type 1 diabetes (T1D) is widely considered to result from the autoimmune destruction of insulin-producing β cells. This concept has been a central tenet for decades of attempts seeking to decipher the disorder's pathogenesis and prevent/reverse the disease. Recently, this and many other disease-related notions have come under increasing question, particularly given knowledge gained from analyses of human T1D pancreas. Perhaps most crucial are findings suggesting that a collective of cellular constituents-immune, endocrine, and exocrine in origin-mechanistically coalesce to facilitate T1D. This review considers these emerging concepts, from basic science to clinical research, and identifies several key remaining knowledge voids.

The Human Islet: Mini-Organ With Mega-Impact

This review focuses on the human pancreatic islet-including its structure, cell composition, development, function, and dysfunction. After providing a historical timeline of key discoveries about human islets over the past century, we describe new research approaches and technologies that are being used to study human islets and how these are providing insight into human islet physiology and pathophysiology. We also describe changes or adaptations in human islets in response to physiologic challenges such as pregnancy, aging, and insulin resistance and discuss islet changes in human diabetes of many forms. We outline current and future interventions being developed to protect, restore, or replace human islets. The review also highlights unresolved questions about human islets and proposes areas where additional research on human islets is needed.

Type 1 diabetes mellitus as a disease of the β-cell (do not blame the immune system?)

Type 1 diabetes mellitus is believed to result from destruction of the insulin-producing β-cells in pancreatic islets that is mediated by autoimmune mechanisms. The classic view is that autoreactive T cells mistakenly destroy healthy ('innocent') β-cells. We propose an alternative view in which the β-cell is the key contributor to the disease. By their nature and function, β-cells are prone to biosynthetic stress with limited measures for self-defence. β-Cell stress provokes an immune attack that has considerable negative effects on the source of a vital hormone. This view would explain why immunotherapy at best delays progression of type 1 diabetes mellitus and points to opportunities to use therapies that revitalize β-cells, in combination with immune intervention strategies, to reverse the disease. We present the case that dysfunction occurs in both the immune system and β-cells, which provokes further dysfunction, and present the evidence leading to the consensus that islet autoimmunity is an essential component in the pathogenesis of type 1 diabetes mellitus. Next, we build the case for the β-cell as the trigger of an autoimmune response, supported by analogies in cancer and antitumour immunity. Finally, we synthesize a model ('connecting the dots') in which both β-cell stress and islet autoimmunity can be harnessed as targets for intervention strategies.

Advances in Current Diabetes Proteomics: From the Perspectives of Label- free Quantification and Biomarker Selection

BACKGROUND

Due to its prevalence and negative impacts on both the economy and society, the diabetes mellitus (DM) has emerged as a worldwide concern. In light of this, the label-free quantification (LFQ) proteomics and diabetic marker selection methods have been applied to elucidate the underlying mechanisms associated with insulin resistance, explore novel protein biomarkers, and discover innovative therapeutic protein targets.

OBJECTIVE

The purpose of this manuscript is to review and analyze the recent computational advances and development of label-free quantification and diabetic marker selection in diabetes proteomics.

METHODS

Web of Science database, PubMed database and Google Scholar were utilized for searching label-free quantification, computational advances, feature selection and diabetes proteomics.

RESULTS

In this study, we systematically review the computational advances of label-free quantification and diabetic marker selection methods which were applied to get the understanding of DM pathological mechanisms. Firstly, different popular quantification measurements and proteomic quantification software tools which have been applied to the diabetes studies are comprehensively discussed. Secondly, a number of popular manipulation methods including transformation, pretreatment (centering, scaling, and normalization), missing value imputation methods and a variety of popular feature selection techniques applied to diabetes proteomic data are overviewed with objective evaluation on their advantages and disadvantages. Finally, the guidelines for the efficient use of the computationbased LFQ technology and feature selection methods in diabetes proteomics are proposed.

CONCLUSION

In summary, this review provides guidelines for researchers who will engage in proteomics biomarker discovery and by properly applying these proteomic computational advances, more reliable therapeutic targets will be found in the field of diabetes mellitus.

Pancreatic Tissue Proteomics Unveils Key Proteins, Pathways, and Networks Associated with Type 1 Diabetes

PURPOSE

Type 1 diabetes (T1D) is characterized by autoimmune mediated self-destruction of the pancreatic islet beta cells and the resultant insulin deficiency. However, little is known about the underlying molecular pathogenesis at the pancreatic tissue level given the limited availability of clinical specimens.

EXPERIMENTAL DESIGN

Quantitative proteomic studies is performed on age-matched T1D and healthy cadaveric pancreatic tissues (n = 18 each) using TMT 10plex-based isobaric labeling and BoxCar-based label-free LC-MS/MS approaches. ELISA is used to validate the differentially expressed proteins (DEPs).

RESULTS

Overall, the two quantitative proteomics approaches identified 8824 proteins, of which 261 are DEPs. KEGG pathway and functional network analyses of the DEPs reveal dysregulations to pancreatic exocrine function, complement coagulation cascades, and extracellular matrix receptor interaction pathways in T1D. A selected list of the DEPs associated with pathways, subnetworks, and plasma proteome of T1D are validated using ELISA.

CONCLUSIONS AND CLINICAL RELEVANCE

Integrating labeling and label-free approaches improve the confidence in quantitative profiling of pancreatic tissue proteome, which furthers the understanding of the dysregulated pathways and functional subnetworks associated with T1D pathogenesis and may aid to develop diagnostic and therapeutic strategies for T1D.

Diabetes-induced Proteome Changes Throughout Development

BACKGROUND

Diabetes Mellitus (DM) is a multisystemic disease involving the homeostasis of insulin secretion by the pancreatic islet beta cells (β-cells). It is associated with hypertension, renal disease, and arterial and arteriolar vascular diseases.

DISCUSSION

The classification of diabetes is identified as type 1 (gene linked β-cell destruction in childhood) and type 2 (late onset associated with β-cell overload and insulin resistance in peripheral tissues. Type 1 diabetes is characterized by insulin deficiency, type 2 diabetes by both insulin deficiency and insulin resistance. The former is a genetically programmed loss of insulin secretion whereas the latter constitutes a disruption of the homeostatic relationship between the opposing activity of β- cell insulin and alpha cell (α-cell) glucagon of the Islets of Langerhans. The condition could also occur in pregnancy, as a prenatal occurring event, possibly triggered by the hormonal changes of pregnancy combined with β-cell overload. This review discusses the molecular basis of the biomolecular changes that occur with respect to glucose homeostasis and related diseases in DM. The underlying link between pancreatic, renal, and microvascular diseases in DM is based on oxidative stress and the Unfolded Protein Response (UPR).

CONCLUSION

Studying proteome changes in diabetes can deepen our understanding of the biomolecular basis of disease and help us acquire more efficient therapies.

Protein Signatures and Tissue Diagnosis of Pancreatic Cancer

BACKGROUND

Endoscopic ultrasound-guided fine-needle aspiration fails to diagnose up to 25% of patients with pancreatic ductal adenocarcinoma (PDAC). Proteomics can help to overcome this clinical dilemma. We hypothesized that soluble protein signatures can differentiate PDAC from benign tissues.

STUDY DESIGN

Tissues were obtained from resected surgical specimens, lysed, and homogenates collected for analysis with a 41-protein multiplex assay. Analyte concentrations were normalized to total protein. Statistical analysis was performed to evaluate for differences in PDAC vs benign tissue.

RESULTS

Tissues were obtained from 159 patients, 82 patients with PDAC naïve to therapy and 77 with benign pancreatic pathology. Fourteen analytes had a receiver operating characteristic curve area of >0.75 for predicting PDAC vs benign tissue. A recursive partitioning model using only 2 analytes, interleukin 1 receptor antagonist and transforming growth factor-α, provided an accuracy, sensitivity, and specificity of 91.2%, 90.2%, and 92.2%, respectively. A penalized logistic regression model found 12 analytes that provide diagnostic value to a protein signature. The mean area under the receiver operating characteristic after 50 tenfold cross-validations was 0.951. Accuracy, sensitivity, and specificity of this model were 91.2%, 87.8%, and 94.8%, respectively. Applying the scenario of 80% disease prevalence in patients undergoing endoscopic ultrasound with fine-needle aspiration for a pancreatic head mass, positive predictive value is 98.5% (95% CI 93.0% to 99.7%) and negative predictive value is 66.0% (95% CI 54.9% to 75.6%).

CONCLUSIONS

Protein signatures from pancreatic specimens can differentiate PDAC from benign tissue. Additional work to validate these findings in a unique sample set is warranted.

Pediatric endocrine and metabolic diseases and proteomics

The principles of Predictive, Preventive and Personalized Medicine (PPPM) dictate the need to recognize individual susceptibility to disease in a timely fashion and to offer targeted preventive interventions and treatments. Proteomics is a state-of-the art technology- driven science aiming at expanding our understanding of the pathophysiologic mechanisms that underlie disease, but also at identifying accurate predictive, diagnostic and therapeutic biomarkers, that will eventually promote the implementation of PPPM. In this review, we summarize the wide spectrum of the applications of Mass Spectrometry-based proteomics in the various fields of Pediatric Endocrinology, including Inborn Errors of Metabolism, type 1 diabetes, Adrenal Disease, Metabolic Syndrome and Thyroid disease, ranging from neonatal screening to early recognition of specific at-risk populations for disease manifestations or complications in adult life and to monitoring of disease progression and response to treatment.

SIGNIFICANCE

Proteomics is a state-of-the art technology- driven science aiming at expanding our understanding of the pathophysiologic mechanisms that underlie disease, but also at identifying accurate predictive, diagnostic and therapeutic biomarkers that will eventually lead to successful, targeted, patient-centric, individualized approach of each patient, as dictated by the principles of Predictive, Preventive and Personalized Medicine. In this review, we summarize the wide spectrum of the applications of Mass Spectrometry-based proteomics in the various fields of Pediatric Endocrinology, including Inborn Errors of Metabolism, type 1 diabetes, Adrenal Disease, Metabolic Syndrome and Thyroid disease, ranging from neonatal screening, accurate diagnosis, early recognition of specific at-risk populations for the prevention of disease manifestation or future complications.

Unraveling endometriosis-associated ovarian carcinomas using integrative proteomics

Background: To elucidate potential markers of endometriosis and endometriosis-associated endometrioid and clear cell ovarian carcinomas using mass spectrometry-based proteomics.

Methods: A total of 21 fresh, frozen tissues from patients diagnosed with clear cell carcinoma, endometrioid carcinoma, endometriosis and benign endometrium were subjected to an in-depth liquid chromatography-tandem mass spectrometry analysis on the Q-Exactive Plus. Protein identification and quantification were performed using MaxQuant, while downstream analyses were performed using Perseus and various bioinformatics databases.

Results: Approximately 9000 proteins were identified in total, representing the first in-depth proteomic investigation of endometriosis and its associated cancers. This proteomic data was shown to be biologically sound, with minimal variation within patient cohorts and recapitulation of known markers. While moderate concordance with genomic data was observed, it was shown that such data are limited in their abilities to represent tumours on the protein level and to distinguish tumours from their benign precursors.

Conclusions: The proteomic data suggests that distinct markers may differentiate endometrioid and clear cell carcinoma from endometriosis. These markers may be indicators of pathobiology but will need to be further investigated. Ultimately, this dataset may serve as a basis to unravel the underlying biology of the endometrioid and clear cell cancers with respect to their endometriotic origins.

Temporal expression profiling of plasma proteins reveals oxidative stress in early stages of Type 1 Diabetes progression

Blood markers other than islet autoantibodies are greatly needed to indicate the pancreatic beta cell destruction process as early as possible, and more accurately reflect the progression of Type 1 Diabetes Mellitus (T1D). To this end, a longitudinal proteomic profiling of human plasma using TMT-10plex-based LC-MS/MS analysis was performed to track temporal proteomic changes of T1D patients (n=11) across 9 serial time points, spanning the period of T1D natural progression, in comparison with those of the matching healthy controls (n=10). To our knowledge, the current study represents the largest (>2000 proteins measured) longitudinal expression profiles of human plasma proteome in T1D research. By applying statistical trend analysis on the temporal expression patterns between T1D and controls, and Benjamini-Hochberg procedure for multiple-testing correction, 13 protein groups were regarded as having statistically significant differences during the entire follow-up period. Moreover, 16 protein groups, which play pivotal roles in response to oxidative stress, have consistently abnormal expression trend before seroconversion to islet autoimmunity. Importantly, the expression trends of two key reactive oxygen species-decomposing enzymes, Catalase and Superoxide dismutase were verified independently by ELISA.

BIOLOGICAL SIGNIFICANCE

The temporal changes of >2000 plasma proteins (at least quantified in two subjects), spanning the entire period of T1D natural progression were provided to the research community. Oxidative stress related proteins have consistently different dysregulated patterns in T1D group than in age-sex matched healthy controls, even prior to appearance of islet autoantibodies - the earliest sign of islet autoimmunity and pancreatic beta cell stress.

A proteome analysis of pig pancreatic islets and exocrine tissue by liquid chromatography with tandem mass spectrometry

Liquid chromatography with tandem mass spectrometry (LC-MS/MS) is a proteome analysis method, and the shotgun analysis by LC-MS/MS comprehensively identifies proteins from tissues and cells with high resolving power. In this study, we analyzed the protein expression in pancreatic tissue by LC-MS/MS. Islets isolated from porcine pancreata (purity ≥95%) and exocrine tissue (purity ≥99%) were used in this study. LC-MS/MS showed that 13 proteins were expressed in pancreatic islets only (Group I), 43 proteins were expressed in both islets and exocrine tissue (Group I&E), and 102 proteins were expressed in exocrine tissue only (Group E). Proteins involved in islet differentiation and cell proliferation were identified in Group I (e.g. CLUS, CMGA, MIF). In addition, various functional proteins (e.g. SCG2, TBA1A) were identified in islet by using the new method of 'principal component analysis (PCA)'. However, the function of such proteins on islets remains unclear. EPCAM was identified in Group E. Group E was found to include proteins involved in clinical inflammatory diseases such as pancreatitis (e.g. CBPA1, CGL, CYTB, ISK1 and PA21B). Many of these identified proteins were reported less frequently in previous studies, and HS71B, NEC2, PRAF3 and SCG1 were newly detected in Group I while CPNS1, DPEP1, GANAB, GDIB, GGT1, HSPB1, ICTL, VILI, MUTA, NDKB, PTGR1, UCHL3, VAPB and VINC were newly detected in Group E. These results show that comprehensive expression analysis of proteins by LC-MS/MS is useful as a method to investigate new factors constructing cellular component, biological process, and molecular function.

Isobaric Labeling-Based LC-MS/MS Strategy for Comprehensive Profiling of Human Pancreatic Tissue Proteome

The pancreas is an organ with both endocrine and exocrine functions, and various pathologies, such as pancreatic cancer and diabetes are associated with this organ. Owing to the limited pancreatic biopsy samples available for research, it is critical to make the best use of cadaveric pancreatic tissue for biomarker studies and mechanistic understanding of pancreas-related pathologies. Discovery-phase quantitative proteomics has attracted a lot of attention for its capabilities in large-scale protein identification and accurate protein quantification. Here, we describe a workflow using isobaric labeling (tandem mass tag or TMT) based quantitative proteomics to confidently identify and quantify human pancreatic tissue proteome, including sample preparation, isobaric tag labeling, peptide level fractionation, LC-MS/MS, database search, and statistical analysis.

The progress and potential of proteomic biomarkers for type 1 diabetes in children

INTRODUCTION

Although it is possible to identify the genetic risk for type 1 diabetes (T1D), it is not possible to predict who will develop the disease. New biomarkers are needed that would help understand the mechanisms of disease onset and when to administer targeted therapies and interventions. Areas covered: An overview is presented of international study efforts towards understanding the cause of T1D, including the collection of several extensive temporal sample series that follow the development of T1D in at risk children. The results of the proteomics analysis of these materials are presented, which have included bodily fluids, such as serum or plasma and urine, as well as tissue samples from the pancreas. Expert commentary: Promising recent reports have indicated detection of early proteomic changes in the serum of patients prior to diagnosis, potentially providing new measures for risk assessment. Similarly, there has been evidence that post-translational modification (PTM) may result in the recognition of islet cell proteins as autoantigens; modified proteins could thus be used as targets for immunomodulation to overcome the threat of the autoimmune response.

Temporal profiles of plasma proteome during childhood development

Human blood plasma proteome reflects physiological changes associated with a child's development as well as development of disease states. While age-specific normative values are available for proteins routinely measured in clinical practice, there is paucity of comprehensive longitudinal data regarding changes in human plasma proteome during childhood. We applied TMT-10plex isobaric labeling-based quantitative proteomics to longitudinally profile the plasma proteome in 10 healthy children during their development, each with 9 serial time points from 9months to 15years of age. In total, 1828 protein groups were identified at peptide and protein level false discovery rate of 1% and with at least two razor and unique peptides. The longitudinal expression profiles of 1747 protein groups were statistically modeled and their temporal changes were categorized into 7 different patterns. The patterns and relative abundance of proteins obtained by LC-MS were also verified with ELISA. To our knowledge, this study represents the most comprehensive longitudinal profiling of human plasma proteome to date. The temporal profiles of plasma proteome obtained in this study provide a comprehensive resource and reference for biomarker studies in childhood diseases. Biological significance: A pediatric plasma proteome database with longitudinal expression patterns of 1747 proteins from neonate to adolescence was provided to the research community. 970 plasma proteins had age-dependent expression trends, which demonstrated the importance of longitudinal profiling study to identify the potential biomarkers specific to childhood diseases, and the requirement of strictly age-matched clinical samples in a cross-sectional study in pediatric population.

Proteomic profiling of human islets collected from frozen pancreata using laser capture microdissection

The etiology of Type 1 Diabetes (T1D) remains elusive. Enzymatically isolated and cultured (EIC) islets cannot fully reflect the natural protein composition and disease process of in vivo islets, because of the stress from isolation procedures. In order to study islet protein composition in conditions close to the natural environment, we performed proteomic analysis of EIC islets, and laser capture microdissected (LCM) human islets and acinar tissue from fresh-frozen pancreas sections of three cadaveric donors. 1104 and 706 proteins were identified from 6 islets equivalents (IEQ) of LCM islets and acinar tissue, respectively. The proteomic profiles of LCM islets were reproducible within and among cadaveric donors. The endocrine hormones were only detected in LCM islets, whereas catalytic enzymes were significantly enriched in acinar tissue. Furthermore, high overlap (984 proteins) and similar function distribution were found between LCM and EIC islets proteomes, except that EIC islets had more acinar contaminants and stress-related signal transducer activity proteins. The comparison among LCM islets, LCM acinar tissue and EIC islets proteomes indicates that LCM combined with proteomic methods enables accurate and unbiased profiling of islet proteome from frozen pancreata. This paves the way for proteomic studies on human islets during the progression of T1D.

SIGNIFICANCE

The etiological agent triggering autoimmunity against beta cells in Type 1 diabetes (T1D) remains obscure. The in vitro models available (enzymatically isolated and cultured islets, EIC islets) do not accurately reflect what happens in vivo due to lack of the natural environment where islets exist and the preparation-induced changes in cell physiology. The importance of this study is that we investigated the feasibility of laser capture microdissection (LCM) for the isolation of intact islets from frozen cadaveric pancreatic tissue sections. We compared the protein profile of LCM islets (9 replicates from 3 cadaveric donors) with that of both LCM acinar tissues (6 replicates from the same 3 cadaveric donor as LCM islets) and EIC islets (at least 4 replicates for each sample with the same islets equivalents) by using proteomics techniques with advanced instrumentation, nanoLC-Q Exactive HF Orbitrap mass spectrometry (nano LC-MS/MS). The results demonstrate that the LCM method is reliable in isolating islets with an intact environment. LCM-based islet proteomics is a feasible approach to obtain good proteome coverage for assessing the pathology of T1D using cadaveric pancreatic samples, even from very small sample amounts. Future applications of this LCM-based proteomic method may help us understand the pathogenesis of T1D and identify potential biomarkers for T1D diagnosis at an early stage.